def diagramme_gant(liste_taches): durees =[] arivees =[] for tache in liste_taches: durees.append(tache.duree) arivees.append(tache.arivee) plt(arivees,"r",linewidth=10)
def make_CMD(short_w_file, long_w_file):
"""Links the two data sets it is given so the RA/Decs agree and plots a CMD.
Compares second data set to first data set and assigns second data set the
DataNum of first data set where the RA and Dec columns are within a
reasonable range. Uses function from another module to do so. Removes the
points not in both data sets so they can be ploted together and creates a
CM Diagram of the data.
Parameters
------
short_w_file, long_w_file: filename
short_wave, long_wave: list
short_w, long_w: astropy table
"""
short_wave = glob.glob(short_w_file)
short_w = Table.read(short_wave[0])
long_wave = glob.glob(long_w_file)
long_w = Table.read(long_wave[0])
# send through matching function in Sort module to make the DataNum's match
long_w = assign_id(short_w, long_w, "AvgRA", "AvgDec")
# print(long_w["DataNum", "AvgRA"], short_w["DataNum", "AvgRA"])
short_w = match(short_w, long_w)
# long_w = match(long_w, short_w)
# print(long_w["DataNum", "AvgRA"], short_w["DataNum", "AvgRA"])
# plot short - long vs instrumental mag
plt(short_w["AvgFlux"] - long_w["AvgFlux"], short_w["InstruMag"])
def save_all_series(df):
def plt(graph):
graph.plot().save().close()
cols = [x for x in dfm.columns if x not in ['year', 'month', 'qtr']]
for col in cols:
plt(Spline(col))
plt(Chart(col))
def render(self, renderObj, name): if self.ndims == 1: x_axis = np.linspace(-self.half_boxboundary, self.half_boxboundary, self.binNum) x_axis = np.delete(x_axis, -1 , 0) # prevent boundary error if hasattr(renderObj, '__call__'): # is function if renderObj.__name__ == "boltz1D" or renderObj.__name__ == "freeE1D": plt.plot(x_axis, renderObj(x_axis, self.temperature)) else: plt.plot(x_axis, renderObj(x_axis)) else: # is array plt(x_axis, renderObj) plt.xticks(np.linspace(-self.half_boxboundary, self.half_boxboundary, 8)) plt.savefig(name + ".png") plt.gcf().clear() if self.ndims == 2: x_axis = np.linspace(-self.half_boxboundary, self.half_boxboundary, self.binNum) y_axis = np.linspace(-self.half_boxboundary, self.half_boxboundary, self.binNum) x_axis = np.delete(x_axis, -1 , 0) # prevent boundary error y_axis = np.delete(y_axis, -1 , 0) # prevent boundary error A, B = np.meshgrid(x_axis, y_axis, indexing="ij") if hasattr(renderObj, '__call__'): if renderObj.__name__ == "boltz2D": #TODO freeE2D cs = plt.contourf(A, B, renderObj(A, B, self.temperature), 8, cmap=plt.cm.plasma) R = plt.contour(A, B, renderObj(A, B, self.temperature), 8, colors='black', linewidth=.25, linestyles="solid", extend="both") else: cs = plt.contourf(A, B, renderObj(A, B), 8, cmap=plt.cm.plasma) R = plt.contour(A, B, renderObj(A, B), 8, colors='black', linewidth=.25, linestyles="solid", extend="both") else: cs = plt.contourf(A, B, renderObj, 8, cmap=plt.cm.plasma) R = plt.contour(A, B, renderObj, 8, colors='black', linewidth=.25, linestyles="solid", extend="both") plt.clabel(R, inline=True, fontsize=8) plt.xlim(x_axis[0],x_axis[-1]) plt.ylim(y_axis[0],y_axis[-1]) plt.xticks(np.linspace(-self.half_boxboundary, self.half_boxboundary-2*self.half_boxboundary/(self.binNum-1), 6)) plt.yticks(np.linspace(-self.half_boxboundary, self.half_boxboundary-2*self.half_boxboundary/(self.binNum-1), 6)) plt.colorbar(cs) plt.savefig(name + ".png") plt.gcf().clear()
def winrate_statistics(self, dataset_df, mmr_info="1"):
x_data, y_data = dataset_df
wins = np.zeros(129)
games = np.zeros(129)
winrate = np.zeros(129)
for idx, game in enumerate(x_data):
for i in range(258):
if game[i] == 1:
games[i % 129] += 1
if y_data[idx] == 1:
if i < 129:
wins[i] += 1
else:
if i >= 129:
wins[i - 129] += 1
winrate = wins / games
winrate_dict = dict()
hero_dict = self.hero_id
for i in range(129):
try:
winrate_dict[hero_dict[str(i)]] = winrate[i]
except:
continue
sorted_winrates = sorted(winrate_dict.items(),
key=operator.itemgetter(1))
x_plot_data = [x[0] for x in sorted_winrates]
y_plot_data = [x[1] for x in sorted_winrates]
title = 'Hero winrates at ' + mmr_info + ' MMR'
data = [go.Bar(y=x_plot_data, x=y_plot_data, orientation='h')]
layout = go.Layout(title=title,
width=1000,
height=1400,
yaxis=dict(title='hero',
ticks='',
nticks=129,
tickfont=dict(size=8, color='black')),
xaxis=dict(title='win rate',
nticks=30,
tickfont=dict(size=10, color='black')))
fig = go.Figure(data=data, layout=layout)
