def test_kuiper_anglerange():
alpha1 = np.asarray([
0.291662278945, 5.899415544666, 5.402236718096, 3.728212505263,
5.303188109786, 3.737946900082, 3.850015526787, 4.902154536516,
3.631621444982, 5.341562525096
])
alpha2 = np.asarray([
0.613650458799, 2.109660249330, 3.617555161298, 6.196794760548,
1.856071575830, 2.991480015107, 1.789200626487, 4.835921843822,
2.767491245457, 1.744565591973
])
alpha1b = alpha1 - np.pi
alpha2b = alpha2 - np.pi
with warnings.catch_warnings():
warnings.simplefilter("ignore")
p, k = pycircstat.kuiper(alpha1, alpha2)
pb, kb = pycircstat.kuiper(alpha1b, alpha2b)
assert_allclose(p,
pb,
rtol=1e-4,
atol=1e-4,
err_msg="P-values are not equal")
assert_allclose(k,
kb,
rtol=1e-4,
atol=1e-4,
err_msg="K statistics not equal")
def test_kuiper():
alpha1 = np.asarray([0.291662278945,
5.899415544666,
5.402236718096,
3.728212505263,
5.303188109786,
3.737946900082,
3.850015526787,
4.902154536516,
3.631621444982,
5.341562525096])
alpha2 = np.asarray([0.613650458799,
2.109660249330,
3.617555161298,
6.196794760548,
1.856071575830,
2.991480015107,
1.789200626487,
4.835921843822,
2.767491245457,
1.744565591973])
p0 = 0.1
k0 = 70
K0 = 67.395
with warnings.catch_warnings():
warnings.simplefilter("ignore")
p, k = pycircstat.kuiper(alpha1, alpha2)
assert_allclose(p, p0, rtol=1e-4, atol=1e-4, err_msg="Error in p-values.")
assert_allclose(
k,
k0,
rtol=1e-4,
atol=1e-4,
err_msg="Error in statistic k.")
def test_kuiper_warning():
alpha1 = np.asarray([0.291662278945,
5.899415544666,
5.402236718096,
3.728212505263,
5.303188109786,
3.737946900082,
3.850015526787,
4.902154536516,
3.631621444982,
5.341562525096])
alpha2 = np.asarray([0.613650458799,
2.109660249330,
3.617555161298,
6.196794760548,
1.856071575830,
2.991480015107,
1.789200626487,
4.835921843822,
2.767491245457,
1.744565591973])
p0 = 0.1
k0 = 70
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
p, k = pycircstat.kuiper(alpha1, alpha2)
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
assert "N=10 not found in table" in str(w[-1].message)
def test_kuiper_anglerange():
alpha1 = np.asarray([0.291662278945,
5.899415544666,
5.402236718096,
3.728212505263,
5.303188109786,
3.737946900082,
3.850015526787,
4.902154536516,
3.631621444982,
5.341562525096])
alpha2 = np.asarray([0.613650458799,
2.109660249330,
3.617555161298,
6.196794760548,
1.856071575830,
2.991480015107,
1.789200626487,
4.835921843822,
2.767491245457,
1.744565591973])
alpha1b = alpha1 - np.pi
alpha2b = alpha2 - np.pi
with warnings.catch_warnings():
warnings.simplefilter("ignore")
p, k = pycircstat.kuiper(alpha1, alpha2)
pb, kb = pycircstat.kuiper(alpha1b, alpha2b)
assert_allclose(p,
pb,
rtol=1e-4,
atol=1e-4,
err_msg="P-values are not equal"
)
assert_allclose(k,
kb,
rtol=1e-4,
atol=1e-4,
err_msg="K statistics not equal"
)
def test_kuiper_warning():
unknown_N = 26
alpha1 = np.random.rand(unknown_N)*2.*np.pi
alpha2 = np.random.rand(unknown_N)*2.*np.pi
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
p, k = pycircstat.kuiper(alpha1, alpha2)
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
assert "N=%d not found in table" % unknown_N in str(w[-1].message)
def _compute_novel_rep_spike_stats(novel_phases, rep_phases):
# compute rayleigh test for each condition
p_novel, z_novel = pycircstat.rayleigh(novel_phases, axis=0)
p_rep, z_rep = pycircstat.rayleigh(rep_phases, axis=0)
# test whether the means are different
ww_pvals, ww_tables = pycircstat.watson_williams(novel_phases, rep_phases, axis=0)
ww_fstat = np.array([x.loc['Columns'].F for x in ww_tables])
# test whether the medians are different
med_pvals, med_stat = pycircstat.cmtest(novel_phases, rep_phases, axis=0)
# finall run kuiper test for difference in mean and/or dispersion
p_kuiper, stat_kuiper = pycircstat.kuiper(novel_phases, rep_phases, axis=0)
return p_novel, z_novel, p_rep, z_rep, ww_pvals, ww_fstat, med_pvals, med_stat, p_kuiper, stat_kuiper
def compute_phase_stats_with_shuffle(events, spike_rel_times, phase_data_hilbert, phase_bin_start,
phase_bin_stop, do_permute=False, shuffle_type=1):
spike_rel_times_tmp = spike_rel_times.copy()
e_tmp = events.copy()
if do_permute:
if shuffle_type == 1:
# permute the novel
novel_events = np.where(events.isFirst.values)[0]
perm_novel_events = np.random.permutation(novel_events)
spike_rel_times_tmp[novel_events] = spike_rel_times_tmp[perm_novel_events]
# and repeated separately
rep_events = np.where(~events.isFirst.values)[0]
