def freqz(sosmat, nsamples=44100, sample_rate=44100, plot=True):
"""Plots Frequency response of sosmat."""
from pylab import np, plt, fft, fftfreq
x = np.zeros(nsamples)
x[nsamples/2] = 0.999
y, states = sosfilter_double_c(x, sosmat)
Y = fft(y)
f = fftfreq(len(x), 1.0/sample_rate)
if plot:
plt.grid(True)
plt.axis([0, sample_rate / 2, -100, 5])
L = 20*np.log10(np.abs(Y[:len(x)/2]) + 1e-17)
plt.semilogx(f[:len(x)/2], L, lw=0.5)
plt.hold(True)
plt.title('freqz sos filter')
plt.xlabel('Frequency / Hz')
plt.ylabel('Damping /dB(FS)')
plt.xlim((10, sample_rate/2))
plt.hold(False)
return x, y, f, Y
def freqz(sosmat, nsamples=44100, sample_rate=44100, plot=True):
"""Plots Frequency response of sosmat."""
from pylab import np, plt, fft, fftfreq
x = np.zeros(nsamples)
x[int(nsamples/2)] = 0.999
y, states = sosfilter_double_c(x, sosmat)
Y = fft(y)
f = fftfreq(len(x), 1.0/sample_rate)
if plot:
plt.grid(True)
plt.axis([0, sample_rate / 2, -100, 5])
L = 20*np.log10(np.abs(Y[:int(len(x)/2)]) + 1e-17)
plt.semilogx(f[:int(len(x)/2)], L, lw=0.5)
plt.hold(True)
plt.title(u'freqz sos filter')
plt.xlabel('Frequency / Hz')
plt.ylabel(u'Damping /dB(FS)')
plt.xlim((10, sample_rate/2))
plt.hold(False)
return x, y, f, Y
def plot_L_curve(
files,
nlin_pars=['log10_He_', 'log10_visM_', 'rake'],
nlin_pars_ylabels=[r'$log_{10}(He)$', r'$log_{10}(visM)$', 'rake'],
):
nreses = collect_from_result_files(files, 'residual_norm_weighted')
nroughs = collect_from_result_files(files, 'roughening_norm')
num_subplots = 1 + len(nlin_pars)
x1 = amin(nreses)
x2 = amax(nreses)
dx = x2 - x1
xlim = (x1 - dx * 0.02, x2 + dx * 0.2)
xticks = range(int(x1), int(x2), 5)
plt.subplot(num_subplots, 1, 1)
plt.loglog(nreses, nroughs, 'o-')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.gca().get_xaxis().set_major_formatter(
matplotlib.ticker.ScalarFormatter())
plt.ylabel('roughening')
plt.xlabel('Residual Norm')
plt.grid('on')
nth = 2
for par, par_label in zip(nlin_pars, nlin_pars_ylabels):
y = collect_from_result_files(files, par)
plt.subplot(num_subplots, 1, nth)
plt.semilogx(nreses, y, 'o-')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.gca().get_xaxis().set_major_formatter(
matplotlib.ticker.ScalarFormatter())
plt.ylabel(par_label)
plt.xlabel('Residual Norm')
plt.grid('on')
nth += 1
def plot_L_curve(files,
nlin_pars = ['log10_He_','log10_visM_','rake'],
nlin_pars_ylabels = [r'$log_{10}(He)$',
r'$log_{10}(visM)$',
'rake'],
):
nreses = collect_from_result_files(files, 'residual_norm_weighted')
nroughs = collect_from_result_files(files, 'roughening_norm')
num_subplots = 1 + len(nlin_pars)
x1 = amin(nreses)
x2 = amax(nreses)
dx = x2 - x1
xlim = (x1-dx*0.02, x2+dx*0.2)
xticks = range(int(x1), int(x2),5)
plt.subplot(num_subplots,1,1)
plt.loglog(nreses, nroughs,'o-')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.ylabel('roughening')
plt.xlabel('Residual Norm')
plt.grid('on')
nth = 2
for par, par_label in zip(nlin_pars, nlin_pars_ylabels):
y = collect_from_result_files(files, par)
plt.subplot(num_subplots,1,nth)
plt.semilogx(nreses, y,'o-')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.ylabel(par_label)
plt.xlabel('Residual Norm')
plt.grid('on')
nth += 1
import h5py
from pylab import plt
nreses = []
rakes = []
for ano in range(30):
with h5py.File('outs/ano_%02d.h5' % ano, 'r') as fid:
nres = fid['residual_norm_weighted'][...]
