Python pylab.load函数代码示例

def doit():
    map = Basemap(projection='lcc',
		    llcrnrlon=80,
		    urcrnrlon=160,
		    llcrnrlat=-50,
		    urcrnrlat=-8,
		    #lat_ts=-35,
		    lat_0=-35,
		    lon_0=120,
		    resolution='c',
		    area_thresh=1000.)
    p.clf()
    map.drawcoastlines()
    # map.drawcountries()
    
    # map.drawrivers()

    map.drawmeridians(p.arange(0,360,10),labels=[0,0,1,0])
    map.drawparallels(p.arange(-90,0,10),labels=[1,0,0,0])

    traj=p.load('example_traj.dat')
    coast=p.load('/media/sda4/map-data/aust-coast-noaa-2000000-1.dat')

    traj_x,traj_y   = map(traj[:,1],traj[:,0]) 
    # coast_x,coast_y = map(coast[:,0],coast[:,1])
    
    p.plot(traj_x,traj_y)    
    p.plot(coast_x,coast_y,color='black')    

    map.drawmapboundary()
    p.show()
    return map 

def InvokeMap(coastfile='/media/sda4/map-data/aust-coast-noaa-2000000-1.dat',
		    lllon=80,
		    urlon=166,
		    lllat=-47,
		    urlat=-9,
		    draw_map=True):
    global PYLIB_PATH

    map = Basemap(projection='cyl',
			llcrnrlon=lllon,
			urcrnrlon=urlon,
			llcrnrlat=lllat,
			urcrnrlat=urlat,
			#lat_ts=-35,
			lat_0=-35,
			lon_0=120,
			resolution='l',
			area_thresh=1000.)


    try: 
	coast = p.load(coastfile)
	coast = p.load(coastfile)
	coast_x,coast_y = map(coast[:,0],coast[:,1])
	p.plot(coast_x,coast_y,color='black')    
    except IOError:
	map.drawcoastlines()

    map.drawmapboundary()
    map.drawmeridians(p.arange(0,360,10),labels=[0,0,1,0])
    map.drawparallels(p.arange(-90,0,10),labels=[1,0,0,0])

    return map

	def load_csv(self,f):
		"""
		Loading data from a csv file. Uses pylab's load function. Seems much faster
		than scipy.io.read_array.
		"""
		varnm = f.readline().split(',')

		# what is the date variable's key if any, based on index passed as argument
		if self.date_key != '':
			try:
				rawdata = pylab.load(f, delimiter=',',converters={self.date_key:pylab.datestr2num})			# don't need to 'skiprow' here
			except ValueError:																				# if loading via pylab doesn't work use csv
				rawdata = self.load_csv_nf(f)	

				# converting the dates column to a date-number
				rawdata[self.date_key] = pylab.datestr2num(rawdata[self.date_key])

			self.date_key = varnm[self.date_key]
		else:
			try:
				rawdata = pylab.load(f, delimiter=',')														# don't need to 'skiprow' here
			except ValueError:																				# if loading via pylab doesn't work use csv
				rawdata = self.load_csv_nf(f)	

		# making sure that the variable names contain no leading or trailing spaces
		varnm = [i.strip() for i in varnm]

		# transforming the data into a dictionary
		if type(rawdata) == list:
			# if the csv module was used
			self.data = dict(zip(varnm,rawdata))
		else:
			# if the pylab.load module was used
			self.data = dict(zip(varnm,rawdata.T))

