如何使用 Matplotlib 从预渲染图像创建子图

如何解决如何使用 Matplotlib 从预渲染图像创建子图

目标是从由某些程序生成的数字列表中创建一个子图。

这里，从函数 SomeClass::someMethod() 生成单个图像，并附加到 plot_conn()

最终，我想对这些数字进行子图，如下所示：

这可以通过 all_fig 或任何其他 Python 包实现吗？

这 3 个数字是使用下面的代码生成的，并列在 Matplotlib

all_figure

如果有任何提示，我将不胜感激。

解决方法

一种肮脏的解决方案是将每个 Figure 转换为 Numpy array，并垂直或水平堆叠数组。

1. 生成Numpy array

• 使用 plot_connectivity_circle 重绘 canvas.draw () 输出
• 通过使用 np.frombuffer 转换新的重绘图像获得数组形式

    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
    canvas = FigureCanvas ( fig )
    plot_connectivity_circle ( conmat,all_ch,n_lines=300,node_angles=node_angles,title=f'All-to-All Connectivity_ band_{bands}',fig=fig )
    
    canvas.draw ()
    s,(width,height) = canvas.print_to_buffer ()
    im0 = np.frombuffer ( s,np.uint8 ).reshape ( (height,width,4) )

2. 通过堆叠数组创建子图

np.hstack ( all_fig ) # all_fig is a list of array

完整代码如下：

import mne
from mne.connectivity import spectral_connectivity
from mne.viz import circular_layout,plot_connectivity_circle
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas

def generate_conn ():
    # Generate data

    label_names = ['FP1','FP2','F3','F4','F7','F8','C3','C4','T3','T4','O1','O2']

    np.random.seed ( 42 )
    n_epochs = 5
    n_channels = len ( label_names )
    n_times = 1000
    data = np.random.rand ( n_epochs,n_channels,n_times )
    # Set sampling freq
    sfreq = 250  # A reasonable random choice

    # 10Hz sinus waves with random phase differences in each channel and epoch
    # Generate 10Hz sinus waves to show difference between connectivity
    # over time and over trials. Here we expect con over time = 1
    for i in range ( n_epochs ):
        for c in range ( n_channels ):
            wave_freq = 10
            epoch_len = n_times / sfreq
            # Introduce random phase for each channel
            phase = np.random.rand ( 1 ) * 10
            # Generate sinus wave
            x = np.linspace ( -wave_freq * epoch_len * np.pi + phase,wave_freq * epoch_len * np.pi + phase,n_times )
            data [i,c] = np.squeeze ( np.sin ( x ) )

    info = mne.create_info ( ch_names=label_names,ch_types=['eeg'] * len ( label_names ),sfreq=sfreq )

    epochs = mne.EpochsArray ( data,info )

    # Define freq bands
    Freq_Bands = {"delta": [1.25,4.0],"theta": [4.0,8.0],"alpha": [8.0,13.0],"beta": [13.0,30.0],"gamma": [30.0,49.0]}

    n_freq_bands = len ( Freq_Bands )
    # Convert to tuples for the mne function
    fmin = tuple ( [list ( Freq_Bands.values () ) [f] [0] for f in range ( len ( Freq_Bands ) )] )
    fmax = tuple ( [list ( Freq_Bands.values () ) [f] [1] for f in range ( len ( Freq_Bands ) )] )

    # Connectivity methods
    connectivity_methods = ["plv"]
    n_con_methods = len ( connectivity_methods )

    # # Calculate PLV and wPLI - the MNE python implementation is over trials
    con,freqs,times,n_epochs,n_tapers = spectral_connectivity (
        epochs,method=connectivity_methods,mode="multitaper",sfreq=sfreq,fmin=fmin,fmax=fmax,faverage=True,verbose=0 )
    all_ch = epochs.ch_names

    return con,all_ch


def plot_conn (conmat,idx,bands):
    lh_labels = ['FP1','O1']
    rh_labels = ['FP2','O2']
    node_order = lh_labels + rh_labels  # Is this order tally with the con arrangement?
    node_angles = circular_layout ( all_ch,node_order,start_pos=90,group_boundaries=[0,len ( all_ch ) // 2] )

    fig = plt.figure ( num=None,figsize=(8,8),facecolor='black' )
    

    canvas = FigureCanvas ( fig )
    plot_connectivity_circle ( conmat,4) )
    return im0


con,all_ch = generate_conn ()

all_fig = [plot_conn ( con [:,:,idx],band ) for idx,band in enumerate ( ["delta","theta","alpha"] )]

SUBPLOT = np.hstack ( all_fig )



plt.imsave ( 'myimage.png',SUBPLOT )