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How to use measure_duration method in avocado

Best Python code snippet using avocado_python

DataGatherThread.py

Source:DataGatherThread.py

1'''2Created on Jul 31, 20203@author: duicul4'''5import threading6import os7from moviepy.video.io.VideoFileClip import VideoFileClip8import traceback9class DataGatherThread (threading.Thread):10    11    def __init__(self, files,measure_duration,thread_no):12        threading.Thread.__init__(self)13        self.files = files # 0-file_name 1-fullpath 2-duration 3-size14        self.measure_duration = measure_duration15        self.thread_no=thread_no16        17    def video_len(self,path,measure_len):18        if not measure_len:19            return False20        try:21            clip = VideoFileClip(path)22        except Exception:23            #print(path)24            #print(str(traceback.format_exc()))25            return False26        if clip is None:27            return False28        len_vid=clip.duration29        #print(len_vid)30        if len_vid is None:31            return False32        clip.reader.close()33        if clip.audio is None:34            return False35        else:36            clip.audio.reader.close_proc()37        return len_vid38    39    40    def gather_data(self,file_range_search,measure_duration):41        #if range_search[len(range_search)-1]>=len(self.file_data):42        #    return43        44        ret_file_data=[]45        for file in file_range_search:46            #print(self.file_data[i][1])47            file[2]=self.video_len(file[1],measure_duration)48            file[3]=os.path.getsize(file[1])49            #print(file)50            ret_file_data.append(file)51        52        self.files=ret_file_data53        #print(self.files)54    55    def run(self):56        print("Starting: "+str(len(self.files))+" files no:"+str(self.thread_no))57        self.gather_data(self.files, self.measure_duration)58        print("Finish: "+" no: "+str(self.thread_no))59if __name__ == '__main__':...

experiment_10_post_sim_plotting.py

Source:experiment_10_post_sim_plotting.py

1from brian2.units import *2from echo_time import *3import pickle4import scipy.io as spio5import matplotlib.pyplot as plt6import power_spectral_density as psd7fname = "experiment_data/exp10_20sec.pickle"8echo = echo_start("Reading data from {}... ".format(fname))9try:10    with open(fname, 'rb') as f:11        DATA = pickle.load(f)12except Exception as e:13    print('Error', e)14    exit()15echo_end(echo)16X = DATA['X']17Y = DATA['Y']18duration = DATA['duration']/ms19echo = echo_start("Post-processing and plotting... \n")20MEASURE_START = 100021MEASURE_DURATION = 50022print("\t{:,} excitatory neuron spikes in total".format(len(X)))23tt = time.time()24X1, Y1 = [], []25for spike_t, spike_idx in zip(X, Y):26    if MEASURE_START <= spike_t < MEASURE_START + MEASURE_DURATION and \27            spike_idx < 3*40:28        X1.append(spike_t)29        Y1.append(spike_idx)30print('\tCollect relevant spikes: {}s'.format(time.time() - tt))31#X, Y = M.t/ms, M.i32X, Y = X1, Y133tt = time.time()34dt = 10 # ms35shift = 5 # ms36total_steps = int(duration/(shift*ms))37ma, time_scale = psd.moving_average(X, dt, shift, total_steps, True)38print('\tCalculate moving average: {}s'.format(time.time() - tt))39tt = time.time()40X2, Y2 = [], []41for ma_val, t in zip(ma, time_scale):42    if MEASURE_START <= t[0] < MEASURE_START + MEASURE_DURATION:43        Y2.append(ma_val)44        X2.append(t[0])45print('\tCollect relvant moving average data points: {}s'.format(time.time() - tt))46plt.subplot(211)47plt.plot(X1, Y1, '.b')48plt.ylabel('Neuron Index')49plt.xlabel('Simulation Time (ms)')50plt.subplot(212)51plt.plot(X2, Y2)52plt.xlabel('Simulation Time (ms)')53plt.ylabel('Mean firing rate')54echo_end(echo)55plt.show()...

main_without_esp32.py

Source:main_without_esp32.py

1from imu import gy8012import time3from plot import plot_trajectory, fft, plot_trajectory_attenuated4if __name__ == '__main__':5    sensor = gy801()6    7    MEASURE_DURATION = 5 # unit: s8    df = sensor.measure_complementary(MEASURE_DURATION)9    print(df)10    freq = MEASURE_DURATION / len(df)11    plot_trajectory(df, freq)12    df = fft(df, freq)...

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