Python axes.Subplot方法代碼示例

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _add_update_events(subplot: plt_axes.Subplot, dna_helix_graph: nx.DiGraph,
                       nucleotide_plots: Dict[Nucleotide, _NUCLEOTIDE_PLOT]):
    subplot.figure.canvas.mpl_connect(
        'draw_event', lambda x: subplot.pchanged())
    subplot.figure.canvas.mpl_connect(
        'resize_event', lambda x: subplot.pchanged())

    text_initial_position = list(nucleotide_plots.values())[0].body.center
    text_object = subplot.text(
        text_initial_position[0], text_initial_position[1], "",
        ha="right", va="top", ma="left",
        bbox=dict(facecolor='white', edgecolor='blue', pad=5.0))
    text_object.set_visible(False)

    subplot.figure.canvas.mpl_connect(
        'button_press_event',
        partial(_remove_nucleotide_info_text, text_object=text_object))
    subplot.figure.canvas.mpl_connect(
        'pick_event',
        partial(_draw_nucleotide_info, dna_helix_graph=dna_helix_graph,
                text_object=text_object, subplot=subplot)) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_nucleotide_body(nucleotide, center, subplot: plt_axes.Subplot,
                          radius=10.0):
    nucleotide_color, nucleotide_base_class = _get_nucleotide_color(nucleotide)
    nucleotide_name = nucleotide.name
    if len(nucleotide_name) > 10:
        nucleotide_name = nucleotide_name.replace("_", "_\n")

    nucleotide_body = patches.Circle(
        center, radius=radius, color=nucleotide_color)
    text_object = subplot.text(
        center[0], center[1], nucleotide_name, va="center", ha="center")
    text_object.draw(subplot.figure.canvas.renderer)
    subplot.add_patch(nucleotide_body)
    nucleotide_body.add_callback(
        partial(_nucleotide_name_callback, text_object=text_object))
    nucleotide_body.set_label(":".join([nucleotide_base_class.__name__,
                                        nucleotide.name]))
    nucleotide_body.set_picker(True)
    return nucleotide_body 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_click_instructions(subplot: plt_axes.Subplot,
                             doubleclick=True, singleclck=True):
    instruction_texts = list()
    instruction_texts.append("Interactive instructions:")
    if singleclck:
        instruction_texts.append(
            "Click once on nucleotide to see its information")
    if doubleclick:
        instruction_texts.append(
            "Make double clock on nucleotide to cut the subgraph with its "
            "incoming and outgoing nucleotides in new figure")

    instruction_text = "\n".join(instruction_texts)
    subplot.annotate(
        instruction_text, (0.5, 0.01), xycoords="figure fraction",
        ha="center", va="bottom", ma="left",
        bbox=dict(facecolor='white', edgecolor='blue', pad=5.0)) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def test_AxonMapSpatial_plot():
    model = AxonMapSpatial()
    for use_dva, xlim in zip([True, False], [(-18, 18), (-5000, 5000)]):
        ax = model.plot(use_dva=use_dva)
        npt.assert_equal(isinstance(ax, Subplot), True)
        npt.assert_equal(ax.get_xlim(), xlim)

    # Quadrants can be annotated:
    for ann_q, n_q in [(True, 6), (False, 0)]:
        fig, ax = plt.subplots()
        model.plot(annotate=ann_q, ax=ax)
        npt.assert_equal(len(ax.child_axes), int(n_q > 0))
        if len(ax.child_axes) > 0:
            npt.assert_equal(len(ax.child_axes[0].texts), n_q)
        plt.close(fig) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def test_Percept_plot():
    y_range = (-1, 1)
    x_range = (-2, 2)
    grid = Grid2D(x_range, y_range)
    percept = Percept(np.arange(15).reshape((3, 5, 1)), space=grid)

    # Basic usage of pcolor:
    ax = percept.plot(kind='pcolor')
    npt.assert_equal(isinstance(ax, Subplot), True)
    npt.assert_almost_equal(ax.axis(), [*x_range, *y_range])
    frame = percept.get_brightest_frame()
    npt.assert_almost_equal(ax.collections[0].get_clim(),
                            [frame.min(), frame.max()])

    # Basic usage of hex:
    ax = percept.plot(kind='hex')
    npt.assert_equal(isinstance(ax, Subplot), True)
    npt.assert_almost_equal(ax.axis(), [percept.xdva[0], percept.xdva[-1],
                                        percept.ydva[0], percept.ydva[-1]])
    npt.assert_almost_equal(ax.collections[0].get_clim(),
                            [percept.data[..., 0].min(),
                             percept.data[..., 0].max()])

    # Verify color map:
    npt.assert_equal(ax.collections[0].cmap, plt.cm.gray)

    # Specify figsize:
    ax = percept.plot(kind='pcolor', figsize=(6, 4))
    npt.assert_almost_equal(ax.figure.get_size_inches(), (6, 4))

    # Invalid calls:
    with pytest.raises(ValueError):
        percept.plot(kind='invalid')
    with pytest.raises(TypeError):
        percept.plot(ax='invalid')

