下面是相应的代码: x,y=m(lon,lat)m.plot(x,y,'ro',markersize=8)plt.show() 1. 2. 3. 3. 类图 Basemapprojection: strllcrnrlat: floaturcrnrlat: floatllcrnrlon: floaturcrnrlon: floatdrawcoastlines()drawcountries()drawmapboundary()drawparallels(range)drawmeridians(range)plot(x, y, 'ro',...
j)):lat,lon,ele=read_return(i,j)lim=np.arange(1,2401,200)ax.contourf(lon,lat,ele,lim,cmap='binary')else:print(i,j)returnaxif__name__=='__main__':fig,ax=plt.subplots()ax=plot_d(ax)plt.show()
接下来设置 curve 函数,进而使用 .FuncAnimation 让它动起来: def buildmebarchart(i=int): plt.legend(df1.columns) p = plt.plot(df1[:i].index, df1[:i].values) #note it only returns the dataset, up to the point i for i in range(0,4): p[i].set_color(color[i]) #set the colour...
lat_0=0, lon_0=0) #Fill the globe with a blue color map.drawmapboundary(fill_color='aq...
lat,lon,population,area=c['latd'],c['longd'],c['population_total'],c['area_total_km2'] #原本想用面向对象的画图风格,但是没找到ax的颜色条设置方法 fig,ax=plt.subplots() ax.scatter(lon,lat,label=None,c=np.log10(population),cmap='viridis',s=area,alpha=0.5) ax.axis(aspect='equal'...
%matplotlib inline %config InlineBackend.figure_format = 'retina' import matplotlib.pyplot as plt import numpy as np x = np.linspace(0,1,300) for w in range(2,6,2): plt.plot(x, np.sin(np.pi*x)*np.sin(2*w*np.pi*x)) <Figure size 640x480 with 1 Axes> ...
ax = data.plot( column="childrenNum", scheme="QUANTILES",# 设置分层设色标准 edgecolor='lightgrey', k=7,# 分级数量 cmap="Blues", legend=True, # 通过fmt设置位数 legend_kwds={"loc":"center left","bbox_to_anchor": (1,0.5),"fmt":"{:.2f}"} ...
hoverinfo='text')]layout=go.Layout(title="The City of Jiangsu",autosize=True,hovermode="closest",showlegend=False,mapbox=dict(accesstoken=mapbox_token,bearing=0,center=dict(lat=31,lon=120),pitch=0,zoom=5,style='light'),)fig=dict(data=data,layout=layout)plotly.offline.plot(fig,filename...
ggplot(china_data,aes(long,lat))+ geom_polygon(aes(group=group),fill="white",colour="grey60")+ geom_point(data =province_city,aes(x = jd,y = wd),colour="red")+ coord_map("polyconic")+ theme_void() 过程何其辛苦! 为什么使用maptools+geom_ploygon技术组合这么辛苦呢,问题出在数据源上...
())#将绘制台风路径,并将逐六小时坐标点及其对应的台风强度标记ax1.plot(lon,lat,linewidth=2)s1=ax1.scatter(lon,lat,c=pressure,s=(level+1)*13,cmap='Reds_r',vmax=1050,vmin=900,alpha=1)fig.colorbar(s1,ax=ax1,fraction=0.04)#绘制台风路径ax2=fig.add_subplot(1,2,2,projection=ccrs.Plate...