Python pyplot.twinx方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twin_axis_locaters_formatters():
    vals = np.linspace(0, 1, num=5, endpoint=True)
    locs = np.sin(np.pi * vals / 2.0)

    majl = plt.FixedLocator(locs)
    minl = plt.FixedLocator([0.1, 0.2, 0.3])

    fig = plt.figure()
    ax1 = fig.add_subplot(1, 1, 1)
    ax1.plot([0.1, 100], [0, 1])
    ax1.yaxis.set_major_locator(majl)
    ax1.yaxis.set_minor_locator(minl)
    ax1.yaxis.set_major_formatter(plt.FormatStrFormatter('%08.2lf'))
    ax1.yaxis.set_minor_formatter(plt.FixedFormatter(['tricks', 'mind',
                                                      'jedi']))

    ax1.xaxis.set_major_locator(plt.LinearLocator())
    ax1.xaxis.set_minor_locator(plt.FixedLocator([15, 35, 55, 75]))
    ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%05.2lf'))
    ax1.xaxis.set_minor_formatter(plt.FixedFormatter(['c', '3', 'p', 'o']))
    ax2 = ax1.twiny()
    ax3 = ax1.twinx() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twinx_cla():
    fig, ax = plt.subplots()
    ax2 = ax.twinx()
    ax3 = ax2.twiny()
    plt.draw()
    assert not ax2.xaxis.get_visible()
    assert not ax2.patch.get_visible()
    ax2.cla()
    ax3.cla()

    assert not ax2.xaxis.get_visible()
    assert not ax2.patch.get_visible()
    assert ax2.yaxis.get_visible()

    assert ax3.xaxis.get_visible()
    assert not ax3.patch.get_visible()
    assert not ax3.yaxis.get_visible()

    assert ax.xaxis.get_visible()
    assert ax.patch.get_visible()
    assert ax.yaxis.get_visible() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def shared_axis_remover(request):
    def _helper_x(ax):
        ax2 = ax.twinx()
        ax2.remove()
        ax.set_xlim(0, 15)
        r = ax.xaxis.get_major_locator()()
        assert r[-1] > 14

    def _helper_y(ax):
        ax2 = ax.twiny()
        ax2.remove()
        ax.set_ylim(0, 15)
        r = ax.yaxis.get_major_locator()()
        assert r[-1] > 14

    return {"x": _helper_x, "y": _helper_y}[request.param] 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True,
                   verbose=False, two_gauss_model=False, lw=2.5, color='k',
                   alpha=0.5, fillcolor=None):
    """Plot a fitted model overlay on a FRET histogram."""
    if ax is None:
        ax2 = gca()
    else:
        ax2 = plt.twinx(ax=ax)
        ax2.grid(False)

    if d.fit_E_curve and show_model:
        x = r_[-0.2:1.21:0.002]
        y = d.fit_E_model(x, d.fit_E_res[i, :])
        scale = F*d.fit_E_model_F[i]
        if two_gauss_model:
            assert d.fit_E_res.shape[1] > 2
            if d.fit_E_res.shape[1] == 5:
                m1, s1, m2, s2, a1 = d.fit_E_res[i, :]
                a2 = (1-a1)
            elif d.fit_E_res.shape[1] == 6:
                m1, s1, a1, m2, s2, a2 = d.fit_E_res[i, :]
            y1 = a1*normpdf(x, m1, s1)
            y2 = a2*normpdf(x, m2, s2)
            ax2.plot(x, scale*y1, ls='--', lw=lw, alpha=alpha, color=color)
            ax2.plot(x, scale*y2, ls='--', lw=lw, alpha=alpha, color=color)
        if fillcolor is None:
            ax2.plot(x, scale*y, lw=lw, alpha=alpha, color=color)
        else:
            ax2.fill_between(x, scale*y, lw=lw, alpha=alpha, edgecolor=color,
                             facecolor=fillcolor, zorder=10)
        if verbose:
            print('Fit Integral:', np.trapz(scale*y, x))

    ax2.axvline(d.E_fit[i], lw=3, color=red, ls='--', alpha=0.6)
    xtext = 0.6 if d.E_fit[i] < 0.6 else 0.2
    if d.nch > 1 and not no_E:
        ax2.text(xtext, 0.81, "CH%d: $E_{fit} = %.3f$" % (i+1, d.E_fit[i]),
                 transform=gca().transAxes, fontsize=16,
                 bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5)) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twinx_axis_scales():
    x = np.array([0, 0.5, 1])
    y = 0.5 * x
    x2 = np.array([0, 1, 2])
    y2 = 2 * x2