nreses.append(nres)
m = fid['m'][...]
rakes.append(m[-1])
plt.semilogx(nreses, rakes, 'o')
plt.show()
nreses.append(nres)
m = fid['m'][...]
visMs.append(m[-3])
Hes.append(m[-2])
rakes.append(m[-1])
nrough = fid['regularization/roughening/norm'][...]
nroughs.append(nrough)
xlim = (7, 22)
xlim = None
xticks = range(7,22)
plt.subplot(411)
plt.semilogx(nreses, visMs,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('log10(visM/(Pa.s))')
plt.subplot(412)
plt.semilogx(nreses, Hes,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('He/km')
plt.subplot(413)
plt.semilogx(nreses, rakes,'o')
plt.xlim(xlim)
def freqz(ofb, length_sec=6, ffilt=False, plot=True):
"""Computes the IR and FRF of a digital filter.
Parameters
----------
ofb : FractionalOctaveFilterbank object
length_sec : scalar
Length of the impulse response test signal.
ffilt : bool
Backard forward filtering. Effectiv order is doubled then.
plot : bool
Create Plots or not.
Returns
-------
x : ndarray
Impulse test signal.
y : ndarray
Impules responses signal of the filters.
f : ndarray
Frequency vector for the FRF.
Y : Frequency response (FRF) of the summed filters.
"""
from pylab import np, plt, fft, fftfreq
x = np.zeros(length_sec * ofb.sample_rate)
x[int(length_sec * ofb.sample_rate / 2)] = 0.9999
if not ffilt:
y, states = ofb.filter_mimo_c(x)
y = y[:, :, 0]
else:
y, states = ofb.filter(x, ffilt=ffilt)
s = np.zeros(len(x))
len_x_2 = int(len(x) / 2)
for i in range(y.shape[1]):
s += y[:, i]
X = fft(y[:, i]) # sampled frequency response
f = fftfreq(len(x), 1.0 / ofb.sample_rate)
if plot:
fig = plt.figure('freqz filter bank')
plt.grid(True)
plt.axis([0, ofb.sample_rate / 2, -100, 5])
L = 20 * np.log10(np.abs(X[:len_x_2]) + 1e-17)
plt.semilogx(f[:len_x_2], L, lw=0.5)
Y = fft(s)
if plot:
plt.title(u'freqz() Filter Bank')
plt.xlabel('Frequency / Hz')
plt.ylabel(u'Damping /dB(FS)')
plt.xlim((10, ofb.sample_rate / 2))
plt.figure('sum')
L = 20 * np.log10(np.abs(Y[:len_x_2]) + 1e-17)
plt.semilogx(f[:len_x_2], L, lw=0.5)
level_input = 10 * np.log10(np.sum(x**2))
level_output = 10 * np.log10(np.sum(s**2))
plt.axis([5, ofb.sample_rate / 1.8, -50, 5])
plt.grid(True)
plt.title('Sum of filter bands')
plt.xlabel('Frequency / Hz')
plt.ylabel(u'Damping /dB(FS)')
print('sum level', level_output, level_input)
return x, y, f, Y
visMs.append(m[-3])
Hes.append(m[-2])
print(Hes)
rakes.append(m[-1])
nrough = fid['regularization/roughening/norm'][...]
