def plot_logp(sol, save=False, draw=True, save_as_png=False, dpi=None):
"""
Plots the model log-likelihood
"""
ext = ['png' if save_as_png else 'pdf'][0]
fig, ax = plt.subplots(figsize=(4, 3))
logp = logp_trace(sol.MDL)
sampler_state = sol.MDL.get_state()["sampler"]
x = np.arange(sampler_state["_burn"] + 1, sampler_state["_iter"] + 1,
sampler_state["_thin"])
plt.plot(x, logp, "-")
plt.xlabel("Iteration")
plt.ylabel("Log-likelihood")
plt.grid('on')
if sampler_state["_burn"] == 0:
plt.xscale('log')
else:
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
if save:
fn = 'LOGP-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='LogLikelihood', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_deviance(sol, save=False, draw=True, save_as_png=False, dpi=None):
"""
Plots the model deviance trace
"""
ext = ['png' if save_as_png else 'pdf'][0]
fig, ax = plt.subplots(figsize=(4, 3))
deviance = sol.MDL.trace('deviance')[:]
sampler_state = sol.MDL.get_state()["sampler"]
x = np.arange(sampler_state["_burn"] + 1, sampler_state["_iter"] + 1,
sampler_state["_thin"])
plt.plot(x,
deviance,
"-",
color="C3",
label="DIC = %d\nBPIC = %d" % (sol.MDL.DIC, sol.MDL.BPIC))
plt.xlabel("Iteration")
plt.ylabel("Model deviance")
plt.legend(numpoints=1, loc="best", fontsize=9)
plt.grid('on')
if sampler_state["_burn"] == 0:
plt.xscale('log')
else:
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
if save:
fn = 'MDEV-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='ModelDeviance', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_logp(sol, save=False, draw=True, save_as_png=False, dpi=None):
"""
Plots the model log-likelihood
"""
ext = ['png' if save_as_png else 'pdf'][0]
fig, ax = plt.subplots(figsize=(4,3))
logp = logp_trace(sol.MDL)
sampler_state = sol.MDL.get_state()["sampler"]
x = np.arange(sampler_state["_burn"]+1, sampler_state["_iter"]+1, sampler_state["_thin"])
plt.plot(x, logp, "-")
plt.xlabel("Iteration")
plt.ylabel("Log-likelihood")
plt.grid('on')
if sampler_state["_burn"] == 0:
plt.xscale('log')
else:
plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
if save:
fn = 'LOGP-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='LogLikelihood', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_deviance(sol, save=False, draw=True, save_as_png=False, dpi=None):
"""
Plots the model deviance trace
"""
ext = ['png' if save_as_png else 'pdf'][0]
fig, ax = plt.subplots(figsize=(4,3))
deviance = sol.MDL.trace('deviance')[:]
sampler_state = sol.MDL.get_state()["sampler"]
x = np.arange(sampler_state["_burn"]+1, sampler_state["_iter"]+1, sampler_state["_thin"])
plt.plot(x, deviance, "-", color="C3", label="DIC = %d\nBPIC = %d" %(sol.MDL.DIC,sol.MDL.BPIC))
plt.xlabel("Iteration")
plt.ylabel("Model deviance")
plt.legend(numpoints=1, loc="best", fontsize=9)
plt.grid('on')
if sampler_state["_burn"] == 0:
plt.xscale('log')
else:
plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
if save:
fn = 'MDEV-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='ModelDeviance', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_KDE(sol, var1, var2, fig=None, ax=None, draw=True, save=False, save_as_png=False, dpi=None):
"""
Like the hexbin plot but a 2D KDE
Pass mcmcinv object and 2 variable names as strings
"""
ext = ['png' if save_as_png else 'pdf'][0]
if fig == None or ax == None:
fig, ax = plt.subplots(figsize=(3,3))
MDL = sol.MDL
if var1 == "R0":
stoc1 = "R0"
else:
stoc1 = ''.join([i for i in var1 if not i.isdigit()])
stoc_num1 = [int(i) for i in var1 if i.isdigit()]
try:
x = MDL.trace(stoc1)[:,stoc_num1[0]-1]
except:
x = MDL.trace(stoc1)[:]
if var2 == "R0":
stoc2 = "R0"
else:
stoc2 = ''.join([i for i in var2 if not i.isdigit()])
stoc_num2 = [int(i) for i in var2 if i.isdigit()]
try:
y = MDL.trace(stoc2)[:,stoc_num2[0]-1]
except:
y = MDL.trace(stoc2)[:]
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = gaussian_kde(values)
kernel.set_bandwidth(bw_method='silverman')
