Python pylab.scatter方法代碼示例

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = list(zip(xs, ys))
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def test_proj():
    import pylab
    M = test_proj_make_M()

    ts = ['%d' % i for i in [0,1,2,3,0,4,5,6,7,4]]
    xs, ys, zs = [0,1,1,0,0, 0,1,1,0,0], [0,0,1,1,0, 0,0,1,1,0], \
            [0,0,0,0,0, 1,1,1,1,1]
    xs, ys, zs = [np.array(v)*300 for v in (xs, ys, zs)]
    #
    test_proj_draw_axes(M, s=400)
    txs, tys, tzs = proj_transform(xs, ys, zs, M)
    ixs, iys, izs = inv_transform(txs, tys, tzs, M)

    pylab.scatter(txs, tys, c=tzs)
    pylab.plot(txs, tys, c='r')
    for x, y, t in zip(txs, tys, ts):
        pylab.text(x, y, t)

    pylab.xlim(-0.2, 0.2)
    pylab.ylim(-0.2, 0.2)

    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = zip(xs, ys)
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def polyfitting():
    '''
    helper function to play around with polyfit from:
    http://www.wired.com/2011/01/linear-regression-with-pylab/
    '''
    x = [0.2, 1.3, 2.1, 2.9, 3.3]
    y = [3.3, 3.9, 4.8, 5.5, 6.9]
    slope, intercept = pylab.polyfit(x, y, 1)
    print 'slope:', slope, 'intercept:', intercept

    yp = pylab.polyval([slope, intercept], x)
    pylab.plot(x, yp)
    pylab.scatter(x, y)
    pylab.show()

#polyfitting() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot_question7():
    '''
    graph of total resources generated as a function of time,
    for upgrade_cost_increment == 1
    '''
    data = resources_vs_time(1.0, 50)
    time = [item[0] for item in data]
    resource = [item[1] for item in data]
    a, b, c = pylab.polyfit(time, resource, 2)
    print 'polyfit with argument \'2\' fits the data, thus the degree of the polynomial is 2 (quadratic)'

    # plot in pylab on logarithmic scale (total resources over time for upgrade growth 0.0)
    #pylab.loglog(time, resource, 'o')

    # plot fitting function
    yp = pylab.polyval([a, b, c], time)
    pylab.plot(time, yp)
    pylab.scatter(time, resource)
    pylab.title('Silly Homework, Question 7')
    pylab.legend(('Resources for increment 1', 'Fitting function' + ', slope: ' + str(a)))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.grid()
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight') 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def __init__(self, table, lens='mds', metric='correlation', precomputed=False, **kwargs):
        """
        Initializes the class by providing the mapper input table generated by Preprocess.save(). The parameter 'metric'
        specifies the metric distance to be used ('correlation', 'euclidean' or 'neighbor'). The parameter 'lens'
        specifies the dimensional reduction algorithm to be used ('mds' or 'pca'). The rest of the arguments are
        passed directly to sklearn.manifold.MDS or sklearn.decomposition.PCA. It plots the low-dimensional projection
        of the data.
        """
        self.df = pandas.read_table(table + '.mapper.tsv')
        if lens == 'neighbor':
            self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, **kwargs)
        elif lens == 'mds':
            if precomputed:
                self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, metric=metric,
                                                          dissimilarity='precomputed', **kwargs)
            else:
                self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, metric=metric, **kwargs)
        else:
            self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, **kwargs)
        pylab.figure()
        pylab.scatter(numpy.array(self.lens_data_mds)[:, 0], numpy.array(self.lens_data_mds)[:, 1], s=10, alpha=0.7)
        pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot_CDR_correlation(self, doplot=True):
        """
        Displays correlation between sampling time points and CDR. It returns the two
        parameters of the linear fit, Pearson's r, p-value and standard error. If optional argument 'doplot' is
        False, the plot is not displayed.
        """
        pel2, tol = self.get_gene(self.rootlane, ignore_log=True)
        pel = numpy.array([pel2[m] for m in self.pl])*tol
        dr2 = self.get_gene('_CDR')[0]
        dr = numpy.array([dr2[m] for m in self.pl])
        po = scipy.stats.linregress(pel, dr)
        if doplot:
            pylab.scatter(pel, dr, s=9.0, alpha=0.7, c='r')
            pylab.xlim(min(pel), max(pel))
            pylab.ylim(0, max(dr)*1.1)
            pylab.xlabel(self.rootlane)
            pylab.ylabel('CDR')
            xk = pylab.linspace(min(pel), max(pel), 50)
            pylab.plot(xk, po[1]+po[0]*xk, 'k--', linewidth=2.0)
            pylab.show()
        return po 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot(t, plots, shot_ind):
    n = len(plots)

    for i in range(0,n):
        label, data = plots[i]

        plt = py.subplot(n, 1, i+1)
        plt.tick_params(labelsize=8)
        py.grid()
        py.xlim([t[0], t[-1]])
        py.ylabel(label)