plt(data, filename='hero_win_1.html')
def main():
generator = Generator()
discriminator = Discriminator()
#setup the optimizer for two MLP networks
D_solver = tf.train.AdadeltaOptimizer()
G_solver = tf.train.AdadeltaOptimizer()
out_path = os.path.join(config.OUTPUT_DIR, "conditional_gan")
if not os.path.exists(out_path):
os.makedirs(out_path)
#training data
i = 0
for it in range(100000):
if it % 1000 == 0:
n_sample = 16
z_sample = sample_z(n_sample, z_dim)
y_sample = np.zeros(shape=[n_sample, y_dim])
y_sample[:, 6] = 1
samples = generator.forward(z_sample, y_sample)
fig = plt(samples.numpy())
file_path = os.path.join(out_path,
"{}.png".format(str(i).zfill(3)))
plt.savefig(file_path, bbox_inches='tight')
i += 1
plt.close(fig)
images, labels = mnist.train.next_batch(batchSize)
images = images.astpye(np.float32)
labels = images.astpye(np.float32)
z_sample = sample_z(batchSize, z_dim)
with tf.GradientTape() as tape_d:
g_sample = generator.forward(z_sample, labels)
d_real, d_logit_real = discriminator.forward(images, labels)
d_fake, d_logit_fake = discriminator.forward(g_sample, labels)
d_loss = discriminator_loss(d_logit_real, d_logit_fake)
# backward propagation of discriminator
grad = tape_d.gradient(d_loss, discriminator.theta_D)
D_solver.apply_gradients(
zip(grad, discriminator.theta_D),
global_step=tf.train.get_or_create_global_step())
with tf.GradientTape() as tape_g:
g_sample = generator.forward(z_sample, labels)
d_fake, d_logit_fake = discriminator.forward(g_sample, labels)
g_loss = generator_loss(d_logit_fake)
if it % 1000 == 0:
print("Iter: {}".format(it))
print("D loss: {:.4}".format(d_loss))
print("G loss: {:.4}".format(g_loss))
print()
def learning_curve(model,
inputs,
inputs_val=None,
train_args=None,
metric='mse',
ax=None,
log=True,
labels=None):
if ax is None:
_, ax = subplots(1, 1, size=figsize(1, 2))
if train_args is None:
train_args = {}
res = model.train(inputs, learning_curve=metric, **train_args)
plt = ax.semilogy if log else ax.plot
if labels is None:
labels = ["Training Set", "Validation Set"]
p = plt(res[0], res[1], label=labels[0])
if inputs_val is not None:
plt(res[0],
res[2],
color=p[0].get_color(),
linestyle='--',
label=labels[1])
ax.set_xlabel("Epoch")
metric_name = {
'mse': 'MSE',
'total': 'Total Error',
}[metric]
ax.set_ylabel(metric_name)
ax.legend()
def DrawACC(t=None, Rec_ACC=None, *args, **kwargs):
# varargin = DrawACC.varargin
# nargin = DrawACC.nargin
figure(5)
plt.subplot(3, 1, 1)
plt(t, Rec_ACC(1, arange()), '-bo')
title('\theta1')
xlabel('Sec')
ylabel('Acc')
plt.grid
plt.subplt(3, 1, 2)
plt(t, Rec_ACC(2, arange()), '-bo')
title('\theta2')
xlabel('Sec')
ylabel('Acc')
plt.grid
plt.subplot(3, 1, 3)
plt(t, Rec_ACC(3, arange()), '-bo')
title('\theta3')
xlabel('Sec')
ylabel('Acc')
plt.grid
return
def DrawAngle(t=None, Rec_Ang=None, *args, **kwargs):
# varargin = DrawAngle.varargin
# nargin = DrawAngle.nargin
figure(3)
subplot(3, 1, 1)
plt(t, Rec_Ang(1, arange()))
title('\theta1')
xlabel('Sec')
ylabel('degree')
plt.grid
subplot(3, 1, 2)
plt(t, Rec_Ang(2, arange()))
title('\theta2')
xlabel('Sec')
ylabel('degree')
plt.grid
subplot(3, 1, 3)
plt(t, Rec_Ang(3, arange()))
title('\theta3')
xlabel('Sec')
ylabel('degree')
plt.grid
return
def DrawRPM(t=None, Rec_RPM=None, *args, **kwargs):
# varargin = DrawRPM.varargin
# nargin = DrawRPM.nargin
figure(4)
subplot(3, 1, 1)
plt(t, Rec_RPM(1, arange()), '-bo')
title('\theta1')
xlabel('Sec')
ylabel('RPM')
plt.grid
subplot(3, 1, 2)
plt(t, Rec_RPM(2, arange()), '-bo')
title('\theta2')
xlabel('Sec')
ylabel('RPM')
plt.grid
subplot(3, 1, 3)
plt(t, Rec_RPM(3, arange()), '-bo')
title('\theta3')
xlabel('Sec')
ylabel('RPM')
plt.grid
return
print('N_3e: |', 'count')
for i in range(time_running):
N_3e = find_out_3_coincidences(lmax, theta) #number of mu
count += 1
print(N_3e, "|", count)
L.append([N_3e])
if N_3e > 0:
L_3e.append([N_3e])
if len(L_3e) == 0:
print("Probability <", 1 / len(L))
else:
print("probability =", len(L_3e) / len(L))
return len(L_3e), time_running
'''L_dx = np.linspace(1,5,4)
L_coincidence = []
for i in range(len(L_dx)):
dx = L_dx[i]
dy,dz = dx,dx
N_coincidence, N_total = simulation_3_events(lmax = l, theta = angle, time_running=10**7)
prob = N_coincidence/N_total
L_coincidence.append(N_coincidence)
plt(L_dx,L_coincidence)
plt.xlabel('dx (mm)')
plt.ylabel('N_coincidence')'''
def estimate_prob_at_dx(
dx, N
): # this function(method) require the high performance of computers which is not suitable for my laptop.
