understar

import gdal,  osr
import numpy as np

levelsdetail = {18:(262144, 131072, 256),
                17:(131072, 65536, 256)
               }

def lat_lon2index(lat, lon, level): # top left corner
    width = levelsdetail[level][0]
    height = levelsdetail[level][1]

understar

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 23 10:41:47 2014

@author: shuaiyi
"""
import logging
from landez import TilesManager

down = False

understar

#include <io.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <stdlib.h>
#include <stdio.h>
#include <windows.h>
#include <Share.h>
#include <math.h>

understar

using System;
using System.Net;
using System.IO;
using Gdk;

namespace TileSticher
{
	class App
	{
		public static int Main (string[] args)

understar

#include <sys/stat.h>
#include <sys/mman.h>
#include <fcntl.h>
#include <unistd.h>
#include <stdlib.h>
#include <stdio.h>

struct xyz
{
	float x, y, z;

understar

"""
==========
Kernel PCA
==========

This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)

understar

# -*- coding: utf-8 -*-
from numpy import mean,cov,double,cumsum,dot,linalg,array,rank
from pylab import plot,subplot,axis,stem,show,figure

def princomp(A):
 """ performs principal components analysis 
     (PCA) on the n-by-p data matrix A
     Rows of A correspond to observations, columns to variables. 

 Returns :  

understar

# -*- coding: utf-8 -*-
"""
Created on Wed Jan 08 18:02:10 2014

@author: understar
"""
from __future__ import division
from collections import Counter
import sys
from random import randint