nroughs.append(nrough)
x1 = amin(nreses)
x2 = amax(nreses)
dx = x2 - x1
xlim = (x1 - dx * 0.02, x2 + dx * 0.2)
xticks = range(int(x1), int(x2), 5)
plt.subplot(411)
plt.semilogx(nreses, visMs, 'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.axhline(18.8, color='red')
plt.grid('on')
plt.ylabel('log10(visM/(Pa.s))')
plt.subplot(412)
plt.semilogx(nreses, Hes, 'o')
#plt.xlim(xlim)
plt.gca().set_xticks(xticks)
#plt.axhline(40, color='red')
plt.grid('on')
plt.ylabel('He/km')
plt.subplot(413)
return outs
com = vj.MomentCalculator('../../../fault_model/fault_bott60km.h5',
'../earth.model_He63km_VisM1.0E19')
files = sorted(glob.glob('../outs/ano_??.h5'))
Bm1 = collect_results(files, 'Bm')
slip1 = [ii[:-1, :] for ii in Bm1]
mo1 = [com.moment(ii)[0] for ii in slip1]
nres1 = collect_results(files, 'misfit/norm_weighted')
files = sorted(glob.glob('../../run0/outs/ano_??.h5'))
Bm0 = collect_results(files, 'Bm')
slip0 = [ii[:-1, :] for ii in Bm0]
mo0 = [com.moment(ii)[0] for ii in slip0]
nres0 = collect_results(files, 'misfit/norm_weighted')
plt.semilogx(nres0, mo0, '.', label='Result0')
plt.semilogx(nres1, mo1, '.', label='Result1')
plt.grid('on')
plt.xlabel('norm of weighted residual')
plt.ylabel('Mo')
plt.xlim([.7, 5])
plt.legend()
plt.savefig('compare_mo.png')
plt.show()
def freqz(ofb, length_sec=6, ffilt=False, plot=True):
"""Computes the IR and FRF of a digital filter.
Parameters
----------
ofb : FractionalOctaveFilterbank object
length_sec : scalar
Length of the impulse response test signal.
ffilt : bool
Backard forward filtering. Effectiv order is doubled then.
plot : bool
Create Plots or not.
Returns
-------
x : ndarray
Impulse test signal.
y : ndarray
Impules responses signal of the filters.
f : ndarray
Frequency vector for the FRF.
Y : Frequency response (FRF) of the summed filters.
"""
from pylab import np, plt, fft, fftfreq
x = np.zeros(length_sec*ofb.sample_rate)
x[length_sec*ofb.sample_rate/2] = 0.9999
if not ffilt:
y, states = ofb.filter_mimo_c(x)
y = y[:, :, 0]
else:
y, states = ofb.filter(x, ffilt=ffilt)
s = np.zeros(len(x))
for i in range(y.shape[1]):
s += y[:, i]
X = fft(y[:, i]) # sampled frequency response
f = fftfreq(len(x), 1.0/ofb.sample_rate)
if plot:
fig = plt.figure('freqz filter bank')
plt.grid(True)
plt.axis([0, ofb.sample_rate / 2, -100, 5])
L = 20*np.log10(np.abs(X[:len(x)/2]) + 1e-17)
plt.semilogx(f[:len(x)/2], L, lw=0.5)
plt.hold(True)
Y = fft(s)
if plot:
plt.title('freqz() Filter Bank')
plt.xlabel('Frequency / Hz')
plt.ylabel('Damping /dB(FS)')
plt.xlim((10, ofb.sample_rate/2))
plt.hold(False)
plt.figure('sum')
L = 20*np.log10(np.abs(Y[:len(x)/2]) + 1e-17)
plt.semilogx(f[:len(x)/2], L, lw=0.5)
level_input = 10*np.log10(np.sum(x**2))
level_output = 10*np.log10(np.sum(s**2))
plt.axis([5, ofb.sample_rate/1.8, -50, 5])
plt.grid(True)
plt.title('Sum of filter bands')
plt.xlabel('Frequency / Hz')
plt.ylabel('Damping /dB(FS)')
print('sum level', level_output, level_input)
return x, y, f, Y
nres = vj.collect_from_result_files(files, 'residual_norm_weighted')
log10_He_ = vj.collect_from_result_files(files, 'log10_He_')
log10_visM_ = vj.collect_from_result_files(files, 'log10_visM_')
rakes = vj.collect_from_result_files(files, 'rake')
nroughs = vj.collect_from_result_files(files, 'roughening_norm')
x1 = amin(nres)
x2 = amax(nres)
dx = x2 - x1
xlim = (x1-dx*0.02, x2+dx*0.2)
xticks = range(int(x1), int(x2),5)
plt.subplot(411)
plt.semilogx(nres, log10_visM_,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.axhline(18.8, color='red')
plt.grid('on')
plt.ylabel('log10(visM/(Pa.s))')
plt.subplot(412)
plt.semilogx(nres, log10_He_,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
#plt.axhline(40, color='red')
plt.grid('on')
plt.ylabel('He/km')
plt.subplot(413)
nres = fid['misfit/norm_weighted'][...]
nreses.append(nres)
m = fid['m'][...]