        py.plot(t, data, 'k-')
        py.scatter(t[shot_ind], data[shot_ind], marker='*', c='g')

    py.xlabel("Time")
    py.show()
    py.close() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def coinc_timeseries_plot(coinc_file, start, end):
    fig = pylab.figure()
    f = h5py.File(coinc_file, 'r')

    stat1 = f['foreground/stat1']
    stat2 = f['foreground/stat2']
    time1 = f['foreground/time1']
    time2 = f['foreground/time2']
    ifo1 = f.attrs['detector_1']
    ifo2 = f.attrs['detector_2']

    pylab.scatter(time1, stat1, label=ifo1, color=ifo_color[ifo1])
    pylab.scatter(time2, stat2, label=ifo2, color=ifo_color[ifo2])

    fmt = '.12g'
    mpld3.plugins.connect(fig, mpld3.plugins.MousePosition(fmt=fmt))
    pylab.legend()
    pylab.xlabel('Time (s)')
    pylab.ylabel('NewSNR')
    pylab.grid()
    return mpld3.fig_to_html(fig) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def trigger_timeseries_plot(file_list, ifos, start, end):

    fig = pylab.figure()
    for ifo in ifos:
        trigs = columns_from_file_list(file_list,
                                       ['snr', 'end_time'],
                                       ifo, start, end)
        print(trigs)
        pylab.scatter(trigs['end_time'], trigs['snr'], label=ifo,
                      color=ifo_color[ifo])

        fmt = '.12g'
        mpld3.plugins.connect(fig, mpld3.plugins.MousePosition(fmt=fmt))
    pylab.legend()
    pylab.xlabel('Time (s)')
    pylab.ylabel('SNR')
    pylab.grid()
    return mpld3.fig_to_html(fig) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot_facade_cuts(self):

        facade_sig = self.facade_edge_scores.sum(0)
        facade_cuts = find_facade_cuts(facade_sig, dilation_amount=self.facade_merge_amount)
        mu = np.mean(facade_sig)
        sigma = np.std(facade_sig)

        w = self.rectified.shape[1]
        pad=10

        gs1 = pl.GridSpec(5, 5)
        gs1.update(wspace=0.5, hspace=0.0)  # set the spacing between axes.

        pl.subplot(gs1[:3, :])
        pl.imshow(self.rectified)
        pl.vlines(facade_cuts, *pl.ylim(), lw=2, color='black')
        pl.axis('off')
        pl.xlim(-pad, w+pad)

        pl.subplot(gs1[3:, :], sharex=pl.gca())
        pl.fill_between(np.arange(w), 0, facade_sig, lw=0, color='red')
        pl.fill_between(np.arange(w), 0, np.clip(facade_sig, 0, mu+sigma), color='blue')
        pl.plot(np.arange(w), facade_sig, color='blue')

        pl.vlines(facade_cuts, facade_sig[facade_cuts], pl.xlim()[1], lw=2, color='black')
        pl.scatter(facade_cuts, facade_sig[facade_cuts])

        pl.axis('off')

        pl.hlines(mu, 0, w, linestyle='dashed', color='black')
        pl.text(0, mu, '$\mu$ ', ha='right')

        pl.hlines(mu + sigma, 0, w, linestyle='dashed', color='gray',)
        pl.text(0, mu + sigma, '$\mu+\sigma$ ', ha='right')
        pl.xlim(-pad, w+pad) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot(Y, labels):
    pylab.scatter(Y[:, 0], Y[:, 1], s=30, c=labels, cmap=colors, linewidth=0)
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def plot_polynomial_regression():
    rng = np.random.RandomState(0)
    x = 2*rng.rand(100) - 1

    f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
    y = f(x) + .4 * rng.normal(size=100)

    x_test = np.linspace(-1, 1, 100)

    pl.figure()
    pl.scatter(x, y, s=4)

    X = np.array([x**i for i in range(5)]).T
    X_test = np.array([x_test**i for i in range(5)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='4th order')

    X = np.array([x**i for i in range(10)]).T
    X_test = np.array([x_test**i for i in range(10)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='9th order')

    pl.legend(loc='best')
    pl.axis('tight')
    pl.title('Fitting a 4th and a 9th order polynomial')

    pl.figure()
    pl.scatter(x, y, s=4)
    pl.plot(x_test, f(x_test), label="truth")
    pl.axis('tight')
    pl.title('Ground truth (9th order polynomial)') 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import scatter [as 別名]
def test_kmeans(K=5):
    """Test k-means with the synthetic data."""
    X = XMeans.generate_2d_data(K=4)
    wX = vq.whiten(X)
    dic, dist = vq.kmeans(wX, K, iter=100)

    plt.scatter(wX[:, 0], wX[:, 1])
    plt.scatter(dic[:, 0], dic[:, 1], color="m")
    plt.show()