Python Basemap.scatter方法代码示例

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
    def plot_global_map(self, outputfile=None):
        """
        Plot global map of event and stations
        """
        # ax = plt.subplot(211)
        fig = plt.figure()
        ax = plt.gca()
        plt.title("Station and Event distribution")

        m = Basemap(projection='moll', lon_0=0.0, lat_0=0.0,
                    resolution='c')
        m.drawcoastlines()
        m.fillcontinents()
        m.drawparallels(np.arange(-90., 120., 30.))
        m.drawmeridians(np.arange(0., 420., 60.))
        m.drawmapboundary()

        cm = plt.cm.get_cmap('RdYlBu')
        x, y = m(self.station_loc[:, 1], self.station_loc[:, 0])
        m.scatter(x, y, 30, color=self.weight, marker="^", edgecolor="k",
                  linewidth='0.3', zorder=3, cmap=cm)
        plt.colorbar(shrink=0.8)

        cmt_lat = self.event.latitude
        cmt_lon = self.event.longitude
        src_x, src_y = m(cmt_lon, cmt_lat)
        m.scatter(src_x, src_y, 60, color="g", marker="o", edgecolor="k",
                  linewidth='0.3', zorder=3)

        if outputfile is None:
            plt.show()
        else:
            plt.savefig(outputfile)
            plt.close(fig)

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
    def WorkBook_plotPrisons(self, coords):
        lons = coords[0]
        lats = coords[1]

        print("Graphing image...")
        m = Basemap(llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-64,
            urcrnrlat=49, projection='lcc', lat_1=33, lat_2=45,
            lon_0=-95, resolution='h', area_thresh=10000)
        x, y = m(lons, lats)

        m.drawmapboundary(fill_color='white')
        m.fillcontinents(color='white',lake_color='white')
        m.scatter(x,y,10,marker='D',color='m')

        plt.figure(frameon=False)
        plt.title('US Prison Locations',fontsize=12)
        plt.savefig("USPrisons.png")

        background = Image.open("SocioEconomicBackground.png")
        overlay = Image.open("USPrisons.png")

        background = background.convert("RGBA")
        overlay = overlay.convert("RGBA")

        new_img = Image.blend(background, overlay, 0.5)
        new_img.save("SocioEconomic_Prison.png","PNG")

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
    def Map(self):
        m = Basemap(projection='cyl',  # stere, tmerc, lcc
                    lat_0=39.828127, lon_0=-98.579404,
                    urcrnrlon=-62.208289, urcrnrlat=51.342619,
                    llcrnrlon=-128.936426, llcrnrlat=19.06875)

        m.drawcoastlines()  # draw coastlines
        m.drawmapboundary()  # draw a line around the map region
        m.drawparallels(np.arange(-90., 120., 30.), labels=[1, 0, 0, 0])  # draw parallels

        m.drawmeridians(np.arange(0., 420., 60.), labels=[0, 0, 0, 1])  # draw meridians

        m.drawstates()
        m.drawcountries()

        lon = list()
        lon.append(-80.633333)
        lon.append(-74.364684)
        lon.append(-75.387778)
        lon.append(-84.253333)

        lat = list()
        lat.append(28.116667)
        lat.append(40.715622)
        lat.append(40.043889)
        lat.append(30.455)

        m.scatter(lon, lat, latlon=True, c=np.random.rand(3))
        #m.pcolor(lon, lat, latlon=True)
        plt.title('United States Fair Market Rent')  # add a title
        plt.show()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def plot_map(lons, lats, c, legend_label, projection='mill',
             llcrnrlat=-80, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180, resolution='i'):
    
    ''' Optional Arguments: projection - map projection, default set as 'mill'
                            llcrnrlat - lower left corner latitude value, default is -80
                            urcrnrlat - upper right corner latitude value, default is 90
                            llcrnrlon - lower left corner longitude value, default is -180
                            urcrnrlon - upper right corner longitude value, default is 180
                            resolution - the resolution of the plot, default is 'i'
                            
        Required Arguments: lons - list of longitude values to be plotted
                            lats - list of latitude values to be plotted
                            c - the color of the points to be plotted
                            legend_label - how this set of points will be labeled on the legend
                            
        Returns:            m - a basemap object defined by input bounds with input points included '''
    
    # Creates a basic plot of a series of lat,lon points over a defined region
    m = Basemap(projection=projection, llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat,
                llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon, resolution=resolution)
    m.drawcoastlines()
    m.drawmapboundary()
    m.drawcountries()
    m.etopo()
    m.drawmeridians(np.arange(llcrnrlon, urcrnrlon, 5), labels=[0,0,0,1], fontsize=10)
    m.drawparallels(np.arange(llcrnrlat, urcrnrlat, 5), labels=[1,0,0,0], fontsize=10)
    x,y = m(lons, lats)
    m.scatter(x, y, color=c, label=legend_label, marker='o', edgecolor='none', s=10)
    return m

