def plot_dfc_timeseries(dfc, limitaxis=500, cm='Set2', fig_dir = None, fig_prefix=None,mi=[]):
if isinstance(mi,str):
mi = [mi]
if fig_prefix:
fig_prefix += '_'
else:
fig_prefix = ''
if not fig_dir:
fig_dir = './'
if not os.path.exists(fig_dir):
os.makedirs(fig_dir,exist_ok=True)
params = {}
for m in mi:
params[m] = np.unique(dfc.index.get_level_values(m))
mi,mi_num,mi_parameters,mi_param_list = tvc_benchmarker.multiindex_preproc(params,mi)
colormap=tvc_benchmarker.get_discrete_colormap(cm)
for sim_it, mi_params in enumerate(mi_parameters):
param_sname = [p[0] + '-' + str(p[1]) for p in list(zip(mi,mi_params))]
param_sname = '_'.join(param_sname)
if param_sname:
param_sname = '_' + param_sname.replace(' ','')
param_title = [p[0] + '=' + str(p[1]) for p in list(zip(mi,mi_params))]
param_title = ','.join(param_title)
param_title = param_title.replace(' ','').replace(',',', ')
if mi_params == ():
mi_params = np.arange(0,len(dfc))
fig,ax=plt.subplots(len(dfc.columns), 1, sharex=True,sharey=True, figsize=(5,len(dfc.columns)*2))
for i,dfc_method in enumerate(sorted(dfc.columns)):
ax[i].plot(dfc[dfc_method][mi_params][:limitaxis].values,color=colormap(i),alpha=0.5,linewidth=2)
ax[i].set_ylabel('DFC ('+ dfc_method + ')')
ax[i].get_yaxis().set_major_locator(LinearLocator(numticks=5))
ax[i].set_xlim(1,limitaxis)
ax[-1].set_xlabel('time')
plt.suptitle(param_title,fontsize=11)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig(fig_dir + '/' + fig_prefix + 'dfc-timeseries' + param_sname + '.pdf',r=600)
plt.close('all')
def plot_fluctuating_covariance(x, fig_dir = None, lags=10,limitaxis=500,cm = 'Set2',mi='alpha', fig_prefix=None):
# if labels == None:
# labels=np.unique(x.index.get_level_values(mi))
if isinstance(mi,str):
mi = [mi]
if not fig_dir:
fig_dir = './'
if fig_prefix:
fig_prefix += '_'
else:
fig_prefix = ''
if not os.path.exists(fig_dir):
os.makedirs(fig_dir,exist_ok=True)
params = {}
for m in mi:
params[m] = np.unique(x.index.get_level_values(m))
mi,mi_num,mi_parameters,mi_param_list = tvc_benchmarker.multiindex_preproc(params,mi)
colormap=tvc_benchmarker.get_discrete_colormap(cm)
for sim_it, mi_params in enumerate(mi_parameters):
param_sname = [p[0] + '-' + str(p[1]) for p in list(zip(mi,mi_params))]
param_sname = '_'.join(param_sname)
if param_sname:
param_sname = '_' + param_sname.replace(' ','')
param_title = [p[0] + '=' + str(p[1]) for p in list(zip(mi,mi_params))]
param_title = ','.join(param_title)
param_title = param_title.replace(' ','').replace(',',', ')
if mi_params == ():
mi_params = np.arange(0,len(x))
covariance_autocorrelation = tvc_benchmarker.autocorr(x['covariance_parameter'][mi_params],lags=lags)
# Create grid
fig = plt.figure()
ax = []
ax.append(plt.subplot2grid((2,2),(0,0),colspan=2))
ax.append(plt.subplot2grid((2,2),(1,0)))
ax.append(plt.subplot2grid((2,2),(1,1)))
ax[0].plot(np.arange(1,limitaxis+1),x['covariance_parameter'][mi_params][:limitaxis],color=colormap(0),alpha=0.5,linewidth=2)
ax[0].set_xlabel('Time')