# kernel.set_bandwidth(bw_method=kernel.factor * 2.)
f = np.reshape(kernel(positions).T, xx.shape)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.sca(ax)
# Contourf plot
plt.grid(None)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0,0))
plt.xticks(rotation=90)
plt.locator_params(axis = 'y', nbins = 7)
plt.locator_params(axis = 'x', nbins = 7)
ax.contourf(xx, yy, f, cmap=plt.cm.viridis, alpha=0.8)
ax.scatter(x, y, color='k', s=1, zorder=2)
plt.ylabel("%s" %var2)
plt.xlabel("%s" %var1)
if save:
fn = 'KDE-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='2D-KDE', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_autocorr(
sol,
save=False,
draw=True,
save_as_png=False,
dpi=None,
ignore=subplots_to_ignore,
):
"""
Plots autocorrelations
"""
ext = ['png' if save_as_png else 'pdf'][0]
MDL = sol.MDL
keys = [k for k in sol.var_dict.keys() if k not in ignore]
for (i, k) in enumerate(keys):
vect = old_div((MDL.trace(k)[:].size), (len(MDL.trace(k)[:])))
if vect > 1:
keys[i] = [k + "%d" % n for n in range(1, vect + 1)]
keys = list(flatten(keys))
ncols = 2
nrows = int(ceil(len(keys) * 1.0 / ncols))
fig, ax = plt.subplots(nrows, ncols, figsize=(10, nrows * 2))
plt.ticklabel_format(style='sci', axis='both', scilimits=(0, 0))
for (a, k) in zip(ax.flat, keys):
if k[-1] not in ["%d" % d for d in range(1, 8)] or k == "R0":
data = sorted(MDL.trace(k)[:].ravel())
else:
data = sorted(MDL.trace(k[:-1])[:][:, int(k[-1]) - 1].ravel())
plt.sca(a)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
plt.ylabel(k, fontsize=12)
to_thin = old_div(len(data), 50)
if to_thin != 0: plt.xlabel("Lags / %d" % to_thin, fontsize=12)
else: plt.xlabel("Lags", fontsize=12)
max_lags = None
if len(data) > 50: data = data[::to_thin]
plt.acorr(data,
usevlines=True,
maxlags=max_lags,
detrend=plt.mlab.detrend_mean)
plt.grid(None)
fig.tight_layout()
for a in ax.flat[ax.size - 1:len(keys) - 1:-1]:
a.set_visible(False)
if save:
fn = 'AC-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='Autocorrelations', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_traces(sol, save=False, draw=True, save_as_png=False, dpi=None,
ignore=default_ignore,
):
"""
Plots the traces of stochastic and
deterministic parameters in mcmcinv object (sol)
Ignores the ones in list argument ignore
"""
# Get some settings
ext = ['png' if save_as_png else 'pdf'][0] # get figure format
mcmc = sol.mcmc # get MCMC parameters
# Get all variable names from mcmcinv object
headers = sorted(sol.trace_dict.keys()) # In alphabetical order
# Remove unwanted headers
headers = [h for h in headers if h.strip('0123456789') not in ignore]
# Extract the needed traces
traces = [sol.trace_dict[h] for h in headers]
# Subplot settings
ncols = 2
nrows = int(ceil(len(headers)*1.0 / ncols))
fig, ax = plt.subplots(nrows, ncols, figsize=(8, nrows*1.5), sharex=True)
# Plot traces
for i in range(len(headers)):
data = traces[i]
x = np.arange(mcmc["nb_burn"]+1, mcmc["nb_iter"]+1, mcmc["thin"])
plt.sca(ax.flat[i])
plt.ylabel(parlbl_dic[headers[i]])
plt.plot(x, data,'-', color='0.8', alpha=1)
av = np.mean(data)*np.ones(len(x))
sd = np.std(data)*np.ones(len(x))
plt.plot(x, av, linestyle='--', linewidth=1.5)
plt.plot(x, av+sd, color='0.2',linestyle=':', linewidth=1)
plt.plot(x, av-sd, color='0.2',linestyle=':', linewidth=1)
if x[0] == 1:
plt.xscale('log')
else:
plt.ticklabel_format(style='sci', axis='x', scilimits=(-1,1))
for a in ax.flat:
a.grid(False)
for a in ax[-1]:
a.set_xlabel("Iteration number")
plt.tight_layout(pad=0, w_pad=0.5, h_pad=0.5)
if save:
fn = 'TRA-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='Traces', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_histo(sol, save=False, draw=True, save_as_png=False, dpi=None,
ignore=default_ignore,
):
"""
Plots the traces of stochastic and
deterministic parameters in mcmcinv object (sol)
Ignores the ones in list argument ignore
"""
# Get some settings
ext = ['png' if save_as_png else 'pdf'][0] # get figure format
# Get all variable names from mcmcinv object
headers = sorted(sol.trace_dict.keys())
# Remove unwanted headers
headers = [h for h in headers if h.strip('0123456789') not in ignore]
# Extract the needed traces
traces = [sol.trace_dict[h] for h in headers]
# Subplot settings
ncols = 2
nrows = int(ceil(len(headers)*1.0 / ncols))
fig, ax = plt.subplots(nrows, ncols, figsize=(8,nrows*1.8))
# Plot histograms
for i in range(len(headers)):
data = sorted(traces[i])
plt.sca(ax.flat[i])
plt.xlabel(parlbl_dic[headers[i]])
try:
hist = plt.hist(data, bins=20, histtype='stepfilled', density=False, linewidth=1.0, color='0.95', alpha=1)
plt.hist(data, bins=20, histtype='step', density=False, linewidth=1.0, alpha=1)
fit = norm.pdf(data, np.mean(data), np.std(data))
xh = [0.5 * (hist[1][r] + hist[1][r+1]) for r in range(len(hist[1])-1)]
binwidth = (max(xh) - min(xh)) / len(hist[1])
fit *= len(data) * binwidth
plt.plot(data, fit, "-", color='k', linewidth=1)
except:
print("File %s: failed to plot %s histogram.\nNot enough accepted moves." %(sol.filename,headers[i]))
plt.grid(False)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0,0))
