agpredict.py 文件源码

def matrix(self):

        """
        This function transforms the images into a single numpy array with dimensions
        pixels by observations.  If the image has 100 pixels for 1 year (23 observations)
        then this matrix should have dimensions 100 rows by 23 columns.  The matrix
        includes the quality mask dataset.  This matrix is not yet masked for quality control.
        """

        dataCount = self.subset.count('1')
        dataNames = sorted(glob.glob(self.fullPath + '/*.tif'))
        dataNames = dataNames[0:dataCount]
        subsetInt = [int(s) for s in self.subset.split() if s.isdigit()] 

        DC = np.empty(shape = (self.rows*self.columns*self.observations,0))  
        DCs = np.empty(shape = (self.rows*self.columns*self.observations, subsetInt.count(1)))  

        for i in range(dataCount):
            name = str(dataNames[i])
            dataList = sorted(glob.glob(self.fullPath + '/*' + name[-10:-4] + '.tif'))  
            bandDC = np.empty((0, 1))      
            for b in dataList:
                data = gdal.Open(str(b), GA_ReadOnly).ReadAsArray()
                vec = data.reshape((self.rows*self.columns, 1))
                bandDC = np.append(bandDC, vec, axis = 0)  
            DC = np.append(DC, bandDC, axis = 1) 
            del vec, bandDC, data

        #apply fill values    
        if self.dataset == 'MOD15A2.005' or self.dataset == 'MOD17A2.005':
            DC[DC>self.fillValue] = 9999.0                
        if self.dataset == 'MOD11A2.005':
            DC[:,0][DC[:,0] == self.fillValue] = 9999.0 #this should have fixed it!
        else:
            DC[DC == self.fillValue] = 9999.0 


        #scale dataset
        count = 0    
        for i in range(len(subsetInt)):
            if subsetInt[i] == 1:
                DCs[:,count] = np.multiply(DC[:,count], self.scale[i])
                count += 1
        DCs[DC == 9999.0] = 9999.0
        self.DC = DCs
        del DC

        #metadata function        
        with open(self.fullPath + '/' + 'metadata_' + self.dataset + '.txt', 'w') as f:
            f.write(' '.join(["self.%s = %s" % (k,v) for k,v in self.__dict__.iteritems()]))

        logger.log('SUCCESS', 'The %s data was transformed into an array with dimensions %d rows by %d columns.  No data value set to 9999.  A metadata file with object attributes was created.  To access the matrix, simply call object.DC' % (str(self.outformat), self.DC.shape[0], self.DC.shape[1]))

        tif = sorted(glob.glob(self.fullPath + '/*.tif'))
        for t in tif:
            os.remove(t)