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def generate_map(output, latlng, wesn=None):
    """
    Using Basemap and the matplotlib toolkit, this function generates a map and
    puts a red dot at the location of every IP addresses found in the list.
    The map is then saved in the file specified in `output`.
    """
    print("Generating map and saving it to {}".format(output))
    lats=[]
    lngs=[]
    for i in latlng:
        if i[1]:
            lats.append(i[1])
            lngs.append(i[2])

    if wesn:
        wesn = [float(i) for i in wesn.split('/')]
        m = Basemap(projection='cyl', resolution='l',
                llcrnrlon=wesn[0], llcrnrlat=wesn[2],
                urcrnrlon=wesn[1], urcrnrlat=wesn[3])
    else:
        m = Basemap(projection='cyl', resolution='l')
    m.bluemarble()
    x, y = m(lngs, lats)
    m.scatter(x, y, s=1, color='#ff0000', marker='o', alpha=0.3)
    plt.savefig(output, dpi=300, bbox_inches='tight')

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def example_2():
    
    # retrieve data
    dir  = "/homespace/gaubert/viirs/Mband-SDR"
    geo_file     = h5py.File("%s/%s" %(dir,"GMODO_npp_d20030125_t0847056_e0848301_b00015_c20090513182937526121_gisf_pop.h5"))
    
    lats = geo_file['All_Data']['VIIRS-MOD-GEO_All']['Latitude'][:]
    lons = geo_file['All_Data']['VIIRS-MOD-GEO_All']['Longitude'][:]
    # draw map with markers for float locations
    #m = Basemap(projection='hammer',lon_0=180)
    lon_ref = lons[0][0]-lons[-1][-1]/2
    lat_ref = 10
    #m = Basemap(projection='ortho',lat_0=lat_ref,lon_0=lon_ref,resolution='l')
    m = Basemap(projection='nsper',lat_0=lat_ref,lon_0=lon_ref,satellite_height=2000*1000,resolution='l')
    
    #x, y = m(lons[0:10],lats[0:10])
    x,y  = m(lons,lats)
    
    #m.drawcoastlines()
    
    m.drawmapboundary(fill_color='#99ffff')
    m.fillcontinents(color='#cc9966',lake_color='#99ffff')
    m.scatter(x,y,s = 1 ,color='k')
    #m.drawgreatcircle(lons[0][0],lats[0][0],lons[0][-1],lats[0][-1],linewidth=2,color='b')
    #m.drawgreatcircle(lons[-1][0],lats[0][0],lons[-1][-1],lats[0][-1],linewidth=2,color='b')
    
    plt.title('Locations of %s ARGO floats active between %s and %s' %\
            (len(lats),'2002', '2003'))
    plt.savefig('/tmp/plotargo.png')

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
 def show(self, proj='moll', lon_0=180, tmap=None, coord=None):
     from mpl_toolkits.basemap import Basemap
     import pylab as plt
     import resources.figures as figures
     figures.set_fancy()
     if coord==None:
         ra = np.rad2deg(self.grid['points'][:,0])
         dec = np.rad2deg(self.grid['points'][:,1])
     else:
         ra = np.rad2deg(coord[:,0])
         dec = np.rad2deg(coord[:,1])
     
     fig = plt.figure()
     m = Basemap(projection=proj,lon_0=lon_0)#,celestial=True) # celestial=True inverses alpha (East towards the right)
     m.drawparallels(np.arange(-60.,90.,30.),labels=[1,0,0,0])
     m.drawmeridians(np.arange(0.,360.,30.))
     
     ra__ = np.arange(0., 360., 30.)
     x, y = m(ra__,ra__*0)
     for x,y,t in zip(x,y,ra__):
         plt.text(x, y, figures.format_degree(t), color='black', ha='center', weight='black', size='small') ##93c6ed
     if tmap==None:
         m.scatter(ra,dec,latlon=True,marker='x',s=20,cmap=plt.cm.binary)
     else:
         m.scatter(ra,dec,c=tmap,latlon=True,marker='x',s=20,cmap=plt.cm.binary)
     plt.show()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def triangulation_map(station_lon, station_lat, station_val, cmap=None, *args, **kwargs):
    """ Using matplotlib.pyplot.tricontourf() function to plot contourplot on an irreguar grid by using triangulation. """