    def __init__(self,xlmrpath,xlmipath,maptype):
        xlmr = pylab.load(xlmrpath)
        xlmi = pylab.load(xlmipath)
        xlmrname = os.path.basename(xlmrpath)
        self.outfileprefix = '-'.join( re.split( '-' , xlmrname )[:-1] )
        try:
            assert numpy.shape(xlmr) == numpy.shape(xlmi)
            self.xlmr , self.xlmi = xlmr , xlmi
        except AssertionError:
            print 'The two arrays loaded must have the same dimensions.'
        self.xlm = self.xlmr + 1j*self.xlmi
        print 'Multiple moments files read successfully!'
        ntrunc = int( numpy.sqrt( numpy.shape(self.xlm)[0] ) - 1 )
        self.ntrunc = ntrunc
        self.gotpix = False
        self.maptype = maptype
#        print 'Applying quick-fix I to multiple moments...'
#        indxpn = LISAresponse.getMLvec( self.ntrunc , 'pn' )
#        xlm = numpy.zeros( numpy.shape(self.xlm) , dtype=complex )
#        for i,ml in enumerate(indxpn):
#            m , l = ml[0] , ml[1]
#            k = indxpn.index( ( -m,l ) )
#            xlm[i] = (-1)**m*self.xlm[k]
#        self.xlm = xlm    
        return

def plot_covs(filein, fileout):
    import pylab as P
    data1 = P.load(filein)
    data2 = P.load(fileout)
    P.plot(data1[:,0], data1[:,1], 'o')
    P.plot(data2[:,0], data2[:,1])
    P.grid('True')
    P.show()

def compute_wishart_A(p):
    g = pylab.load('81vectors.txt')
    B = prepareB(math.sqrt(1500.0)*g)
    ew = [0.0015,0.0004,0.0004]
    ev1 = pylab.load('81vectors.txt')
    ev2 = pylab.load('321vectors.txt')
    A1 = assemble_wishart_matrix(B,ev1,ew,p)
    A2 = assemble_wishart_matrix(B,ev2,ew,p)
    return A1,A2

def getParamCovMat(prefix,dlogpower = 2, theoconstmult = 1.,dlogfilenames = ['dlogpnldloga.dat'],volume=256.**3,startki = 0, endki = 0, veff = [0.]):
    """
    Calculates parameter covariance matrix from the power spectrum covariance matrix and derivative term
    in the prefix directory
    """
    nparams = len(dlogfilenames)

    kpnl = M.load(prefix+'pnl.dat')
    k = kpnl[startki:,0]

    nk = len(k)
    if (endki == 0):
        endki = nk
        
    pnl = M.array(kpnl[startki:,1],M.Float64)
    covarwhole = M.load(prefix+'covar.dat')
    covar = covarwhole[startki:,startki:]
    if len(veff) > 1:
        sqrt_veff = M.sqrt(veff[startki:])
    else:
        sqrt_veff = M.sqrt(volume*M.ones(nk))

    dlogs = M.reshape(M.ones(nparams*nk,M.Float64),(nparams,nk))
    paramFishMat = M.reshape(M.zeros(nparams*nparams*(endki-startki),M.Float64),(nparams,nparams,endki-startki))
    paramCovMat = paramFishMat * 0.

    # Covariance matrices of dlog's
    for param in range(nparams):
        if len(dlogfilenames[param]) > 0:
            dlogs[param,:] = M.load(prefix+dlogfilenames[param])[startki:,1]

    normcovar = M.zeros(M.shape(covar),M.Float64)
    for i in range(nk):
        normcovar[i,:] = covar[i,:]/(pnl*pnl[i])

    M.save(prefix+'normcovar.dat',normcovar)

    f = k[1]/k[0]

    if (volume == -1.):
        volume = (M.pi/k[0])**3

    #theoconst = volume * k[1]**3 * f**(-1.5)/(12.*M.pi**2) #1 not 0 since we're starting at 1
    for ki in range(1,endki-startki):
        for p1 in range(nparams):
            for p2 in range(nparams):
                paramFishMat[p1,p2,ki] = M.sum(M.sum(\
                M.inverse(normcovar[:ki+1,:ki+1]) *
                M.outerproduct(dlogs[p1,:ki+1]*sqrt_veff[:ki+1],\
                               dlogs[p2,:ki+1]*sqrt_veff[:ki+1])))
                
