Python pylab.xlabel函数代码示例

    def check_models(self):
        plt.figure('Bandgap narrowing')
        Na = np.logspace(12, 20)
        Nd = 0.
        dn = 1e14
        temp = 300.

        for author in self.available_models():
            BGN = self.update(Na=Na, Nd=Nd, nxc=dn,
                              author=author,
                              temp=temp)

            if not np.all(BGN == 0):
                plt.plot(Na, BGN, label=author)

        test_file = os.path.join(
            os.path.dirname(os.path.realpath(__file__)),
            'Si', 'check data', 'Bgn.csv')

        data = np.genfromtxt(test_file, delimiter=',', names=True)

        for name in data.dtype.names[1:]:
            plt.plot(
                data['N'], data[name], 'r--',
                label='PV-lighthouse\'s: ' + name)

        plt.semilogx()
        plt.xlabel('Doping (cm$^{-3}$)')
        plt.ylabel('Bandgap narrowing (K)')

        plt.legend(loc=0)

def make_corr1d_fig(dosave=False):
    corr = make_corr_both_hemi()
    lw=2; fs=16
    pl.figure(1)#, figsize=(8, 7))
    pl.clf()
    pl.xlim(4,300)
    pl.ylim(-400,+500)    
    lambda_titles = [r'$20 < \lambda < 30$',
                     r'$30 < \lambda < 40$',
                     r'$\lambda > 40$']
    colors = ['blue','green','red']
    for i in range(3):
        corr1d, rcen = corr_1d_from_2d(corr[i])
        ipdb.set_trace()
        pl.semilogx(rcen, corr1d*rcen**2, lw=lw, color=colors[i])
        #pl.semilogx(rcen, corr1d*rcen**2, 'o', lw=lw, color=colors[i])
    pl.xlabel(r'$s (Mpc)$',fontsize=fs)
    pl.ylabel(r'$s^2 \xi_0(s)$', fontsize=fs)    
    pl.legend(lambda_titles, 'lower left', fontsize=fs+3)
    pl.plot([.1,10000],[0,0],'k--')
    s_bao = 149.28
    pl.plot([s_bao, s_bao],[-9e9,+9e9],'k--')
    pl.text(s_bao*1.03, 420, 'BAO scale')
    pl.text(s_bao*1.03, 370, '%0.1f Mpc'%s_bao)
    if dosave: pl.savefig('xi1d_3bin.pdf')

 def plot_bernoulli_matrix(self, show_npfs=False):
   """
   Plot the heatmap of the Bernoulli matrix 
   @self
   @show_npfs - Highlight NPFS detections [Boolean] 
   """
   matrix = self.Bernoulli_matrix
   if show_npfs == False:
     plot = plt.imshow(matrix)
     plot.set_cmap('hot')
     plt.colorbar()
     plt.xlabel("Bootstraps")
     plt.ylabel("Feature")
     plt.show()
   else:
     for i in self.selected_features:
       for k in range(len(matrix[i])):
         matrix[i,k] = .5
     plot = plt.imshow(matrix)
     plot.set_cmap('hot')
     plt.xlabel("Bootstraps")
     plt.ylabel("Feature")
     plt.colorbar()
     plt.show()
   return None

    def plot(self, title=None, **kwargs):
        """Generates a pylab plot from the result set.

        ``matplotlib`` must be installed, and in an
        IPython Notebook, inlining must be on::

            %%matplotlib inline

        The first and last columns are taken as the X and Y
        values.  Any columns between are ignored.

        Parameters
        ----------
        title: Plot title, defaults to names of Y value columns

        Any additional keyword arguments will be passsed
        through to ``matplotlib.pylab.plot``.
        """
        import matplotlib.pylab as plt
        self.guess_plot_columns()
        self.x = self.x or range(len(self.ys[0]))
        coords = reduce(operator.add, [(self.x, y) for y in self.ys])
        plot = plt.plot(*coords, **kwargs)
        if hasattr(self.x, 'name'):
            plt.xlabel(self.x.name)
        ylabel = ", ".join(y.name for y in self.ys)
        plt.title(title or ylabel)
        plt.ylabel(ylabel)
        return plot

