import matplotlib
import matplotlib.pyplot as plt
import numpy as np
[文档]def plot_2d_heatmap(array: np.ndarray, title: str, xlabel: str, ylabel: str, int_x_ticks=True, int_y_ticks=True,
plot_colorbar=True, colorbar_y_label='magnitude', dpi=200):
'''
:param array: shape=[N, M]的任意数组
:param title: 热力图的标题
:param xlabel: 热力图的x轴的label
:param ylabel: 热力图的y轴的label
:param int_x_ticks: x轴上是否只显示整数刻度
:param int_y_ticks: y轴上是否只显示整数刻度
:param plot_colorbar: 是否画出显示颜色和数值对应关系的colorbar
:param colorbar_y_label: colorbar的y轴label
:param dpi: 绘图的dpi
:return: 绘制好的figure
绘制一张二维的热力图。可以用来绘制一张表示多个神经元在不同时刻的电压的热力图，示例代码：
.. code-block:: python
neuron_num = 32
T = 50
lif_node = neuron.LIFNode(monitor=True)
w = torch.rand([neuron_num]) * 50
for t in range(T):
lif_node(w * torch.rand(size=[neuron_num]))
v_t_array = np.asarray(lif_node.monitor['v']).T # v_t_array[i][j]表示神经元i在j时刻的电压值
visualizing.plot_2d_heatmap(array=v_t_array, title='voltage of neurons', xlabel='simulating step',
ylabel='neuron index', int_x_ticks=True, int_y_ticks=True,
plot_colorbar=True, colorbar_y_label='voltage magnitude', dpi=200)
plt.show()
.. image:: ./_static/API/plot_2d_heatmap.png
'''
fig, heatmap = plt.subplots(dpi=dpi)
im = heatmap.imshow(array, aspect='auto')
heatmap.set_title(title)
heatmap.set_xlabel(xlabel)
heatmap.set_ylabel(ylabel)
heatmap.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_x_ticks))
heatmap.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_y_ticks))
if plot_colorbar:
cbar = heatmap.figure.colorbar(im)
cbar.ax.set_ylabel(colorbar_y_label, rotation=90, va='top')
return fig
[文档]def plot_2d_bar_in_3d(array: np.ndarray, title: str, xlabel: str, ylabel: str, zlabel: str, int_x_ticks=True, int_y_ticks=True, int_z_ticks=False, dpi=200):
'''
:param array: shape=[N, M]的任意数组
:param title: 图的标题
:param xlabel: x轴的label
:param ylabel: y轴的label
:param zlabel: z轴的label
:param int_x_ticks: x轴上是否只显示整数刻度
:param int_y_ticks: y轴上是否只显示整数刻度
:param int_z_ticks: z轴上是否只显示整数刻度
:param dpi: 绘图的dpi
:return: 绘制好的figure
将shape=[N, M]的任意数组，绘制为三维的柱状图。可以用来绘制多个神经元的脉冲发放频率，随着时间的变化情况，示例代码：
.. code-block:: python
Epochs = 5
N = 10
spiking_rate = torch.zeros(N, Epochs)
init_spiking_rate = torch.rand(size=[N])
for i in range(Epochs):
spiking_rate[:, i] = torch.softmax(init_spiking_rate * (i + 1) ** 2, dim=0)
visualizing.plot_2d_bar_in_3d(spiking_rate.numpy(), title='spiking rates of output layer', xlabel='neuron index',
ylabel='training epoch', zlabel='spiking rate', int_x_ticks=True, int_y_ticks=True,
int_z_ticks=False, dpi=200)
plt.show()
.. image:: ./_static/API/plot_2d_bar_in_3d.png
也可以用来绘制一张表示多个神经元在不同时刻的电压的热力图，示例代码：
.. code-block:: python
neuron_num = 4
T = 50
lif_node = neuron.LIFNode(monitor=True)
w = torch.rand([neuron_num]) * 10
for t in range(T):
lif_node(w * torch.rand(size=[neuron_num]))
v_t_array = np.asarray(lif_node.monitor['v']).T # v_t_array[i][j]表示神经元i在j时刻的电压值
visualizing.plot_2d_bar_in_3d(v_t_array, title='voltage of neurons', xlabel='neuron index',
ylabel='simulating step', zlabel='voltage', int_x_ticks=True, int_y_ticks=True,
int_z_ticks=False, dpi=200)
plt.show()
.. image:: ./_static/API/plot_2d_bar_in_3d_1.png
'''
fig = plt.figure(dpi=dpi)
ax = fig.add_subplot(111, projection='3d')
ax.set_title(title)
colormap = plt.get_cmap('tab10') # cmap的种类参见https://matplotlib.org/gallery/color/colormap_reference.html
xs = np.arange(array.shape[1])
for i in range(array.shape[0]):
ax.bar(xs, array[i], i, zdir='x', color=colormap(i % 10), alpha=0.8)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_zlabel(zlabel)
ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_x_ticks))
ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_y_ticks))
ax.zaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_z_ticks))
[文档]def plot_1d_spikes(spikes: np.asarray, title: str, xlabel: str, ylabel: str, int_x_ticks=True, int_y_ticks=True,
plot_spiking_rate=True, spiking_rate_map_title='spiking rate', dpi=200):
'''
:param spikes: shape=[N, T]的np数组，其中的元素只为0或1，表示N个时长为T的脉冲数据
:param title: 热力图的标题
:param xlabel: 热力图的x轴的label
:param ylabel: 热力图的y轴的label
:param int_x_ticks: x轴上是否只显示整数刻度
:param int_y_ticks: y轴上是否只显示整数刻度
:param plot_spiking_rate: 是否画出各个脉冲发放频率
:param spiking_rate_map_title: 脉冲频率发放图的标题
:param dpi: 绘图的dpi
:return: 绘制好的figure
画出N个时长为T的脉冲数据。可以用来画N个神经元在T个时刻的脉冲发放情况，示例代码：
.. code-block:: python
neuron_num = 32
T = 50
lif_node = neuron.LIFNode(monitor=True)
w = torch.rand([neuron_num]) * 50
for t in range(T):
lif_node(w * torch.rand(size=[neuron_num]))
s_t_array = np.asarray(lif_node.monitor['s']).T # s_t_array[i][j]表示神经元i在j时刻释放的脉冲，为0或1
visualizing.plot_1d_spikes(spikes=s_t_array, title='spikes of neurons', xlabel='simulating step',
ylabel='neuron index', int_x_ticks=True, int_y_ticks=True,
plot_spiking_rate=True, spiking_rate_map_title='spiking rate', dpi=200)
plt.show()
.. image:: ./_static/API/plot_1d_spikes.png
'''
if plot_spiking_rate:
fig = plt.figure(tight_layout=True, dpi=dpi)
gs = matplotlib.gridspec.GridSpec(1, 5)
spikes_map = fig.add_subplot(gs[0, 0:4])
spiking_rate_map = fig.add_subplot(gs[0, 4])
else:
fig, spikes_map = plt.subplots()
spikes_map.set_title(title)
spikes_map.set_xlabel(xlabel)
spikes_map.set_ylabel(ylabel)
spikes_map.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_x_ticks))
spikes_map.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=int_y_ticks))
spikes_map.set_xlim(-0.5, spikes.shape[1] + 0.5)
spikes_map.set_ylim(-0.5, spikes.shape[0] + 0.5)
spikes_map.invert_yaxis()
N = spikes.shape[0]
T = spikes.shape[1]
t = np.arange(0, T)
t_spike = spikes * t
mask = (spikes == 1) # eventplot中的数值是时间发生的时刻，因此需要用mask筛选出
colormap = plt.get_cmap('tab10') # cmap的种类参见https://matplotlib.org/gallery/color/colormap_reference.html
for i in range(N):
spikes_map.eventplot(t_spike[i][mask[i]], lineoffsets=i, colors=colormap(i % 10))
if plot_spiking_rate:
spiking_rate = np.mean(spikes, axis=1, keepdims=True)
spiking_rate_map.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True))
spiking_rate_map.imshow(spiking_rate, cmap='plasma', aspect='auto')
for i in range(spiking_rate.shape[0]):
spiking_rate_map.text(0, i, spiking_rate[i][0], ha='center', va='center', color='w')
spiking_rate_map.get_xaxis().set_visible(False)
spiking_rate_map.set_title(spiking_rate_map_title)
[文档]def plot_2d_spiking_feature_map(spikes: np.asarray, nrows, ncols, title: str, dpi=200):
'''
:param spikes: shape=[C, W, H]，C个尺寸为W * H的脉冲矩阵，矩阵中的元素为0或1。这样的矩阵一般来源于卷积层后的脉冲神经元的输出
:param nrows: 画成多少行
:param ncols: 画成多少列
:param title: 热力图的标题
:param dpi: 绘图的dpi
:return: 一个figure，将C个矩阵全部画出，然后排列成nrows行ncols列
将C个尺寸为W * H的脉冲矩阵，全部画出，然后排列成nrows行ncols列。这样的矩阵一般来源于卷积层后的脉冲神经元的输出，通过这个函数\\
可以对输出进行可视化。示例代码：
.. code-block:: python
C = 48
W = 8
H = 8
spikes = (np.random.rand(C, W, H) > 0.8).astype(float)
visualizing.plot_2d_spiking_feature_map(spikes=spikes, nrows=6, ncols=8, title='spiking feature map', dpi=200)
plt.show()
.. image:: ./_static/API/plot_2d_spiking_feature_map.png
'''
C = spikes.shape[0]
assert nrows * ncols == C, 'nrows * ncols != C'
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, dpi=dpi)
fig.suptitle(title)
for i in range(nrows):
for j in range(ncols):
axs[i][j].imshow(spikes[i * ncols + j], cmap='gray')
axs[i][j].get_xaxis().set_visible(False)
axs[i][j].get_yaxis().set_visible(False)