Python scipy.savetxt函数代码示例

def Corr(GDP,I,C):
	m = sp.shape(GDP)[1]
	GDPIcorr = []
	GDPCcorr = []
	for i in range(0, m):
		gdp = GDP[:,i]
		inv = I[:,i]
		con = C[:,i]
		#Correlation between output and investment for each series
		gdpi = sp.corrcoef(gdp,inv)
		GDPIcorr.append(gdpi[0,1])
		#Correlation between output and consumption for each series
		gdpc = sp.corrcoef(gdp,con)
		GDPCcorr.append(gdpc[0,1])
	#Mean and standard deviation of correlation between GDP and
	#Investment and Consumption over total number of simulations
	GDPICORR = sp.array(GDPIcorr)
	gdpimean = sp.mean(GDPICORR)
	gdpistdev = sp.std(GDPICORR)
	GDPCCORR = sp.array(GDPCcorr)
	gdpcmean = sp.mean(GDPCCORR)
	gdpcstdev = sp.std(GDPCCORR)
	sp.savetxt('GDPICORR.csv',GDPICORR)
	sp.savetxt('GDPCCORR.csv',GDPCCORR)
	print "The mean and standard deviation between GDP and"
	print "Investment and GDP and Consumption followed by"
	print "The lists of each correlation coefficient for"
	print "each series are saved in csv files"
	return gdpimean, gdpistdev, gdpcmean, gdpcstdev

def gen_IC(sigma,rn,outfile="workfile",icdir="ICs", M=5, N=50, lapfile="Laplacian.txt", tries=10, iclist=[]):
   lap = loadtxt(lapfile)
   spa = sparse.csr_matrix(lap)
   success=0
   attempts=0
   while success==0 and attempts<tries:
     try:
	tag='s%.2fr%.3d'%(sigma,rn)
	tag=tag.replace(".", "")

	parameters = [35.0, 16.0, 9.0, 0.4, 0.12, sigma]
	x0=10*(random.random(2*N)-0.5)

	tic=time.time()
	trajectory = integrate.odeint(mimura, x0, range(0,1000), args=(parameters,spa))
	print "integration took", time.time()-tic, "seconds"

	x1=trajectory[-1]

	sol=fsolve(mimura3, x1, args=(parameters,spa),full_output=True)
	x2=sol[0]
	if x2 not in iclist:
	    savetxt(icdir+'/init_cond_'+tag+'.txt',x2)
	    write_mimu(lap,par=parameters,ic=x2,outfile=outfile)
    	    iclist.append(x2)
	    success=1
	tries+=1
     except: pass
   return iclist

def processdataset(globpath='iimage-*', outputpath='./', margin=17):

    global sp
    sp = margin

    files = glob.glob(globpath)
    files.sort()

    datasets = []

    imagedir = outputpath+'images/'
    if not os.path.exists(imagedir):
        os.makedirs(imagedir)

    datadir = outputpath+'datasets/'
    if not os.path.exists(datadir):
        os.makedirs(datadir)


    for filename in files:
        print filename
        datasets = processimage(filename, imagedir, datasets)

    for i,dataset in enumerate(datasets):
        scipy.savetxt(datadir+'results-'+str(dataset[2])+'.dat',dataset[-1])

def apm_generate_aperture_map():
    r"""
    Driver function to generate an aperture map from a TIF image.
    """
    # parsing commandline args
    namespace = parser.parse_args()
    if namespace.verbose:
        set_main_logger_level('debug')

    # checking path to prevent accidental overwritting
    if not namespace.aperture_map_name:
        map_name = os.path.basename(namespace.image_file)
        map_name = map_name.replace( os.path.splitext(map_name)[1], '-aperture-map.txt')
        namespace.aperture_map_name = map_name

    #
    map_path = os.path.join(namespace.output_dir, namespace.aperture_map_name)
    if os.path.exists(map_path) and not namespace.force:
        msg = '{} already exists, use "-f" option to overwrite'
        raise FileExistsError(msg.format(map_path))

