test2, p_value2 = stats.ks_2samp(poisson, poisson1) print p_value, p_value2 test3, p3 = stats.normaltest(poisson_std) print p3 x = stats.norm(mu, sigma) pl.plot(t, x.pdf(t), label=u"norm") pl.plot(t, x1, label=u"poisson", color="red") pl.ylabel(u"概率") pl.legend() pl.show() inputFile = "result_v1.txt" data = np.loadtxt(util.getRelativePath(inputFile)) m, n = data.shape tags = data[:, n - 1] subClassData = {} for i in range(m): tag = tags[i] for j in range(n - 1): if data[i][j] == 99: continue if not subClassData.has_key(tag): subClassData[tag] = [] while len(subClassData[tag]) - 1 < j: subClassData[tag].append([]) subClassData[tag][j].append(data[i][j])
import os import Astronomy.Util.CommonUtil as astroUtil import CodeLib.Util.CommonUtil as util # waveLens = [1188,1453,2023,2217,2313,2356,2491,3262,3613,3635,3696] waveLens = [1188, 2023, 2217, 2356, 2491, 3613, 3262, 3635, 3696] features = ["MAG1", "MAG2", "MAG3"] subClassKey = "SUBCLASS" classMap = {"O": 1, "B": 2, "A": 3, "F": 4, "G": 5, "K": 6, "M": 7, "N": 8} classCount = {"O": 0, "B": 0, "A": 0, "F": 0, "G": 0, "K": 0, "M": 0, "N": 0} inputPath = "C:\dr2" outFile = "data.txt" outFilePath = util.getRelativePath(outFile) output = open(outFilePath, "w") dirs = os.listdir(inputPath) count = 0 for dir in dirs: files = os.listdir(os.path.join(inputPath, dir)) for fileName in files: filePath = os.path.join(inputPath, dir, fileName) try: keywords, flux = astroUtil.getFitsHeaderAndFlux(filePath) record = [] for fea in features: record.append(keywords[fea])