Python matplotlib.pyplot方法代碼示例

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def cortex_cmap_plot_2D(the_map, zs, cmap, vmin=None, vmax=None, axes=None, triangulation=None):
    '''
    cortex_cmap_plot_2D(map, zs, cmap, axes) plots the given cortical map values zs on the given
      axes using the given given color map and yields the resulting polygon collection object.
    cortex_cmap_plot_2D(map, zs, cmap) uses matplotlib.pyplot.gca() for the axes.

    The following options may be passed:
      * triangulation (None) may specify the triangularion object for the mesh if it has already
        been created; otherwise it is generated fresh.
      * axes (None) specify the axes on which to plot; if None, then matplotlib.pyplot.gca() is
        used. If Ellipsis, then a tuple (triangulation, z, cmap) is returned; to recreate the plot,
        one would call:
          axes.tripcolor(triangulation, z, cmap, shading='gouraud', vmin=vmin, vmax=vmax)
      * vmin (default: None) specifies the minimum value for scaling the property when one is passed
        as the color option. None means to use the min value of the property.
      * vmax (default: None) specifies the maximum value for scaling the property when one is passed
        as the color option. None means to use the max value of the property.
    '''
    if triangulation is None:
        triangulation = matplotlib.tri.Triangulation(the_map.coordinates[0], the_map.coordinates[1],
                                                     triangles=the_map.tess.indexed_faces.T)
    if axes is Ellipsis: return (triangulation, zs, cmap)
    return axes.tripcolor(triangulation, zs, cmap=cmap, shading='gouraud', vmin=vmin, vmax=vmax) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def task_3_IQR(flight_data):
    plot=plt.boxplot(flight_data['Price'],patch_artist=True)
    for median in plot['medians']:
        median.set(color='#fc0004', linewidth=2)
    for flier in plot['fliers']:
        flier.set(marker='+', color='#e7298a')
    for whisker in plot['whiskers']:
        whisker.set(color='#7570b3', linewidth=2)
    for cap in plot['caps']:
        cap.set(color='#7570b3', linewidth=2)
    for box in plot['boxes']:
        box.set(color='#7570b3', linewidth=2)
        box.set(facecolor='#1b9e77')
    plt.matplotlib.pyplot.savefig('task_3_iqr.png')
    clean_data=[]
    for index,row in flight_data.loc[flight_data['Price'].isin(plot['fliers'][0].get_ydata())].iterrows():
        clean_data.append([row['Price'],row['Date_of_Flight']])
    return pd.DataFrame(clean_data, columns=['Price', 'Date_of_Flight']) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def plot_coordinates(coordinates, plot_path, markers, label_names, fig_num):
    matplotlib.use('svg')
    import matplotlib.pyplot as plt

    plt.figure(fig_num)
    for i in range(len(markers) - 1):
        plt.scatter(x=coordinates[markers[i]:markers[i + 1], 0],
                    y=coordinates[markers[i]:markers[i + 1], 1],
                    marker=plot_markers[i % len(plot_markers)],
                    c=colors[i % len(colors)],
                    label=label_names[i], alpha=0.75)

    plt.legend(loc='upper right', fontsize='x-large')
    plt.axis('off')
    plt.savefig(fname=plot_path, format="svg", bbox_inches='tight', transparent=True)
    plt.close() 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def assert_is_valid_plot_return_object(objs):  # pragma: no cover
    import matplotlib.pyplot as plt

    if isinstance(objs, (pd.Series, np.ndarray)):
        for el in objs.ravel():
            msg = (
                "one of 'objs' is not a matplotlib Axes instance, "
                "type encountered {}".format(repr(type(el).__name__))
            )
            assert isinstance(el, (plt.Axes, dict)), msg
    else:
        msg = (
            "objs is neither an ndarray of Artist instances nor a single "
            "ArtistArtist instance, tuple, or dict, 'objs' is a {}".format(
                repr(type(objs).__name__))
        )
        assert isinstance(objs, (plt.Artist, tuple, dict)), msg 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def plot_images(ax, images, shape, color = False):
     # finally save to file
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt

    # flip 0 to 1
    images = 1.0 - images

    images = reshape_and_tile_images(images, shape, n_cols=len(images))
    if color:
        from matplotlib import cm
        plt.imshow(images, cmap=cm.Greys_r, interpolation='nearest')
    else:
        plt.imshow(images, cmap='Greys')
    ax.axis('off') 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def test_plot_irf(self):
        import matplotlib.pyplot as plt
        self.irf.plot()
        plt.close('all')
        self.irf.plot(plot_stderr=False)
        plt.close('all')

        self.irf.plot(impulse=0, response=1)
        plt.close('all')
        self.irf.plot(impulse=0)
        plt.close('all')
        self.irf.plot(response=0)
        plt.close('all')

        self.irf.plot(orth=True)
        plt.close('all')
        self.irf.plot(impulse=0, response=1, orth=True)
        close_plots() 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def save_plot(niters, loss, args):
    print('Saving training loss-iteration figure...')
    try:
        import matplotlib
        matplotlib.use('Agg')
        import matplotlib.pyplot as plt

        name = 'Train-{}_hs-{}_lr-{}_bs-{}'.format(args.train_file, args.hs,
                                                   args.lr, args.batch_size)
        plt.title(name)
        plt.plot(niters, loss)
        plt.xlabel('iteration')
        plt.ylabel('loss')
        plt.savefig(name + '.jpg')
        print('{} saved!'.format(name + '.jpg'))

    except ImportError:
        print('matplotlib not installed and no figure is saved.') 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def initialize_pyplot(self):
        """
        Call me before use!
        [Supposed to be done inside already running server process]
        """
        if not self.ready:
            from multiprocessing import Pipe
            self.out_pipe, self.in_pipe = Pipe()

            if self.plt is None:
                import matplotlib
                matplotlib.use(self.plt_backend, force=True)
                import matplotlib.pyplot as plt

            self.plt = plt
            self.ready = True 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def visualize_waveform(audio_signal, ch=0, do_mono=False, x_axis='time', **kwargs):
    """
    Wrapper around `librosa.display.waveplot` for usage with AudioSignals.
    
    Args:
        audio_signal (AudioSignal): AudioSignal to plot
        ch (int, optional): Which channel to plot. Defaults to 0.
        do_mono (bool, optional): Make the AudioSignal mono. Defaults to False.
        x_axis (str, optional): x_axis argument to librosa.display.waveplot. Defaults to 'time'.
        kwargs: Additional keyword arguments to librosa.display.waveplot.
    """
    import librosa.display
    import matplotlib.pyplot as plt

    if do_mono:
        audio_signal = audio_signal.to_mono(overwrite=False)
    
    data = np.asfortranarray(audio_signal.audio_data[ch])
    librosa.display.waveplot(data, sr=audio_signal.sample_rate, x_axis=x_axis, **kwargs)
    plt.ylabel('Amplitude') 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def draw():
    """
    Redraw the current figure.

    This is used in interactive mode to update a figure that
    has been altered using one or more plot object method calls;
    it is not needed if figure modification is done entirely
    with pyplot functions, if a sequence of modifications ends
    with a pyplot function, or if matplotlib is in non-interactive
    mode and the sequence of modifications ends with :func:`show` or
    :func:`savefig`.

    A more object-oriented alternative, given any
    :class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that
    was created using a :mod:`~matplotlib.pyplot` function, is::

        fig.canvas.draw()


    """
    get_current_fig_manager().canvas.draw() 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def xlabel(s, *args, **kwargs):
    """
    Set the *x* axis label of the current axis.

    Default override is::

      override = {
          'fontsize'            : 'small',
          'verticalalignment'   : 'top',
          'horizontalalignment' : 'center'
          }

    .. seealso::

        :func:`~matplotlib.pyplot.text`
            For information on how override and the optional args work
    """
    l =  gca().set_xlabel(s, *args, **kwargs)
    draw_if_interactive()
    return l 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def polar(*args, **kwargs):
    """
    Make a polar plot.

    call signature::

      polar(theta, r, **kwargs)

    Multiple *theta*, *r* arguments are supported, with format
    strings, as in :func:`~matplotlib.pyplot.plot`.

    """
    ax = gca(polar=True)
    ret = ax.plot(*args, **kwargs)
    draw_if_interactive()
    return ret 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def activate_matplotlib(backend):
    """Activate the given backend and set interactive to True."""

    import matplotlib
    matplotlib.interactive(True)
    
    # Matplotlib had a bug where even switch_backend could not force
    # the rcParam to update. This needs to be set *before* the module
    # magic of switch_backend().
    matplotlib.rcParams['backend'] = backend

    import matplotlib.pyplot
    matplotlib.pyplot.switch_backend(backend)

    # This must be imported last in the matplotlib series, after
    # backend/interactivity choices have been made
    import matplotlib.pylab as pylab

    pylab.show._needmain = False
    # We need to detect at runtime whether show() is called by the user.
    # For this, we wrap it into a decorator which adds a 'called' flag.
    pylab.draw_if_interactive = flag_calls(pylab.draw_if_interactive) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def plot_f(f, filenm='test_function.eps'):
    # only for 2D functions
    import matplotlib.pyplot as plt
    import matplotlib
    font = {'size': 20}
    matplotlib.rc('font', **font)

    delta = 0.005
    x = np.arange(0.0, 1.0, delta)
    y = np.arange(0.0, 1.0, delta)
    nx = len(x)
    X, Y = np.meshgrid(x, y)

    xx = np.array((X.ravel(), Y.ravel())).T
    yy = f(xx)

    plt.figure()
    plt.contourf(X, Y, yy.reshape(nx, nx), levels=np.linspace(yy.min(), yy.max(), 40))
    plt.xlim([0, 1])
    plt.ylim([0, 1])
    plt.colorbar()
    plt.scatter(f.argmax[0], f.argmax[1], s=180, color='k', marker='+')
    plt.savefig(filenm) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import pyplot [as 別名]
def Cont(imG):
#This is meant to create plots similar to the ones from
#https://www.bu.edu/blazars/VLBA_GLAST/3c454.html
#for the visual comparison

    import matplotlib.pyplot as plt
    plt.figure()
    Z = np.reshape(imG.imvec,(imG.xdim,imG.ydim))
    pov = imG.xdim*imG.psize
    pov_mas = pov/(RADPERUAS*1.e3)
    Zmax = np.amax(Z)
    print(Zmax)

    levels = np.array((-0.00125*Zmax,0.00125*Zmax,0.0025*Zmax, 0.005*Zmax, 0.01*Zmax,
                        0.02*Zmax, 0.04*Zmax, 0.08*Zmax, 0.16*Zmax, 0.32*Zmax, 0.64*Zmax))
    CS = plt.contour(Z, levels,
                     origin='lower',
                     linewidths=2,
                     extent=(-pov_mas/2., pov_mas/2., -pov_mas/2., pov_mas/2.))
    plt.show()