    # loading image data
    data_array = load_image_data(namespace.image_file, namespace.invert)
    data_array = data_array.astype(sp.int8)

    # summing data array down into a 2-D map
    logger.info('creating 2-D aperture map...')
    aperture_map = sp.sum(data_array, axis=1, dtype=int)

    # saving map
    logger.info('saving aperture map as {}'.format(map_path))
    sp.savetxt(map_path, aperture_map.T, fmt='%d', delimiter='\t')

def create_histogram(parameter_name, nbins=100, writeFile=True, skipfirst=0, truncate=False, smooth=False):
	"""
	Returns a histogram and some statistics about this parameter.
		
	@param writeFile: if true, write the histogram to paramname.histogram
	"""
	f = "%s-chain-0.prob.dump" % parameter_name
	values = numpy.recfromtxt(f)[skipfirst::nevery]

	statistics = {
		'min':   float(values.min()),
		'max':   float(values.max()),
		'stdev': float(values.std()),
		'mean':  float(values.mean()),
		'median':float(numpy.median(values)),
		'q1':    float(scipy.stats.scoreatpercentile(values, 25)),
		'q3':    float(scipy.stats.scoreatpercentile(values, 75)),
		'p5':    float(scipy.stats.scoreatpercentile(values, 5)),
		'p95':    float(scipy.stats.scoreatpercentile(values, 95)),
	}
	
	hist = scipy.histogram(values, bins = nbins if not smooth else nbins*10, normed=True)
	histwithborders = numpy.dstack([hist[1][0:nbins], hist[1][1:nbins+1], hist[0]])
	if writeFile:
		scipy.savetxt('%s.histogram' % parameter_name, histwithborders[0], delimiter="\t")
	return histwithborders[0], statistics

def main():
    # Do not modify
    start = time.time()

    parser = argparse.ArgumentParser(description='Build context vectors.')
    parser.add_argument('textfile', type=str, help='name of text file')
    parser.add_argument('window', type=int, help='context window')
    parser.add_argument('threshold', type=int, help='vocabulary minimum frequency')
    parser.add_argument('--ndims', type=int, default=100, help='number of SVD dimensions')
    parser.add_argument('--debug', type=bool, default=False)
    args = parser.parse_args()

    vocab, points, vocabFreq = count_context_vectors(args.textfile+'.txt', args.window, args.threshold)
    print 'Estimated count context vectors'
    if not args.debug:  # compute PPMI and SVD before writing. if debug is True, just write the count vectors
        points = ppmi(points)
        print 'Converted to positive pointwise mutual information'
        points = dimensionality_reduce(points, args.ndims)
        print 'Reduced dimensionality'
        outfile = args.textfile+'.window'+str(args.window)+'.thresh'+str(args.threshold)
    else:
        outfile = args.textfile+'.window'+str(args.window)+'.thresh'+str(args.threshold)+'.todebug'

    with open(outfile+'.labels', 'w') as o:
        o.write('\n'.join(vocab)+'\n')
    scipy.savetxt(outfile+'.vecs', points, fmt='%.4e')
    with open(outfile+'.csv', 'w') as do:
        do.write('\n'.join(vocabFreq)+'\n')
    print 'Saved to file'

    print time.time()-start, 'seconds'

 def run(self):
 
     # Parameters passed are current data array, along with time step
     # between current data points
     self.times = sp.arange(0,self.Tfinal,self.dt)
     self.sim = odeint(self.eqns,self.init,self.times,(self.inj,self.injdt))
     sp.savetxt('simulation.txt',sp.column_stack((self.times,self.sim)))

def main():
    # Do not modify
    start = time.time()

    parser = argparse.ArgumentParser(description='Build document-term vectors.')
    parser.add_argument('textfile', type=str, help='name of text file with documents on each line')
    parser.add_argument('threshold', type=int, help='term minimum frequency')
    parser.add_argument('--ndims', type=int, default=100, help='number of SVD dimensions')
    parser.add_argument('--debug', type=bool, default=False, help='debug mode?')
    args = parser.parse_args()