    fig = plt.figure()
    ax = fig.add_axes((0, 0, 1, 1), autoscalex_on=False, autoscaley_on=False)
    ax.plot(x, y, color='blue', lw=10)

    ax2 = plt.twinx(ax)
    ax2.plot(x2, y2, 'r--', lw=5)

    ax.margins(0, 0)
    ax2.margins(0, 0) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twin_inherit_autoscale_setting():
    fig, ax = plt.subplots()
    ax_x_on = ax.twinx()
    ax.set_autoscalex_on(False)
    ax_x_off = ax.twinx()

    assert ax_x_on.get_autoscalex_on()
    assert not ax_x_off.get_autoscalex_on()

    ax_y_on = ax.twiny()
    ax.set_autoscaley_on(False)
    ax_y_off = ax.twiny()

    assert ax_y_on.get_autoscaley_on()
    assert not ax_y_off.get_autoscaley_on() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_subplot_key_hash():
    ax = plt.subplot(np.float64(5.5), np.int64(1), np.float64(1.2))
    ax.twinx()
    assert ax.get_subplotspec().get_geometry() == (5, 1, 0, 0) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twin_spines_on_top():
    matplotlib.rcParams['axes.linewidth'] = 48.0
    matplotlib.rcParams['lines.linewidth'] = 48.0

    fig = plt.figure()
    ax1 = fig.add_subplot(1, 1, 1)

    data = np.array([[1000, 1100, 1200, 1250],
                     [310, 301, 360, 400]])

    ax2 = ax1.twinx()

    ax1.plot(data[0], data[1]/1E3, color='#BEAED4')
    ax1.fill_between(data[0], data[1]/1E3, color='#BEAED4', alpha=.8)

    ax2.plot(data[0], data[1]/1E3, color='#7FC97F')
    ax2.fill_between(data[0], data[1]/1E3, color='#7FC97F', alpha=.5)

    # Reuse testcase from above for a labeled data test
    data = {"i": data[0], "j": data[1]/1E3}
    fig = plt.figure()
    ax1 = fig.add_subplot(1, 1, 1)
    ax2 = ax1.twinx()
    ax1.plot("i", "j", color='#BEAED4', data=data)
    ax1.fill_between("i", "j", color='#BEAED4', alpha=.8, data=data)
    ax2.plot("i", "j", color='#7FC97F', data=data)
    ax2.fill_between("i", "j", color='#7FC97F', alpha=.5, data=data) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twin_with_aspect(twin):
    fig, ax = plt.subplots()
    # test twinx or twiny
    ax_twin = getattr(ax, 'twin{}'.format(twin))()
    ax.set_aspect(5)
    ax_twin.set_aspect(2)
    assert_array_equal(ax.bbox.extents,
                       ax_twin.bbox.extents) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def test_twinx_knows_limits():
    fig, ax = plt.subplots()

    ax.axvspan(1, 2)
    xtwin = ax.twinx()
    xtwin.plot([0, 0.5], [1, 2])
    # control axis
    fig2, ax2 = plt.subplots()

    ax2.axvspan(1, 2)
    ax2.plot([0, 0.5], [1, 2])

    assert_array_equal(xtwin.viewLim.intervalx, ax2.viewLim.intervalx) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import twinx [as 別名]
def buVsTime(reactor, scalars):
    r"""
    produces a burnup and DPA vs. time plot for this case

    Will add a second axis containing DPA if the scalar column maxDPA exists.

    Parameters
    ----------
    scalars : dict
        Scalar values for this case


    """
    plt.figure()
    try:
        plt.plot(scalars["time"], scalars["maxBuI"], ".-", label="Driver")
    except ValueError:
        runLog.warning(
            "Incompatible axis length in burnup plot. Time has {0}, bu has {1}. Skipping"
            "".format(len(scalars["time"]), len(scalars["maxBuI"]))
        )
        plt.close(1)
        return
    plt.plot(scalars["time"], scalars["maxBuF"], ".-", label="Feed")
    plt.xlabel("Time (yr)")
    plt.ylabel("BU (%FIMA)")
    plt.grid(color="0.70")
    plt.legend(loc="lower left")
    title = "Maximum burnup"
    if scalars["maxDPA"]:
        plt.twinx()
        plt.plot(scalars["time"], scalars["maxDPA"], "r--", label="dpa")
        plt.legend(loc="lower right")
        plt.ylabel("dpa")
        title += " and DPA"
    title += " for " + reactor.name

    plt.title(title)
    plt.legend(loc="lower right")
    figName = reactor.name + ".bu." + settings.getMasterCs()["outputFileExtension"]
    plt.savefig(figName)
    plt.close(1)

    report.setData("Burnup Plot", os.path.abspath(figName), report.BURNUP_PLOT)