                
        paramCovMat[:,:,ki] = M.inverse(paramFishMat[:,:,ki])

    return k[1:],paramCovMat[:,:,1:]

def test_mrf_EM():
    """EXAMPLE: EM learning on a MRF"""
    """Define MRF graph structure"""
    C = 0
    S = 1
    R = 2
    W = 3
    nodes = 4
    adj_mat = sparse.lil_matrix((nodes, nodes), dtype=int)
    adj_mat[C, [R, S]] = 1
    adj_mat[R, W] = 1
    adj_mat[S, W] = 1
    adj_mat[R, S] = 1

    """Define clique domains and node sizes"""
    ns = 2 * np.ones((1, nodes))
    clq_doms = [[0], [0, 1], [0, 2], [1, 2, 3]]

    """Define cliques and potentials"""
    clqs = []
    clqs.append(cliques.discrete_clique(0, clq_doms[0], np.array([2])))
    clqs.append(cliques.discrete_clique(1, clq_doms[1], np.array([2, 2])))
    clqs.append(cliques.discrete_clique(2, clq_doms[2], np.array([2, 2])))
    clqs.append(cliques.discrete_clique(3, clq_doms[3], np.array([2, 2, 2])))

    """Create the MRF"""
    net = models.mrf(adj_mat, ns, clqs)
    
    """
    Load the samples, and set one sample of one node to be unobserved, this
    should not effect the learnt parameter much, and will demonstrate that
    the algorithm can handle unobserved samples.
    """
    samples = (np.array(pylab.load('./Data/lawn_samples.txt')) - 1).tolist()
    samples[0][0] = []

    """Learn the parameters"""
    net.learn_params_EM(samples[:])
   
    """Initialize the inference engine"""
    net.init_inference_engine(exact=True)

    """Create and enter evidence"""
    evidences = create_all_evidence(4, 2)
    mlcs = np.array([[0, 0, 0, 0]])
    for evidence in evidences:
        mlc = net.max_sum(evidence)
        mlcs = np.vstack((mlcs, mlc))
   
    """Read in expected values"""
    exp_mlcs = np.array(pylab.load('./Data/mrf_em_exact_max_sum_res.txt'))

    """Assert that the output matched the expected values"""
    assert_array_equal(mlcs, exp_mlcs)

def degraderesolution(prefix,factor,dlogstring):
    covar = M.load(prefix+'covar.dat')
    pnl = M.load(prefix+'pnl.dat')
    dlog = M.load(prefix+dlogstring)[:,1]
    k = pnl[:,0]*1.
    p = pnl[:,1]*1.
    gausspart = M.load(prefix+'gausspart.dat')
    nbins = len(k)

    nongausspart = covar - gausspart

    nongausspartnew = nongausspart[:nbins-factor:factor,:nbins-factor:factor]*0.
    knew = k[:nbins-factor:factor]*0.
    pnew = p[:nbins-factor:factor]*0.
    gausspartnew = gausspart[:nbins-factor:factor,:nbins-factor:factor]*0.
    nbinsnew = len(knew)
    dlognew = dlog[:nbins-factor:factor]*0.

    for i1 in range(0,nbins-factor,factor):
        i1new = i1/factor
        print i1,i1+factor-1,nbins
        print i1new,nbinsnew
        weights = k[i1:i1+factor-1]**3
        sumweights = M.sum(weights)
        pnew[i1new] = M.sum(p[i1:i1+factor-1]*weights)/sumweights
        knew[i1new] = M.sum(k[i1:i1+factor-1]*weights)/sumweights
        dlognew[i1new] = M.sum(dlog[i1:i1+factor-1]*weights)/sumweights

    sqrtkfact = M.sqrt(k[1]/k[0])
        
    for i1 in range(0,nbins-factor,factor):
        i1new = i1/factor
        for i2 in range(0,nbins-factor,factor):
            i2new = i2/factor
                                                                       
            weights2 = M.outer(k[i1:i1+factor-1]**3,k[i2:i2+factor-1]**3)
            sumweights2 = M.sum(M.sum(weights2))
            nongausspartnew[i1new,i2new] = M.sum(M.sum(nongausspart[i1:i1+factor-1,i2:i2+factor-1]*weights2))/sumweights2

