it is possible to use basemap to create regular spaced lat/lon grids?

Hi,
i want to interpolate irregular spaced satellite data onto a regular
spaced grid. The regular spaced grid should have cell sizes of 1km^2. Is
it possible to use basemap to create such a grid. It looked like it
includes some facilities like that, but i am not sure if they are meant
to be used by end user or more like internal fcns (the makegrid fcn for
example).

Any advice would be appreciated.

thanks
matt

Hi Matt,

Something like this?:

def create_map(ax, llcrnrlon,llcrnrlat,urcrnrlon,urcrnrlat):
m = Basemap(llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat,urcrnrlon=urcrnrlon,urcrnrlat=urcrnrlat,resolution=‘i’,projection=‘cyl’,lon_0=(urcrnrlon+llcrnrlon)/2,lat_0=(urcrnrlat+llcrnrlat)/2)

m.drawcoastlines()
m.drawmapboundary()
m.drawstates(linewidth=3)
m.fillcontinents(color='lightgrey',lake_color='white')
m.drawcountries(linewidth=3)
return m

def plotMapData(ax,data):

lats = []
lons = []
val = []

for k,v in data.iteritems():
    lats.append(float(k[0]))
    lons.append(float(k[1]))
    val.append(float(v))

  
value = np.array(val)
lat = np.array(lats)
lon = np.array(lons)
       
llcrnlon = lon.min()-0.5
llcrnlat = lat.min()-0.5
urcrnlon = lon.max()+0.5
urcrnlat = lat.max()+0.5

xi = np.linspace(llcrnlon,urcrnlon,1000)
yi = np.linspace(llcrnlat,urcrnlat,1000)
zi = griddata(lon,lat,value,xi,yi)

cmap = cm.jet
m = create_map(ax,llcrnlon,llcrnlat,urcrnlon,urcrnlat)

cs = ax.contour(xi,yi,zi,15,linewidth=0.5,cmap=cmap,alpha=0.5)   
ax.contourf(xi,yi,zi,15,cmap=cmap,zorder=1000,alpha=0.5)

colorscale = cm.ScalarMappable()
colorscale.set_array(value)
colorscale.set_cmap(cmap)

colors = colorscale.to_rgba(value)
ax.scatter(lon,lat,c=colors,zorder=1000,cmap=cmap,s=10)
colorbar(colorscale, shrink=0.50, ax=ax,extend='both')
···

On Tue, Sep 6, 2011 at 1:28 PM, Matt Funk <matze999@…287…> wrote:

Hi,

i want to interpolate irregular spaced satellite data onto a regular

spaced grid. The regular spaced grid should have cell sizes of 1km^2. Is

it possible to use basemap to create such a grid. It looked like it

includes some facilities like that, but i am not sure if they are meant

to be used by end user or more like internal fcns (the makegrid fcn for

example).

Any advice would be appreciated.

thanks

matt


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Hi Aman,
thanks for your code. I am testing it right now, but i think this
might what i need.
Not sure if you know this: what is the difference between:
1) scipy.interpolate.griddata
2) matplotlib.mlab.griddata

For 2) you have specify the interpolation method and i think the

calling convention is different. Is one a wrapper for the other?

thanks
matt
···
-- Matt Funk
Research Associate
Plant and Environmental Scienc. Dept.
New Mexico State University

No, they are not wrappers.

I don’t know the full details, but the idea was that we didn’t want to have SciPy as a dependency, so mlab was used to replicate many of the functions found in SciPy. I don’t know why the calling conventions are different, though.

Ben Root

···

On Tue, Sep 6, 2011 at 2:48 PM, Matt Funk <matze999@…2015…87…> wrote:

Hi Aman,

thanks for your code. I am testing it right now, but i think this

might what i need.

Not sure if you know this: what is the difference between:

1) scipy.interpolate.griddata

2) matplotlib.mlab.griddata



For 2) you have specify the interpolation method and i think the

calling convention is different. Is one a wrapper for the other?

thanks

matt

Apologies for drifting off-topic. I understand the desire to not
require scipy, but how likely is that to change? I've written a BCa
bootstrapper[1] for boxplots, but it needs scipy, so I can't
contribute it back to the community.

[1] - http://staff.ustc.edu.cn/~zwp/teach/Stat-Comp/Efron_Bootstrap_CIs.pdf

Cheers,
-paul

···

On Tue, Sep 6, 2011 at 12:58 PM, Benjamin Root <ben.root@...1304...> wrote:

I don't know the full details, but the idea was that we didn't want to have
SciPy as a dependency, so mlab was used to replicate many of the functions
found in SciPy. I don't know why the calling conventions are different,
though.

