def plot_dat(self, anomaly, data, standardise = 1):
""" For a given detected anomaly, plots the data around that time point """
# Need to add input checks
sample_half = int(round( self.p['zt_sample_size']/2.))
dat = data[anomaly-sample_half:anomaly+sample_half,:]
if standardise:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(zscore(dat))
bpList = bp_lookup(self.p['SAX_alphabet_size'])
for bp in bpList:
ax.axhline(y=bp, xmin=0, xmax=dat.shape[0], ls = '--', color = 'k')
adjust_spines(ax, ['bottom'])
ax.set_yticklabels([])
ax.yaxis.set_ticks([])
ax.set_xticklabels(range(anomaly-sample_half,anomaly+sample_half+1))
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(18)
else:
plt.figure()
plt.plot(dat)
bpList = bp_lookup(self.p['SAX_alphabet_size'])
plt.hlines(bpList, xmin=0, xmax=dat.shape[0]-1, linestyles = 'dashed', color = 'k')
def plot_EgSAX(data, segMeans, alphaSize, compRatio):
from matplotlib.ticker import NullFormatter
from plot_utils import adjust_spines
# Stored originally as wordSize x numStreams. Becaule plot func plots columsn by default
if segMeans.ndim == 1:
numStreams = 1
segMeans = np.atleast_2d(segMeans).T
else:
numStreams = segMeans.shape[1]
wordSize = segMeans.shape[0]
bpList = bp_lookup(alphaSize)
PAAStreams = np.zeros((wordSize*compRatio, numStreams))
for stream in xrange(numStreams):
temp = []
for mean in segMeans[:,stream]:
temp.extend([mean]*int(compRatio))
PAAStreams[:,stream] = np.array(temp)
# Multiple Ploting
nullfmt = NullFormatter() # no labels
# start with a rectangular Figure
plt.figure()
# Plot Gaussina Axes (l,b r,t)
axGauss = plt.axes([0.05,0.1, 0.2, 0.85])
adjust_spines(axGauss, [])
# Plot Sax Axes
axSAX = plt.axes([0.3,0.1, 0.65, 0.85], )
# no labels
#axSAX.xaxis.set_major_formatter(nullfmt)
axGauss.yaxis.set_major_formatter(nullfmt)
axGauss.yaxis.set_minor_formatter(nullfmt)
# the SAX plot:
axSAX.plot(PAAStreams, drawstyle = 'steps', color = 'r', lw = 1.5)
for bp in bpList:
axSAX.axhline(y=bp, xmin=0, xmax=PAAStreams.shape[0], ls = '--', color = 'k')
axSAX.plot(data, color = 'b', lw = 1)
# the Gaussian plot
y = np.linspace(-3, 3, 1000)
gauss_data = gauss_dist.pdf(y)
axGauss.plot(-gauss_data, y)
for bp in bpList:
axGauss.axhline(y=bp, xmin=0, xmax=PAAStreams.shape[0], ls = '--', color = 'k')
# Tweek Axis
axSAX.set_ylim( axGauss.get_ylim() )
#axGauss.set_xlim( 0, -0.5 )
adjust_spines(axGauss, [])
adjust_spines(axSAX, ['left', 'bottom'])
plt.xlabel('Time Steps')
plt.show()
return PAAStreams
})
## Turn off one axis to display accordingly to the other figure in example_4_synrel.py
ax[0].axis('off')
ax[1].errorbar(f_vals / Hz,
np.mean(syn_mon.r_S[2 * N_synapses:], axis=1),
np.std(syn_mon.r_S[2 * N_synapses:], axis=1),
fmt='o',
color='black',
lw=0.5)
ax[1].set(xlim=(0.08, 100),
xscale='log',
ylim=(0., 0.7),
ylabel=r'$\langle r_S \rangle$')
pu.adjust_spines(ax[1], ['left'])
ax[2].errorbar(f_vals / Hz,
np.mean(syn_mon.r_S[N_synapses:2 * N_synapses], axis=1),
np.std(syn_mon.r_S[N_synapses:2 * N_synapses], axis=1),
fmt='o',
color='C2',
lw=0.5)
ax[2].set(xlim=(0.08, 100),
xscale='log',
ylim=(0., 0.2),