perm_rep_events = np.random.permutation(rep_events)
spike_rel_times_tmp[rep_events] = spike_rel_times_tmp[perm_rep_events]
else:
e_tmp['isFirst'] = np.random.permutation(e_tmp.isFirst)
# get the phases at which the spikes occurred and bin into novel and repeated items for each hilbert band
spike_phases_hilbert = _compute_spike_phase_by_freq(spike_rel_times_tmp,
phase_bin_start,
phase_bin_stop,
phase_data_hilbert,
events)
# bin into repeated and novel phases
novel_phases, rep_phases = _bin_phases_into_cond(spike_phases_hilbert, e_tmp)
if (len(novel_phases) > 0) & (len(rep_phases) > 0):
# test phase locking for all spikes comboined
all_spikes_phases = np.vstack([novel_phases, rep_phases])
rayleigh_pval_all, rayleigh_z_all = pycircstat.rayleigh(all_spikes_phases, axis=0)
rvl_all = pycircstat.resultant_vector_length(all_spikes_phases, axis=0)
# rayleigh test for uniformity
rvl_novel = pycircstat.resultant_vector_length(novel_phases, axis=0)
rvl_rep = pycircstat.resultant_vector_length(rep_phases, axis=0)
rvl_diff = rvl_novel - rvl_rep
# compute rayleigh test for each condition
rayleigh_pval_novel, rayleigh_z_novel = pycircstat.rayleigh(novel_phases, axis=0)
rayleigh_pval_rep, rayleigh_z_rep = pycircstat.rayleigh(rep_phases, axis=0)
rayleigh_diff = rayleigh_z_novel - rayleigh_z_rep
# watson williams test for equal means
ww_pval, ww_tables = pycircstat.watson_williams(novel_phases, rep_phases, axis=0)
ww_fstat = np.array([x.loc['Columns'].F for x in ww_tables])
# kuiper test, to test for difference in dispersion (not mean, because I'm making them equal)
kuiper_pval, stat_kuiper = pycircstat.kuiper(novel_phases - pycircstat.mean(novel_phases),
rep_phases - pycircstat.mean(rep_phases), axis=0)
return (rvl_novel, rvl_rep, rvl_diff, ww_fstat, stat_kuiper, rayleigh_z_novel, rayleigh_z_rep, rayleigh_diff,
rayleigh_z_all, rvl_all), \
(rayleigh_pval_novel, rayleigh_pval_rep, ww_pval, kuiper_pval, rayleigh_pval_all), novel_phases, rep_phases
else:
return (np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2])), \
(np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2])), novel_phases, rep_phases
def compute_phase_stats_with_shuffle(events, spike_rel_times, phase_data_hilbert, phase_bin_start,
phase_bin_stop, do_permute=False):
spike_rel_times_tmp = spike_rel_times.copy()
if do_permute:
# permute the novel
novel_events = np.where(events.isFirst.values)[0]
perm_novel_events = np.random.permutation(novel_events)
spike_rel_times_tmp[novel_events] = spike_rel_times_tmp[perm_novel_events]
# and repeated separately
rep_events = np.where(~events.isFirst.values)[0]
perm_rep_events = np.random.permutation(rep_events)
spike_rel_times_tmp[rep_events] = spike_rel_times_tmp[perm_rep_events]
# get the phases at which the spikes occurred and bin into novel and repeated items for each hilbert band
spike_phases_hilbert = _compute_spike_phase_by_freq(spike_rel_times_tmp,
phase_bin_start,
phase_bin_stop,
phase_data_hilbert,
events)
# bin into repeated and novel phases
novel_phases, rep_phases = _bin_phases_into_cond(spike_phases_hilbert, events)
if (len(novel_phases) > 0) & (len(rep_phases) > 0):
# rayleigh test for uniformity
rvl_novel = pycircstat.resultant_vector_length(novel_phases, axis=0)
rvl_rep = pycircstat.resultant_vector_length(rep_phases, axis=0)
rvl_diff = rvl_novel - rvl_rep
# compute rayleigh test for each condition
rayleigh_pval_novel, rayleigh_z_novel = pycircstat.rayleigh(novel_phases, axis=0)
rayleigh_pval_rep, rayleigh_z_rep = pycircstat.rayleigh(rep_phases, axis=0)
rayleigh_diff = rayleigh_z_novel - rayleigh_z_rep
# watson williams test for equal means
ww_pval, ww_tables = pycircstat.watson_williams(novel_phases, rep_phases, axis=0)
ww_fstat = np.array([x.loc['Columns'].F for x in ww_tables])
# kuiper test, to test for difference in dispersion (not mean, because I'm making them equal)
kuiper_pval, stat_kuiper = pycircstat.kuiper(novel_phases - pycircstat.mean(novel_phases),
rep_phases - pycircstat.mean(rep_phases), axis=0)
return (rvl_novel, rvl_rep, rvl_diff, ww_fstat, stat_kuiper, rayleigh_z_novel, rayleigh_z_rep, rayleigh_diff), \
(rayleigh_pval_novel, rayleigh_pval_rep, ww_pval, kuiper_pval), novel_phases, rep_phases
else:
return (np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2])), \
(np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2]),
np.array([np.nan] * phase_data_hilbert.shape[2])), novel_phases, rep_phases