            if i1new == i2new:
                vk = (4.*M.pi/3.)*((k[i1+factor-1]*sqrtkfact)**3 - (k[i1]/sqrtkfact)**3)
                gausspartnew[i1new,i2new] = (2.*M.pi)**3 * 2.*(pnew[i1new]**2)/vk
                                                                       
    covarnew = gausspartnew + nongausspartnew

    prefixnew = prefix+'degrade'+str(factor)+'/'
    os.system('mkdir '+prefixnew)
    M.save(prefixnew+'pnl.dat',M.transpose([knew,pnew]), fmt = '%18.16e')
    M.save(prefixnew+'covar.dat',covarnew, fmt = '%18.16e')
    M.save(prefixnew+'gausspart.dat',gausspartnew, fmt = '%18.16e')
    M.save(prefixnew+dlogstring,M.transpose([knew,dlognew]), fmt = '%18.16e')
    M.save(prefix+'nbins.dat',M.array([nbinsnew],shape=(1,1,)), fmt = '%d')

def LoadColormapMirrored(filename):
	data = pylab.load(filename)

	samples = len(data)/2
	t = linspace(0,1,samples)
	r = list(data[0::4])
	g = list(data[1::4])
	b = list(data[2::4])

	r.reverse()
	g.reverse()
	b.reverse()

	r = list(reversed(b)) + r 
	g = list(reversed(g)) + g  
	b = list(reversed(r)) + b 

	red = []
	green = []
	blue = []

	for i in range(samples):

		red.append((t[i], r[i], r[i]))
		green.append((t[i], g[i], g[i]))
		blue.append((t[i], b[i], b[i]))

	cdict = { "red": red, "green": green, "blue": blue }
	cmap = matplotlib.colors.LinearSegmentedColormap("my_colors", cdict, 1024)

	return cmap

def readBinnedPower(file):
    """
    @brief reads in a binned power spectrum from a file
    The file must have columns specficed as : binLeft,binRight,l,cl
    """
    binLeft,binRight,l,cl = pylab.load(file,skiprows= 50,unpack=True,usecols=[0,1,2,3])
    return l,cl

def GetSparseMatrix(psi, config):
	matrix = pylab.load("d130_50stk-matel")
	row = array(matrix[:,0], dtype=int) - 1
	col = array(matrix[:,1], dtype=int) - 1
	matelem = array(matrix[:,2], dtype=complex)

	return row, col, matelem

def makeplot(filename):
    T0 = 2452525.374416
    P = 0.154525
    
    X = pl.load(filename)
    x = X[:,0]
    y = X[:,1]
    print x[0] # check for HJD faults
    
    #orbital phase
    p = (x-T0)/P
    
    pl.figure(figsize=(6,4))
    pl.subplots_adjust(hspace=0.47,left=0.16)
    
    pl.subplot(211)
    pl.scatter(p,y,marker='o',s=0.1,color='k')
    pl.ylim(-0.06,0.06)
    pl.xlim(pl.average(p)-1.25,pl.average(p)+1.25)
    pl.ylabel('Intensity')
    pl.xlabel('Orbital Phase')
    
    pl.subplot(212)
    f,a = ast.signal.dft(x,y,0,4000,1)
    pl.plot(f,a,'k')
    pl.ylabel('Amplitude')
    pl.xlabel('Frequency (c/d)')
    #pl.ylim(yl[0],yl[1])
    
    #pl.vlines(3636,0.002,0.0025,color='k',linestyle='solid')
    #pl.vlines(829,0.002,0.0025,color='k',linestyle='solid')
    #pl.text(3500,0.00255,'DNO',fontsize=11)
    #pl.text(700,0.00255,'lpDNO',fontsize=11)
    pl.ylim(0.0,0.004)
    pl.savefig('%spng'%filename[:-3])

def load_default(path, closure):
    from pylab import load, save
    try:
        return load(path)
    except IOError: 
        obj = closure()
        save(obj, path)
        return obj