for c in range(nrows):
ax[c][0].set_ylabel("Frequency")
for a in ax.flat[ax.size - 1:len(headers) - 1:-1]:
a.set_visible(False)
plt.tight_layout(pad=1, w_pad=1, h_pad=0)
if save:
fn = 'HST-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='Histograms', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_hexbin(sol,
var1,
var2,
draw=True,
save=False,
save_as_png=False,
dpi=None):
"""
Like the 2D KDE plot but a hexbin
Pass mcmcinv object and 2 variable names as strings
"""
ext = ['png' if save_as_png else 'pdf'][0]
MDL = sol.MDL
if var1 == "R0":
stoc1 = "R0"
else:
stoc1 = ''.join([i for i in var1 if not i.isdigit()])
stoc_num1 = [int(i) for i in var1 if i.isdigit()]
try:
x = MDL.trace(stoc1)[:, stoc_num1[0] - 1]
except:
x = MDL.trace(stoc1)[:]
if var2 == "R0":
stoc2 = "R0"
else:
stoc2 = ''.join([i for i in var2 if not i.isdigit()])
stoc_num2 = [int(i) for i in var2 if i.isdigit()]
try:
y = MDL.trace(stoc2)[:, stoc_num2[0] - 1]
except:
y = MDL.trace(stoc2)[:]
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
fig, ax = plt.subplots(figsize=(4, 3))
plt.grid(None)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.hexbin(x, y, gridsize=15, cmap=plt.cm.magma_r)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0, 0))
plt.xticks(rotation=90)
plt.locator_params(axis='y', nbins=5)
plt.locator_params(axis='x', nbins=5)
cb = plt.colorbar()
cb.set_label('Number of observations')
plt.ylabel("%s" % var2)
plt.xlabel("%s" % var1)
if save:
fn = 'HEX-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='Hexbins', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_rtd(sol, save=False, draw=True, save_as_png=False, dpi=None):
"""
Plots the relaxation time distribution (RTD)
for a polynomial decomposition or ccdt results
"""
ext = ['png' if save_as_png else 'pdf'][0]
fig, ax = plt.subplots(figsize=(4,3))
try:
bot95 = 10**sol.MDL.stats()["log_m_i"]['95% HPD interval'][0]
top95 = 10**sol.MDL.stats()["log_m_i"]['95% HPD interval'][1]
log_tau = 10**sol.MDL.stats()["log_tau_i"]['mean']
log_m = 10**sol.MDL.stats()["log_m_i"]['mean']
except:
bot95 = sol.MDL.stats()["m_i"]['95% HPD interval'][0]
top95 = sol.MDL.stats()["m_i"]['95% HPD interval'][1]
log_tau = 10**sol.MDL.log_tau
log_m = sol.MDL.stats()["m_i"]['mean']
plt.errorbar(log_tau, log_m, None, None, color="C7", linestyle='-', label="RTD")
try:
peaks = 10**np.atleast_1d(sol.MDL.stats()["log_peak_tau"]["mean"])
uncer_peaks = 10**sol.MDL.stats()["log_peak_tau"]['95% HPD interval'].T.reshape(len(np.atleast_1d(sol.MDL.stats()["log_peak_tau"]['mean'])),2)
m_peaks = log_m[[list(log_tau).index(find_nearest(log_tau, peaks[x])) for x in range(len(peaks))]]
if len(peaks) >= 1:
plt.errorbar(peaks, m_peaks*1.2, None, None, color="C3", marker="v", markersize=5, linestyle="", label=r"$\tau_{peak}$")
for i, u in enumerate(uncer_peaks):
plt.axvspan(u[0], u[1], alpha=0.2, color="C3")
except:
pass
plt.axvline(10**sol.MDL.stats()["log_half_tau"]['mean'],color="C0",linestyle=':', label=r"$\tau_{50}$")
plt.axvline(10**sol.MDL.stats()["log_mean_tau"]['mean'],color='C2',linestyle='--', label=r"$\bar{\tau}$")
inter = 10**sol.MDL.stats()["log_half_tau"]['95% HPD interval']
plt.axvspan(inter[0], inter[1], alpha=0.2, color="C0")
inter = 10**sol.MDL.stats()["log_mean_tau"]['95% HPD interval']
plt.axvspan(inter[0], inter[1], alpha=0.2, color='C2')
plt.axvspan(min(log_tau), min(log_tau)*10, alpha=0.1, color='C7')
plt.axvspan(max(log_tau)/10, max(log_tau), alpha=0.1, color='C7')
plt.fill_between(log_tau, bot95, top95, color="C7", alpha=0.2)
plt.xlim([10**np.ceil(np.log10(min(log_tau))), 10**np.floor(np.log10(max(log_tau)))])
ax.set_xlabel(r'$\tau$ (s)')
ax.set_ylabel(r'$m$')
plt.grid(False)
plt.legend(fontsize=9, loc=1,labelspacing=0.2, handlelength=1.5)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
fig.tight_layout()
if save:
fn = 'RTD-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='RTD', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_autocorr(sol, save=False, draw=True, save_as_png=False, dpi=None,
ignore=default_ignore,
):
"""
Plots autocorrelations
"""
ext = ['png' if save_as_png else 'pdf'][0]
MDL = sol.MDL
keys = [k for k in sol.var_dict.keys() if k not in ignore]
for (i, k) in enumerate(keys):
vect = old_div((MDL.trace(k)[:].size),(len(MDL.trace(k)[:])))
if vect > 1:
keys[i] = [k+"%d"%n for n in range(1,vect+1)]
keys = list(flatten(keys))
ncols = 2
nrows = int(ceil(len(keys)*1.0 / ncols))
fig, ax = plt.subplots(nrows, ncols, figsize=(10,nrows*2))
plt.ticklabel_format(style='sci', axis='both', scilimits=(0,0))
for (a, k) in zip(ax.flat, keys):
if k[-1] not in ["%d"%d for d in range(1,8)] or k =="R0":
data = sorted(MDL.trace(k)[:].ravel())
else:
data = sorted(MDL.trace(k[:-1])[:][:,int(k[-1])-1].ravel())
plt.sca(a)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.yticks(fontsize=12)
plt.xticks(fontsize=12)
plt.ylabel(k, fontsize=12)
to_thin = old_div(len(data),50)
if to_thin != 0: plt.xlabel("Lags / %d"%to_thin, fontsize=12)
else: plt.xlabel("Lags", fontsize=12)
max_lags = None
if len(data) > 50: data= data[::to_thin]
plt.acorr(data, usevlines=True, maxlags=max_lags, detrend=plt.mlab.detrend_mean)
plt.grid(None)
fig.tight_layout()
for a in ax.flat[ax.size - 1:len(keys) - 1:-1]:
a.set_visible(False)
if save:
fn = 'AC-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='Autocorrelations', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_hexbin(sol, var1, var2, draw=True, save=False, save_as_png=False, dpi=None):
"""
Like the 2D KDE plot but a hexbin
Pass mcmcinv object and 2 variable names as strings
"""
ext = ['png' if save_as_png else 'pdf'][0]
MDL = sol.MDL
if var1 == "R0":
stoc1 = "R0"
else:
stoc1 = ''.join([i for i in var1 if not i.isdigit()])
stoc_num1 = [int(i) for i in var1 if i.isdigit()]
try:
x = MDL.trace(stoc1)[:,stoc_num1[0]-1]
except:
x = MDL.trace(stoc1)[:]
if var2 == "R0":
stoc2 = "R0"
else:
stoc2 = ''.join([i for i in var2 if not i.isdigit()])
stoc_num2 = [int(i) for i in var2 if i.isdigit()]
try:
y = MDL.trace(stoc2)[:,stoc_num2[0]-1]
except:
y = MDL.trace(stoc2)[:]
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
fig, ax = plt.subplots(figsize=(4,3))
plt.grid(None)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.hexbin(x, y, gridsize=15, cmap=plt.cm.magma_r)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0,0))
plt.xticks(rotation=90)
plt.locator_params(axis = 'y', nbins = 5)
plt.locator_params(axis = 'x', nbins = 5)
cb = plt.colorbar()
cb.set_label('Number of observations')
plt.ylabel("%s" %var2)
plt.xlabel("%s" %var1)
if save:
fn = 'HEX-%s-%s.%s'%(sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='Hexbins', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_data(filename, headers, ph_units, save=False,
save_as_png=False, dpi=None, fig_nb=None):
"""
Plots data before doing inversion
Pass full file path, number of headers and phase units
"""
ext = ['png' if save_as_png else 'pdf'][0]
data = get_data(filename,headers,ph_units)
# Graphiques du data
Z = data["Z"]
dZ = data["Z_err"]
f = data["freq"]
zn_dat = Z
zn_err = dZ
Pha_dat = 1000*data["pha"]
Pha_err = 1000*data["pha_err"]
Amp_dat = data["amp"]
Amp_err = data["amp_err"]
fig, ax = plt.subplots(2, 2, figsize=(8,5), sharex=True)
# Real-Imag
plt.axes(ax[0,0])
plt.errorbar(f, zn_dat.real, zn_err.real, None, fmt='o', mfc='white', markersize=5, label='Data', zorder=0)
ax[0,0].set_xscale("log")
plt.ylabel(sym_labels['realrho'])
plt.axes(ax[0,1])
plt.errorbar(f, -zn_dat.imag, zn_err.imag, None, fmt='o', mfc='white', markersize=5, label='Data', zorder=0)
ax[0,1].set_xscale("log")
plt.ylabel(sym_labels['imagrho'])
# Freq-Phas
plt.axes(ax[1,1])
plt.errorbar(f, -Pha_dat, Pha_err, None, fmt='o', mfc='white', markersize=5, label='Data', zorder=0)
ax[1,1].set_yscale("log", nonposy='clip')
ax[1,1].set_xscale("log")
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['phas'])
# Adjust for low or high phase response
if (-Pha_dat < 1).any() and (-Pha_dat >= 0.1).any():
plt.ylim([0.1,10**np.ceil(max(np.log10(-Pha_dat)))])
if (-Pha_dat < 0.1).any() and (-Pha_dat >= 0.01).any():
plt.ylim([0.01,10**np.ceil(max(np.log10(-Pha_dat)))])
# Freq-Ampl
plt.axes(ax[1,0])
plt.errorbar(f, Amp_dat, Amp_err, None, fmt='o', mfc='white', markersize=5, label='Data', zorder=0)
ax[1,0].set_xscale("log")
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['resi'])
for a in ax.flat:
a.grid('on')
fig.tight_layout()
if save:
fn = 'DAT-%s.%s'%(filename,ext)
save_figure(fig, subfolder='Data', fname=fn, dpi=dpi)
plt.close(fig)
return fig
def plot_summary(
sol,
save=False,
draw=True,
save_as_png=False,
dpi=None,
ignore=subplots_to_ignore,
fig_nb="",
):
"""
Plots a parameter summary and
Gelman-Rubin R-hat for multiple chains
"""
ext = ['png' if save_as_png else 'pdf'][0]
ch_nb = sol.mcmc["nb_chain"]
keys = sorted([k for k in sol.var_dict.keys() if k not in ignore])
trac = [[sol.var_dict[x].trace(chain=n).mean(axis=0) for x in keys]
for n in range(ch_nb)]
deps = [var_depth(sol.var_dict[x]) for x in keys]
lbls = list(
reversed(
flatten([[k + '%s' % (x + 1) for x in range(d)] if d > 1 else k
for k, d in zip(keys, deps)])))
if ch_nb >= 2:
rhat = [
gelman_rubin([
sol.MDL.trace(var, -x)[:] for x in range(sol.mcmc['nb_chain'])
]) for var in keys
]
R = np.array(flatten(rhat))
R[R > 5] = 5
else:
print(
"\nTwo or more chains of equal length required for Gelman-Rubin convergence"
)
R = len(lbls) * [None]
fig, axes = plt.subplots(figsize=(6, 4))
gs2 = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(gs2[:, :-1])
ax2 = plt.subplot(gs2[:, -1], sharey=ax1)
for i in range(len(lbls)):
for c in range(ch_nb):
val_m = np.array(flatten(trac[c]))
ax1.scatter(val_m[i],
len(val_m) - (i + 1),
color="C0",
marker=".",
s=50,
facecolor='k',
edgecolors='k',
alpha=1)
ax2.scatter(R[i], i, color="C3", marker="<", s=50, alpha=1)
ax1.set_ylim([-1, len(lbls)])
ax1.set_yticks(list(range(0, len(lbls))))
ax1.set_yticklabels([parlbl_dic[l] for l in lbls])
ax1.set_axisbelow(True)
ax1.yaxis.grid(True)
ax1.xaxis.grid(False)
ax1.set_xlim(ax1.get_xlim())
ax1.set_xlabel(r'Parameter value')
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.set_xlim([0.5, 5.5])
ax2.set_xticklabels(["", "1", "2", "3", "4", "5+"])
