<jupyter_start><jupyter_text>Example manual analysis<jupyter_code>srcsub = mre.input_handler('/Users/harangju/Developer/activity_i5_j2.txt')
print('imported trials from wildcard: ', srcsub.shape[0])
oful = mre.OutputHandler()
oful.add_ts(srcsub)
rk = mre.coefficients(srcsub)
print(rk.coefficients)
print('this guy has the following attributes: ', rk._fields)
m = mre.fit(rk)
m.mre<jupyter_output>INFO     Unbound fit to $|A| e^{-k/\tau}$
INFO     Finished 4 fit(s)
INFO     Finished fitting the data to f_exponential, mre = 0.70596, tau = 2.87ms, ssres = 0.15697
WARNING  The obtained autocorrelationtime is small compared to the fitrange: tmin~1ms, tmax~2904ms, tau~3ms
WARNING  Consider fitting with smaller 'minstep' and 'maxstep'<jupyter_text>Full analysis<jupyter_code># m = np.zeros((16,12))
mact = np.zeros((16,12))
for i in range(0,16):
    for j in range(0,12):
        fname = '/Users/harangju/Developer/avalanche paper data/mr estimation/activity/activity_i' +\
        str(i+1) + '_j' + str(j+1) + '.txt'
        act = np.loadtxt(fname)
        mact[i,j] = max(act)
#         srcsub = mre.input_handler(fname)
#         rk = mre.coefficients(srcsub, steps=(1,10000))
#         me = mre.fit(rk)
#         m[i,j] = me.mre
import scipy.io as sio
sio.savemat('/Users/harangju/Developer/mre.mat',{'mre':m,'mact':mact})<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1467551622418879, 'fraction_numerical': 0.03466076696165192, 'mean_word_length': 4.239382239382239, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '511155', 'license': 'MIT', 'n_tokens_mistral': 562, 'n_tokens_neox': 521, 'n_words': 108, 'path': 'figures/supplement/sfig6.ipynb', 'repo_name': 'harangju/avalanche', 'size': '1356'}