def report(self, x_name=None, **kwargs):
"""
Save eigenvalue analysis reports.
Returns
-------
None
"""
if x_name is None:
x_name = self.x_name
text, header, rowname, data = list(), list(), list(), list()
neig = len(self.mu)
mu_real = self.mu.real()
mu_imag = self.mu.imag()
n_positive = sum(1 for x in mu_real if x > 0)
n_zeros = sum(1 for x in mu_real if x == 0)
n_negative = sum(1 for x in mu_real if x < 0)
numeral = []
for idx, item in enumerate(range(neig)):
if mu_real[idx] == 0:
marker = '*'
elif mu_real[idx] > 0:
marker = '**'
else:
marker = ''
numeral.append('#' + str(idx + 1) + marker)
# compute frequency, un-damped frequency and damping
freq = [0] * neig
ufreq = [0] * neig
damping = [0] * neig
for idx, item in enumerate(self.mu):
if item.imag == 0:
freq[idx] = 0
ufreq[idx] = 0
damping[idx] = 0
else:
ufreq[idx] = abs(item) / 2 / pi
freq[idx] = abs(item.imag / 2 / pi)
damping[idx] = -div(item.real, abs(item)) * 100
# obtain most associated variables
var_assoc = []
for prow in range(neig):
temp_row = self.part_fact[prow, :]
name_idx = list(temp_row).index(max(temp_row))
var_assoc.append(x_name[name_idx])
pf = []
for prow in range(neig):
temp_row = []
for pcol in range(neig):
temp_row.append(round(self.part_fact[prow, pcol], 5))
pf.append(temp_row)
# opening info section
text.append(report_info(self.system))
header.append(None)
rowname.append(None)
data.append(None)
text.append('')
header.append([''])
rowname.append(['EIGENVALUE ANALYSIS REPORT'])
data.append('')
text.append('STATISTICS\n')
header.append([''])
rowname.append(['Positives', 'Zeros', 'Negatives'])
data.append([n_positive, n_zeros, n_negative])
text.append('EIGENVALUE DATA\n')
header.append([
'Most Associated', 'Real', 'Imag.', 'Damped Freq.', 'Frequency',
'Damping [%]'
])
rowname.append(numeral)
data.append(
[var_assoc,
list(mu_real),
list(mu_imag), freq, ufreq, damping])
n_cols = 7 # columns per block
n_block = int(ceil(neig / n_cols))
if n_block <= 100:
for idx in range(n_block):
start = n_cols * idx
end = n_cols * (idx + 1)
text.append('PARTICIPATION FACTORS [{}/{}]\n'.format(
idx + 1, n_block))
header.append(numeral[start:end])
rowname.append(x_name)
data.append(pf[start:end])
dump_data(text, header, rowname, data, self.system.files.eig)
logger.info('Report saved to "%s".', self.system.files.eig)
def report(self, x_name=None, **kwargs):
"""
Save eigenvalue analysis reports.
Returns
-------
None
"""
if x_name is None:
x_name = self.x_name
n_states = len(self.mu)
mu_real = self.mu.real
mu_imag = self.mu.imag
freq, ufreq, damping, numeral = self.post_process()
# obtain most associated variables
var_assoc = []
for prow in range(n_states):
temp_row = self.pfactors[prow, :]
name_idx = list(temp_row).index(max(temp_row))
var_assoc.append(x_name[name_idx])
text, header, rowname, data = list(), list(), list(), list()
# opening info section
text.append(report_info(self.system))
header.append(None)
rowname.append(None)
data.append(None)
text.append('')
text.append('EIGENVALUE ANALYSIS REPORT')
header.append([])
rowname.append([])
data.append([])
text.append('STATISTICS\n')
header.append([''])
rowname.append(['Positives', 'Zeros', 'Negatives'])
data.append((self.n_positive, self.n_zeros, self.n_negative))
text.append('EIGENVALUE DATA\n')
header.append([
'Most Associated', 'Real', 'Imag.', 'Damped Freq.', 'Frequency',
'Damping [%]'
])
rowname.append(numeral)
data.append(
[var_assoc,
list(mu_real),
list(mu_imag), freq, ufreq, damping])
n_cols = 7 # columns per block
n_block = int(ceil(n_states / n_cols))
if n_block <= 100:
for idx in range(n_block):
start = n_cols * idx
end = n_cols * (idx + 1)
text.append('PARTICIPATION FACTORS [{}/{}]\n'.format(
idx + 1, n_block))
header.append(numeral[start:end])
rowname.append(x_name)
data.append(self.pfactors[start:end, :])
dump_data(text, header, rowname, data, self.system.files.eig)
logger.info('Report saved to "%s".', self.system.files.eig)