    # TODO
    with pytest.raises(NotImplementedError):
        percept.plot(time=3.3) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _get_subplots(self):
        from matplotlib.axes import Subplot
        return [ax for ax in self.axes[0].get_figure().get_axes()
                if isinstance(ax, Subplot)] 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def draw_dna_connections(dna_helix_graph: nx.DiGraph,
                         nucleotide_plots: Dict[Nucleotide, _NUCLEOTIDE_PLOT],
                         subplot: plt_axes.Subplot,
                         verbosity: int = 0):
    """
    Draw dna connections on given subplot according to verbosity level

    Parameters
    ----------
    dna_helix_graph
        directed graph with nucleotides as nodes
    nucleotide_plots
        mapping of nucleotide to its plot
    subplot
        subplot to draw on
    verbosity
        verbosity of the visualization;
        if verbosity == 0, then only the connections between
        nucleotide are drawn, otherwise connections between nucleotide keys
        are drawn
    """
    nucleotide_positions = {
        each_nucleotide: each_nucleotide_plot.body.center
        for each_nucleotide, each_nucleotide_plot in nucleotide_plots.items()}
    _draw_dna_connections(
        subplot, dna_helix_graph, nucleotide_positions, nucleotide_plots,
        verbosity=verbosity)
    subplot.add_callback(
        partial(_draw_dna_connections,
                dna_helix_graph=dna_helix_graph,
                nucleotide_positions=nucleotide_positions,
                nucleotide_plots=nucleotide_plots, verbosity=verbosity)) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _create_figure_with_subplot(figsize=None
                                ) -> Tuple[plt.Figure, plt_axes.Subplot]:
    figure = plt.figure(figsize=figsize)
    subplot = figure.add_subplot(111)
    figure.show()
    figure.canvas.draw()
    return figure, subplot 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_legend(subplot: plt_axes.Subplot):
    nucleotide_patches, nucleotide_class_names_with_names = (
        subplot.figure.gca().get_legend_handles_labels())
    nucleotide_class_names = [
        each_class_name_with_name.split(":")[0]
        for each_class_name_with_name in nucleotide_class_names_with_names]
    legend_labels, legend_items = zip(*OrderedDict(
        zip(nucleotide_class_names, nucleotide_patches)).items())
    subplot.legend(legend_items, legend_labels, loc="lower right",
                   bbox_to_anchor=(0, 0), title="Nucleotide types") 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_dna_connections(
        subplot: plt_axes.Subplot,
        dna_helix_graph: nx.DiGraph,
        nucleotide_positions: Dict[Nucleotide, tuple],
        nucleotide_plots: Dict[Nucleotide, _NUCLEOTIDE_PLOT],
        verbosity: int = 0):
    # assumes that the coordinates are equal scaled in the view
    edge_label = "_dna_edge"
    _remove_dna_edge_patches(subplot, edge_label)

    nucleotide_body_patch = list(nucleotide_plots.values())[0].body
    body_patch_window_extent = nucleotide_body_patch.get_window_extent()
    if verbosity == 0:
        node_size_pixels = (body_patch_window_extent.width / 2 * 1.5
                            * subplot.figure.dpi)
        edge_patches = _draw_without_key_connections(
            dna_helix_graph, node_size_pixels, nucleotide_positions, subplot)
        if edge_patches:
            for each_edge_patch in edge_patches:
                each_edge_patch.set_zorder(0)
    else:
        edge_patches = _draw_with_key_connections(
            dna_helix_graph, nucleotide_plots, subplot)

    edge_patches = edge_patches or []
    for each_edge_patch in edge_patches:
        each_edge_patch.set_label(edge_label) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_nucleotide_incoming_keys(nucleotide, center,
                                   nucleotide_body_patch: patches.Circle,
                                   subplot: plt_axes.Subplot,
                                   radius=10.0, width=5.0):
    theta_min, theta_max = 0, 180
    keys_color = _get_key_color("incoming")
    incoming_keys_required = nucleotide.incoming_keys_required
    if nucleotide.dynamic_incoming_keys:
        incoming_keys_required = incoming_keys_required + ["DYNAMIC"]
    key_patches = _draw_nucleotide_keys(
        incoming_keys_required, nucleotide.incoming_keys_optional,
        center, nucleotide_body_patch=nucleotide_body_patch,
        subplot=subplot, color=keys_color, radius=radius, width=width,
        theta_min=theta_min, theta_max=theta_max)
    return key_patches 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_nucleotide_generated_keys(nucleotide, center,
                                    nucleotide_body_patch: patches.Circle,
                                    subplot: plt_axes.Subplot,
                                    radius=10.0, width=5.0):
    theta_min, theta_max = 180, 360
    keys_color = _get_key_color("generated")
    generated_keys_required = nucleotide.generated_keys_required
    if nucleotide.dynamic_generated_keys:
        generated_keys_required = generated_keys_required + ["DYNAMIC"]
    key_patches = _draw_nucleotide_keys(
        generated_keys_required, nucleotide.generated_keys_optional,
        center, nucleotide_body_patch=nucleotide_body_patch,
        subplot=subplot, color=keys_color, radius=radius, width=width,
        theta_min=theta_min, theta_max=theta_max)
    return key_patches 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def _draw_key_text(text: str, center, theta1, theta2, radius, width,
                   subplot: plt_axes.Subplot, **text_kwargs):
    text_center, theta = _get_wedge_center_and_angle(
        center, radius, theta1, theta2, width)
    text_angle = theta - 90
    if text_angle > 90:
        text_angle = text_angle - 180
    if len(text) > 10:
        text = text.replace("_", "_\n")
    text_object = subplot.annotate(text, xy=text_center,
                                   verticalalignment="center",
                                   horizontalalignment="center",
                                   rotation=text_angle, **text_kwargs)
    text_object.draw(subplot.figure.canvas.renderer)
    return text_object 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def test_plot_axon_map():
    ax = plot_axon_map()
    npt.assert_equal(isinstance(ax, Subplot), True)