ax[0].set_ylabel(r'Covariance ($r_t$)')
ymin = x['covariance_parameter'][mi_params][:limitaxis].min()
ymax = x['covariance_parameter'][mi_params][:limitaxis].max()
ax[0].axis([1,limitaxis+1,np.around(ymin-0.05,1),np.around(ymax+0.05,1)])
ax[1].hist(x['covariance_parameter'][mi_params],np.arange(-.1,1,0.02),color=colormap(1),alpha=0.9,linewidth=0,histtype='stepfilled',normed='true')
ax[1].set_xlabel('Covariance')
ax[1].set_ylabel('Frequency')
xmin = x['covariance_parameter'][mi_params].min()
xmax = x['covariance_parameter'][mi_params].max()
ax[1].axis([np.around(xmin-0.05,1),np.around(xmax+0.05,1),0,np.ceil(ax[1].get_ylim()[-1])])
tvc_benchmarker.square_axis(ax[1])
ax[2].plot(np.arange(0,11),covariance_autocorrelation,color=colormap(2),alpha=0.9,linewidth=2)
ax[2].set_ylabel('Correlation (r)')
ax[2].set_xlabel('Lag')
ymin = covariance_autocorrelation.min()
ymax = 1
ax[2].axis([0,10,np.around(ymin-0.05,1),np.around(ymax+0.05,1)])
tvc_benchmarker.square_axis(ax[2])
plt.suptitle(param_title,fontsize=11)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig(fig_dir + '/' + fig_prefix + 'fluctuating-covariance' + param_sname + '.pdf',r=600)
plt.close('all')
def plot_betadfc_distribution(dfc, dat_dir, fig_dir = None, model_prefix=None, burn=1000, mi='alpha', cm='Set2'):
if isinstance(mi,str):
mi = [mi]
if model_prefix:
model_prefix += '_'
if not fig_dir:
fig_dir = './'
if not os.path.exists(fig_dir):
os.makedirs(fig_dir,exist_ok=True)
params = {}
for m in mi:
params[m] = np.unique(dfc.index.get_level_values(m))
mi,mi_num,mi_parameters,mi_param_list = tvc_benchmarker.multiindex_preproc(params,mi)
colormap=tvc_benchmarker.get_discrete_colormap(cm)
for sim_it, mi_params in enumerate(mi_parameters):
param_sname = [p[0] + '-' + str(p[1]) for p in list(zip(mi,mi_params))]
param_sname = '_'.join(param_sname)
if param_sname:
param_sname = '_' + param_sname.replace(' ','')
param_title = [p[0] + '=' + str(p[1]) for p in list(zip(mi,mi_params))]
param_title = ','.join(param_title)
param_title = param_title.replace(' ','').replace(',',', ')
if mi_params == ():
mi_params = np.arange(0,len(dfc))
fig,ax=plt.subplots(len(dfc.columns),sharex=True,sharey=True,figsize=(5,len(dfc.columns)))
beta_col = []
lines = []
for i,method in enumerate(sorted(dfc.columns)):
beta_dfc=tvc_benchmarker.load_bayes_model(dat_dir,model_prefix + 'method-' + method + param_sname)[0][burn:].get_values('beta')
#Plot
ltmp = ax[i].hist(beta_dfc,np.arange(-1,1,0.001),histtype='stepfilled',color=colormap(i),density=True,alpha=0.4, linewidth=2,label=method)
lines.append(ltmp)
ax[i].set_yticklabels([])
ax[i].set_ylabel(method)
beta_col.append(beta_dfc)
#ax[i].set_ylabel('Posterior Frequency (' + method + ')')
beta_col = np.vstack(beta_col)
xmin = beta_col.min()
xmax = beta_col.max()
ax[0].get_yaxis().set_major_locator(LinearLocator(numticks=4))
ax[0].set_xlim([np.around(xmin-0.005,2),np.around(xmax+0.005,2)])
ax[-1].set_xlabel('Posterior (' + r'$β$' + ')')
fig.suptitle(param_title,fontsize=11)