visMs.append(m[-3])
Hes.append(m[-2])
rakes.append(m[-1])
nrough = fid['regularization/roughening/norm'][...]
nroughs.append(nrough)
xlim = (4, 7)
xticks = range(4, 7)
plt.subplot(411)
plt.semilogx(nreses, visMs, 'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('log10(visM/(Pa.s))')
plt.subplot(412)
plt.semilogx(nreses, 10**(np.asarray(Hes)), 'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('He/km')
plt.subplot(413)
plt.semilogx(nreses, rakes, 'o')
plt.xlim(xlim)
com = vj.MomentCalculator('../../../fault_model/fault_bott60km.h5',
'../earth.model_He63km_VisM1.0E19'
)
files = sorted(glob.glob('../outs/ano_??.h5'))
Bm1 = collect_results(files, 'Bm')
slip1 = [ii[:-1, :] for ii in Bm1]
mo1 = [com.moment(ii)[1] for ii in slip1]
nres1 = collect_results(files, 'misfit/norm_weighted')
files = sorted(glob.glob('../../run0/outs/ano_??.h5'))
Bm0 = collect_results(files, 'Bm')
slip0 = [ii[:-1, :] for ii in Bm0]
mo0 = [com.moment(ii)[1] for ii in slip0]
nres0 = collect_results(files, 'misfit/norm_weighted')
plt.semilogx(nres0, mo0, '.', label='Result0')
plt.semilogx(nres1, mo1, '.', label='Result1')
plt.grid('on')
plt.xlabel('norm of weighted residual')
plt.ylabel('Mw')
plt.xlim([.7,5])
plt.legend()
plt.savefig('compare_mw.png')
plt.show()
nres = fid['misfit/norm_weighted'][...]
nreses.append(nres)
m = fid['m'][...]
visMs.append(m[-3])
Hes.append(m[-2])
rakes.append(m[-1])
nrough = fid['regularization/roughening/norm'][...]
nroughs.append(nrough)
xlim = (4,7)
xticks = range(4,7)
plt.subplot(411)
plt.semilogx(nreses, visMs,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('log10(visM/(Pa.s))')
plt.subplot(412)
plt.semilogx(nreses, 10**(np.asarray(Hes)),'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('He/km')
plt.subplot(413)
plt.semilogx(nreses, rakes,'o')
plt.xlim(xlim)
marker = itertools.cycle(('+', 'o', '*'))
class Newton(Experimento):
metodo = 'newton'
funcion = 'f'
criterio = 'relativo'
limite = 0.0001
plt.figure(1)
plt.xlabel('x0')
plt.ylabel('iteraciones')
#plt.xscale('semilog')
plt.semilogx()
# codigo
# voy iterando por cada alpha
resultados = {}
for alpha in alphas:
resultado_real = sqrt(alpha)
new_x0s = deepcopy(x0s)
mi_newton = Newton(entradas=[alpha] * len(new_x0s), x0s=new_x0s)
mi_newton.run()
resultados[alpha] = mi_newton.resultados
for alpha in alphas:
resultado = resultados[alpha]
import h5py
from pylab import plt
nreses =[]
rakes = []
for ano in range(30):
with h5py.File('outs/ano_%02d.h5'%ano,'r') as fid:
nres = fid['residual_norm_weighted'][...]
nreses.append(nres)
m = fid['m'][...]
rakes.append(m[-1])
plt.semilogx(nreses, rakes,'o')
plt.show()
from pylab import plt
def collect_results(outs_files, key):
outs = []
for file in outs_files:
with h5py.File(file, 'r') as fid:
out = fid[key][...]
outs.append(out)
return outs
files = sorted(glob.glob('../outs/ano_??.h5'))
rake1 = collect_results(files, 'nlin_pars/rake')
nres1 = collect_results(files, 'misfit/norm_weighted')
files = sorted(glob.glob('../../run0/outs/ano_??.h5'))
rake0 = collect_results(files, 'nlin_pars/rake')
nres0 = collect_results(files, 'misfit/norm_weighted')
plt.semilogx(nres0, rake0, '.', label='Result0')
plt.semilogx(nres1, rake1, '.', label='Result1')
plt.grid('on')
plt.xlabel('weighted residual norm')
plt.ylabel('rake')
plt.xlim([.7, 5])
plt.legend()
plt.savefig('compare_rake.png')
plt.show()