# coding: utf-8
import numpy
import h5py
import numpy as np
np.read
np.loadtxt('data/ecg_101.txt')
a = np.loadtxt('data/ecg_101.txt')
a
a.shape
import matplotlib
import matplotlib.pyplot as plt
plt(a[0], a[1])
plt.plot(a[0], a[1])
plt.show()
a[0]
a[]
a[1]
get_ipython().magic('pinfo plt.plot')
plt.plot(a[1])
plt.show()
plt.plot(a[0])
plt.show()
a
a[0]
a[0][0]
a[;0]
a[,0]
a[,:]
a[:,]
a.shape
a[0][:]
if epoch >3:
done_looping = True
break
end_time = timeit.default_timer()
print(('Optimization complete. Best validation score of %f %% '
'obtained at iteration %i, with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)
epoch_loss_np = np.reshape(epoch_loss_list,newshape=(len(epoch_loss_list),3))
plt(epoch_loss_np)
epoch_val_np = np.reshape(epoch_loss_list,newshape=(len(epoch_val_list),3))
#trying to plot this this
"""
for i in range(10000):
cost = train_model(Xtr_rows, Ytr)
if cost_aux > cost:
learning_rate.set_value(learning_rate.get_value()*1.1)
else:
learning_rate.set_value(learning_rate.get_value()*0.9)
cost_aux = cost
print("cost is: ", cost, "learning rate: ", learning_rate.get_value())
# -*- coding: utf-8 -*- """ Created on Thu May 10 16:10:56 2018 @author: sapta """ import matplotlib.pyplot as plt plt()
l_sec = df['total_seconds'].tolist()
l_binsx =[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
l_bins_n = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
h = plt.hist2d(l_day, l_sec, bins=15)
plt.grid(False)
plt.xlabel("day of the month")
plt.ylabel("seconds since midnight")
plt.colorbar()
plt.savefig("time_Vs_response.pdf")
for i, group in df.groupby('statu'):
plt.figure()
group.plot(x='time', y='responsetime', title="status = "+str(i), legend=False)
group.plot(df['responsetime'], bins=20, alpha=0.5, title="status = "+str(i), kind="hist")
plt.ylabel("responsetime (s)")
title=str(i)+".pdf"
plt(marker='o')
plt.savefig(title)
print(df["stat_freq"])
for i, group, in df.groupby(['id']):
print(group)
print(group["status"])
count = group["status"].value_counts().sort_index(0).to_dict()
o_count = collections.OrderedDict(sorted(count.items()))
print(o_count)
plt.bar(range(len(o_count)), o_count.values(), align='center')
plt.xticks(range(len(o_count)), list(o_count.keys()))
group["status"].plot(x='status', y=group['status'].value_counts(), title="user status - "+str(i), legend=False, kind="bar")
my['cat']='고양이'
my['cat']
my['고양이']
dic={'1':1,'2':2,'3':3,'4':4,'5':5,'6':6}
dic['1']+dic['6']==7 and dic['2']+dic['5']==7 and dic['3']+dic['4']==7
def max_min(a):
print(max(a))
print(min(a))
my_list=[1,3,2,9,6,5,3]
max_min(my_list)
import matplotlib.pyplot as plt
for i in range(0,6,0.1):
print(i)
plt(-(i-1)(i-3)(i-4))
x=[a for a in range(0,6)]
y=[(-(b-1)(b-3)(b-4)) for b in range(6)]
y=[a*a for a in x]
plt.plot(x,y)
import numpy as np
x=np.arange(-1,6,0.1)
y=-(x-1)*(x-3)*(x-4)
plt.plot(x,y)
x=np.arange(-1,11,0.1)
y=-(x-1)*(x-6)*(x-9)
from scipy import optimize
num_points = 150
Tx = linspace(5., 8., num_points)
Ty = Tx
tX = 11.86*cos(2*pi/0.81*Tx-1.32) + 0.64*Tx+4*((0.5-scipy.rand(num_points))*exp(2*scipy.rand(num_points)**2))
tY = -32.14*cos(2*pi/0.8*Ty-1.94) + 0.15*Ty+7*((0.5-scipy.rand(num_points))*exp(2*scipy.rand(num_points)**2))
# Fit the first set
fitfunc = lambda p, x: p[0]*cos(2*pi/p[1]*x+p[2]) + p[3]*x # Target function
errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function
p0 = [-15., 0.8, 0., -1.] # Initial guess for the parameters
p1, success = optimize.leastsq(errfunc, p0[:], args=(Tx, tX))
time = linspace(Tx.min(), Tx.max(), 1000)
plt(Tx, tX, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the fit
# Fit the second set
p0 = [-15., 0.8, 0., -1.]
p2,success = optimize.leastsq(errfunc, p0[:], args=(Ty, tY))
time = linspace(Ty.min(), Ty.max(), 1000)
plt(Ty, tY, "b^", time, fitfunc(p2, time), "b-")
# Legend the plot
plt.title("Oscillations in the compressed trap")
plt.xlabel("time [ms]")
plt.ylabel("displacement [um]")
plt.legend(('x position', 'x fit', 'y position', 'y fit'))
maxchange = current > maxi - radius
mini_h = (current - mini)[minchange]
maxi_h = (maxi - current)[maxchange]
vshell += np.sum(ne.evaluate("1/3.0*pi*(2*(radius**3 - inn**3) + 3*mini_h*(radius**2 - inn**2))"))
vshell += np.sum(ne.evaluate("1/3.0*pi*(2*(radius**3 - inn**3) + 3*maxi_h*(radius**2 - inn**2))"))
vshell += np.sum(((-minchange + -maxchange))*ne.evaluate("4/3.0*pi*(radius**3 - inn**3)"))
count += np.sum((r<radius) * (r>inn))
gofrset[i,0] = radius
gofrset[i,1] = count/(vshell*density)
gofrset[i,2] = count
gofrset[i,3] = vshell
#print gofrset[i,1]
#results.writerow(gofrset[:,0:1])
for i in range(len(gofrset[:])):
plt(gofrset[i,0:1])
raw_input()
print "I'm Done!"