Ben Root

I don't know the full details, but the idea was that we didn't want to have
SciPy as a dependency, so mlab was used to replicate many of the functions
found in SciPy. I don't know why the calling conventions are different,
though.

Ben Root

Apologies for drifting off-topic. I understand the desire to not
require scipy, but how likely is that to change? I've written a BCa
bootstrapper[1] for boxplots, but it needs scipy, so I can't
contribute it back to the community.

If you are asking whether matplotlib will ever depend on scipy, the answer is "no", not in any future I can foresee. Its purpose is plotting, not calculating. There are some simple deviations from this mission--spectral plots and histograms, for example--but they depend only on numpy. Maybe your BCa code can be contributed to scipy?

Eric

···

On 09/06/2011 12:55 PM, Paul Hobson wrote:

On Tue, Sep 6, 2011 at 12:58 PM, Benjamin Root<ben.root@...1304...> wrote:

[1] - http://staff.ustc.edu.cn/~zwp/teach/Stat-Comp/Efron_Bootstrap_CIs.pdf

Cheers,
-paul

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Hi,
sorry that it has taken me so long to reply. Anyway, i could be
wrong, but i don’t think that the code:
xi = np.linspace(llcrnlon,urcrnlon,1000)
yi = np.linspace(llcrnlat,urcrnlat,1000)

will produce a grid which gives the lat/lon coordinates with 1km

spacing. The reason being is that the distance between 2 lons (say
-117.731659 and -91.303642) is different depending on where you are
in terms of the latitude (i.e. the extreme examples are of course
the north pole vs the equator). So the above gives a regular grid in
terms of degrees but not in terms of distance.
Anyway, but the example was still helpful in terms of getting me
started with the griddata issue. In my experience the mlab.griddate
fcn did not work as well as the scipy.griddata (but that could be a
user error as well … ). Not sure why though. It might be the size
of my source data and the destination grid. I had to upgrade to the
64-bit python to be able to access enough memory.

thanks
matt
···
-- Matt Funk
Research Associate
Plant and Environmental Scienc. Dept.
New Mexico State University

Yes, that's correct. You'll need to project your original data
locations into a cartesian co-ordinate system before interpolating
their values onto a regular grid in that co-ordinate system using
griddata et al.

You might like to use pyproj (included with the basemap toolkit) to
help you project from lat/lon to your chosen co-ordinate system..

Cheers,
Scott

···

On 8 September 2011 19:20, Matt Funk <matze999@...287...> wrote:

Hi,
sorry that it has taken me so long to reply. Anyway, i could be wrong, but i
don't think that the code:
xi = np.linspace(llcrnlon,urcrnlon,1000)
yi = np.linspace(llcrnlat,urcrnlat,1000)

will produce a grid which gives the lat/lon coordinates with 1km spacing.
The reason being is that the distance between 2 lons (say -117.731659 and
-91.303642) is different depending on where you are in terms of the latitude
(i.e. the extreme examples are of course the north pole vs the equator). So
the above gives a regular grid in terms of degrees but not in terms of
distance.

Hi,
sorry that it has taken me so long to reply. Anyway, i could be wrong, but i
don't think that the code:
    xi = np.linspace(llcrnlon,urcrnlon,1000)
    yi = np.linspace(llcrnlat,urcrnlat,1000)

will produce a grid which gives the lat/lon coordinates with 1km spacing.
The reason being is that the distance between 2 lons (say -117.731659 and
-91.303642) is different depending on where you are in terms of the latitude
(i.e. the extreme examples are of course the north pole vs the equator). So
the above gives a regular grid in terms of degrees but not in terms of
distance.

Yes, that's correct. You'll need to project your original data
locations into a cartesian co-ordinate system before interpolating
their values onto a regular grid in that co-ordinate system using
griddata et al.

You might like to use pyproj (included with the basemap toolkit) to
help you project from lat/lon to your chosen co-ordinate system..

I have been using gdal for many of my geographic needs. Is there an
advantage/disadvantage using pyproj vs capabilities found in gdal (from
what i understand both are based on PROJ.4)? Can you comment on this?
Also, i was thinking of projecting things to UTM for interpolation
purposes. Is there any apparent reason this is a bad idea vs a different
projected coordinate system?

matt

···

On 9/9/2011 6:42 AM, Scott Sinclair wrote:

On 8 September 2011 19:20, Matt Funk <matze999@...287...> wrote:

Cheers,
Scott

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--
Matt Funk
Research Associate
Plant and Environmental Scienc. Dept.
New Mexico State University