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Fram Straße: [[http://modis-atmos.gsfc.nasa.gov/IMAGES/index_myd021km.html|30.3.2003]] |
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: {{{#!python from pylab import * 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) test=a[1500,500:1500,0] psd(test,NFFT=512,Fs=250,Fc=0,detrend=mlab.detrend_none,window=mlab.window_hanning, noverlap=0) show() }}} {{{#!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) }}} |
Modis-Daten:
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 : Modis Images
Bilder als Dateianhänge
- 30.03.2003 10:40
- 31.03.2009 11:35
Spektrum erzeugen mit der Funktion pylab.psd. Spektrum über eine Zeile:
1 from pylab import *
2
3 def load_img(filename):
4 im=Image.open(filename)
5 return resize(fromstring(im.tostring(),uint8),(im.size[1],im.size[0],3))
6
7 filename='AERONET_Hornsund.2009072.aqua.250m.jpg'
8
9 a=load_img(filename)
10
11 test=a[1500,500:1500,0]
12 psd(test,NFFT=512,Fs=250,Fc=0,detrend=mlab.detrend_none,window=mlab.window_hanning, noverlap=0)
13
14 show()
1 from scipy import *
2 import Image
3 def profmean(img,y1=0,ylen=5,x1=0,xlen=512):
4 """
5
6
7 """
8 y2=y1+ylen
9 xlist=range(x1,x1+xlen)
10 #print xlist
11 p=[]
12 for x in xlist:
13 p.append(a[y1:y2,x,0].mean())
14 print p
15
16 return p
17
18 def load_img(filename):
19 im=Image.open(filename)
20 return resize(fromstring(im.tostring(),uint8),(im.size[1],im.size[0],3))
21
22 filename='AERONET_Hornsund.2009072.terra.250m.jpg'
23
24 a=load_img(filename)
25
26 print profmean(a)