    # map boundries
    lat_0 = 51
    lat_min = 47
    lat_max = 55

    lon_0 = 10
    lon_min =  5
    lon_max = 16

    m = Basemap(projection='tmerc', lat_0=lat_0, lon_0=lon_0, 
                llcrnrlat=lat_min, llcrnrlon=lon_min, urcrnrlat=lat_max, 
                urcrnrlon=lon_max, resolution='i')

    m.drawcoastlines()
    m.drawcountries()
    m.drawmapboundary()

    m.contourf(station_lon, station_lat, station_val, cmap=cmap, latlon=True, tri=True, *args, **kwargs)
    m.scatter(station_lon, station_lat, color='k', s=5, latlon=True)

    # Set Triangulation manual?:
    #station_x, station_y = m(station_lon, station_lat)
    #triang = tri.Triangulation(station_x, station_y)
    #plt.tricontour(station_x, station_y, station_val, 15, linewidths=0.5, colors='k')
    #plt.tricontourf(x, y, z, 15, cmap=plt.cm.rainbow, norm=plt.Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
   
    plt.show()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def address_map( data ) :
    a = addresses_with_locations( data )
    plt.figure()
    m = Basemap( projection = "robin",
                 llcrnrlon = -180,
                 llcrnrlat = -80,
                 urcrnrlon = 180,
                 urcrnrlat = 80,
                 lon_0 = 0,
                 lat_0 = 0,
                 resolution = "c" )

    m.drawcoastlines()
    m.fillcontinents( color = 'coral', lake_color = 'aqua', alpha=0.3 )
    m.drawmeridians( arange( -180, 180, 60 ) )
    m.drawparallels( arange( -80, 80, 60 ) )

    xys = array([m(x[2][1], x[2][0]) for x in a])
    xs = xys[:,0]
    ys = xys[:,1]
    counts = array([x[1] for x in a]) * 200

    m.scatter( xs, ys, s=counts, marker='o', c='b', alpha=0.3 )
    plt.title("Research by sites, top 500 papers Ultraintense Lasers")
    plt.savefig("map.png")
    plt.close()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def run(FILE_NAME):

    with h5py.File(FILE_NAME, mode="r") as f:

        name = "/RetrievalResults/xco2"
        data = f[name][:]
        units = f[name].attrs["Units"][0]
        longname = f[name].attrs["Description"][0]

        # Get the geolocation data
        latitude = f["/RetrievalGeometry/retrieval_latitude"][:]
        longitude = f["/RetrievalGeometry/retrieval_longitude"][:]

    m = Basemap(projection="cyl", resolution="l", llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
    m.drawcoastlines(linewidth=0.5)
    m.drawparallels(np.arange(-90, 91, 45))
    m.drawmeridians(np.arange(-180, 180, 45), labels=[True, False, False, True])
    m.scatter(longitude, latitude, c=data, s=1, cmap=plt.cm.jet, edgecolors=None, linewidth=0)
    # cb = m.colorbar(orientation='horizontal', format='%.1e')
    cb = m.colorbar(location="bottom", format="%.1e", pad="10%")
    cb.set_label(units)

    basename = os.path.basename(FILE_NAME)
    plt.title("{0}\n{1}".format(basename, longname))
    fig = plt.gcf()
    # plt.show()
    pngfile = "{0}.py.png".format(basename)
    fig.savefig(pngfile)

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def draw_point(lats, lons):
    m = Basemap(resolution='i')
    m.drawcoastlines()
    m.drawcountries()
    x, y = m(lons, lats)
    m.scatter(x, y, s=0.3, color='#ff0000', marker='.')
    plt.show()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def Plotcat(ra_0, dec_0, fov_size=15., grid_size=2., allsky=False):
	pkscat = np.loadtxt('source_list_pks_140MHz',usecols=(0,1))
	pkscat[:,0] *= 15.
	
	sky = Basemap(projection='ortho', lon_0=-ra_0, lat_0=dec_0, celestial=True)
	if allsky==True:
		sky.drawmeridians(np.arange(0.,360.,grid_size))
		sky.drawparallels(np.arange(90,-90,-grid_size))
		sky.drawmapboundary(fill_color='White')
		catx, caty = sky(pkscat[:,0],pkscat[:,1])
		sky.scatter(catx,caty,3,marker='o',color='Black')
	else:
		cnreq = np.array([[ra_0+fov_size/2., dec_0-fov_size/2.], 
				[ra_0-fov_size/2., dec_0+fov_size/2.]])
			# = [[ll_ra, ll_dec], [ur_ra, ur_dec]]
		cnrxy = np.transpose(np.array(sky(cnreq[:,0],cnreq[:,1])))
			# = [[ll_x, ll_y], [ur_x, ur_y]]
		cenxy = np.array(sky(ra_0,dec_0))
		cnrmap = cnrxy-np.array([cenxy,cenxy])
		m = Basemap(projection='ortho', lon_0=-ra_0, lat_0=dec_0, 	
			celestial=True, llcrnrx=cnrxy[0,0]-cenxy[0], 
			llcrnry=cnrxy[0,1]-cenxy[1], urcrnrx=cnrxy[1,0]-cenxy[0], 
			urcrnry=cnrxy[1,1]-cenxy[1])
		m.drawmeridians(np.arange(cnreq[0,0], cnreq[1,0], -grid_size), 
						labels=[0,0,0,1],fmt=format_label)
		m.drawparallels(np.arange(cnreq[0,1], cnreq[1,1], grid_size), 
						labels=[1,0,0,0])
		m.drawmapboundary(fill_color='White')
		catx, caty = m(pkscat[:,0],pkscat[:,1])
		m.scatter(catx,caty,3,marker='o',color='Black')