"""
for m in range(num_part):
if (current[m,0] - radius < mini[0]) | current[m,0] + radius > maxi[0]:
continue
elif (current[m,1] - radius < mini[1]) | current[m,1] + radius > maxi[1]:
continue
elif (current[m,2] - radius < mini[2]):
h = current(m,2) - mini[2]
vshell += ne.evaluate("1/3.0*np.pi*(2*(radius**3 - inn**3) + 3*h*(radius**2 - inn**2))")
def competition(t, state, parameters):
# array
# constants
k = 0.11
m = 0.01
g = 0.1
n = 1
C = 0.99
q = 2
# state derivatives
dw1 = (w1 ^ q/(w1 ^ q + C*w2 ^ q))*(k*(w1+w2)/(1 + g*(w1+w2) ^ n)) - m*w1
dw2 = (C*(w2 ^ q/(w1 ^ q + C*w2 ^ q))) * \
(k*(w1+w2)/(1 + g*(w1+w2) ^ n)) - m*w2
return [dw1, dw2]
state0 = [0.20, 0.20] # initial state
t = np.arange(.0, 10.0, 0.1) # timestep
state = odeint(competition, state0, t)
plt(t, state)
xlabel('TIME (sec)')
ylabel('STATES')
title('2 Plant System')
legend((''))
show()
# Checked OK, for the three ions independently, with fig.1 of Ercolano & Storey 2006
check_fb = False
if check_fb :
f = open('Ercolano_fig1_H.dat','r') # Datathiefed from fig.1 para HI, HeI and HeII.
aa = f.readlines()
f.close()
lenaa = len(aa)
xnu = np.zeros(lenaa)
yfb = np.zeros(lenaa)
for i in range(0,lenaa) :
tt = string.split(aa[i])
xnu[i] = float(tt[0])
yfb[i] = float(tt[1])
Gamma_fb=gamma_fb_func('HI_t3_elec.ascii')
plt(nu*1e-14,Gamma_fb,xnu,yfb*1e-40,'o')
f = open('Ercolano_fig1_HeI.dat','r') # Datathiefed from fig.1 para HI, HeI and HeII.
aa = f.readlines()
f.close()
lenaa = len(aa)
xnu = np.zeros(lenaa)
yfb = np.zeros(lenaa)
for i in range(0,lenaa) :
tt = string.split(aa[i])
xnu[i] = float(tt[0])
yfb[i] = float(tt[1])
Gamma_fb=gamma_fb_func('HeI_t5_elec.ascii')
plt(nu*1e-14,Gamma_fb,'--',xnu,yfb*1e-40,'o')
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
import matplotlib.pyplot as plt
import numpy as np
def lafunc(x):
return np.exp(-(x**2))
minx = -5.0
maxx = 5.0
x = np.linspace(minx,maxx,1000)
gausiana = lafunc(x)
plt(x,gausiana)
# <codecell>
miny = 0.0
maxy = amax(gausiana)
print miny, maxy
# <codecell>
nrandom = 10000
randomx = random.rand(nrandom) * (maxx - minx) + minx
randomy = random.rand(nrandom) * (maxy - miny) + miny
# <codecell>
delta = lafunc(randomx) - randomy
import matplotlib.pyplot as plt
Samples=100 #描画するサンプルパスの数
Steps=10000 #時間区切りの数
S_T=np.zeros((Samples,Steps)) #
S_T[:,0]=S_t
for i in range(1,Samples):
for j in range(1,Steps):
S_T[i,j]=gBM(S_T[i-1,j-1],sigma,r,T/Steps,random.gauss(0,1))
plt(S_T[:,Steps])
plt.show
dir_out = 'data' file_out= 'Hw4_1_fixed.png' N = 1000 f_Tm = 10 # mean T in C f_dA = 40 # in degree C w = 3600 ## noise / error f_sigma = 15.2 #======================================================================================= # create synthetic data #======================================================================================= a_t = np.linspace( 0, 7*w) # convert to martian days a_T = fct_T( a_t, f_Tm, f_dA, w) # add some noise a_T_noise = a_T + np.dot(np.random.randn( N, 1), (1, f_sigma) #======================================================================================= # save to file #======================================================================================= #fig, ax = plt.subplots() plt( a_t, a_T_noise, 'ko', ms = 2) plt( a_t, a_T, 'r-', lw = 1.5) # save this figure as .png plt.savefig( 'dir_out') plt.show()
def render(self, renderObj, name):
if self.ndims == 1:
x_axis = np.linspace(-self.half_boxboundary, self.half_boxboundary,
self.binNum)
x_axis = np.delete(x_axis, -1, 0) # prevent boundary error
if hasattr(renderObj, '__call__'): # is function
if renderObj.__name__ == "boltz1D":
plt.plot(x_axis, renderObj(x_axis, self.temperature))
else:
plt.plot(x_axis, renderObj(x_axis))
else: # is array
plt(x_axis, renderObj)
plt.xticks(
np.linspace(-self.half_boxboundary, self.half_boxboundary, 8))
plt.savefig(name + ".png")
plt.gcf().clear()
if self.ndims == 2:
x_axis = np.linspace(-self.half_boxboundary, self.half_boxboundary,
self.binNum)
y_axis = np.linspace(-self.half_boxboundary, self.half_boxboundary,
self.binNum)
x_axis = np.delete(x_axis, -1, 0) # prevent boundary error
y_axis = np.delete(y_axis, -1, 0) # prevent boundary error
A, B = np.meshgrid(x_axis, y_axis, indexing="ij")
if hasattr(renderObj, '__call__'):
if renderObj.__name__ == "boltz2D":
cs = plt.contourf(A,
B,
renderObj(A, B, self.temperature),
6,
alpha=.75,
cmap=plt.cm.hot)
R = plt.contour(A,
B,
renderObj(A, B, self.temperature),
6,
colors='black',
linewidth=.5)
else:
cs = plt.contourf(A,
B,
renderObj(A, B),
6,
alpha=.75,
cmap=plt.cm.hot)
R = plt.contour(A,
B,
renderObj(A, B),
6,
colors='black',
linewidth=.5)
else:
cs = plt.contourf(A,
B,
renderObj,
6,
alpha=.75,
cmap=plt.cm.hot)
R = plt.contour(A,
B,
renderObj,
6,
colors='black',
linewidth=.5)
plt.clabel(R, inline=True, fontsize=10)
plt.xlim(x_axis[0], x_axis[-2])
plt.ylim(y_axis[0], y_axis[-2])
plt.xticks(
np.linspace(-self.half_boxboundary, self.half_boxboundary, 6))
plt.yticks(
np.linspace(-self.half_boxboundary, self.half_boxboundary, 6))
plt.colorbar(cs)
plt.savefig(name + ".png")
plt.gcf().clear()
Noise power spectrum for angular frequency omega.