def rejuice(d63,d63_2,d63_4,d63_8):
    #pinit = M.load('mill/s63/pm.pnl.dat')

    p1 = M.load('mill/s63/pm.pnl.dat')
    plog1 = M.load('mill/s63/plogm.pnl.dat')
    p2 = M.load('mill/s63r2/pm.pnl.dat')
    plog2 = M.load('mill/s63r2/plogm.pnl.dat')
    p4 = M.load('mill/s63r4/pm.pnl.dat')
    plog4 = M.load('mill/s63r4/plogm.pnl.dat')
    p8 = M.load('mill/s63r8/pm.pnl.dat')
    plog8 = M.load('mill/s63r8/plogm.pnl.dat')

    f63= N.exp(-N.mean(N.log(d63.flatten())))
    f63_2= N.exp(-N.mean(N.log(d63_2.flatten())))
    f63_4= N.exp(-N.mean(N.log(d63_4.flatten())))
    f63_8= N.exp(-N.mean(N.log(d63_8.flatten())))

    #M.loglog(p1[:,0],p1[:,1]/(plog1[:,1]*f63),'b--')
    #M.loglog(p2[:,0],p2[:,1]/(plog2[:,1]*f63_2),'g--')
    #M.loglog(p4[:,0],p4[:,1]/(plog4[:,1]*f63_4),'r--')
    #M.loglog(p8[:,0],p8[:,1]/(plog8[:,1]*f63_8),'y--')

    #xis = N.mean(d63.flatten()**2)
    #xis_2 = N.mean(d63_2.flatten()**2)
    #xis_4 = N.mean(d63_4.flatten()**2)
    #xis_8 = N.mean(d63_8.flatten()**2)

    xis = (1.+ 0.5*N.sqrt(N.var(d63.flatten())))
    xis_2 = (1.+0.5*N.sqrt(N.var(d63_2.flatten())))
    xis_4 = (1.+0.5*N.sqrt(N.var(d63_4.flatten())))
    xis_8 = 1.+0.5*N.sqrt(N.var(d63_8.flatten()))
    
    print 'exps:',f63,f63_2,f63_4,f63_8
    print 'xis:',xis, xis_2,xis_4,xis_8

    M.loglog(plog1[:,0],p1[:,1]/(plog1[:,1]*f63)*(1.+2.*xis**2),'b')
    M.loglog(plog2[:,0],p2[:,1]/(plog2[:,1]*f63_2)*(1.+2.*xis_2**2),'g')
    M.loglog(plog4[:,0],p4[:,1]/(plog4[:,1]*f63_4)*(1.+2.*xis_4**2),'r')
    M.loglog(plog8[:,0],p8[:,1]/(plog8[:,1]*f63_8)*(1.+2.*xis_8**2),'y')

    M.loglog(plog1[:,0],p1[:,1]/(plog1[:,1]*xis),'b')
    M.loglog(plog2[:,0],p2[:,1]/(plog2[:,1]*xis_2),'g')
    M.loglog(plog4[:,0],p4[:,1]/(plog4[:,1]*xis_4),'r')
    M.loglog(plog8[:,0],p8[:,1]/(plog8[:,1]*xis_8),'y')


    M.xlabel(r'$k\ [\rm{Mpc}/h]$',fontsize=20)
    M.ylabel(r'$P_\delta(k)/P_{\log (1+\delta)}(k)$',fontsize=20)

    bias1 = N.sum(p1[:5,1]*p1[:5,2])/N.sum(plog1[:5,1]*plog1[:5,2])
    bias2 = N.sum(p2[:5,1]*p2[:5,2])/N.sum(plog2[:5,1]*plog2[:5,2])
    bias4 = N.sum(p4[:5,1]*p4[:5,2])/N.sum(plog4[:5,1]*plog4[:5,2])
    bias8 = N.sum(p8[:5,1]*p8[:5,2])/N.sum(plog8[:5,1]*plog8[:5,2])

    print bias1,bias2,bias4,bias8#, N.log(bias1),N.log(bias2),N.log(bias4)       
    M.show()