ylabel=r'$\langle r_S \rangle$')
pu.adjust_spines(ax[2], ['left'])
ax[3].errorbar(f_vals / Hz,
np.mean(syn_mon.r_S[:N_synapses], axis=1),
ast_mon.C[0] / umolar,
'-',
color='C2')
ax[0].plot((ast_mon.t - transient) / second,
ast_mon.C[1] / umolar,
'-',
color='C3')
## Add threshold for gliotransmitter release
ax[0].plot(np.asarray([-transient / second, 0.0]),
np.asarray([C_Theta, C_Theta]) / umolar,
':',
color='gray')
ax[0].set(xlim=[-transient / second, 0.0],
yticks=[0., 0.4, 0.8, 1.2],
ylabel=r'$C$ ($\mu$M)')
pu.adjust_spines(ax[0], ['left'])
## Gliotransmitter concentration in the extracellular space
ax[1].plot((ast_mon.t - transient) / second,
ast_mon.G_A[0] / umolar,
'-',
color='C2')
ax[1].plot((ast_mon.t - transient) / second,
ast_mon.G_A[1] / umolar,
'-',
color='C3')
ax[1].set(yticks=[0., 50., 100.],
xlim=[-transient / second, 0.0],
xlabel='time (s)',
ylabel=r'$G_A$ ($\mu$M)')
pu.adjust_spines(ax[1], ['left', 'bottom'])
################################################################################
run(duration, report='text')
################################################################################
# Analysis and plotting
################################################################################
plt.style.use('figures.mplstyle')
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6.26894, 6.26894 * 0.66),
gridspec_kw={'left': 0.1, 'bottom': 0.12})
scaling = 1.2
step = 10
ax.plot(astro_mon.t/second,
(astro_mon.C[0:N_astro//2-1].T/astro_mon.C.max() +
np.arange(N_astro//2-1)*scaling), color='black')
ax.plot(astro_mon.t/second, (astro_mon.C[N_astro//2:].T/astro_mon.C.max() +
np.arange(N_astro//2, N_astro)*scaling),
color='black')
ax.plot(astro_mon.t/second, (astro_mon.C[N_astro//2-1].T/astro_mon.C.max() +
np.arange(N_astro//2-1, N_astro//2)*scaling),
color='C0')
ax.set(xlim=(0., duration/second), ylim=(0, (N_astro+1.5)*scaling),
xticks=np.arange(0., duration/second, 500), xlabel='time (s)',
yticks=np.arange(0.5*scaling, (N_astro + 1.5)*scaling, step*scaling),
yticklabels=[str(yt) for yt in np.arange(0, N_astro + 1, step)],
ylabel='$C/C_{max}$ (cell index)')
pu.adjust_spines(ax, ['left', 'bottom'])
pu.adjust_ylabels([ax], x_offset=-0.08)
plt.show()
fig, ax = plt.subplots(nrows=4, ncols=1, figsize=(3.07, 3.07*1.33), sharex=False,
gridspec_kw={'height_ratios': [1, 3, 3, 3],
'top': 0.98, 'bottom': 0.12,
'left': 0.22, 'right': 0.93})
## Turn off one axis to display accordingly to the other figure in example_4_synrel.py
ax[0].axis('off')
ax[1].errorbar(f_vals/Hz, np.mean(syn_mon.r_S[2*N_synapses:], axis=1),
np.std(syn_mon.r_S[2*N_synapses:], axis=1),
fmt='o', color='black', lw=0.5)
ax[1].set(xlim=(0.08, 100), xscale='log',
ylim=(0., 0.7),
ylabel=r'$\langle r_S \rangle$')
pu.adjust_spines(ax[1], ['left'])
ax[2].errorbar(f_vals/Hz, np.mean(syn_mon.r_S[N_synapses:2*N_synapses], axis=1),
np.std(syn_mon.r_S[N_synapses:2*N_synapses], axis=1),
fmt='o', color='C2', lw=0.5)
ax[2].set(xlim=(0.08, 100), xscale='log',
ylim=(0., 0.2), ylabel=r'$\langle r_S \rangle$')
pu.adjust_spines(ax[2], ['left'])
ax[3].errorbar(f_vals/Hz, np.mean(syn_mon.r_S[:N_synapses], axis=1),