"""
return 0.5 * gamma1 * omega/(2*np.pi)
# find the floquet modes for the time-dependent hamiltonian
f_modes_0, f_energies = qt.floquet_modes(H, T, args)
# precalculate mode table
f_modes_table_t = qt.floquet_modes_table(f_modes_0, f_energies, np.linspace(0, T, 500 + 1), H, T, args)
# solve the floquet-markov master equation
output = qt.fmmesolve(H, psi0, tlist, [sigmax()], [], [noise_spectrum], T, args)
# calculate expectation values in the computational basis
p_ex = np.zeros(np.shape(tlist), dtype=complex)
for idx, t in enumerate(tlist):
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
p_ex[idx] = expect(num(2), output.states[idx].transform(f_modes_t, True))
# For reference: calculate the same thing with mesolve
output = qt.mesolve(H, psi0, tlist, [np.sqrt(gamma1) * qt.sigmax()], [num(2)], args)
p_ex_ref = output.expect[0]
# plot the results
plt(tlist, real(p_ex), "r--", tlist, 1-real(p_ex), "b--")
plt(tlist, real(p_ex_ref), "r", tlist, 1-real(p_ex_ref), "b")
plt.xlabel("Time")
plt.ylabel("Occupation probability")
plt.legend(("Floquet $P_1$", "Floquet $P_0$", "Lindblad $P_1$", "Lindblad $P_0$"))
plt.show()
import numpy as np
from matplotlib.patches import Circle, Rectangle, Wedge, Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
def atlok(szam):
megoldás = (szam * (szam - 3)) / 2
return megoldás
patches = []
for szám in range(100):
#plt(0.1*szám, 0.1*atlok(szám))
patches.append(plt(0.2, 0.2))
p = PatchCollection(patches, alpha=0.4)
plt.show()
def DrawRobotManipulator(DOF, JointPos, JointDir, FigNum, View):
# varargin = DrawRobotManipulator.varargin
# nargin = DrawRobotManipulator.nargin
figure(FigNum)
plt(JointPos(1, arange(1, end())), JointPos(2, arange(1, end())),
JointPos(3, arange(1, end())), 'linewidth', 4)
plt(JointPos(1, arange(1, end())), JointPos(2, arange(1, end())),
JointPos(3, arange(1, end())), 'ro', 'linewidth', 7)
xlabel('X axis')
ylabel('Y axis')
zlabel('Z axis')
X = 10
Y = 10
Z = 20
BaseX = np.array([X, X, -X, -X, X])
BaseY = np.array([Y, -Y, -Y, Y, Y])
BaseZ = np.array([0, 0, 0, 0, 0])
patch(BaseX, BaseY, BaseZ, 'k')
#-------------------------------------
BaseX = np.array([X, X, X, X, X])
BaseY = np.array([Y, -Y, -Y, Y, Y])
BaseZ = np.array([0, 0, -Z, -Z, 0])
patch(BaseX, BaseY, BaseZ, 'k')
#-------------------------------------
BaseX = np.array([X, -X, -X, X, X])
BaseY = np.array([Y, Y, Y, Y, Y])
BaseZ = np.array([0, 0, -Z, -Z, 0])
patch(BaseX, BaseY, BaseZ, 'k')
#-------------------------------------
BaseX = np.array([-X, -X, -X, -X, -X])
BaseY = np.array([-Y, Y, Y, -Y, -Y])
BaseZ = np.array([0, 0, -Z, -Z, 0])
patch(BaseX, BaseY, BaseZ, 'k')
#-------------------------------------
BaseX = np.array([X, -X, -X, X, X])
BaseY = np.array([-Y, -Y, -Y, -Y, -Y])
BaseZ = np.array([0, 0, -Z, -Z, 0])
patch(BaseX, BaseY, BaseZ, 'k')
for i in arange(1, DOF + 1):
#-----------------------------------------
nUnit_v = JointPos(arange(), i) + np.dot(
5, JointDir(arange(),
np.dot(3, (i - 1)) + 1))
nBase = np.array([JointPos(arange(), i), nUnit_v])
plt(nBase(1, arange(1, end())), nBase(2, arange(1, end())),
nBase(3, arange(1, end())), 'y', 'linewidth', 2)
sUnit_v = JointPos(arange(), i) + np.dot(
5, JointDir(arange(),
np.dot(3, (i - 1)) + 2))
sBase = np.array([JointPos(arange(), i), sUnit_v])
plt(sBase(1, arange(1, end())), sBase(2, arange(1, end())),
sBase(3, arange(1, end())), 'g', 'linewidth', 2)
aUnit_v = JointPos(arange(), i) + np.dot(
5, JointDir(arange(),
np.dot(3, (i - 1)) + 3))