def study_redmapper_2d():
    # I just want to know the typical angular separation for RM clusters.
    # I'm going to do this in a lazy way.
    hemi = 'north'
    rm = load_redmapper(hemi=hemi)
    ra = rm['ra']
    dec = rm['dec']
    ncl = len(ra)
    dist = np.zeros((ncl, ncl))
    for i in range(ncl):
        this_ra = ra[i]
        this_dec = dec[i]
        dra = this_ra-ra
        ddec = this_dec-dec
        dxdec = dra*np.cos(this_dec*np.pi/180.)
        dd = np.sqrt(dxdec**2. + ddec**2.)
        dist[i,:] = dd
        dist[i,i] = 99999999.
    d_near_arcmin = dist.min(0)*60.
    pl.clf(); pl.hist(d_near_arcmin, bins=100)
    pl.title('Distance to Nearest Neighbor for RM clusters')
    pl.xlabel('Distance (arcmin)')
    pl.ylabel('N')
    fwhm_planck_217 = 5.5 # arcmin
    sigma = fwhm_planck_217/2.355
    frac_2sigma = 1.*len(np.where(d_near_arcmin>2.*sigma)[0])/len(d_near_arcmin)
    frac_3sigma = 1.*len(np.where(d_near_arcmin>3.*sigma)[0])/len(d_near_arcmin)
    print '%0.3f percent of RM clusters are separated by 2-sigma_planck_beam'%(100.*frac_2sigma)
    print '%0.3f percent of RM clusters are separated by 3-sigma_planck_beam'%(100.*frac_3sigma)    
    ipdb.set_trace()

def plot_confusion_matrix(cm, title='', cmap=plt.cm.Blues):
    #print cm
    #display vehicle, idle, walking accuracy respectively
    #display overall accuracy
    print type(cm)
   # plt.figure(index
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    #plt.figure("")
    plt.title("Confusion Matrix")
    plt.colorbar()
    tick_marks = [0,1,2]
    target_name = ["driving","idling","walking"]


    plt.xticks(tick_marks,target_name,rotation=45)

    plt.yticks(tick_marks,target_name,rotation=45)
    print len(cm[0])

    for i in range(0,3):
        for j in range(0,3):
         plt.text(i,j,str(cm[i,j]))
    plt.tight_layout()
    plt.ylabel("Actual Value")
    plt.xlabel("Predicted Outcome")

 def test_simple_gen(self):
     self_con = .8
     other_con = 0.05
     g = self.gen.gen_stoch_blockmodel(min_degree=1, blocks=5, self_con=self_con, other_con=other_con,
                                       powerlaw_exp=2.1, degree_seq='powerlaw', num_nodes=1000, num_links=3000)
     deg_hist = vertex_hist(g, 'total')
     res = fit_powerlaw.Fit(g.degree_property_map('total').a, discrete=True)
     print 'powerlaw alpha:', res.power_law.alpha
     print 'powerlaw xmin:', res.power_law.xmin
     if len(deg_hist[0]) != len(deg_hist[1]):
         deg_hist[1] = deg_hist[1][:len(deg_hist[0])]
     print 'plot degree dist'
     plt.plot(deg_hist[1], deg_hist[0])
     plt.xscale('log')
     plt.xlabel('degree')
     plt.ylabel('#nodes')
     plt.yscale('log')
     plt.savefig('deg_dist_test.png')
     plt.close('all')
     print 'plot graph'
     pos = sfdp_layout(g, groups=g.vp['com'], mu=3)
     graph_draw(g, pos=pos, output='graph.png', output_size=(800, 800),
                vertex_size=prop_to_size(g.degree_property_map('total'), mi=2, ma=30), vertex_color=[0., 0., 0., 1.],
                vertex_fill_color=g.vp['com'],
                bg_color=[1., 1., 1., 1.])
     plt.close('all')
     print 'init:', self_con / (self_con + other_con), other_con / (self_con + other_con)
     print 'real:', gt_tools.get_graph_com_connectivity(g, 'com')

    def handle(self, *args, **options):
        try:
            from matplotlib import pylab as pl
            import numpy as np
        except ImportError:
            raise Exception('Be sure to install requirements_scipy.txt before running this.')

