def plot_correlation_hist(data):
""" Plot histogram of all correlations
"""
# gather data
corrs = []
for raw_res, enh_list in data:
_, raw_mat, _ = raw_res
if not raw_mat is None:
raw_vec = extract_sig_entries(raw_mat)
corrs.extend(raw_vec)
for enh_res in enh_list:
_, enh_mat, _ = enh_res
if not enh_mat is None:
enh_vec = extract_sig_entries(enh_mat)
corrs.extend(enh_vec)
# plot result
fig = plt.figure()
plot_histogram(corrs, plt.gca())
plt.xlabel('simulated correlations')
fig.savefig('images/all_sim_corrs.pdf')
def plot_correlation_hist(data):
""" Plot histogram of all correlations
"""
# gather data
corrs = []
for raw_res, enh_list in data:
_, raw_mat, _ = raw_res
if not raw_mat is None:
raw_vec = extract_sig_entries(raw_mat)
corrs.extend(raw_vec)
for enh_res in enh_list:
_, enh_mat, _ = enh_res
if not enh_mat is None:
enh_vec = extract_sig_entries(enh_mat)
corrs.extend(enh_vec)
# plot result
fig = plt.figure()
plot_histogram(corrs, plt.gca())
plt.xlabel('simulated correlations')
fig.savefig('images/all_sim_corrs.pdf')
def extract_entries(row):
""" Extract needed amount of entries from each matrix
"""
return pd.Series({
'type': row.type,
'id': row.id,
'raw_res': row.raw_res,
'enh_res': row.enh_res,
'raw_vals': extract_sig_entries(row.raw_mat),
'enh_vals': extract_sig_entries(row.enh_mat[:3, :3])
})
def extract_entries(row):
""" Extract needed amount of entries from each matrix
"""
return pd.Series({
'type': row.type,
'id': row.id,
'raw_res': row.raw_res,
'enh_res': row.enh_res,
'raw_vals': extract_sig_entries(row.raw_mat),
'enh_vals': extract_sig_entries(row.enh_mat[:3,:3])
})
def find_threshold(data):
""" Use std/2 of correlation distribution closest to 0 (most likely) to switch sign as detection threshold
"""
cur = []
for raw, enh_res in data:
_, rd = raw
_, rdm, _ = rd
cur.append(extract_sig_entries(rdm))
for enh in enh_res:
_, ed = enh
_, edm, _ = ed
if not edm is None:
cur.append(extract_sig_entries(edm[:-1, :-1]))
idx = np.argmin(abs(np.mean(cur, axis=0)))
return np.std(cur, axis=0)[idx] / 2
def handle_enh_entry(raw_res, enh_res, val_func):
""" Compare given networks with given function
"""
raw_sde, raw_odesde = raw_res
enh_sde, enh_odesde = enh_res
raw, raw_mat, raw_sol = raw_odesde
enh, enh_mat, enh_sol = enh_odesde
if raw_mat is None or enh_mat is None:
return -1
enh_mat = enh_mat[:-1, :-1] # disregard fourth node
raw_vals = extract_sig_entries(raw_mat)
enh_vals = extract_sig_entries(enh_mat)
return val_func(raw_vals, enh_vals)
def handle_enh_entry(raw_res, enh_res, val_func):
""" Compare given networks with given function
"""
raw_sde, raw_odesde = raw_res
enh_sde, enh_odesde = enh_res
raw, raw_mat, raw_sol = raw_odesde
enh, enh_mat, enh_sol = enh_odesde
if raw_mat is None or enh_mat is None:
return -1
enh_mat = enh_mat[:-1,:-1] # disregard fourth node
raw_vals = extract_sig_entries(raw_mat)
enh_vals = extract_sig_entries(enh_mat)
return val_func(raw_vals, enh_vals)
def get_correlation_median(syst, mat):
vals = extract_sig_entries(mat)
avg = np.median(vals)
return avg
def get_correlation_variance(syst, mat):
vals = extract_sig_entries(mat)
var = np.var(vals)
return var
def get_correlation_median(syst, mat):
vals = extract_sig_entries(mat)
avg = np.median(vals)
return avg
def get_correlation_variance(syst, mat):
vals = extract_sig_entries(mat)
var = np.var(vals)
return var