aBase = np.array([JointPos(arange(), i), aUnit_v])
plt(aBase(1, arange(1, end())), aBase(2, arange(1, end())),
aBase(3, arange(1, end())), 'r', 'linewidth', 2)
plt.show()
view(View)
#axis equal
axis(np.array([-50, 50, -30, 80, -20, 80]))
return
# coding: utf-8
get_ipython().magic(u'clear ')
import numpy as np
x = np.arange(0, 1, 50)
x
x = np.linspace(0, 1, 50)
y = np.exp(-x)
from matplotlib import pyplot as plt
plt.plot(x, y)
plt.show()
plt.plot(x, y, '*')
plt.show()
x
idx = np.arange(2, 48)
idx
dy = -0.5 * y[idx - 2] + y[idx - 1] - y[idx + 1] + 0.5 * y[idx + 2]
dy
x
dy = dy / 0.02040816**3
plt(x, y, '*', x[idx], dy, 'o')
plt.plot(x, y, '*', x[idx], dy, 'o')
plt.show()
plt.plot(x, y, '*', x[idx], dy, 'o')
plt.legend('funcion', 'derivada')
plt.legend(['funcion', 'derivada'])
plt.show()
get_ipython().magic(u'ls ')
get_ipython().magic(u'save ejemplo_salon_clase.py 1-27')
plt.xscale('log')
# Show plot
plt.show()
print('3 HISTOGRAM')
print('use a histogram to show distribution ')
print(
'range is diveded into bins and graph shows how many results are in each bin'
)
print(' Too few bins will oversimplify reality and won'
't show you the details. ')
print('prToo many bins will overcomplicate reality and won'
't show the bigger picture.')
plt.clf()
plt(life_exp, bins=10)
print('customisations!!')
print('lables for x and y')
plt.ylabel('this is the x')
plt.xlabel('this is the life expectency')
print('title')
plt.title('This is a crazy graph')
print('Ticks = values for x and y axis - can use this to make it start at 0')
print(' can also set lables for each tick')
plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
plt.yticks([0, 4, 8, 12, 16, 20],
["None", "4 times", "8 times", "12 times", "16 times", "20 times"])
plt.grid(True)
print('put text into the graph at given coordinates')
data.head()
data.describe()
data['bedrooms'].value_counts().plot(kind='bar')
plt.title('number of Bedroom')
plt.xlabel('Bedrooms')
plt.ylabel('Count')
sns.despine
plt.figure(figsize=(10, 10))
sns.jointplot(x=data.lat.values, y=data.long.values, size=10)
plt.ylabel('Longitude', fontsize=12)
plt.xlabel('Latitude', fontsize=12)
plt.show()
plt1 = plt()
sns.despine
plt.scatter(data.price, data.sqft_living)
plt.title("Price vs Square Feet")
plt.scatter(data.bedrooms, data.price)
plt.title("Bedroom and Price ")
plt.xlabel("Bedrooms")
plt.ylabel("Price")
plt.show()
sns.despine
plt.scatter((data['sqft_living'] + data['sqft_basement']), data['price'])
from sklearn.linear_model import LinearRegression
def tst7(look,xrange,velplotmin, velplotmax,amg_1666,\
nrcnm,tde,tbaseline,tbg,xd,zrocnm, taucnm, cencnm, widcnm, tspincnm, ordercnm,\
continuum_em, hgtwnm, cenwnm, widwnm, fwnm,\
zrocnmyn, taucnmyn, cencnmyn, widcnmyn, tspincnmyn,\
continuum_emyn, hgtwnmyn, cenwnmyn, widwnmyn, fwnmyn):
# Tst 7 #
zrocnmyn = 0
continuum_emyn = 0
taucnmyn = [0]*nrcnm
cencnmyn = [0]*nrcnm
widcnmyn = [0]*nrcnm
ordercnm = [0]*nrcnm
tdee = tde - tbaseline + tbg
tb_cont, tb_wnm_tot, tb_cnm_tot, tb_tot, exp_tau_sum = tb_exp(xd, \
zrocnm, taucnm, cencnm, widcnm, tspincnm, ordercnm, \
continuum_em, hgtwnm, cenwnm, widwnm, fwnm)
plt.plot(xd,tdee)
plt.xlim(velplotmin, velplotmax)
plt.plot(xd,tb_cnm_tot+tbg)
plt(xd, tb_tot)
# plt.show()
tfita, sigma, \
zrocnm1, taucnm1, cencnm1, widcnm1, tspincnm1, \
sigzrocnm1, sigtaucnm1, sigcencnm1, sigwidcnm1, sigtspincnm1, \
continuum_em1, hgtwnm1, cenwnm1, widwnm1, fwnm1, \
sigcontinuum_em1, sighgtwnm1, sigcenwnm1, sigwidwnm1, sigfwnm1, \
cov, problem, nloop, \