ax2.set_xticks([
0.5,
1,
2,
3,
4,
5,
])
ax2.set_axisbelow(True)
ax2.yaxis.grid(True)
ax2.xaxis.grid(False)
ax2.set_xlabel(r'$\hat{R}$')
ax2.axvline(1, ls='--', color='C0', zorder=0)
plt.tight_layout()
plt.close(fig)
if save:
fn = '%sSUM-%s-%s.%s' % (fig_nb, sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='Summaries', fname=fn, dpi=dpi)
if draw: return fig
else: return None
def plot_KDE(sol,
var1,
var2,
fig=None,
ax=None,
draw=True,
save=False,
save_as_png=False,
dpi=None):
"""
Like the hexbin plot but a 2D KDE
Pass mcmcinv object and 2 variable names as strings
"""
ext = ['png' if save_as_png else 'pdf'][0]
if fig == None or ax == None:
fig, ax = plt.subplots(figsize=(3, 3))
MDL = sol.MDL
if var1 == "R0":
stoc1 = "R0"
else:
stoc1 = ''.join([i for i in var1 if not i.isdigit()])
stoc_num1 = [int(i) for i in var1 if i.isdigit()]
try:
x = MDL.trace(stoc1)[:, stoc_num1[0] - 1]
except:
x = MDL.trace(stoc1)[:]
if var2 == "R0":
stoc2 = "R0"
else:
stoc2 = ''.join([i for i in var2 if not i.isdigit()])
stoc_num2 = [int(i) for i in var2 if i.isdigit()]
try:
y = MDL.trace(stoc2)[:, stoc_num2[0] - 1]
except:
y = MDL.trace(stoc2)[:]
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = gaussian_kde(values)
kernel.set_bandwidth(bw_method='silverman')
# kernel.set_bandwidth(bw_method=kernel.factor * 2.)
f = np.reshape(kernel(positions).T, xx.shape)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.sca(ax)
# Contourf plot
plt.grid(None)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0, 0))
plt.xticks(rotation=90)
plt.locator_params(axis='y', nbins=7)
plt.locator_params(axis='x', nbins=7)
ax.contourf(xx, yy, f, cmap=plt.cm.viridis, alpha=0.8)
ax.scatter(x, y, color='k', s=1, zorder=2)
plt.ylabel("%s" % var2)
plt.xlabel("%s" % var1)
if save:
fn = 'KDE-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='2D-KDE', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_traces(
sol,
save=False,
draw=True,
save_as_png=False,
dpi=None,
ignore=subplots_to_ignore,
):
"""
Plots the traces of stochastic and
deterministic parameters in mcmcinv object (sol)
Ignores the ones in list argument ignore
"""
ext = ['png' if save_as_png else 'pdf'][0]
MDL = sol.MDL
sampler = MDL.get_state()["sampler"]
keys = [k for k in sol.var_dict.keys() if k not in ignore]
for (i, k) in enumerate(keys):
vect = old_div((MDL.trace(k)[:].size), (len(MDL.trace(k)[:])))
if vect > 1:
keys[i] = [k + "%d" % n for n in range(1, vect + 1)]
keys = list(flatten(keys))
ncols = 2
nrows = int(ceil(len(keys) * 1.0 / ncols))
fig, ax = plt.subplots(nrows, ncols, figsize=(8, nrows * 1.5), sharex=True)
for c, (a, k) in enumerate(zip(ax.flat, keys)):
if k == "R0":
stoc = "R0"
else:
stoc = ''.join([i for i in k if not i.isdigit()])
stoc_num = [int(i) for i in k if i.isdigit()]
try:
data = MDL.trace(stoc)[:][:, stoc_num[0] - 1]
except:
data = MDL.trace(stoc)[:]
x = np.arange(sampler["_burn"] + 1, sampler["_iter"] + 1,
sampler["_thin"])
plt.sca(a)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0, 0))
plt.ylabel(parlbl_dic[k])
plt.plot(x, data, '-', alpha=0.8)
plt.plot(x,
np.mean(data) * np.ones(len(x)),
color='k',
linestyle='--',
linewidth=2)
# plt.plot(x, np.median(data)*np.ones(len(x)), color='k',linestyle=':', linewidth=2)
if sampler["_burn"] == 0:
plt.xscale('log')
else:
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
plt.grid(False)
plt.tight_layout(pad=0, w_pad=0.5, h_pad=0)
for a in ax[-1]:
a.set_xlabel("Iteration number")
if save:
fn = 'TRA-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='Traces', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_histo(
sol,
save=False,
draw=True,
save_as_png=False,
dpi=None,
ignore=subplots_to_ignore,
):
"""
Plots the traces of stochastic and
deterministic parameters in mcmcinv object (sol)
Ignores the ones in list argument ignore
"""
ext = ['png' if save_as_png else 'pdf'][0]
MDL = sol.MDL
keys = [k for k in sol.var_dict.keys() if k not in ignore]
for (i, k) in enumerate(keys):
vect = old_div((MDL.trace(k)[:].size), (len(MDL.trace(k)[:])))
if vect > 1:
keys[i] = [k + "%d" % n for n in range(1, vect + 1)]
keys = list(flatten(keys))
ncols = 2
nrows = int(ceil(len(keys) * 1.0 / ncols))
fig, ax = plt.subplots(nrows, ncols, figsize=(8, nrows * 1.8))
for c, (a, k) in enumerate(zip(ax.flat, keys)):
if k == "R0":
stoc = "R0"
else:
stoc = ''.join([i for i in k if not i.isdigit()])
stoc_num = [int(i) for i in k if i.isdigit()]
try:
data = sorted(MDL.trace(stoc)[:][:, stoc_num[0] - 1])
except:
data = sorted(MDL.trace(stoc)[:])
plt.sca(a)
plt.xlabel(parlbl_dic[k])
try:
hist = plt.hist(data,
bins=20,
histtype='stepfilled',
density=False,
linewidth=1.0,
color='0.95',
alpha=1)
plt.hist(data,
bins=20,
histtype='step',
density=False,
linewidth=1.0,
alpha=1)
fit = norm.pdf(data, np.mean(data), np.std(data))
xh = [
0.5 * (hist[1][r] + hist[1][r + 1])
for r in range(len(hist[1]) - 1)
]
binwidth = old_div((max(xh) - min(xh)), len(hist[1]))
fit *= len(data) * binwidth
plt.plot(data, fit, "-", color='k', linewidth=1)
except:
print(
"File %s: failed to plot %s histogram. Parameter not mobile enough (see traces)."
% (sol.filename, k))
plt.grid(False)
plt.ticklabel_format(style='sci', axis='both', scilimits=(0, 0))
for c in range(nrows):
ax[c][0].set_ylabel("Frequency")
plt.tight_layout(pad=1, w_pad=1, h_pad=0)
for a in ax.flat[ax.size - 1:len(keys) - 1:-1]:
a.set_visible(False)
if save:
fn = 'HST-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='Histograms', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_fit(sol,
save=False,
draw=True,
save_as_png=False,
dpi=None,
fig_nb=""):
"""
Plots the average fit and uncertainty
Pass mcmcinv object (sol)
"""
ext = ['png' if save_as_png else 'pdf'][0]
# Prepare data for plotting
f = sol.data["freq"]
Zr0 = sol.data["Z_max"]
zn_dat = sol.data["Z"] / Zr0
zn_err = sol.data["Z_err"] / Zr0
zn_fit = sol.fit["best"] / Zr0
zn_min = sol.fit["lo95"] / Zr0
zn_max = sol.fit["up95"] / Zr0
Pha_dat = 1000 * sol.data["pha"]
Pha_err = 1000 * sol.data["pha_err"]
Pha_fit = 1000 * np.angle(sol.fit["best"])
Pha_min = 1000 * np.angle(sol.fit["lo95"])
Pha_max = 1000 * np.angle(sol.fit["up95"])
Amp_dat = sol.data["amp"] / Zr0
Amp_err = sol.data["amp_err"] / Zr0
Amp_fit = abs(sol.fit["best"]) / Zr0
Amp_min = abs(sol.fit["lo95"]) / Zr0
Amp_max = abs(sol.fit["up95"]) / Zr0
fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True)
# Freq-Imag
plt.sca(ax[0, 0])
plt.errorbar(f,
-zn_dat.imag,
zn_err.imag,
None,
color='k',
fmt='o',
mfc='white',
markersize=5,
label='Data',
zorder=0)
p = plt.plot(f, -zn_fit.imag, ls='-', label="Model", zorder=2)
plt.fill_between(f,
-zn_max.imag,
-zn_min.imag,
alpha=0.4,
color=p[0].get_color(),
zorder=1,
label='95% HPD')
plt.ylabel(sym_labels['imag'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
# Freq-Real
plt.sca(ax[0, 1])
plt.errorbar(f,
zn_dat.real,
zn_err.real,
None,
color='k',
fmt='o',
mfc='white',
markersize=5,
label='Data',
zorder=0)
p = plt.plot(f, zn_fit.real, ls='-', label="Model", zorder=2)
plt.fill_between(f,
zn_max.real,
zn_min.real,
alpha=0.4,
color=p[0].get_color(),
zorder=1,
label='95% HPD')
plt.ylabel(sym_labels['imag'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
# Freq-Phas
plt.sca(ax[1, 0])
plt.errorbar(f,
-Pha_dat,
Pha_err,
None,
fmt='o',
color='k',
mfc='white',
markersize=5,
label='Data',
zorder=0)
p = plt.plot(f, -Pha_fit, ls='-', label='Model', zorder=2)
ax[1, 0].set_yscale("log", nonposy='clip')
plt.xscale('log')
plt.fill_between(f,
-Pha_max,
-Pha_min,
color=p[0].get_color(),
alpha=0.4,
zorder=1,
label='95% HPD')
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['phas'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
# Freq-Ampl
plt.sca(ax[1, 1])
plt.errorbar(f,
Amp_dat,
Amp_err,
None,
fmt='o',
color='k',
mfc='white',
markersize=5,
label='Data',
zorder=0)
p = plt.semilogx(f, Amp_fit, ls='-', label='Model', zorder=2)
plt.fill_between(f,
Amp_max,
Amp_min,
color=p[0].get_color(),
alpha=0.4,
zorder=1,
label='95% HPD')
plt.xscale('log')
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['ampl'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
for a in ax.flat:
a.grid(True)
plt.tight_layout(pad=0, h_pad=0.5, w_pad=1)
if save:
fn = '%sFIT-%s-%s.%s' % (fig_nb, sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='Fit figures', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_rtd(sol, save=False, draw=True, save_as_png=False, dpi=None):
"""
Plots the relaxation time distribution (RTD)
for a polynomial decomposition or ccdt results
"""
ext = ['png' if save_as_png else 'pdf'][0]
fig, ax = plt.subplots(figsize=(4, 3))
try:
bot95 = 10**sol.MDL.stats()["log_m_i"]['95% HPD interval'][0]
top95 = 10**sol.MDL.stats()["log_m_i"]['95% HPD interval'][1]
log_tau = 10**sol.MDL.stats()["log_tau_i"]['mean']
log_m = 10**sol.MDL.stats()["log_m_i"]['mean']
except:
bot95 = sol.MDL.stats()["m_i"]['95% HPD interval'][0]
top95 = sol.MDL.stats()["m_i"]['95% HPD interval'][1]
log_tau = 10**sol.MDL.log_tau
log_m = sol.MDL.stats()["m_i"]['mean']
plt.errorbar(log_tau,
log_m,
None,
None,
color="C7",
linestyle='-',
label="RTD")
try:
peaks = 10**np.atleast_1d(sol.MDL.stats()["log_peak_tau"]["mean"])
uncer_peaks = 10**sol.MDL.stats(
)["log_peak_tau"]['95% HPD interval'].T.reshape(