fig.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig(fig_dir + '/' + model_prefix + 'beta-posterior' + param_sname + '.pdf',r=600)
plt.close('all')
def plot_timeseries(x,plot_autocorr='no',fig_dir=None,fig_prefix=None,cm='Set2',limitaxis=100,mi='alpha'):
if isinstance(mi,str):
mi = [mi]
if fig_prefix:
fig_prefix += '_'
else:
fig_prefix = ''
if not fig_dir:
fig_dir = './'
if not os.path.exists(fig_dir):
os.makedirs(fig_dir,exist_ok=True)
params = {}
for m in mi:
params[m] = np.unique(x.index.get_level_values(m))
mi,mi_num,mi_parameters,mi_param_list = tvc_benchmarker.multiindex_preproc(params,mi)
colormap=tvc_benchmarker.get_discrete_colormap(cm)
for sim_it, mi_params in enumerate(mi_parameters):
param_sname = [p[0] + '-' + str(p[1]) for p in list(zip(mi,mi_params))]
param_sname = '_'.join(param_sname)
if param_sname:
param_sname = '_' + param_sname.replace(' ','')
param_title = [p[0] + '=' + str(p[1]) for p in list(zip(mi,mi_params))]
param_title = ','.join(param_title)
param_title = param_title.replace(' ','').replace(',',', ')
if mi_params == ():
mi_params = np.arange(0,len(x))
if plot_autocorr == 'no':
fig,ax=plt.subplots(1)
ax.plot(np.arange(1,limitaxis+1),x['timeseries_1'][mi_params][:limitaxis],color=colormap(0),alpha=0.9,linewidth=2)
ax.plot(np.arange(1,limitaxis+1),x['timeseries_2'][mi_params][:limitaxis],color=colormap(1),alpha=0.9,linewidth=2)
ax.set_xlim(1,limitaxis)
ax.set_ylabel('Signal Amplitude')
ax.set_xlabel('Time')
else:
autocorrelation = np.array([tvc_benchmarker.autocorr(x[ts][mi_params]) for ts in ['timeseries_1','timeseries_2']])
fig=plt.figure()
ax=[]
ax.append(plt.subplot2grid((2,3),(0,0),colspan=3))
for n in range(0,3):
ax.append(plt.subplot2grid((2,3),(1,n)))
# Plot 1: raw time series
ax[0].plot(np.arange(1,limitaxis+1),x['timeseries_1'][mi_params][:limitaxis],color=colormap(0),alpha=0.9,linewidth=2)
ax[0].plot(np.arange(1,limitaxis+1),x['timeseries_2'][mi_params][:limitaxis],color=colormap(1),alpha=.9,linewidth=2)
ax[0].set_xlim(1,limitaxis)
ax[0].set_ylabel('Signal Amplitude')
ax[0].set_xlabel('Time')
# Plot 2 and 3: autocorrelation of timeseries 1 and 2
for p in range(1,3):
ax[p].plot(np.arange(0,autocorrelation.shape[1]),autocorrelation[p-1,:],color=colormap(p-1),alpha=0.9,linewidth=2)
ax[p].set_ylabel('Correlation (r)')
ax[p].set_xlabel('Lag')
ax[p].axis([0,autocorrelation.shape[1]-1,0,1])
ax[p].set_yticks(np.arange(0,1.05,0.25))
ax[p].set_xticks(np.arange(0,autocorrelation.shape[1],2))
# Plot 4: correlation of timeseries 1 and 2
cmap = sns.cubehelix_palette(start=1/3, light=1, as_cmap=True)
ax[3] = sns.kdeplot(x['timeseries_1'][mi_params], x['timeseries_2'][mi_params], shade=True,cmap=cmap)
ax[3].set_xlabel('Signal 1 amplitude')
ax[3].set_ylabel('Signal 2 amplitude')
[tvc_benchmarker.square_axis(ax[n]) for n in [1,2,3]]
plt.suptitle(param_title,fontsize=11)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig(fig_dir + '/' + fig_prefix + 'raw-timeseries' + param_sname + '.pdf',r=600)
plt.close('all')