Python matplotlib.pyplot.eventplot() Examples

def make_raster_plot(self):
        
        fname = os.path.join(self.save_dir, 'raster.png')
        if os.path.exists(fname):
            return
        
        plt.figure(figsize=(30,15))
        ptps = self.ptps
        order = np.argsort(ptps)
        sorted_ptps = np.round(np.sort(ptps),2)
        for j in range(self.n_units):
            k = order[j]
            idx = self.spike_train[:,1] == k
            spt = self.spike_train[idx, 0]/self.sampling_rate
            prob = self.soft_assignment[idx]
            if np.sum(prob) > 1:
                spt = np.sort(np.random.choice(
                    spt, int(np.sum(prob)), False, prob/np.sum(prob)))
                plt.eventplot(spt, lineoffsets=j, color='k', linewidths=0.01)
        plt.yticks(np.arange(0,self.n_units,10), sorted_ptps[0:self.n_units:10])
        plt.ylabel('ptps', fontsize=self.fontsize)
        plt.xlabel('time (seconds)', fontsize=self.fontsize)
        plt.title('Raster Plot Sorted by PTP', fontsize=self.fontsize)
        plt.savefig(fname, bbox_inches='tight', dpi=100)
        plt.close() 

def test_eventplot_colors(colors):
    '''Test the *colors* parameter of eventplot. Inspired by the issue #8193.
    '''
    data = [[i] for i in range(4)]  # 4 successive events of different nature

    # Build the list of the expected colors
    expected = [c if c is not None else 'C0' for c in colors]
    # Convert the list into an array of RGBA values
    # NB: ['rgbk'] is not a valid argument for to_rgba_array, while 'rgbk' is.
    if len(expected) == 1:
        expected = expected[0]
    expected = np.broadcast_to(mcolors.to_rgba_array(expected), (len(data), 4))

    fig, ax = plt.subplots()
    if len(colors) == 1:  # tuple with a single string (like '0.5' or 'rgbk')
        colors = colors[0]
    collections = ax.eventplot(data, colors=colors)

    for coll, color in zip(collections, expected):
        assert_allclose(coll.get_color(), color) 

def test_eventplot_colors(colors):
    '''Test the *colors* parameter of eventplot. Inspired by the issue #8193.
    '''
    data = [[i] for i in range(4)]  # 4 successive events of different nature

    # Build the list of the expected colors
    expected = [c if c is not None else 'C0' for c in colors]
    # Convert the list into an array of RGBA values
    # NB: ['rgbk'] is not a valid argument for to_rgba_array, while 'rgbk' is.
    if len(expected) == 1:
        expected = expected[0]
    expected = broadcast_to(mcolors.to_rgba_array(expected), (len(data), 4))

    fig, ax = plt.subplots()
    if len(colors) == 1:  # tuple with a single string (like '0.5' or 'rgbk')
        colors = colors[0]
    collections = ax.eventplot(data, colors=colors)

    for coll, color in zip(collections, expected):
        assert_allclose(coll.get_color(), color) 

def test_eventplot_colors(colors):
    '''Test the *colors* parameter of eventplot. Inspired by the issue #8193.
    '''
    data = [[i] for i in range(4)]  # 4 successive events of different nature

    # Build the list of the expected colors
    expected = [c if c is not None else 'C0' for c in colors]
    # Convert the list into an array of RGBA values
    # NB: ['rgbk'] is not a valid argument for to_rgba_array, while 'rgbk' is.
    if len(expected) == 1:
        expected = expected[0]
    expected = np.broadcast_to(mcolors.to_rgba_array(expected), (len(data), 4))

    fig, ax = plt.subplots()
    if len(colors) == 1:  # tuple with a single string (like '0.5' or 'rgbk')
        colors = colors[0]
    collections = ax.eventplot(data, colors=colors)

    for coll, color in zip(collections, expected):
        assert_allclose(coll.get_color(), color) 

def test_eventplot_problem_kwargs():
    '''
    test that 'singular' versions of LineCollection props raise an
    IgnoredKeywordWarning rather than overriding the 'plural' versions (e.g.
    to prevent 'color' from overriding 'colors', see issue #4297)
    '''
    np.random.seed(0)

    data1 = np.random.random([20]).tolist()
    data2 = np.random.random([10]).tolist()
    data = [data1, data2]

    fig = plt.figure()
    axobj = fig.add_subplot(111)

