From: Ian B. <ib...@pu...> - 2011-04-19 22:53:01
|
To clarify, you are trying to read in a set of (lat,lon) points in a file that is space delimited, store the data, and then put a text marker at each point, with each point numbered in order? The critical part is that you want to use a list (or numpy array) instead of a dictionary. Something like this ought to do (don't have MPL on this computer though - pretty sure this should work): lines=open('file.txt','r').readlines() (lats,lons)=([],[]) for line in lines: (lat,lon)=line.strip().split(' ') lats.append(float(lat)) lons.append(float(lon)) for i in range(len(lons)): plt.text(lats[i],lon[i],str(i+1),ha='center',va='center',color='white') I'm sure there are a bunch of more compact ways to do this, but this should work. Ian ---- Ian Bell Graduate Research Assistant Herrick Labs Purdue University email: ib...@pu... cell: (607)227-7626 On Tue, Apr 19, 2011 at 4:09 PM, Michael Rawlins <raw...@ya...>wrote: > > I'm trying to plot a series of points/locations on a map. I'm reading the > latitudes and longitudes from a file, with each lat, lon pair on each record > (line). Here is the code: > > def make_float(line): > lati, longi = line.split() > return float(lati), float(longi) > > my_dict = {} > with open("file.txt") as f: > for item in f: > lati,longi = make_float(item) > my_dict[lati] = longi > > xpt,ypt = m(-76.1670,39.4670 ) > plt.text(xpt,ypt,'1',color='white') > > #print my_dict > > The matplotlib code which I've previously used to plot a single point on > the map is below, with longitude and latitude in ( ): > > xpt,ypt = m(-70.758392,42.960445) > plt.text(xpt,ypt,'1',color='white') > > When replacing (-70.758392,42.960445) with (longi,lati), the code plots > only a single '1' at the location of just the last coordinate pair in the > file. So now I only need to plot them all. Does the code I've implemented > have an implicit loop to it? > > Mike > > > > > ------------------------------------------------------------------------------ > Benefiting from Server Virtualization: Beyond Initial Workload > Consolidation -- Increasing the use of server virtualization is a top > priority.Virtualization can reduce costs, simplify management, and improve > application availability and disaster protection. Learn more about boosting > the value of server virtualization. http://p.sf.net/sfu/vmware-sfdev2dev > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > |