Python pylab.gca方法代碼示例

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False):
    """plot_confusion_matrix."""
    cm = confusion_matrix(y_true, y_pred)
    fmt = "%d"
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        fmt = "%.2f"
    xticklabels = list(sorted(set(y_pred)))
    yticklabels = list(sorted(set(y_true)))
    if size is not None:
        plt.figure(figsize=(size, size))
    heatmap(cm, xlabel='Predicted label', ylabel='True label',
            xticklabels=xticklabels, yticklabels=yticklabels,
            cmap=plt.cm.Blues, fmt=fmt)
    if normalize:
        plt.title("Confusion matrix (norm.)")
    else:
        plt.title("Confusion matrix")
    plt.gca().invert_yaxis() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [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 gca [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 gca [as 別名]
def plot_regions(self, fill=True, bgimage=None, alpha=0.5):
        import pylab as pl
        ax = pl.gca()
        assert isinstance(ax, pl.Axes)

        colors = i12.JET_12

        self._plot_background(bgimage)

        for label in self.regions:
            color = colors[i12.LABELS.index(label)] / 255.

            for region in self.regions[label]:
                t = region['top']
                l = self.facade_left + region['left']
                b = region['bottom']
                r = self.facade_left + region['right']
                patch = pl.Rectangle((l, t), r - l, b - t, color=color, fill=fill, alpha=alpha)
                ax.add_patch(patch) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def impose_legend_limit(limit=30, axes="gca", **kwargs):
    """
    This will erase all but, say, 30 of the legend entries and remake the legend.
    You'll probably have to move it back into your favorite position at this point.
    """
    if axes=="gca": axes = _pylab.gca()

    # make these axes current
    _pylab.axes(axes)

    # loop over all the lines_pylab.
    for n in range(0,len(axes.lines)):
        if n >  limit-1 and not n==len(axes.lines)-1: axes.lines[n].set_label("_nolegend_")
        if n == limit-1 and not n==len(axes.lines)-1: axes.lines[n].set_label("...")

    _pylab.legend(**kwargs) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def image_click_xshift(axes = "gca"):
    """
    Takes a starting and ending point, then shifts the image y by this amount
    """
    if axes == "gca": axes = _pylab.gca()

    try:
        p1 = _pylab.ginput()
        p2 = _pylab.ginput()

        xshift = p2[0][0]-p1[0][0]

        e = axes.images[0].get_extent()

        e[0] = e[0] + xshift
        e[1] = e[1] + xshift

        axes.images[0].set_extent(e)

        _pylab.draw()
    except:
        print("whoops") 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def image_shift(xshift=0, yshift=0, axes="gca"):
    """
    This will shift an image to a new location on x and y.
    """

    if axes=="gca": axes = _pylab.gca()

    e = axes.images[0].get_extent()

    e[0] = e[0] + xshift
    e[1] = e[1] + xshift
    e[2] = e[2] + yshift
    e[3] = e[3] + yshift

    axes.images[0].set_extent(e)

    _pylab.draw() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def image_set_clim(zmin=None, zmax=None, axes="gca"):
    """
    This will set the clim (range) of the colorbar.

    Setting zmin or zmax to None will not change them.
    Setting zmin or zmax to "auto" will auto-scale them to include all the data.
    """
    if axes=="gca": axes=_pylab.gca()

    image = axes.images[0]

    if zmin=='auto': zmin = _n.min(image.get_array())
    if zmax=='auto': zmax = _n.max(image.get_array())

    if zmin==None: zmin = image.get_clim()[0]
    if zmax==None: zmax = image.get_clim()[1]

    image.set_clim(zmin, zmax)

    _pylab.draw() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def reverse_draw_order(axes="current"):
    """

    This function takes the graph and reverses the draw order.

    """

    if axes=="current": axes = _pylab.gca()

    # get the lines from the plot
    lines = axes.get_lines()

    # reverse the order
    lines.reverse()

    for n in range(0, len(lines)):
        if isinstance(lines[n], _mpl.lines.Line2D):
            axes.lines[n]=lines[n]

    _pylab.draw() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def scale_x(scale, axes="current"):
    """

    This function scales lines horizontally.