    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        colls = axobj.eventplot(data,
                                colors=['r', 'b'],
                                color=['c', 'm'],
                                linewidths=[2, 1],
                                linewidth=[1, 2],
                                linestyles=['solid', 'dashed'],
                                linestyle=['dashdot', 'dotted'])

        # check that three IgnoredKeywordWarnings were raised
        assert len(w) == 3
        assert all(issubclass(wi.category, IgnoredKeywordWarning) for wi in w) 

def add_ptp_vs_time(self, ptp, spt, template):

        plt.scatter(spt, ptp/self.sampling_rate, c='k')
        plt.plot([np.min(spt), np.max(spt)], [temp.ptp(), temp.ptp()], 'r')

        #plt.eventplot(spt, color='k', linewidths=0.01)
        plt.title('ptp vs spike times (red = template ptp)',
                  fontsize=self.fontsize)
        
        return gs 

def test_eventplot_legend():
    plt.eventplot([1.0], label='Label')
    plt.legend() 

def test_empty_eventplot():
    fig, ax = plt.subplots(1, 1)
    ax.eventplot([[]], colors=[(0.0, 0.0, 0.0, 0.0)])
    plt.draw() 

def test_eventplot_problem_kwargs():
    '''
    test that 'singular' versions of LineCollection props raise an
    IgnoredKeywordWarning rather than overriding the 'plural' versions (e.g.
    to prevent 'color' from overriding 'colors', see issue #4297)
    '''
    np.random.seed(0)

    data1 = np.random.random([20]).tolist()
    data2 = np.random.random([10]).tolist()
    data = [data1, data2]

    fig = plt.figure()
    axobj = fig.add_subplot(111)

    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        colls = axobj.eventplot(data,
                                colors=['r', 'b'],
                                color=['c', 'm'],
                                linewidths=[2, 1],
                                linewidth=[1, 2],
                                linestyles=['solid', 'dashed'],
                                linestyle=['dashdot', 'dotted'])

        # check that three IgnoredKeywordWarnings were raised
        assert len(w) == 3
        assert all(issubclass(wi.category, IgnoredKeywordWarning) for wi in w) 

def test_eventplot_defaults():
    '''
    test that eventplot produces the correct output given the default params
    (see bug #3728)
    '''
    np.random.seed(0)

    data1 = np.random.random([32, 20]).tolist()
    data2 = np.random.random([6, 20]).tolist()
    data = data1 + data2

    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot(data) 

def test_eventplot_legend():
    plt.eventplot([1.0], label='Label')
    plt.legend() 

def test_empty_eventplot():
    fig, ax = plt.subplots(1, 1)
    ax.eventplot([[]], colors=[(0.0, 0.0, 0.0, 0.0)])
    plt.draw() 

def test_eventplot_defaults():
    '''
    test that eventplot produces the correct output given the default params
    (see bug #3728)
    '''
    np.random.seed(0)

    data1 = np.random.random([32, 20]).tolist()
    data2 = np.random.random([6, 20]).tolist()
    data = data1 + data2

    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot(data) 

def test_eventplot_legend():
    plt.eventplot([1.0], label='Label')
    plt.legend() 

def test_empty_eventplot():
    fig, ax = plt.subplots(1, 1)
    ax.eventplot([[]], colors=[(0.0, 0.0, 0.0, 0.0)])
    plt.draw() 

def test_eventplot_problem_kwargs():
    '''
    test that 'singular' versions of LineCollection props raise an
    IgnoredKeywordWarning rather than overriding the 'plural' versions (e.g.
    to prevent 'color' from overriding 'colors', see issue #4297)
    '''
    np.random.seed(0)

    data1 = np.random.random([20]).tolist()
    data2 = np.random.random([10]).tolist()
    data = [data1, data2]

    fig = plt.figure()
    axobj = fig.add_subplot(111)

    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        colls = axobj.eventplot(data,
                                colors=['r', 'b'],
                                color=['c', 'm'],
                                linewidths=[2, 1],
                                linewidth=[1, 2],
                                linestyles=['solid', 'dashed'],
                                linestyle=['dashdot', 'dotted'])

