def fitLossCoeffs(self):
for m in self.mcv:
if 'losses' not in self.mcv[m]:
continue
losses = self.mcv[m]['losses']
cw, alfa, beta = lc.fitsteinmetz(losses['f'], losses['B'],
losses['pfe'], self.mcv[m]['Bo'],
self.mcv[m]['fo'])
losses['cw'] = cw
losses['alfa'] = alfa
losses['beta'] = beta
losses['Bo'] = self.mcv[m]['Bo']
losses['fo'] = self.mcv[m]['fo']
def __init__(self, filename):
self.version_mc_curve = 0
self.mc1_type = femagtools.mcv.MAGCRV
self.curve = []
self.name = os.path.splitext(os.path.basename(filename))[0]
self.mc1_title = 'ThyssenKrupp Steel'
self.curve = [{}]
self.curve[0]['hi'] = []
self.curve[0]['bi'] = []
self.fo = fo
self.Bo = Bo
self.ch = 0.0
self.ch_freq = 0.0
self.cw = None
self.cw_freq = None
self.b_coeff = None
self.rho = 7.6
self.losses = dict(f=[], B=[], pfe=[])
with codecs.open(filename, encoding='utf-8', errors='ignore') as f:
while True:
l = f.readline().strip()
if not l:
if not self.losses['B']:
self.losses = dict()
break
if l.startswith('H(A/m) B(T)') or l.startswith('H[A/m] B[T]'):
h, b, j = readlist(f)
self.curve[0]['hi'] = h
self.curve[0]['bi'] = b
elif l.startswith('Comment'):
self.mc1_title = l.split(':')[1].strip()
elif l.startswith('Mass Density'):
d = numPattern.findall(l.replace(',', '.'))
self.rho = float(d[0])
if l.split()[-1] == 'kg/m^3':
self.rho /= 1e3
elif l.startswith('f='):
fref = numPattern.findall(l.replace(',', '.'))
fxref = float(fref[0])
b, p = readlist(f)
self.losses['f'].append(fxref)
self.losses['B'].append(b)
self.losses['pfe'].append(p)
logger.info("%s Bmax %3.2f", filename, max(self.curve[0]['bi']))
if self.losses and not np.isscalar(self.losses['B'][0]):
import scipy.interpolate as ip
z = lc.fitjordan(self.losses['f'], self.losses['B'],
self.losses['pfe'], self.Bo, self.fo)
logger.info("Jordan loss coeffs %s", z)
self.ch = z[2]
self.ch_freq = z[3]
self.cw = z[0]
self.cw_freq = z[1]
self.b_coeff = z[4]
z = lc.fitsteinmetz(self.losses['f'], self.losses['B'],
self.losses['pfe'], self.Bo, self.fo)
logger.info("Steinmeth loss coeffs %s", z)
self.losses['cw'] = z[0]
self.losses['cw_freq'] = z[1]
self.losses['b_coeff'] = z[2]
self.losses['Bo'] = self.Bo
self.losses['fo'] = self.fo
# must normalize pfe matrix:
bmin = max(list(zip(*(self.losses['B'])))[0])
bmax = max([bx[-1] for bx in self.losses['B']])
Bv = np.arange(
np.ceil(10 * bmin) / 10.0, (np.floor(10 * bmax) + 1) / 10.0,
0.1)
m = []
for i, b in enumerate(self.losses['B']):
pfunc = ip.interp1d(b, self.losses['pfe'][i], kind='cubic')
bx = [x for x in Bv if x < b[-1]]
m.append(pfunc(bx).tolist() + (len(Bv) - len(bx)) * [None])
self.losses['B'] = Bv.tolist()
self.losses['pfe'] = list(zip(*m))
def __init__(self, filename):
self.version_mc_curve = 0
self.mc1_type = femagtools.mcv.MAGCRV
self.curve = []
self.name = os.path.splitext(os.path.basename(filename))[0]
self.mc1_title = 'ThyssenKrupp Steel'
self.curve = [{}]
self.curve[0]['hi'] = []
self.curve[0]['bi'] = []
self.fo = fo
self.Bo = Bo
self.ch = 0.0
self.ch_freq = 0.0
self.cw = None
self.cw_freq = None
self.b_coeff = None
self.rho = 7.6
self.losses = dict(f=[], B=[])
pfe = []
with codecs.open(filename, encoding='utf-8', errors='ignore') as f:
content = [l.strip() for l in f.readlines()]
for i, l in enumerate(content):
if l.startswith('H(A/m) B(T)') or l.startswith('H[A/m] B[T]'):
h, b, j = readlist(content[i + 1:])
self.curve[0]['hi'] = h
self.curve[0]['bi'] = b
elif l.startswith('Comment'):
self.mc1_title = l.split(':')[1].strip()
elif l.startswith('Mass Density'):
d = numPattern.findall(l.replace(',', '.'))
self.rho = float(d[0])
if l.split()[-1] == 'kg/m^3':
self.rho /= 1e3
elif l.startswith('f='):
fref = numPattern.findall(l.replace(',', '.'))
fxref = float(fref[0])
b, p = readlist(content[i + 2:])
self.losses['f'].append(fxref)
self.losses['B'].append(b)
pfe.append(p)
logger.info("%s Bmax %3.2f", filename, max(self.curve[0]['bi']))
if pfe and not np.isscalar(self.losses['B'][0]):
import scipy.interpolate as ip
colsize = max([len(p) for p in pfe])
losses = np.array(
[list(p) + [0] * (colsize - len(p)) for p in pfe]).T
z = lc.fitjordan(self.losses['f'], self.losses['B'], losses,
self.Bo, self.fo)
logger.info("Jordan loss coeffs %s", z)
self.ch = z[0]
self.ch_freq = z[1]
self.cw = z[2]
self.cw_freq = z[3]
self.b_coeff = z[4]
z = lc.fitsteinmetz(self.losses['f'], self.losses['B'], losses,
self.Bo, self.fo)
logger.info("Steinmetz loss coeffs %s", z)
self.losses['cw'] = z[0]
self.losses['cw_freq'] = z[1]
self.losses['b_coeff'] = z[2]
self.losses['Bo'] = self.Bo
self.losses['fo'] = self.fo
# must normalize pfe matrix:
bmin = np.ceil(10 * max([min(b) for b in self.losses['B']])) / 10.0
bmax = round(10 * max([max(b) for b in self.losses['B']])) / 10.0
Bv = np.arange(bmin, bmax + 0.01, 0.1)
m = []
for i, b in enumerate(self.losses['B']):
n = len([x for x in Bv if x < b[-1]])
if n < len(b) and n < len(Bv):
if Bv[n] < b[-1] + 0.1:
b = list(b)
b[-1] = Bv[n]
n += 1
pfunc = ip.interp1d(b, pfe[i], kind='cubic')
m.append([float(pfunc(x))
for x in Bv[:n]] + [None] * (len(Bv) - n))
self.losses['B'] = Bv.tolist()
self.losses['pfe'] = m