tb_cont, tb_wnm_tot, tb_cnm_tot, \
exp_tau_sum, nloopmax, halfasseduse = fit.fit(look, xd, td, vrange, \
zrocnm, taucnm, cencnm, widcnm, tspincnm, ordercnm, \
zrocnmyn, taucnmyn, cencnmyn, widcnmyn, tspincnmyn, \
continuum_defln, hgtwnm, cenwnm, widwnm, fwnm, \
continuum_deflnyn, hgtwnmyn, cenwnmyn, widwnmyn, fwnmyn)
tb_cont, tb_wnm_tot, tb_cnm_tot, tb_tot, exp_tau_sum = tb_exp(xd, \
zrocnm1, taucnm1, cencnm1, widcnm1, tspincnm1, ordercnm, \
continuum_em1, hgtwnm1, cenwnm1, widwnm1, fwnm1)
plt.plot(xd, tb_tot)
plt.plot(xd,tdee-tb_tot)
nrgauss_wnm = len(hgtwnm1)
nrg_wnm = nrgauss_wnm
emg_1666 = {'sname':'', 'ell':0.0, 'bee':0.0, 'gaussnr':0, 'nrgauss':1, \
'tbaseline':0.0, \
'continuum':0.0, 'hgtwnm':0.0, 'cenwnm':0.0, 'widwnm':0.0, 'fwnm':0.0, \
'sigcontinuum':0.0, 'sighgtwnm':0.0, 'sigcenwnm':0.0, 'sigwidwnm':0.0, 'sigfwnm':0.0,\
'continuum_em':0.0}
dassign(emg_1666,'sname', src, nrgauss_wnm)
dassign(emg_1666,'ell', ell[n], nrgauss_wnm)
dassign(emg_1666,'bee', bee[n], nrgauss_wnm)
dassign(emg_1666,'gaussnr', list(range(nrg_wnm)))
dassign(amg_1665,'nrgauss', nrg_wnm, nrgauss_wnm)
dassign(emg_1666,'tbaseline', tbaseline, nrgauss_wnm)
dassign(emg_1666,'continuum_em', continuum_em, nrgauss_wnm)
dassign(emg_1666,'hgtwnm', hgtwnm1)
dassign(emg_1666,'cenwnm', cenwnm1)
dassign(emg_1666,'widwnm', widwnm1)
dassign(emg_1666,'fwnm', fwnm1)
dassign(emg_1666,'sighgtwnm', sighgtwnm1)
dassign(emg_1666,'sigcenwnm', sigcenwnm1)
dassign(emg_1666,'sigwidwnm', sigwidwnm1)
dassign(emg_1666,'sigfwnm', sigfwnm1)
amg_1666['tspincnm'] = tspincnm1 ## DAU DAY
amg_1666['sigtspincnm'] = sigtspincnm1 ## DAU DAY
# End - Tst 7 #
return ''
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
plt.plot([1.5, 3.5, -2, 1.6])
plt.plot(rand(50).cumsum(), 'k--')
_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)
ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))
# Simple plot
x = range(0, 100)
y = [i * i for i in x]
plt(x, y, '-')
plt.plot(x, y, '-')
plt.title('Plotting x * x')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.savefig('simple.png')
# Plotting dates/times
from matplotlib.dates import date2num
from datetime import datetime, timedelta
# Generate a series of timestamps from today to today + 100 years
x = [date2num(datetime.today() + timedelta(days=365 * x)) for x in range(0, 100)]
y = [i * i for i in range(0, 100)]
plt(x, y, '-')
plt.title('Fed Tweets by Date')
plt.xlabel('Date')
plt.ylabel('Fed Tweets')
plt.plot_date(datelistmod, incore, linestyle='-', marker='', color='blue')
plt.show()
plt.title('War Tweets by Date')
plt.xlabel('Date')
plt.ylabel('War Tweets')
plt.plot_date(datelistmod, wacore, linestyle='-', marker='', color='blue')
plt.show()
objects = ('China \n Tweets', 'No \n China \n Tweets', 'Fed \n Tweets',
'No \n Fed \n Tweets', 'War \n Tweets', 'No \n War \n Tweets')
y_pos = np.arange(len(objects))
plt.bar(y_pos,
performance,
align='center',
alpha=0.5,
color=['red', 'red', 'green', 'green', 'blue', 'blue'])
plt.xticks(y_pos, objects)
plt.ylabel('Average SPY Delta')
plt.title('Average SPY Performance')
plt(figsize=(10, 6))
plt.show()
#When does trump tweet about china
import sys
sys.path.append('/usr/local/anaconda3/lib/python3.6/site-packages')
import sys
import matplotlib.pyplot as plt
import numpy as np
from numpy import *
x = linspace(0, 7, 70)
y = cos(x)
from matplotlib import pyplot as plt
plt.grid()
plt.xlabel('x')
plt.ylabel('f(x)')
plt.title('Funkcija $cos(x)$')
plt.plot(x, y)
plt(show)
c = conn.cursor()
