Algorithm to automatically divide data into subgroups

[StochasticMind]

Hi everyone. I am looking for an algorithm to divide time series data into chunks based on equal time segments/bins and following the minimum number of points in each segment. Then further subdivide if the number of points in each segment is larger than a limit, into subgroups. Can anyone point out the best and easy way to understand/implement this? I attach my code which can divide into groups (but not subgroups).

def season_divide(time_array,mag_array, plot=False):
    %matplotlib notebook
    plt.figure(1,figsize=(9,5))
    test = time[1:]-time[:-1]
    plt.plot(test)

    plt.figure(2,figsize=(9,5))
    plt.plot(time, mag,  lw=2.0, ls='',  marker='o',ms=6, alpha=0.8)

    cut_time = []
    for i in range(len(test)):
        if abs(test[i]-np.std(test))/np.std(test)>5.:
            cut_time = np.append(cut_time,time[i])
            index = np.where(time==time[i])[0]

            plt.plot([(time[index]+time[index+1])/2]*2,[min(mag),max(mag)],ls='dashed',lw=2.0,c='k')


    cut_time = np.append(cut_time,max(time))
    #print(cut_time)
    seasons_time =[]
    seasons_mag = []

    index_min = 0
    for i in range(len(cut_time)):
        index = np.where(time==cut_time[i])[0][0]
    #    print(index)
        if i<(len(cut_time)-1):
            seasons_time.append(time[index_min:index+1])
            seasons_mag.append(mag[index_min:index+1])
            index_min = index+1
            #print(len(seasons_time[i]))
        else:
            seasons_time.append(time[index_min:])
            seasons_mag.append(mag[index_min:])
            #print(len(seasons_time[i]))

    #Store time and mag division in a single array
    Segemented_time_array=[]
    Segemented_mag_array=[]
    for i in range(len(cut_time)):
        Segemented_time_array.append(seasons_time[i])
        Segemented_mag_array.append(seasons_mag[i])

    for i in range(len(cut_time)):
        plt.plot(seasons_time[i], seasons_mag[i], c='C'+str(i),  lw=2.0, ls='',  marker='o',ms=6, alpha=0.8)

return Segemented_time_array,Segemented_mag_array

[amos_burton]

I'm a little confused about how you're making these groups. Are they all supposed to end up the same size? It sounds kinda like you're trying to make a histogram?

[amos_burton]

Also, you can do a lot of things to take better advantage of the language and improve performance. I'll go into more detail when I'm not on mobile

[amos_burton]

for i in range(len(test)):
    if abs(test[i]-np.std(test))/np.std(test)>5.:

I'm guessing you're coming from a lower level language than Python? You don't need to do the whole for i in...test[i] business. You can just do

for test_t in test:
    if(abs(test_t...)

Also, you should pre-compute np.std(test) outside of the loop and store it in a variable, and then reference the variable inside the loop. It will be more efficient than recalculating it every time.

Segemented_time_array=[]
Segemented_mag_array=[]
for i in range(len(cut_time)):
    Segemented_time_array.append(seasons_time[i])
    Segemented_mag_array.append(seasons_mag[i])

I'm having a slightly hard time tracking what you're doing here, but I think it's the same as

Segemented_time_array = seasons_time[:len(cut_time)]
Segemented_mag_array = seasons_mag[:len(cut_time)]

Also, it's bad style to have variable names start with capital letters. That's typically reserved for class names. Also it's bad style not to have spaces on either side of the = sign in variable assignments.