## page was renamed from OpenSource2010/Project/Project Idea/Konvektionszellen
Modis-Daten:

[[http://modis.gsfc.nasa.gov/data/|MODIS]]

http://rapidfire.sci.gsfc.nasa.gov/subsets/?subset=AERONET_Hornsund.2009072.terra.250m

http://rapidfire.sci.gsfc.nasa.gov/subsets/?subset=AERONET_Hornsund.2009072&altdates

Satellitenbilder (Aqua Modis) Fram Straße :
[[http://modis-atmos.gsfc.nasa.gov/IMAGES/index_myd021km.html|Modis Images]]

Bilder als Dateianhänge

 * 30.03.2003 10:40
 * 31.03.2009  11:35

{{{#!python
def load_img(filename):
    im=Image.open(filename)
    return resize(fromstring(im.tostring(),uint8),(im.size[1],im.size[0],3))
}}}


Spektrum erzeugen mit der Funktion pylab.psd.
Spektrum über eine Zeile und plot mit basemap (noch ohne Helligkeitsdaten):

{{{#!python
import Image
from pylab import *
from mpl_toolkits.basemap import Basemap
import numpy as np

def load_img(filename):
    im=Image.open(filename)
    return resize(fromstring(im.tostring(),uint8),(im.size[1],im.size[0],3))


filename='AERONET_Hornsund.2009072.aqua.250m.jpg'

a=load_img(filename)

north=80.2363
south=73.7612
east=40.4156
west=-9.3026

[yn,xn,dum]=shape(a)

# make grid
x=linspace(west,east,xn)
y=linspace(north,south,yn)

[lons,lats] = meshgrid(x,y)


start,stop,line=500,1500,1550
test=a[line,start:stop,0]

# power spectrum
figure(1)
psd(test,NFFT=512,Fs=250,Fc=0,detrend=mlab.detrend_linear,window=mlab.window_hanning, noverlap=0)



# plot location of profile
figure(2)
m = Basemap(width=2400000,height=1600000,projection='stere',lat_ts=77.0,lon_0=15.0,lat_0=77.0,resolution='l')

m.drawcoastlines()
m.fillcontinents(color='gray',lake_color='aqua')
m.drawmapboundary(fill_color='aqua')

m.drawmeridians(np.arange(0,360,5),labels=[1,0,0,1])
m.drawparallels(np.arange(-90,90,5),labels=[0,1,0,1])

xm,ym=m(lons,lats)

#m.imshow(xm,ym,a[:,:,0]) # geht nicht!

x1,y1=m(x[start],y[line])
x2,y2=m(x[stop],y[line])
m.plot([x1,x2],[y1,y2],'-r')



show()
}}}

Noch keine genaue Aussage über die Zellengröße machbar!




{{{#!python

from  scipy import *
import Image
def profmean(img,y1=0,ylen=5,x1=0,xlen=512):
    """
        
        
    """
    y2=y1+ylen
    xlist=range(x1,x1+xlen)
    #print xlist
    p=[]
    for x in xlist:
        p.append(a[y1:y2,x,0].mean())
    print p
    return p    

def load_img(filename):
    im=Image.open(filename)
    return resize(fromstring(im.tostring(),uint8),(im.size[1],im.size[0],3))
    
filename='AERONET_Hornsund.2009072.terra.250m.jpg'

a=load_img(filename)

print profmean(a)

}}}