len(np.atleast_1d(sol.MDL.stats()["log_peak_tau"]['mean'])), 2)
m_peaks = log_m[[
list(log_tau).index(find_nearest(log_tau, peaks[x]))
for x in range(len(peaks))
]]
if len(peaks) >= 1:
plt.errorbar(peaks,
m_peaks * 1.2,
None,
None,
color="C3",
marker="v",
markersize=5,
linestyle="",
label=r"$\tau_{peak}$")
for i, u in enumerate(uncer_peaks):
plt.axvspan(u[0], u[1], alpha=0.2, color="C3")
except:
pass
plt.axvline(10**sol.MDL.stats()["log_half_tau"]['mean'],
color="C0",
linestyle=':',
label=r"$\tau_{50}$")
plt.axvline(10**sol.MDL.stats()["log_mean_tau"]['mean'],
color='C2',
linestyle='--',
label=r"$\bar{\tau}$")
inter = 10**sol.MDL.stats()["log_half_tau"]['95% HPD interval']
plt.axvspan(inter[0], inter[1], alpha=0.2, color="C0")
inter = 10**sol.MDL.stats()["log_mean_tau"]['95% HPD interval']
plt.axvspan(inter[0], inter[1], alpha=0.2, color='C2')
plt.axvspan(min(log_tau), min(log_tau) * 10, alpha=0.1, color='C7')
plt.axvspan(max(log_tau) / 10, max(log_tau), alpha=0.1, color='C7')
plt.fill_between(log_tau, bot95, top95, color="C7", alpha=0.2)
plt.xlim([
10**np.ceil(np.log10(min(log_tau))),
10**np.floor(np.log10(max(log_tau)))
])
ax.set_xlabel(r'$\tau$ (s)')
ax.set_ylabel(r'$m$')
plt.grid(False)
plt.legend(fontsize=9, loc=1, labelspacing=0.2, handlelength=1.5)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
fig.tight_layout()
if save:
fn = 'RTD-%s-%s.%s' % (sol.model_type_str, sol.filename, ext)
save_figure(fig, subfolder='RTD', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None
def plot_summary(sol, save=False, draw=True, save_as_png=False, dpi=None,
ignore=default_ignore,
fig_nb="",
):
"""
Plots a parameter summary and
Gelman-Rubin R-hat for multiple chains
"""
ext = ['png' if save_as_png else 'pdf'][0]
ch_nb = sol.mcmc["nb_chain"]
keys = sorted([k for k in sol.var_dict.keys() if k not in ignore])
trac = [[sol.var_dict[x].trace(chain=n).mean(axis=0) for x in keys] for n in range(ch_nb)]
deps = [var_depth(sol.var_dict[x]) for x in keys]
lbls = list(reversed(flatten([[k+'%s'%(x+1) for x in range(d)] if d > 1 else k for k, d in zip(keys,deps)])))
if ch_nb >= 2:
rhat = [gelman_rubin([sol.MDL.trace(var, -x)[:] for x in range(sol.mcmc['nb_chain'])]) for var in keys]
R = np.array(flatten(rhat))
R[R > 5] = 5
else:
print("\nTwo or more chains of equal length required for Gelman-Rubin convergence")
R = len(lbls)*[None]
fig, axes = plt.subplots(figsize=(6,4))
gs2 = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(gs2[:, :-1])
ax2 = plt.subplot(gs2[:, -1], sharey = ax1)
for i in range(len(lbls)):
for c in range(ch_nb):
val_m = np.array(flatten(trac[c]))
ax1.scatter(val_m[i], len(val_m)-(i+1) , color="C0", marker=".",
s=50, facecolor='k', edgecolors='k',alpha=1)
ax2.scatter(R[i], i, color="C3", marker="<", s=50, alpha=1)
ax1.set_ylim([-1, len(lbls)])
ax1.set_yticks(list(range(0,len(lbls))))
ax1.set_yticklabels([parlbl_dic[l] for l in lbls])
ax1.set_axisbelow(True)
ax1.yaxis.grid(True)
ax1.xaxis.grid(False)
ax1.set_xlim(ax1.get_xlim())
ax1.set_xlabel(r'Parameter value')
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.set_xlim([0.5, 5.5])
ax2.set_xticklabels(["","1","2","3","4","5+"])
ax2.set_xticks([0.5, 1, 2, 3, 4, 5, ])
ax2.set_axisbelow(True)
ax2.yaxis.grid(True)
ax2.xaxis.grid(False)
ax2.set_xlabel(r'$\hat{R}$')
ax2.axvline(1, ls='--', color='C0', zorder=0)
plt.tight_layout()
plt.close(fig)
if save:
fn = '%sSUM-%s-%s.%s'%(fig_nb,sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='Summaries', fname=fn, dpi=dpi)
if draw: return fig
else: return None
def plot_data(filename,
headers,
ph_units,
save=False,
save_as_png=False,
dpi=None,
fig_nb=None):
"""
Plots data before doing inversion
Pass full file path, number of headers and phase units
"""
ext = ['png' if save_as_png else 'pdf'][0]
data = get_data(filename, headers, ph_units)
# Graphiques du data
Z = data["Z"]
dZ = data["Z_err"]
f = data["freq"]
zn_dat = Z
zn_err = dZ
Pha_dat = 1000 * data["pha"]
Pha_err = 1000 * data["pha_err"]
Amp_dat = data["amp"]
Amp_err = data["amp_err"]
fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True)
# Real-Imag
plt.axes(ax[0, 0])