    """

    if axes=="current": axes = _pylab.gca()

    # get the lines from the plot
    lines = axes.get_lines()

    # loop over the lines and trim the data
    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):
            line.set_xdata(_pylab.array(line.get_xdata())*scale)

    # update the title
    title = axes.title.get_text()
    title += ", x_scale="+str(scale)
    axes.title.set_text(title)

    # zoom to surround the data properly
    auto_zoom() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def set_all_line_attributes(attribute="lw", value=2, axes="current", refresh=True):
    """

    This function sets all the specified line attributes.

    """

    if axes=="current": axes = _pylab.gca()

    # get the lines from the plot
    lines = axes.get_lines()

    # loop over the lines and trim the data
    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):
            _pylab.setp(line, attribute, value)

    # update the plot
    if refresh: _pylab.draw() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def line_math(fx=None, fy=None, axes='gca'):
    """
    applies function fx to all xdata and fy to all ydata.
    """

    if axes=='gca': axes = _pylab.gca()

    lines = axes.get_lines()

    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):
            xdata, ydata = line.get_data()
            if not fx==None: xdata = fx(xdata)
            if not fy==None: ydata = fy(ydata)
            line.set_data(xdata,ydata)

    _pylab.draw() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def render_sdf(obj_, object_name_):
    plt.figure()
    # ax = h.add_subplot(111, projection='3d')

    # surface_points = np.where(np.abs(sdf.sdf_values) < thresh)
    # surface_points = np.array(surface_points)
    # surface_points = surface_points[:, np.random.choice(surface_points[0].size, 3000, replace=True)]
    # # from IPython import embed; embed()
    surface_points = obj_.sdf.surface_points()[0]
    surface_points = np.array(surface_points)
    ind = np.random.choice(np.arange(len(surface_points)), 1000)
    x = surface_points[ind, 0]
    y = surface_points[ind, 1]
    z = surface_points[ind, 2]

    ax = plt.gca(projection=Axes3D.name)
    ax.scatter(x, y, z, '.', s=np.ones_like(x) * 0.3, c='b')
    ax.set_xlim3d(0, obj_.sdf.dims_[0])
    ax.set_ylim3d(0, obj_.sdf.dims_[1])
    ax.set_zlim3d(0, obj_.sdf.dims_[2])
    plt.title(object_name_)
    plt.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def plot(ifile, varkey, options, before='', after=''):
    import pylab as pl
    outpath = getattr(options, 'outpath', '.')
    var = ifile.variables[varkey]
    dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
    if len(dims) > 1:
        raise ValueError(
            'Plots can have only 1 non-unity dimensions; got %d - %s' %
            (len(dims), str(dims)))
    exec(before)
    ax = pl.gca()
    print(varkey, end='')
    if options.logscale:
        ax.set_yscale('log')

    ax.plot(var[:].squeeze())
    ax.set_xlabel('unknown')
    ax.set_ylabel(getattr(var, 'standard_name',
                          varkey).strip() + ' ' + var.units.strip())
    fmt = 'png'
    figpath = os.path.join(outpath + '_1d_' + varkey + '.' + fmt)
    exec(after)
    pl.savefig(figpath)
    print('Saved fig', figpath)
    return figpath 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import gca [as 別名]
def drawPrfast(tp, fp, tot, show=True, col="g"):
    tp = numpy.cumsum(tp)
    fp = numpy.cumsum(fp)
    rec = tp / tot
    prec = tp / (fp + tp)
    ap = VOColdap(rec, prec)
    ap1 = VOCap(rec, prec)
    if show:
        pylab.plot(rec, prec, '-%s' % col)
        pylab.title("AP=%.1f 11pt(%.1f)" % (ap1 * 100, ap * 100))
        pylab.xlabel("Recall")
        pylab.ylabel("Precision")
        pylab.grid()
        pylab.gca().set_xlim((0, 1))
        pylab.gca().set_ylim((0, 1))
        pylab.show()
        pylab.draw()
    return rec, prec, ap1