        # check that three IgnoredKeywordWarnings were raised
        assert len(w) == 3
        assert all(issubclass(wi.category, IgnoredKeywordWarning) for wi in w) 

def test_eventplot_defaults():
    '''
    test that eventplot produces the correct output given the default params
    (see bug #3728)
    '''
    np.random.seed(0)

    data1 = np.random.random([32, 20]).tolist()
    data2 = np.random.random([6, 20]).tolist()
    data = data1 + data2

    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot(data) 

def test_eventplot():
    '''
    test that eventplot produces the correct output
    '''
    np.random.seed(0)

    data1 = np.random.random([32, 20]).tolist()
    data2 = np.random.random([6, 20]).tolist()
    data = data1 + data2
    num_datasets = len(data)

    colors1 = [[0, 1, .7]] * len(data1)
    colors2 = [[1, 0, 0],
               [0, 1, 0],
               [0, 0, 1],
               [1, .75, 0],
               [1, 0, 1],
               [0, 1, 1]]
    colors = colors1 + colors2

    lineoffsets1 = 12 + np.arange(0, len(data1)) * .33
    lineoffsets2 = [-15, -3, 1, 1.5, 6, 10]
    lineoffsets = lineoffsets1.tolist() + lineoffsets2

    linelengths1 = [.33] * len(data1)
    linelengths2 = [5, 2, 1, 1, 3, 1.5]
    linelengths = linelengths1 + linelengths2

    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot(data, colors=colors, lineoffsets=lineoffsets,
                            linelengths=linelengths)

    num_collections = len(colls)
    assert num_collections == num_datasets

    # Reuse testcase from above for a labeled data test
    data = {"pos": data, "c": colors, "lo": lineoffsets, "ll": linelengths}
    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot("pos", colors="c", lineoffsets="lo",
                            linelengths="ll", data=data)
    num_collections = len(colls)
    assert num_collections == num_datasets 

def test_eventplot():
    '''
    test that eventplot produces the correct output
    '''
    np.random.seed(0)

    data1 = np.random.random([32, 20]).tolist()
    data2 = np.random.random([6, 20]).tolist()
    data = data1 + data2
    num_datasets = len(data)

    colors1 = [[0, 1, .7]] * len(data1)
    colors2 = [[1, 0, 0],
               [0, 1, 0],
               [0, 0, 1],
               [1, .75, 0],
               [1, 0, 1],
               [0, 1, 1]]
    colors = colors1 + colors2

    lineoffsets1 = 12 + np.arange(0, len(data1)) * .33
    lineoffsets2 = [-15, -3, 1, 1.5, 6, 10]
    lineoffsets = lineoffsets1.tolist() + lineoffsets2

    linelengths1 = [.33] * len(data1)
    linelengths2 = [5, 2, 1, 1, 3, 1.5]
    linelengths = linelengths1 + linelengths2

    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot(data, colors=colors, lineoffsets=lineoffsets,
                            linelengths=linelengths)

    num_collections = len(colls)
    assert num_collections == num_datasets

    # Reuse testcase from above for a labeled data test
    data = {"pos": data, "c": colors, "lo": lineoffsets, "ll": linelengths}
    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot("pos", colors="c", lineoffsets="lo",
                            linelengths="ll", data=data)
    num_collections = len(colls)
    assert num_collections == num_datasets 

def test_eventplot():
    '''
    test that eventplot produces the correct output
    '''
    np.random.seed(0)

    data1 = np.random.random([32, 20]).tolist()
    data2 = np.random.random([6, 20]).tolist()
    data = data1 + data2
    num_datasets = len(data)

    colors1 = [[0, 1, .7]] * len(data1)
    colors2 = [[1, 0, 0],
               [0, 1, 0],
               [0, 0, 1],
               [1, .75, 0],
               [1, 0, 1],
               [0, 1, 1]]
    colors = colors1 + colors2

    lineoffsets1 = 12 + np.arange(0, len(data1)) * .33
    lineoffsets2 = [-15, -3, 1, 1.5, 6, 10]
    lineoffsets = lineoffsets1.tolist() + lineoffsets2

    linelengths1 = [.33] * len(data1)
    linelengths2 = [5, 2, 1, 1, 3, 1.5]
    linelengths = linelengths1 + linelengths2