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def plot_cities():
    map = Basemap(llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-64,
                               urcrnrlat=49, projection='lcc', lat_1=33, lat_2=45,
                               lon_0=-95, resolution='i', area_thresh=10000)

    map.drawcoastlines()
    map.drawcountries()
    map.drawstates()
    # key is language, value is pair (longitudes, latitudes)
    plots = { "Java" : ([], []), "Python" : ([], []), "R" : ([], []) }

    # we want each language to have a different marker and color
    markers = { "Java" : "o", "Python" : "s", "R" : "^" }
    colors  = { "Java" : "r", "Python" : "b", "R" : "g" }

    for (longitude, latitude), language in cities:
        plots[language][0].append(longitude)
        plots[language][1].append(latitude)
        
    
    # create a scatter series for each language
    for language, (x, y) in plots.iteritems():
        x1, y1 = map(x, y)
        map.scatter(x1, y1, color=colors[language], marker=markers[language],
                          label=language, zorder=10)

 #   plot_state_borders(plt)    # assume we have a function that does this

    plt.legend(loc=3,prop={'size':12})          # let matplotlib choose the location
    
    plt.title("Favorite Programming Languages")
    plt.show()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def run(FILE_NAME):

    with h5py.File(FILE_NAME, mode="r") as f:

        name = "/S1/surfacePrecipitation"
        data = f[name][:]
        units = f[name].attrs["Units"]
        # The attribute says -9999.900391 but data uses -9999.0.
        # _FillValue = f[name].attrs['CodeMissingValue']
        _FillValue = -9999.0
        data[data == _FillValue] = np.nan
        data = np.ma.masked_where(np.isnan(data), data)

        # Get the geolocation data
        latitude = f["/S1/Latitude"][:]
        longitude = f["/S1/Longitude"][:]

    m = Basemap(projection="cyl", resolution="l", llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
    m.drawcoastlines(linewidth=0.5)
    m.drawparallels(np.arange(-90, 91, 45))
    m.drawmeridians(np.arange(-180, 180, 45), labels=[True, False, False, True])
    m.scatter(longitude, latitude, c=data, s=1, cmap=plt.cm.jet, edgecolors=None, linewidth=0)
    cb = m.colorbar(location="bottom", pad="10%")
    cb.set_label(units)

    basename = os.path.basename(FILE_NAME)
    plt.title("{0}\n{1}".format(basename, name))
    fig = plt.gcf()
    # plt.show()
    pngfile = "{0}.py.png".format(basename)
    fig.savefig(pngfile)

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import scatter [as 别名]
def render_nextbus_dataframe(route, nextbus_df):
    """Plots the NextBus Vehicle Location's Panda Data frame to a
    Matplotlib and Basemap Geospatial image."""

    min_long = min(nextbus_df.lon)
    min_lat = min(nextbus_df.lat)
    max_long = max(nextbus_df.lon)
    max_lat = max(nextbus_df.lat)

    bmap = Basemap(llcrnrlon=min_long, llcrnrlat=min_lat,
                   urcrnrlon=max_long, urcrnrlat=max_lat,
                   ellps='WGS84',
                   resolution='h', area_thresh=1000)

    bmap.drawmapboundary(fill_color='white')

    bmap.scatter(nextbus_df.lon, nextbus_df.lat,
                 marker='d', edgecolor='g', facecolor='g', alpha=0.5)
    plt.xlabel('Longitude')
    plt.ylabel('Latitude')
    plt.axis([min_long, max_long, min_lat, max_lat])
    plt.title('NextBus Vehicle Locations for route {}'.format(route))
    plt.grid(True)
    # plt.legend(loc='lower center')
    plt.savefig('nextbus_vehicle_locations.png', fmt='png', dpi=600)
    # plt.show()
    # Other components (like GMaps plotting) will also use Matplotlib, so
    # it's better to clear the figure that Matplotlib generated
    plt.clf()