def test_dfa_pow_count():
pydfa = fathon.DFA(ts1)
winSizes = fu.powRangeByCount(1, 5, count=4, base=3)
n1, F1 = pydfa.computeFlucVec(winSizes, revSeg=True)
H1, H_int1 = pydfa.fitFlucVec()
assert math.isclose(H1, 0.8465301940909294)
def test_dfa_pow_step():
pydfa = fathon.DFA(ts1)
winSizes = fu.powRangeByStep(2, 7, step=2)
n1, F1 = pydfa.computeFlucVec(winSizes, revSeg=True)
H1, H_int1 = pydfa.fitFlucVec()
assert math.isclose(H1, 0.7316763359204684)
def test_dfa_lin_count():
pydfa = fathon.DFA(ts1)
winSizes = fu.linRangeByCount(10, 200)
n1, F1 = pydfa.computeFlucVec(winSizes, revSeg=True)
H1, H_int1 = pydfa.fitFlucVec()
assert math.isclose(H1, 0.7982194289592676)
def test_mat_dfa_mf():
mf_dfa = fathon.DFA(fu.toAggregated(mf))
n_mf, F_mf = mf_dfa.computeFlucVec(scales, revSeg=False, polOrd=1)
idxs = get_idxs(n_mf, scales)
n_mf = n_mf[idxs]
F_mf = F_mf[idxs]
H, _ = np.polyfit(np.log2(n_mf), np.log2(F_mf), 1)
assert math.isclose(H, 0.77, rel_tol=1e-2, abs_tol=0)
def test_mat_dfa_wn():
w_dfa = fathon.DFA(fu.toAggregated(wn))
n_w, F_w = w_dfa.computeFlucVec(scales, revSeg=False, polOrd=1)
idxs = get_idxs(n_w, scales)
n_w = n_w[idxs]
F_w = F_w[idxs]
H, _ = np.polyfit(np.log2(n_w), np.log2(F_w), 1)
assert math.isclose(H, 0.45, rel_tol=1e-2, abs_tol=0)
def test_dfa_H_log():
pydfa = fathon.DFA(ts1)
winSizes = fu.powRangeByCount(1, 5, count=4, base=3)
n1, F1 = pydfa.computeFlucVec(winSizes, revSeg=True)
H1, H_int1 = pydfa.fitFlucVec()
H2, H_int2 = pydfa.fitFlucVec(logBase=2)
H3, H_int3 = pydfa.fitFlucVec(logBase=10)
H4, H_int4 = pydfa.fitFlucVec(logBase=1.4)
assert (math.isclose(H1, H2) and math.isclose(H1, H3)
and math.isclose(H1, H4) and math.isclose(H2, H3)
and math.isclose(H2, H4) and math.isclose(H3, H4))
def test_dfa_save_load():
dfa = fathon.DFA(fu.toAggregated(ts))
# save and load with empty results
dfa.saveObject(get_object_path('dfa_obj'))
n_load = fu.getObjectMember(get_object_path('dfa_obj', ext=True), 'n')
F_load = fu.getObjectMember(get_object_path('dfa_obj', ext=True), 'F')
assert np.array_equal(n_load, [])
assert np.array_equal(F_load, [])
#save and load with results
n, F = dfa.computeFlucVec(fu.linRangeByStep(10, 500))
H, I = dfa.fitFlucVec(100, 300)
dfa.saveObject(get_object_path('dfa_obj'))
n_load = fu.getObjectMember(get_object_path('dfa_obj', ext=True), 'n')
F_load = fu.getObjectMember(get_object_path('dfa_obj', ext=True), 'F')
assert np.array_equal(n_load, n)
assert np.array_equal(F_load, F)
# DFA from file
dfa_2 = fathon.DFA(get_object_path('dfa_obj', ext=True))
H_2, I_2 = dfa_2.fitFlucVec(100, 300)
assert H_2 == H
assert I_2 == I
mfdfa_n_F_dict[j][i] = dict(zip(n, F))
# dictionary to match all the n and F values this could be
# more efficient
# get the list values of H and intercept
list_H, list_H_intercept = pymfdfa.fitFlucVec() # same for H values
mfdfa_H_dict[j][i] = [list_H, list_H_intercept]
# get the mass exponents
tau = pymfdfa.computeMassExponents()
mfdfa_tau_dict[j][i] = tau
# get the multi-fractal spectrum
alpha, mfSpect = pymfdfa.computeMultifractalSpectrum()
mfdfa_alpha_spect[j][i] = [mfSpect, alpha]
pydfa = fathon.DFA(a)
n, F = pydfa.computeFlucVec(winSizes, revSeg=revSeg, polOrd=polOrd)
dict_dfa[j][i] = dict(zip(n, F)) # dictionary to match all the n and F values
H, H_intercept = pydfa.fitFlucVec() # same for H values
h_dict[j][i] = [H, H_intercept]
print('for date', i, ' and bar type', j, 'you get', H)
# everything below here is to store
# saving DFA files first
# -1-
pickle_out_filename = os.path.join(experimentsDestination, symbol, 'DFA_' +
'n_f_values' + "volume_width" + str(trade_volume_width)
