Shuaiyi Zhang understar

资源记录

python *　plotpy[https://plot.ly/] 交互式绘图 [http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s0_getting-started/s0_getting-started.ipynb] *　颜色搭配很重要[http://colorbrewer2.org/#] *　prettyplotlib[https://github.com/olgabot/prettyplotlib] 增强版matplotlib *　

##This workflow is only for the pansharpened images

####red-green-blue
gdalbuildvrt -separate -q -srcnodata "0 0 0" -vrtnodata "0 0 0" rgb.vrt LC80430352013339LGN00_B4.tif LC80430352013339LGN00_B3.tif LC80430352013339LGN00_B2.tif

####NIR, SWIR, and visible red
gdalbuildvrt -separate -q -srcnodata "0 0 0" -vrtnodata "0 0 0" false.vrt LC80430352013339LGN00_B5.tif LC80430352013339LGN00_B6.tif LC80430352013339LGN00_B4.tif

####color infrared
gdalbuildvrt -separate -q -srcnodata "0 0 0" -vrtnodata "0 0 0" cir.vrt LC80430352013339LGN00_B5.tif LC80430352013339LGN00_B3.tif LC80430352013339LGN00_B4.tif

	# -- coding: cp936--
	__author__ = 'shuaiyi'

	import logging
	import numpy as np
	import calc_tsne as tsne
	import matplotlib.pyplot as plt
	from features import dataset as ds

	logging.getLogger().setLevel(logging.INFO)

	"""
	==============================================================
	Restricted Boltzmann Machine features for digit classification
	==============================================================

	For greyscale image data where pixel values can be interpreted as degrees of
	blackness on a white background, like handwritten digit recognition, the
	Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM
	<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear
	feature extraction.

	# (C) Kyle Kastner, June 2014
	# License: BSD 3 clause

	from sklearn.base import BaseEstimator, TransformerMixin
	from sklearn.utils import array2d, as_float_array
	from scipy.linalg import eigh
	import numpy as np

	class ZCA(BaseEstimator, TransformerMixin):
	def __init__(self, n_components=None, bias=.1, copy=True):

	import numpy as np

	def makeGaussian(size, fwhm = 3, center=None):
	""" Make a square gaussian kernel.

	size is the length of a side of the square
	fwhm is full-width-half-maximum, which
	can be thought of as an effective radius.
	"""

	#!/usr/bin/env python
	# coding=utf8
	# author=evi1m0
	# website=linux.im

	'''
	12306 Captcha Picture:
	author: Evi1m0@20150316
	1. Download Captcha
	2. Pic Conver Text

	# code from http://danieljlewis.org/files/2010/06/Jenks.pdf
	# described at http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/

	def getJenksBreaks( dataList, numClass ):
	dataList.sort()
	mat1 = []
	for i in range(0,len(dataList)+1):
	temp = []
	for j in range(0,numClass+1):
	temp.append(0)