        all_names_and_counts = RawCommitteeTransactions.objects.all().values('attest_by_name').annotate(total=Count('attest_by_name')).order_by('-total')
        all_names_and_counts_as_tuple_and_sorted = sorted([(row['attest_by_name'], row['total']) for row in all_names_and_counts], key=lambda row: row[1])
        print "top ten attestors:  (name, number of transactions they attest for)"
        for row in all_names_and_counts_as_tuple_and_sorted[-10:]:
            print row

        n_bins = 100
        filename = 'attestor_participation_distribution.png'

        x_max = all_names_and_counts_as_tuple_and_sorted[-31][1]  # eliminate top outliers from hist
        x_min = all_names_and_counts_as_tuple_and_sorted[0][1]

        counts = [row['total'] for row in all_names_and_counts]
        pl.figure(1, figsize=(18, 6))
        pl.hist(counts, bins=np.arange(x_min, x_max, (float(x_max)-x_min)/100) )
        pl.title('Histogram of Attestor Participation in RawCommitteeTransactions')
        pl.xlabel('Number of transactions a person attested for')
        pl.ylabel('Number of people')
        pl.savefig(filename)

def fdr(p_values=None, verbose=0):
    """Returns the FDR associated with each p value

    Parameters
    -----------
    p_values : ndarray of shape (n)
        The samples p-value

    Returns
    -------
    q : array of shape(n)
        The corresponding fdr values
    """
    p_values = check_p_values(p_values)
    n_samples = p_values.size
    order = p_values.argsort()
    sp_values = p_values[order]

    # compute q while in ascending order
    q = np.minimum(1, n_samples * sp_values / np.arange(1, n_samples + 1))
    for i in range(n_samples - 1, 0, - 1):
        q[i - 1] = min(q[i], q[i - 1])

    # reorder the results
    inverse_order = np.arange(n_samples)
    inverse_order[order] = np.arange(n_samples)
    q = q[inverse_order]

    if verbose:
        import matplotlib.pylab as mp
        mp.figure()
        mp.xlabel('Input p-value')
        mp.plot(p_values, q, '.')
        mp.ylabel('Associated fdr')
    return q

def flipPlot(minExp, maxExp):
    """假定minEXPy和maxExp是正整数且minExp<maxExp
    绘制出2**minExp到2**maxExp次抛硬币的结果
    """
    ratios = []
    diffs = []
    aAxis = []
    for i in range(minExp, maxExp+1):
        aAxis.append(2**i)
    for numFlips in aAxis:
        numHeads = 0
        for n in range(numFlips):
            if random.random() < 0.5:
                numHeads += 1
        numTails = numFlips - numHeads
        ratios.append(numHeads/numFlips)
        diffs.append(abs(numHeads-numTails))
    plt.figure()
    ax1 = plt.subplot(121)
    plt.title("Difference Between Heads and Tails")
    plt.xlabel('Number of Flips')
    plt.ylabel('Abs(#Heads - #Tails)')
    ax1.semilogx(aAxis, diffs, 'bo')
    ax2 = plt.subplot(122)
    plt.title("Heads/Tails Ratios")
    plt.xlabel('Number of Flips')
    plt.ylabel("#Heads/#Tails")
    ax2.semilogx(aAxis, ratios, 'bo')
    plt.show()

def plot_q(model='cem', r_min=0.0, r_max=6371.0, dr=1.0):
    """
    Plot a radiallysymmetric Q model.

    plot_q(model='cem', r_min=0.0, r_max=6371.0, dr=1.0):

    r_min=minimum radius [km], r_max=maximum radius [km], dr=radius
    increment [km]

    Currently available models (model): cem, prem, ql6
    """
    import matplotlib.pylab as plt

    r = np.arange(r_min, r_max + dr, dr)
    q = np.zeros(len(r))

    for k in range(len(r)):

        if model == 'cem':
            q[k] = q_cem(r[k])
        elif model == 'ql6':
            q[k] = q_ql6(r[k])
        elif model == 'prem':
            q[k] = q_prem(r[k])

    plt.plot(r, q, 'k')
    plt.xlim((0.0, r_max))
    plt.xlabel('radius [km]')
    plt.ylabel('Q')
    plt.show()

def plot_runtime_results(results):
    plt.rcParams["figure.figsize"] = 7,7
    plt.rcParams["font.size"] = 22
    matplotlib.rc("xtick", labelsize=24)
    matplotlib.rc("ytick", labelsize=24)

    params = {"text.fontsize" : 32,
              "font.size" : 32,
              "legend.fontsize" : 30,
              "axes.labelsize" : 32,
              "text.usetex" : False
              }
    plt.rcParams.update(params)
    