    # Check axis limits:
    for xlim, ylim in zip([None, (-2000, 1500)], [(-3000, 1300), None]):
        ax = plot_axon_map(xlim=xlim, ylim=ylim)
        if xlim is None:
            xlim = (-5000, 5000)
        if ylim is None:
            ylim = (-4000, 4000)
        npt.assert_almost_equal(ax.get_xlim(), xlim)
        npt.assert_almost_equal(ax.get_ylim(), ylim)

    # Check optic disc center in both eyes:
    model = AxonMapSpatial()
    for eye in ['RE', 'LE']:
        for loc_od in [(15.5, 1.5), (-17.9, -0.01)]:
            ax = plot_axon_map(eye=eye, loc_od=loc_od)
            npt.assert_equal(len(ax.patches), 1)
            # Wrong sign for x-coord is automatically corrected:
            if eye == 'RE':
                od = (np.abs(loc_od[0]), loc_od[1])
            else:
                od = (-np.abs(loc_od[0]), loc_od[1])
            npt.assert_almost_equal(ax.patches[0].center, model.dva2ret(od))
            close(ax.figure)

    # Electrodes and quadrants can be annotated:
    for ann_q, n_q in [(True, 4), (False, 0)]:
        ax = plot_axon_map(annotate=ann_q)
        npt.assert_equal(len(ax.texts), n_q)

    # Setting upside_down flips y axis:
    ax = plot_axon_map(upside_down=True)
    npt.assert_equal(ax.get_xlim(), (-5000, 5000))
    npt.assert_equal(ax.get_ylim(), (4000, -4000))

    with pytest.raises(ValueError):
        plot_axon_map(loc_od=[3])
    with pytest.raises(ValueError):
        plot_axon_map(eye='foo')
    with pytest.raises(ValueError):
        plot_axon_map(n_bundles=0) 

# 需要導入模塊: from matplotlib import axes [as 別名]
# 或者: from matplotlib.axes import Subplot [as 別名]
def test_plot_implant_on_axon_map():
    ax = plot_implant_on_axon_map(ArgusII())
    npt.assert_equal(isinstance(ax, Subplot), True)

    # Check axis limits:
    for xlim, ylim in zip([None, (-2000, 1500)], [(-3000, 1300), None]):
        ax = plot_implant_on_axon_map(ArgusII(), xlim=xlim, ylim=ylim)
        if xlim is None:
            xlim = (-4000, 4500)
        if ylim is None:
            ylim = (-2500, 3000)
        npt.assert_almost_equal(ax.get_xlim(), xlim)
        npt.assert_almost_equal(ax.get_ylim(), ylim)

    # Check optic disc center in both eyes:
    model = AxonMapSpatial()
    for eye in ['RE', 'LE']:
        for loc_od in [(15.5, 1.5), (17.9, -0.01)]:
            od = (-loc_od[0], loc_od[1]) if eye == 'LE' else loc_od
            ax = plot_implant_on_axon_map(ArgusII(eye=eye), loc_od=od)
            npt.assert_equal(len(ax.patches), 1)
            npt.assert_almost_equal(ax.patches[0].center, model.dva2ret(od))
            close(ax.figure)

    # Electrodes and quadrants can be annotated:
    for ann_el, n_el in [(True, 60), (False, 0)]:
        for ann_q, n_q in [(True, 4), (False, 0)]:
            ax = plot_implant_on_axon_map(ArgusII(), annotate_implant=ann_el,
                                          annotate_quadrants=ann_q)
            npt.assert_equal(len(ax.texts), n_el + n_q)
            npt.assert_equal(len(ax.collections[0]._paths), 60)
            close(ax.figure)

    # Stimulating electrodes are marked:
    ax = plot_implant_on_axon_map(ArgusII(stim=np.ones(60)))

    # Setting upside_down flips y axis:
    ax = plot_implant_on_axon_map(ArgusII(), upside_down=True)
    npt.assert_almost_equal(ax.get_xlim(), (-4000, 4500))
    npt.assert_almost_equal(ax.get_ylim(), (3000, -2500))

    with pytest.raises(TypeError):
        plot_implant_on_axon_map(DiskElectrode(0, 0, 0, 100))
    with pytest.raises(ValueError):
        plot_implant_on_axon_map(ArgusII(), n_bundles=0)