np.std(syn_mon.r_S[:N_synapses], axis=1),
fmt='o', color='C3', lw=0.5)
ax[3].set(xlim=(0.08, 100), xticks=np.logspace(-1, 2, 4), xscale='log',
ylim=(0., 0.7), xlabel='input frequency (Hz)',
ylabel=r'$\langle r_S \rangle$')
ax[3].xaxis.set_major_formatter(ScalarFormatter())
# Plot data
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.plot(res_sp['RSRE'])
#plt.grid(True)
ax2 = fig.add_subplot(312, sharex = ax1)
ax2.plot(res_sp['e_ratio'])
#plt.grid(True)
ax3 = fig.add_subplot(313, sharex = ax1)
ax3.plot(res_sp['orthog_error'])
#plt.grid(True)
# Shift spines
adjust_spines(ax1, ['left', 'bottom'])
adjust_spines(ax2, ['left', 'bottom'])
adjust_spines(ax3, ['left', 'bottom'])
adjust_ticks(ax1, 'y', 5)
adjust_ticks(ax2, 'y', 5)
adjust_ticks(ax3, 'y', 5)
# Axis Labels
plt.suptitle('Error Analysis of SPIRIT', size = 18, verticalalignment = 'top' )
ylabx = -0.1
ax1.set_ylabel('RSRE', horizontalalignment = 'right', transform = ax1.transAxes )
ax1.yaxis.set_label_coords(ylabx, 0.5)
ax2.set_ylabel('Energy Ratio', horizontalalignment = 'right')
ax2.yaxis.set_label_coords(ylabx, 0.5)
ax2 = fig.add_subplot(412, sharex=ax1)
ax2.plot(data['hidden'])
#plt.grid(True)
ax3 = fig.add_subplot(413, sharex=ax1)
ax3.plot(data['e_ratio'])
#plt.grid(True)
plt.axhline(y=e_high, ls='--')
plt.axhline(y=e_low, ls='--')
ax4 = fig.add_subplot(414, sharex=ax1)
ax4.plot(data['orthog_error'])
# Shift spines
adjust_spines(ax1, ['left', 'bottom'])
adjust_spines(ax2, ['left', 'bottom'])
adjust_spines(ax3, ['left', 'bottom'])
adjust_spines(ax4, ['left', 'bottom'])
adjust_ticks(ax1, 'y', 5)
adjust_ticks(ax2, 'y', 5)
adjust_ticks(ax3, 'y', 5)
adjust_ticks(ax4, 'y', 5)
ax1.set_xlim([0, streams.shape[0]])
# Axis Labels
plt.suptitle('Error Analysis of SPIRIT', size=18, verticalalignment='top')
ylabx = -0.13
################################################################################
run(duration, report='text')
################################################################################
# Analysis and plotting
################################################################################
plt.style.use('figures.mplstyle')
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6.26894, 6.26894 * 0.66),
gridspec_kw={'left': 0.1, 'bottom': 0.12})
scaling = 1.2
step = 10
ax.plot(astro_mon.t/second,
(astro_mon.C[0:N_astro//2-1].T/astro_mon.C.max() +
np.arange(N_astro//2-1)*scaling), color='black')
ax.plot(astro_mon.t/second, (astro_mon.C[N_astro//2:].T/astro_mon.C.max() +
np.arange(N_astro//2, N_astro)*scaling),
color='black')
ax.plot(astro_mon.t/second, (astro_mon.C[N_astro//2-1].T/astro_mon.C.max() +
np.arange(N_astro//2-1, N_astro//2)*scaling),
color='C0')
ax.set(xlim=(0., duration/second), ylim=(0, (N_astro+1.5)*scaling),
xticks=np.arange(0., duration/second, 500), xlabel='time (s)',
yticks=np.arange(0.5*scaling, (N_astro + 1.5)*scaling, step*scaling),
yticklabels=[str(yt) for yt in np.arange(0, N_astro + 1, step)],
ylabel='$C/C_{max}$ (cell index)')
pu.adjust_spines(ax, ['left', 'bottom'])
pu.adjust_ylabels([ax], x_offset=-0.08)
plt.show()