def plot(self, datastr):
data = json.loads(unicode(datastr))
if "data" in data and "graphs" in data["data"]:
graphdata = data["data"]["graphs"]
if len(graphdata) < len(self.canvases):
for i in reversed(range(self.layout.count())):
self.layout.itemAt(i).widget().deleteLater()
self.canvases = []
self.figures = []
for maskindex, set in enumerate(graphdata):
if len(self.canvases) <= maskindex:
self.figures.append(plt.figure())
canvas = FigureCanvas(self.figures[maskindex])
canvas.setParent(self)
self.canvases.append(canvas)
self.layout.addWidget(self.canvases[maskindex])
figure = self.figures[maskindex]
figure.clf()
figure.set_frameon(False)
ax = figure.add_subplot(111)
ax.set_yscale('symlog')
ax.set_xlabel(set["columnLabels"][0], fontsize=16)
ax.set_ylabel(set["columnLabels"][1], fontsize=16)
ax.set_title(set["kind"] + " " + data["data"]['filename'])
ax.patch.set_alpha(0)
x = np.array(set["array"][0])[:]
y = np.array(set["array"][1])[:]
e = np.array(set["array"][2])[:]
ppl.plot(ax, x, y, lw=1.0)
ppl.fill_between(ax, x, y - e, y + e)
plt.subplots_adjust(bottom=0.2)
self.canvases[maskindex].draw()
def plot(self,datastr):
data=json.loads(unicode(datastr))
if "data" in data and "graphs" in data["data"]:
graphdata= data["data"]["graphs"]
if len(graphdata)<len(self.canvases):
for i in reversed(range(self.layout.count())):
self.layout.itemAt(i).widget().deleteLater()
self.canvases=[]
self.figures=[]
for maskindex,set in enumerate(graphdata):
if len(self.canvases)<=maskindex:
self.figures.append(plt.figure( ))
canvas=FigureCanvas(self.figures[maskindex])
canvas.setParent(self)
self.canvases.append(canvas)
self.layout.addWidget(self.canvases[maskindex])
figure=self.figures[maskindex]
figure.clf()
figure.set_frameon(False)
ax=figure.add_subplot(111)
ax.set_yscale('symlog')
ax.set_xlabel(set["columnLabels"][0],fontsize=16)
ax.set_ylabel(set["columnLabels"][1],fontsize=16)
ax.set_title( set["kind"]+" "+data["data"]['filename'])
ax.patch.set_alpha(0)
x=np.array(set["array"][0])[:]
y=np.array(set["array"][1])[:]
e=np.array(set["array"][2])[:]
ppl.plot(ax,x,y,lw=1.0)
ppl.fill_between(ax,x,y-e,y+e)
plt.subplots_adjust(bottom=0.2)
self.canvases[maskindex].draw()
def plot_filling(path,plotfolder,x,y,z,condition,videoname,name):
'''simple plot filling maker giving count data'''
fig, ax = plt.subplots(1)
ppl.fill_between(x, y, facecolor='black', alpha = ALPHASINGLE)
ppl.fill_between(x, z, facecolor='pink', alpha = ALPHASINGLE)
ax.set_ylim([0,8])
ax.set_xlabel('time in video (sec)')
ax.set_ylabel('Number of times watched')
ax.set_title('Number of views by a student in condition {0} of the video \n{1}'.format(condition,videoname))
fig.savefig(os.path.join(path,plotfolder,'count_'+name+'.png'))
return None
def plot_mult_counts(path,plotfolder,vlogs, subject, conditions, videoname):
'''plot multiple counts with different summary stats'''
'''for a paticular course subject, condition, and video'''
fig, ax = plt.subplots(1)
#files = []
counts = []
for vlog in vlogs:
for video in vlog.viewage.keys():
if vlog.condition in conditions and vlog.subject == subject and video == videoname:
y = vlog.viewage[videoname]
if sum(y)==0: #only take into account videos that were watched.
continue
x = [i/2 for i in range(len(y))]
ppl.fill_between(x, y, facecolor='black', alpha = ALPHA) #label=str(vlog.filename))
#files.append(vlog.filename)
counts.append(y)
if counts:
newcounts = zip(*counts)
quartile25 = [np.percentile(i,25) for i in newcounts]
quartile50 = [np.percentile(i,50) for i in newcounts]
quartile75 = [np.percentile(i,75) for i in newcounts]
ppl.plot(x,quartile25,'-', color = '#f768a1',label = 'Watched by at least 75% of students', linewidth=2, alpha=0.8)
ppl.plot(x,quartile50,'-', color = '#ae017e',label = 'Watched by at least 50% of students', linewidth=2, alpha=0.7)
ppl.plot(x,quartile75,'-', color = '#49006a', label = 'Watched by at least 25% of students', linewidth=2, alpha=0.6)
ax.set_xlabel('time in video (sec)')
ax.set_ylabel('Number of times watched')
if len(conditions)==1:
ax.set_title('Aggregated views of {0} students in condition {1} for video lecture \n "{2}"'.format(str(len(counts)),conditions[0],videoname))
else:
ax.set_title('Aggregated views of {0} students and all conditions for video lecture \n "{1}"'.format(str(len(counts)),videoname))
ax.set_ylim([0,8])
ppl.legend()
# p = plt.Rectangle((0, 0), 1, 1, fc="r")
# ax.legend([p], files)
fig.savefig(os.path.join(path,plotfolder,'mult_count_'+subject+'_'+str(conditions)+'_'+videoname+'.png'))
else:
print "No counts found under conditions {0} for {1} video called {2}".format(conditions,subject,videoname)
return None
else:
spiketimes = []
thetas = []
ax1.plot(spiketimes,thetas,'yo',label='Spike times')
ax1.plot(times,mg,'b',label='Mean prediction')
ax1.set_title('Gaussian Filter')
ax1.set_ylabel('Signal space')
ax1.legend()
ax2 = plt.gcf().add_subplot(2,1,2)
ax2.plot(times,sp,'r',label='Signal')
if sum(sum(spsp)) !=0:
(tsp,neursp) = np.where(spsp == 1)
spiketimesp = times[tsp]
thetasp = [code.neurons[i].theta for i in neursp]
else:
spiketimesp = []
thetasp = []
ax2.plot(times,mp,label='Mean prediction')
ppl.fill_between(times,mp-np.sqrt(varp),mp+np.sqrt(varp),ax=ax2)
ax2.plot(spiketimesp,thetasp,'.',label='Spike times')
ax2.set_ylabel('Signal space')
ax2.set_xlabel('Time')
ax2.legend()
ax2.set_title('Particle Filter')
plt.savefig('filtering.png',dpi=150)
T_mantle_initial = 1800.