    terms, points = tfidf_docterm(args.textfile+'.txt', args.threshold)
    print 'Estimated document-term TF-IDF vectors'
    if not args.debug:  # compute PPMI and SVD before writing. if debug is True, just write the count vectors
        points = dimensionality_reduce(points, args.ndims)
        print 'Reduced dimensionality'
        outfile = args.textfile+'.tfidf'+'.thresh'+str(args.threshold)
    else:
        outfile = args.textfile+'.tfidf'+'.thresh'+str(args.threshold)+'.todebug'

    with open(outfile+'.dims', 'w') as o:
        o.write('\n'.join(terms)+'\n')
    scipy.savetxt(outfile+'.vecs', points, fmt='%.4e')
    print 'Saved to file'

    print time.time()-start, 'seconds'

def make_S(A0, Ak, G0, Gk, phi):

    R = P = scipy.matrix(scipy.zeros((6,6)))

    for i in range(0, 3, 2):
        for j in range(3):
            R[i, j] = Ak[i, j]
    R[1, 1] = R[3, 3] = R[4, 4] = 1.0;
    R[5, 5] = Ak[5, 5]

    P[0, 0] = (A0[0, 0] * cos(phi)**2.0 + A0[1, 0] * sin(phi)**2.0)
    P[0, 1] = (A0[0, 1] * cos(phi)**2.0 + A0[1, 1] * sin(phi)**2.0)
    P[0, 2] = (A0[0, 2] * cos(phi)**2.0 + A0[1, 2] * sin(phi)**2.0)
    P[0, 3] = (A0[3, 3] * sin(2.0 * phi))
    P[1, 0] = sin(phi)**2.0
    P[1, 1] = cos(phi)**2.0
    P[1, 3] = -sin(2.0*phi)
    P[2, 0] = A0[2, 0]
    P[2, 1] = A0[2, 1]
    P[2, 2] = A0[2, 2]
    P[3, 0] = -0.5*sin(2.0*phi)
    P[3, 1] = 0.5*sin(2.0*phi)
    P[3, 3] = cos(2.0*phi)
    P[4, 4] = cos(phi)
    P[4, 5] = -sin(phi)
    P[5, 4] = A0[4, 4] * sin(phi)
    P[5, 5] = A0[5, 5] * cos(phi)

    scipy.savetxt("R", R)
    scipy.savetxt("P", P)
    return scipy.matrix(R.I) * scipy.matrix(P)

    def test_networkx_matrix(self):
        print('\n---------- Matrix Test Start -----------\n')

        g = nx.barabasi_albert_graph(30, 2)
        nodes = g.nodes()
        edges = g.edges()
        print(edges)

        mx1 = nx.adjacency_matrix(g)
        fp = tempfile.NamedTemporaryFile()
        file_name = fp.name
        sp.savetxt(file_name, mx1.toarray(), fmt='%d')

        # Load it back to matrix
        mx2 = sp.loadtxt(file_name)
        fp.close()

        g2 = nx.from_numpy_matrix(mx2)
        cyjs_g = util.from_networkx(g2)

        #print(json.dumps(cyjs_g, indent=4))

        self.assertIsNotNone(cyjs_g)
        self.assertIsNotNone(cyjs_g['data'])
        self.assertEqual(len(nodes), len(cyjs_g['elements']['nodes']))
        self.assertEqual(len(edges), len(cyjs_g['elements']['edges']))

        # Make sure all edges are reproduced
        print(set(edges))
        diff = compare_edge_sets(set(edges), cyjs_g['elements']['edges'])
        self.assertEqual(0, len(diff))

def TSP(stops, Alg, steps, param, seed = None,
                      coordfile = 'xycoords.txt'):
    '''A wrapper function that attempts to optimize the traveling 
    salesperson problem using a specified algorithm. If coordfile
    exists, a preexisting set of coordinates will be used. Otherwise,
    a new set of "stops" coordinates will be generated for the person to 
    traverse, and will be written to the specified file.'''
    