    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot(data, colors=colors, lineoffsets=lineoffsets,
                            linelengths=linelengths)

    num_collections = len(colls)
    assert num_collections == num_datasets

    # Reuse testcase from above for a labeled data test
    data = {"pos": data, "c": colors, "lo": lineoffsets, "ll": linelengths}
    fig = plt.figure()
    axobj = fig.add_subplot(111)
    colls = axobj.eventplot("pos", colors="c", lineoffsets="lo",
                            linelengths="ll", data=data)
    num_collections = len(colls)
    assert num_collections == num_datasets 

def plot_spike_trains_for_example(
    spikes: torch.Tensor,
    n_ex: Optional[int] = None,
    top_k: Optional[int] = None,
    indices: Optional[List[int]] = None,
) -> None:
    # language=rst
    """
    Plot spike trains for top-k neurons or for specific indices.

    :param spikes: Spikes for one simulation run of shape
        ``(n_examples, n_neurons, time)``.
    :param n_ex: Allows user to pick which example to plot spikes for.
    :param top_k: Plot k neurons that spiked the most for n_ex example.
    :param indices: Plot specific neurons' spiking activity instead of top_k.
    """
    assert n_ex is not None and 0 <= n_ex < spikes.shape[0]

    plt.figure()

    if top_k is None and indices is None:  # Plot all neurons' spiking activity
        spike_per_neuron = [
            np.argwhere(i == 1).flatten() for i in spikes[n_ex, :, :]
        ]
        plt.title("Spiking activity for all %d neurons" % spikes.shape[1])

    elif top_k is None:  # Plot based on indices parameter
        assert indices is not None
        spike_per_neuron = [
            np.argwhere(i == 1).flatten() for i in spikes[n_ex, indices, :]
        ]

    elif indices is None:  # Plot based on top_k parameter
        assert top_k is not None
        # Obtain the top k neurons that fired the most
        top_k_loc = np.argsort(np.sum(spikes[n_ex, :, :], axis=1), axis=0)[
            ::-1
        ]
        spike_per_neuron = [
            np.argwhere(i == 1).flatten()
            for i in spikes[n_ex, top_k_loc[0:top_k], :]
        ]
        plt.title("Spiking activity for top %d neurons" % top_k)

    else:
        raise ValueError('One of "top_k" or "indices" or both must be None')

    plt.eventplot(spike_per_neuron, linelengths=[0.5] * len(spike_per_neuron))
    plt.xlabel("Simulation Time")
    plt.ylabel("Neuron index")
    plt.show() 

def plot_spike_trains_for_example(
    spikes: torch.Tensor,
    n_ex: Optional[int] = None,
    top_k: Optional[int] = None,
    indices: Optional[List[int]] = None,
) -> None:
    # language=rst
    """
    Plot spike trains for top-k neurons or for specific indices.

    :param spikes: Spikes for one simulation run of shape
        ``(n_examples, n_neurons, time)``.
    :param n_ex: Allows user to pick which example to plot spikes for.
    :param top_k: Plot k neurons that spiked the most for n_ex example.
    :param indices: Plot specific neurons' spiking activity instead of top_k.
    """
    assert n_ex is not None and 0 <= n_ex < spikes.shape[0]

    plt.figure()

    if top_k is None and indices is None:  # Plot all neurons' spiking activity
        spike_per_neuron = [np.argwhere(i == 1).flatten() for i in spikes[n_ex, :, :]]
        plt.title("Spiking activity for all %d neurons" % spikes.shape[1])

    elif top_k is None:  # Plot based on indices parameter
        assert indices is not None
        spike_per_neuron = [
            np.argwhere(i == 1).flatten() for i in spikes[n_ex, indices, :]
        ]

    elif indices is None:  # Plot based on top_k parameter
        assert top_k is not None
        # Obtain the top k neurons that fired the most
        top_k_loc = np.argsort(np.sum(spikes[n_ex, :, :], axis=1), axis=0)[::-1]
        spike_per_neuron = [
            np.argwhere(i == 1).flatten() for i in spikes[n_ex, top_k_loc[0:top_k], :]
        ]
        plt.title("Spiking activity for top %d neurons" % top_k)

    else:
        raise ValueError('One of "top_k" or "indices" or both must be None')

    plt.eventplot(spike_per_neuron, linelengths=[0.5] * len(spike_per_neuron))
    plt.xlabel("Simulation Time")
    plt.ylabel("Neuron index")
    plt.show()