T_cmb_initial = T_mantle_initial + T_excess
Tm_low = T_mantle_initial - 100
Tcmb_low = T_cmb_initial - 100
Tm_high = T_mantle_initial + 100
Tcmb_high = T_cmb_initial + 100
t, y_low = mercury.integrate(Tcmb_low, Tm_low, times)
t, y_high = mercury.integrate(Tcmb_high, Tm_high, times)
t, y = mercury.integrate(T_cmb_initial, T_mantle_initial, times)
t = t/1.e9*Julian_year;
c = [ppl.colors.set2[0], ppl.colors.set2[1]]
plt.figure()
ppl.fill_between(t, y_low[:,0], y_high[:,0], color=c[0], label=r'CMB Temperature \pm 100 K')
plt.plot(t, y_low[:,0],'--k')
plt.plot(t, y_high[:,0],'--k')
ppl.fill_between(t, y_low[:,1], y_high[:,1], color=c[1], label=r'Upper Mantle Temperature \pm 100 K')
plt.plot(t, y_low[:,1],'--k')
plt.plot(t, y_high[:,1],'--k')
#mercury.draw()
ppl.plot( t, y[:,0], lw =3, color=c[0], label = 'CMB Temperature')
ppl.plot( t, y[:,0], '--k',lw=1)
ppl.plot( t, y[:,1], lw=3, color=c[1], label = 'Upper Mantle Temperature')
ppl.plot( t, y[:,1], '--k',lw=1)
plt.xlabel('Time [Ga]')
plt.ylabel('Temperature [K]')
plt.title("Thermal Evolution Mercury")
ppl.legend(loc=1)
else:
fig, ax2 = ppl.subplots(1)
times = np.arange(0.0,dt*timewindow,dt)
if gaussian:
if sum(sum(spsg)) !=0:
(ts,neurs) = np.where(spsg == 1)
spiketimes = times[ts]
thetas = [code.neurons[i].theta for i in neurs]
else:
spiketimes = []
thetas = []
l4, = ax1.plot(times,sg,label='True State')
l2, = ax1.plot(times,mg,label='Posterior Mean')
l1, = ax1.plot(spiketimes,thetas,'o',label='Observed Spikes')
l3 = ppl.fill_between(times,mg-stg,mg+stg,ax=ax1,alpha=0.2)
c1 = l1.get_color()
c2 = l2.get_color()
c3 = l3.get_facecolor()
c4 = l4.get_color()
ax1.set_title('Gaussian Assumed Density Filter')
ax1.set_ylabel('Position [cm] (Preferred Stimulus)')
ax1.set_xlabel('Time [s]')
ppl.legend(ax1).get_frame().set_alpha(0.6)
thetas = [code.neurons[i].theta for i in sptrain]
ax2.plot(times,s,color=c4,label = 'True Sate')
ax2.plot(times,m,color=c2,label='Posterior Mean')
ax2.plot(times[sptimes],thetas,'o',color=c1,label='Observed Spikes')
ppl.fill_between(times,m-st,m+st,ax=ax2,facecolor=c3,alpha=0.2)
parse_dates=True)
table = table.to_period(freq='W')
commits_per_period = table.hash.groupby(level=0).aggregate(len)
dates = [p.start_time.date() for p in commits_per_period.index]
ncommits = np.asarray(commits_per_period.values)
total_commits = np.zeros(ncommits.shape[0])
for i in np.arange(0, ncommits.shape[0]):
total_commits[i] = np.sum(ncommits[:i])
fn = interp1d(range(len(dates)), total_commits, 'cubic', bounds_error=False)
fig, ax = plt.subplots(1)
fig.set_size_inches((8, 4.5))
x = np.linspace(0, len(dates), 1000)
ppl.fill_between(x, 0, fn(x), color='blue', alpha=0.5)
ax.set_xlim(0, len(dates))
ax.set_ylim(0, max(total_commits) + 0.1 * max(total_commits))
ax.xaxis.set_ticks(np.linspace(0, len(dates) - 1, 8)[1:-1])
def formatter(x, p):
if x >= len(dates):
return ''
return dates[int(x)].strftime('%b %Y')
formatter = FuncFormatter(formatter)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()