    ## Create the distance matrix, which will be used to calculate
    ## the fitness of a given path
    if os.path.isfile(coordfile):
        coords = scipy.genfromtxt(coordfile)
        distMat = DistanceMatrix(coords)
    else:
        distMat = GenerateMap(stops, fname = coordfile, seed = seed)

    if Alg == 'HC':
        ## param is the number of solutions to try per step
        bestSol, fitHistory = HillClimber(steps, param, distMat, seed)
    elif Alg == 'SA':
        ## param is a placeholder
        bestSol, fitHistory = SimulatedAnnealing(steps, param, distMat, seed)
    elif Alg == 'MC3':
        ## param is the number of chains
        bestSol, fitHistory = MCMCMC(steps, param, distMat, seed)
    elif Alg == 'GA':
        ## param is the population size
        bestSol, fitHistory = GeneticAlgorithm(steps, param, distMat, seed)
    else:
        raise ValueError('Algorithm must be "HC", "SA", "MC3", or "GA".')

    outfname = coordfile + '-' + Alg + '-' + str(steps) + '-' + str(param) + '.txt'
    scipy.savetxt(outfname, scipy.array(bestSol), fmt = '%i')
    return bestSol, fitHistory

def make_B(A0, Ak, G0, Gk, phi):
    x = 0
    y = 1
    z = 2

    B = scipy.zeros((6,6))
    B[0, 0] = cos(phi)**2.0*1
    B[0, 1] = sin(phi)**2.0
    B[0, 3] = sin(2.0 * phi)*1
    B[1, 0] = (A0[0, 0] * sin(phi)**2.0 + (A0[1, 0] - Ak[1, 0]) * cos(phi)**2.0) / Ak[1, 1]
    print B[1,0], A0[0,0]/Ak[1,1]
    B[1, 1] = (A0[1, 1] * cos(phi)**2.0 + (A0[1, 0] - Ak[1, 0]) * sin(phi)**2.0) / Ak[1, 1]
    B[1, 2] = (A0[0, 2] * sin(phi)**2.0 + A0[1, 2] * cos(phi)**2.0 - Ak[1, 2]) / Ak[1, 1]*1
    B[1, 3] = -sin(2.0 * phi) * (2.0 * G0[0, 1] + Ak[1, 0]) / Ak[1, 1]*1
    B[2, 2] = 1.0*1
    B[3, 0] = sin(2.0 * phi) * (A0[1, 0] - A0[0, 0]) / (4.0 * Gk[x, y])*1
    B[3, 1] = sin(2.0 * phi) * (A0[1, 1] - A0[0, 1]) / (4.0 * Gk[x, y])*1
    B[3, 2] = sin(2.0 * phi) * (A0[1, 2] - A0[0, 2]) / (4.0 * Gk[x, y])*1
    B[3, 3] = cos(2.0 * phi) * G0[0, 1] / Gk[x, y]*1
    B[4, 4] = cos(phi) * G0[1, 2] / Gk[y, z]*1
    B[4, 5] = -sin(phi) * G0[0, 2] / Gk[y, z]*1
    B[5, 4] = sin(phi)*1
    B[5, 5] = cos(phi)*1

    scipy.savetxt("B1", B)
    return scipy.matrix(B)

    def request_agreement(self):
        """7. Alice starts key agreement
        generate fingerprint and doing fuzzy commitment
        send hash and delta to Bob
        """
        log.info('7. Alice starts key agreement')

        #===============================================================================
        # Fingerprinting and Fuzzy Cryptography
        #===============================================================================
        # generate fingerprint
        self.fingerprint = fingerprint_energy_diff.get_fingerprint(self.recording_data, self.recording_samplerate)
        
        # save fingerprint for debugging
        scipy.savetxt("client_fingerprint.txt", self.fingerprint)

        log.debug('Alice fingerprint:\n'+str(self.fingerprint))
        