plt.errorbar(f,
zn_dat.real,
zn_err.real,
None,
fmt='o',
mfc='white',
markersize=5,
label='Data',
zorder=0)
ax[0, 0].set_xscale("log")
plt.ylabel(sym_labels['realrho'])
plt.axes(ax[0, 1])
plt.errorbar(f,
-zn_dat.imag,
zn_err.imag,
None,
fmt='o',
mfc='white',
markersize=5,
label='Data',
zorder=0)
ax[0, 1].set_xscale("log")
plt.ylabel(sym_labels['imagrho'])
# Freq-Phas
plt.axes(ax[1, 1])
plt.errorbar(f,
-Pha_dat,
Pha_err,
None,
fmt='o',
mfc='white',
markersize=5,
label='Data',
zorder=0)
ax[1, 1].set_yscale("log", nonposy='clip')
ax[1, 1].set_xscale("log")
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['phas'])
# Adjust for low or high phase response
if (-Pha_dat < 1).any() and (-Pha_dat >= 0.1).any():
plt.ylim([0.1, 10**np.ceil(max(np.log10(-Pha_dat)))])
if (-Pha_dat < 0.1).any() and (-Pha_dat >= 0.01).any():
plt.ylim([0.01, 10**np.ceil(max(np.log10(-Pha_dat)))])
# Freq-Ampl
plt.axes(ax[1, 0])
plt.errorbar(f,
Amp_dat,
Amp_err,
None,
fmt='o',
mfc='white',
markersize=5,
label='Data',
zorder=0)
ax[1, 0].set_xscale("log")
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['resi'])
for a in ax.flat:
a.grid('on')
fig.tight_layout()
if save:
fn = 'DAT-%s.%s' % (filename, ext)
save_figure(fig, subfolder='Data', fname=fn, dpi=dpi)
plt.close(fig)
return fig
def plot_fit(sol, save=False, draw=True,
save_as_png=False, dpi=None, fig_nb=""):
"""
Plots the average fit and uncertainty
Pass mcmcinv object (sol)
"""
ext = ['png' if save_as_png else 'pdf'][0]
# Prepare data for plotting
f = sol.data["freq"]
Zr0 = sol.data["Z_max"]
zn_dat = sol.data["Z"]/Zr0
zn_err = sol.data["Z_err"]/Zr0
zn_fit = sol.fit["best"]/Zr0
zn_min = sol.fit["lo95"]/Zr0
zn_max = sol.fit["up95"]/Zr0
Pha_dat = 1000*sol.data["pha"]
Pha_err = 1000*sol.data["pha_err"]
Pha_fit = 1000*np.angle(sol.fit["best"])
Pha_min = 1000*np.angle(sol.fit["lo95"])
Pha_max = 1000*np.angle(sol.fit["up95"])
Amp_dat = sol.data["amp"]/Zr0
Amp_err = sol.data["amp_err"]/Zr0
Amp_fit = abs(sol.fit["best"])/Zr0
Amp_min = abs(sol.fit["lo95"])/Zr0
Amp_max = abs(sol.fit["up95"])/Zr0
fig, ax = plt.subplots(2, 2, figsize=(8,5), sharex=True)
# Freq-Imag
plt.sca(ax[0,0])
plt.errorbar(f, -zn_dat.imag, zn_err.imag, None, color='k', fmt='o', mfc='white', markersize=5, label='Data', zorder=0)
p=plt.plot(f, -zn_fit.imag, ls='-', label="Model",zorder=2)
plt.fill_between(f, -zn_max.imag, -zn_min.imag, alpha=0.4, color=p[0].get_color(), zorder=1, label='95% HPD')
plt.ylabel(sym_labels['imag'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
# Freq-Real
plt.sca(ax[0,1])
plt.errorbar(f, zn_dat.real, zn_err.real, None, color='k', fmt='o', mfc='white', markersize=5, label='Data', zorder=0)
p=plt.plot(f, zn_fit.real, ls='-', label="Model",zorder=2)
plt.fill_between(f, zn_max.real, zn_min.real, alpha=0.4, color=p[0].get_color(), zorder=1, label='95% HPD')
plt.ylabel(sym_labels['imag'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
# Freq-Phas
plt.sca(ax[1,0])
plt.errorbar(f, -Pha_dat, Pha_err, None, fmt='o', color='k', mfc='white', markersize=5, label='Data', zorder=0)
p=plt.plot(f, -Pha_fit, ls='-', label='Model', zorder=2)
ax[1,0].set_yscale("log", nonposy='clip')
plt.xscale('log')
plt.fill_between(f, -Pha_max, -Pha_min, color=p[0].get_color(), alpha=0.4, zorder=1, label='95% HPD')
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['phas'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
# Freq-Ampl
plt.sca(ax[1,1])
plt.errorbar(f, Amp_dat, Amp_err, None, fmt='o', color='k', mfc='white', markersize=5, label='Data', zorder=0)
p=plt.semilogx(f, Amp_fit, ls='-', label='Model', zorder=2)
plt.fill_between(f, Amp_max, Amp_min, color=p[0].get_color(), alpha=0.4, zorder=1, label='95% HPD')
plt.xscale('log')
plt.xlabel(sym_labels['freq'])
plt.ylabel(sym_labels['ampl'])
plt.legend(loc='best', labelspacing=0.2, handlelength=1, framealpha=1)
for a in ax.flat:
a.grid(True)
plt.tight_layout(pad=0, h_pad=0.5, w_pad=1)
if save:
fn = '%sFIT-%s-%s.%s'%(fig_nb,sol.model_type_str,sol.filename,ext)
save_figure(fig, subfolder='Fit figures', fname=fn, dpi=dpi)
plt.close(fig)
if draw: return fig
else: return None