試圖擺脫matplotlib圖中的列

我們有來自Vicon Nexus運動捕捉軟件的代碼。使用Python，我們生成從EMG模擬源捕獲的圖形。包含的振幅與頻率列是無關的。我們無法評論指出，在第二欄填寫的代碼段，但仍然留下第2欄。試圖擺脫matplotlib圖中的列

def plot(axrow, ax, ay, alim, bx, by, blim, emgLabel): 
    axrow[0].plot(ax, ay, color='blue', label=emgLabel) 
    axrow[0].set_xlim(alim) 
    axrow[0].set_xlabel('Time/s') 
    axrow[0].set_ylabel('Volt./V', fontsize=9) 
    axrow[0].legend() 
    axrow[0].locator_params(axis='y', nbins=3) 
    #axrow[1].plot(bx, by, color='red', label=emgLabel) 
    #axrow[1].set_xlim(blim) 
    #axrow[1].set_xlabel('Frequency/Hz') 
    #axrow[1].set_ylabel('Ampl./ a.u.', fontsize=9) 
    #axrow[1].legend() 
    #axrow[1].locator_params(axis='y', nbins=3)

Second Column Empty

空次要情節當我們改變參數從次要情節2比1，只有一列顯示，但情節是完全空

nrows = len(channelId) 
fig, axes = plt.subplots(nrows, 1)

One Column, No Graphs

有8個通道annelIDs

任何幫助，將不勝感激。謝謝

編輯：對不起，延遲響應。我們終於能夠通過使用「squeeze = false」來找出解決方案。

這裏是FO爲清楚起見，該代碼的條款內容

from __future__ import division, print_function 

import ViconNexus 
import numpy as np 
import matplotlib.pyplot as plt 
from scipy.signal import butter, lfilter 


# define butterworth bandpass filter 
def butter_bandpass(lowcut, highcut, fs, order=5): 
    nyq = 0.5 * fs 
    low = lowcut/nyq 
    high = highcut/nyq 
    b, a = butter(order, [low, high], btype='band') 
    return b, a 


def butter_bandpass_filter(data, lowcut, highcut, fs, order=5): 
    b, a = butter_bandpass(lowcut, highcut, fs, order=order) 
    y = lfilter(b, a, data) 
    return y 

    #plot(row, timeLineCut, x, alim, freq, np.abs(y), blim, emgLabel) 
def plot(axrow, ax, ay, alim, bx, by, blim, emgLabel): 
    axrow[0].plot(ax, ay, color='blue', label=emgLabel) 
    axrow[0].set_xlim(alim) 
    axrow[0].set_xlabel('Time/s') 
    axrow[0].set_ylabel('Volt./V', fontsize=9) 
    axrow[0].legend() 
    axrow[0].locator_params(axis='y', nbins=3) 
    #axrow[1].plot(bx, by, color='red', label=emgLabel) 
    #axrow[1].set_xlim(blim) 
    #axrow[1].set_xlabel('Frequency/Hz') 
    #axrow[1].set_ylabel('Ampl./ a.u.', fontsize=9) 
    #axrow[1].legend() 
    #axrow[1].locator_params(axis='y', nbins=3) 


vicon = ViconNexus.ViconNexus() 

# Extract information from active trial 
subjectName = vicon.GetSubjectNames()[0] 
sessionLoc = vicon.GetTrialName()[0] 
trialName = vicon.GetTrialName()[1] 


analogId = 3 
emgOutId = 1 

channelNames = vicon.GetDeviceOutputDetails(3, 1)[4] 
channelId = vicon.GetDeviceOutputDetails(3, 1)[5] 


nrows = 4 
fig, axes = plt.subplots(nrows, 1, squeeze=False) 


# over all analog channels 
for ii, row in zip(range(nrows), axes): 

    emgId = channelId[ii] 
    emgData = vicon.GetDeviceChannel(analogId, emgOutId, emgId)[0] 
    emgDataRate = vicon.GetDeviceChannel(analogId, emgOutId, emgId)[2] 
    emgDataArray = np.asarray(emgData) 
    timeLine = np.arange(emgDataArray.size) 

    # write routine to select Left/right from trial_name 

    if channelNames[ii][-1] == 'R': 
     testEvent = vicon.GetEvents(subjectName, 'Right', 'Foot Strike') 
     testEventOff = vicon.GetEvents(subjectName, 'Right', 'Foot Off') 
    else: 
     testEvent = vicon.GetEvents(subjectName, 'Left', 'Foot Strike') 
     testEventOff = vicon.GetEvents(subjectName, 'Left', 'Foot Off') 

    trajDataRate = vicon.GetFrameRate() 

    if len(testEventOff[0]) == 1: 
     startFrameTraj = testEvent[0][0] 
     footOffFrame = testEventOff[0][0] 
     stopFrameTraj = testEvent[0][1] 
    else: 
     startFrameTraj = testEvent[0][0] 
     footOffFrame = testEventOff[0][1] 
     stopFrameTraj = testEvent[0][1] 


    startFrameAnal = int(startFrameTraj * (emgDataRate/trajDataRate)) 
    footOffAnal = int(footOffFrame * (emgDataRate/trajDataRate)) 
    stopFrameAnal = int(stopFrameTraj * (emgDataRate/trajDataRate)) 

    emgDataCut = emgDataArray[startFrameAnal:stopFrameAnal] 
    T = 1.0/4000.0 # time per frame 
    tEnd = T * (emgDataCut.size - 1) 
    timeLineCut = np.linspace(0.0, tEnd, num=emgDataCut.size) 
    #timeLineCut = np.arange(emgDataCut.size) 

    # do some filtering 
    fs = emgDataRate # sample rate 
    lowCut = 40.0 # Hz 
    highCut = 800.0 # Hz 
    order = 6 # or 2, 4 ...? 
    x = butter_bandpass_filter(emgDataCut, lowCut, highCut, fs, order) 

    # another style for fft 
    Y = emgDataCut 
    y = np.fft.fft(Y) 
    freq = np.fft.fftfreq(len(Y), T) 

    alim = [0, tEnd+1] 
    blim = [0, 600] 
    emgLabel = channelNames[ii] 
    plot(row, timeLineCut, x, alim, freq, np.abs(y), blim, emgLabel) 

fig.suptitle('EMG and spectrum - %s - %s' % (subjectName, trialName)) 
fig.set_size_inches(6, 10) # for landscape 
fig.savefig('%s%s_%s_EMG.pdf' % (sessionLoc, subjectName, trialName))

來源

2017-10-11 YerevanMotion

請讀[mcve]。即我們需要一個完整的例子來看看這裏發生了什麼。 – ImportanceOfBeingErnest

我認爲axrow變量通過減少數字的數量而變得混亂，但沒有更多的代碼我只能猜測。附：你能修理縮進嗎？ – Jurgy

@YerevanMotion這些評論意味着你可以相應地編輯你的問題。如果你忽視那些不會讓你更接近解決方案的東西。 – ImportanceOfBeingErnest

的解決方案是你的次要情節參數中使用「擠=假」和colmuns的數量切換到1

fig, axes = plt.subplots(nrows, 1, squeeze=False)

來源

2017-10-13 10:18:29 YerevanMotion

試圖擺脫matplotlib圖中的列

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