    #plt.semilogx(results[:,0], results[:,3], 'r-x', lw=3)
    #plt.semilogx(results[:,0], results[:,1], 'g-D', lw=3)
    #plt.semilogx(results[:,0], results[:,2], 'b-s', lw=3)

    plt.plot(results[:,0], results[:,3], 'r-x', lw=3, ms=10)
    plt.plot(results[:,0], results[:,1], 'g-D', lw=3, ms=10)
    plt.plot(results[:,0], results[:,2], 'b-s', lw=3, ms=10)

    plt.legend(["Chain", "Tree", "FFT Tree"], loc="upper left")
    plt.xticks([1e5, 2e5, 3e5])
    plt.yticks([0, 60, 120, 180])

    plt.xlabel("Problem Size")
    plt.ylabel("Runtime (sec)")
    return results

def plotMassFunction(im, pm, outbase, mmin=9, mmax=13, mstep=0.05):
    """
    Make a comparison plot between the input mass function and the 
    predicted projected correlation function
    """
    plt.clf()

    nmbins = ( mmax - mmin ) / mstep
    mbins = np.logspace( mmin, mmax, nmbins )
    mcen = ( mbins[:-1] + mbins[1:] ) /2
    
    plt.xscale( 'log', nonposx = 'clip' )
    plt.yscale( 'log', nonposy = 'clip' )
    
    ic, e, p = plt.hist( im, mbins, label='Original Halos', alpha=0.5, normed = True)
    pc, e, p = plt.hist( pm, mbins, label='Added Halos', alpha=0.5, normed = True)
    
    plt.legend()
    plt.xlabel( r'$M_{vir}$' )
    plt.ylabel( r'$\frac{dN}{dM}$' )
    #plt.tight_layout()
    plt.savefig( outbase+'_mfcn.png' )
    
    mdtype = np.dtype( [ ('mcen', float), ('imcounts', float), ('pmcounts', float) ] )
    mf = np.ndarray( len(mcen), dtype = mdtype )
    mf[ 'mcen' ] = mcen
    mf[ 'imcounts' ] = ic
    mf[ 'pmcounts' ] = pc

    fitsio.write( outbase+'_mfcn.fit', mf )

def plotFeaturePDF(ift, pft, outbase, fmin=0.0, fmax=1.0, fstep=0.01):
    """
    Plot a comparison between the input feature distribution and the 
    feature distribution of the predicted halos
    """
    plt.clf()
    nfbins = ( fmax - fmin ) / fstep
    fbins = np.logspace( fmin, fmax, nfbins )
    fcen = ( fbins[:-1] + fbins[1:] ) / 2

    plt.xscale( 'log', nonposx='clip' )
    plt.yscale( 'log', nonposy='clip' )
    
    ic, e, p = plt.hist( ift, fbins, label='Original Halos', alpha=0.5, normed=True )
    pc, e, p = plt.hist( pft, fbins, label='Added Halos', alpha=0.5, normed=True )

    plt.legend()
    plt.xlabel( r'$\delta$' )
    plt.savefig( outbase+'_fpdf.png' )

    fdtype = np.dtype( [ ('fcen', float), ('ifcounts', float), ('pfcounts', float) ] )
    fd = np.ndarray( len(fcen), dtype = fdtype )
    fd[ 'mcen' ] = fcen
    fd[ 'imcounts' ] = ic
    fd[ 'pmcounts' ] = pc

    fitsio.write( outbase+'_fpdf.fit', fd )

def cdf(x,colsym="",lab="",lw=4):
    """ plot the cumulative density function

    Parameters
    ----------

    x : np.array()
    colsym : string
    lab : string
    lw : int
        linewidth

    Examples
    --------

    >>> import numpy as np

    """
    rcParams['legend.fontsize']=20
    rcParams['font.size']=20

    x  = np.sort(x)
    n  = len(x)
    x2 = np.repeat(x, 2)
    y2 = np.hstack([0.0, repeat(np.arange(1,n) / float(n), 2), 1.0])
    plt.plot(x2,y2,colsym,label=lab,linewidth=lw)
    plt.grid('on')
    plt.legend(loc=2)
    plt.xlabel('Ranging Error[m]')
    plt.ylabel('Cumulative Probability')