        # doing commit, rs codes can correct up to (n-m)/2 errors
        self.hash, self.delta, self.private_key = crypto_fuzzy_jw.JW_commit(self.fingerprint, m=self.rs_code_m, n=self.rs_code_n, symsize=self.rs_code_symsize)
        
        log.debug('Alice Blob:\nHash:\n'+str(self.hash)+'\nDelta:\n'+str(self.delta))
        
        # save delta for debugging
        scipy.savetxt("client_delta.txt", self.delta)
        
        # remote call for key agreement
        # using debug means sending also the fingerprint in clear text!!!
        # meaning no security!
        if self.debug:
            accept_agreement = self.pairing_server.callRemote("agreement_debug", self.fingerprint.tolist(), self.hash, self.delta.tolist())
            accept_agreement.addCallbacks(self.answer_agreement)
        else:
            accept_agreement = self.pairing_server.callRemote("agreement", self.hash, self.delta.tolist())
            accept_agreement.addCallbacks(self.answer_agreement)

def CalculateProjectRg(ProjectInfo,Output,returnRgs = False):
    """
    Calculate Radius of gyration for the Project ie. all the Trajectories.
    ProjectInfo: ProjectInfo.h5 file.
    Output: output file (XXX.dat). 
    The Output default will be set in the scripts and it is './Rgs.dat'.
    """
    Output = checkoutput(Output)
   
    if not isinstance(ProjectInfo,str):
	print "Please input the Path to ProjectInfo.h5"
	raise IOError
    print 'Calculating the Rg for each trajectory......' 
    ProjectInfoPath = '/'.join(os.path.realpath(ProjectInfo).split('/')[:-1])
    os.chdir(ProjectInfoPath)
    Trajfiles = []
    ProjectInfo = Serializer.LoadFromHDF(ProjectInfo)
    for i in range(ProjectInfo['NumTrajs']): 
	Trajfiles.append(ProjectInfo['TrajFilePath']+ProjectInfo['TrajFileBaseName']+'%d'%i+ProjectInfo['TrajFileType'])
    Rgs = computeRg(Trajfiles)
    
    print "Save data to %s"%Output
    savetxt(Output,Rgs)
    print "Done."
    if returnRgs:
	return Rgs

def simPheno(options):

    print 'importing covariance matrix'
    if options.cfile is None: options.cfile=options.bfile
    XX = readCovarianceMatrixFile(options.cfile,readEig=False)['K']

    print 'simulating phenotypes'
    SP.random.seed(options.seed)
    simulator = sim.CSimulator(bfile=options.bfile,XX=XX,P=options.nTraits)
    Xr,region = simulator.getRegion(chrom_i=options.chrom,size=options.windowSize,min_nSNPs=options.nCausalR,pos_min=options.pos_min,pos_max=options.pos_max)
 
    Y,info    = genPhenoCube(simulator,Xr,vTotR=options.vTotR,nCausalR=options.nCausalR,pCommonR=options.pCommonR,vTotBg=options.vTotBg,pHidd=options.pHidden,pCommon=options.pCommon)

    print 'exporting pheno file'
    if options.pfile is not None:
        outdir = os.path.split(options.pfile)[0]
        if not os.path.exists(outdir):
            os.makedirs(outdir)
    else:
        identifier = '_seed%d_nTraits%d_wndSize%d_vTotR%.2f_nCausalR%d_pCommonR%.2f_vTotBg%.2f_pHidden%.2f_pCommon%.2f'%(options.seed,options.nTraits,options.windowSize,options.vTotR,options.nCausalR,options.pCommonR,options.vTotBg,options.pHidden,options.pCommon)
        options.pfile = os.path.split(options.bfile)[-1] + '%s'%identifier

    pfile  = options.pfile + '.phe'
    rfile  = options.pfile + '.phe.region'

    SP.savetxt(pfile,Y)
    SP.savetxt(rfile,region)