# c.execute('''CREATE TABLE POPULATION
# (ID INT PRIMARY KEY NOT NULL,
# EN_NAME TEXT NOT NULL,
# ZH_NAME TEXT NOT NULL);''')
# print("Population Table Created!")
conn.commit()
c.execute("select name from sqlite_master where type='table' order by name;")
print(c.fetchall())
c.execute("PRAGMA table_info(POPULATION)")
print(c.fetchall())
# make sure you already have the dict td in the memory
for i, k in enumerate(td):
sql = "INSERT INTO POPULATION (ID,EN_NAME,ZH_NAME) \
VALUES (%d, '%s', '%s');" % (i, k, td[k])
print(sql)
c.execute(sql)
print("Records created successfully")
conn.close()
x=[1999:1:2018]
y=[tdplus]
fig.ax = plt.subplots()
plt(x,y)
plt.savefig("static/image/graduate_figure.png")
plt.show()
__cpiplus__()
crawl_graduate()
import csv
import numpy as np
filename = '/Users/MahdiMoslemi/Desktop/xyz_b'
with open(filename) as csvfile:
data = [
tuple(map(float, row)) for row in csv.reader(csvfile, delimiter=" ")
]
points = np.array(data)
from scipy.spatial import Voronoi, voronoi_plot_2d
vor = Voronoi(points)
import matplotlib.pyplot as plt
plt(vor)
plt.show()
plt.hist(sample, bins=100, orientation='horizontal')
# bar-plot
plt.bar(FCT.index, FCT['Volume'])
# horizontal bar-plot
plt.hbar(FCT.index, FCT['Volume'])
# boxplot
plt.boxplot([df['normal'], df['random'], df['gamma']])
# heatmap
plt.hist2d(X, Y, bins=25)
plt.colorbar() #add color range legend
# error bars
plt.errorbar(x, y, xerr=0.2)
# OR.... use kind = graph_type
plt(kind=bar, x, y)
### ADD HORIZONTAL/VERTICAL LINE, axhline---------------------------------------
df2.plot(figsize(15,5)).axhline(y = 0, color = "red", lw=1)
#or just add to a new line
plt.axhline(y = 0, color = "red", lw=1)
plt.axvline(y = 0, color = "red", lw=1) #vertical lines
### ADD ANNOTATIONS---------------------------------------
plt.text('2015-10-23', 2.25, 'SMA 10-20',rotation=90) #(x, y, text, rotate-label)
# http://matplotlib.org/users/annotations_guide.html#plotting-guide-annotation
# label name, data points coord, annotation text coord, textcoords???, horizontal alignment, vertical alignment, arrow settings
def construct_boxplot(self, alpha=.05, k_a=1):
"""
This function constructs phase boxplot for functional data using the elastic
square-root slope (srsf) framework.
:param alpha: quantile value (e.g.,=.05, i.e., 95\%)
:param k_a: scalar for outlier cutoff (e.g.,=1)
"""
if self.warp_data.rsamps:
gam = self.warp_data.gams
else:
gam = self.warp_data.gam
M, N = gam.shape
t = np.linspace(0, 1, M)
time = t
lam = 0.5
# compute phase median
median_x, psi_median, psi, vec = uf.SqrtMedian(gam)
# compute phase distances
dx = np.zeros(N)
v = np.zeros((M, N))
for k in range(0, N):
v[:, k], d = geo.inv_exp_map(psi_median, psi[:, k])
dx[k] = np.sqrt(trapz(v[:, k]**2, t))
dx_ordering = dx.argsort()
CR_50 = dx_ordering[0:np.ceil(N / 2).astype('int')]
tmp = dx[CR_50]
m = tmp.max()
# identify phase quartiles
angle = np.zeros((CR_50.shape[0], CR_50.shape[0]))
energy = np.zeros((CR_50.shape[0], CR_50.shape[0]))
for i in range(0, CR_50.shape[0] - 1):
for j in range(i + 1, CR_50.shape[0]):
q1 = v[:, CR_50[i]]
q3 = v[:, CR_50[j]]
q1 /= np.sqrt(trapz(q1**2, time))
q3 /= np.sqrt(trapz(q3**2, time))
angle[i, j] = trapz(q1 * q3, time)
energy[i, j] = (1 - lam) * (dx[CR_50[i]] / m + dx[CR_50[j]] /
m) - lam * (angle[i, j] + 1)
maxloc = energy.argmax()
maxloc_row, maxloc_col = np.unravel_index(maxloc, energy.shape)
Q1_index = CR_50[maxloc_row]
Q3_index = CR_50[maxloc_col]
Q1 = gam[:, Q1_index]
Q3 = gam[:, Q3_index]
Q1_psi = np.sqrt(np.gradient(Q1, 1 / (M - 1)))
Q3_psi = np.sqrt(np.gradient(Q3, 1 / (M - 1)))
# identify phase quantiles
dx_ordering = dx.argsort()
CR_alpha = dx_ordering[0:np.round(N * (1 - alpha)).astype('int')]
tmp = dx[CR_alpha]
m = tmp.max()
angle = np.zeros((CR_alpha.shape[0], CR_alpha.shape[0]))
energy = np.zeros((CR_alpha.shape[0], CR_alpha.shape[0]))
for i in range(0, CR_alpha.shape[0] - 1):
for j in range(i + 1, CR_alpha.shape[0]):
q1 = v[:, CR_alpha[i]]
q3 = v[:, CR_alpha[j]]
q1 /= np.sqrt(trapz(q1**2, time))
q3 /= np.sqrt(trapz(q3**2, time))
angle[i, j] = trapz(q1 * q3, time)
energy[i,
j] = (1 - lam) * (dx[CR_alpha[i]] / m + dx[CR_alpha[j]]
/ m) - lam * (angle[i, j] + 1)
maxloc = energy.argmax()
maxloc_row, maxloc_col = np.unravel_index(maxloc, energy.shape)
Q1a_index = CR_alpha[maxloc_row]
Q3a_index = CR_alpha[maxloc_col]
Q1a = gam[:, Q1a_index]
Q3a = gam[:, Q3a_index]
Q1a_psi = np.sqrt(np.gradient(Q1a, 1 / (M - 1)))
Q3a_psi = np.sqrt(np.gradient(Q3a, 1 / (M - 1)))
# check quartile and quantile going in same direction
tst = trapz(v[:, Q1a_index] * v[:, Q1_index])
if tst < 0:
Q1a = gam[:, Q3a_index]
Q3a = gam[:, Q1a_index]
# compute phase whiskers
IQR = dx[Q1_index] + dx[Q3_index]
v1 = v[:, Q3a_index]
v3 = v[:, Q3a_index]
upper_v = v3 + k_a * IQR * v3 / np.sqrt(trapz(v3**2, time))
lower_v = v1 + k_a * IQR * v1 / np.sqrt(trapz(v1**2, time))
upper_dis = np.sqrt(trapz(v3**2, time))
lower_dis = np.sqrt(trapz(v1**2, time))
whisker_dis = max(upper_dis, lower_dis)
# identify phase outliers
outlier_index = np.array([])
for i in range(0, N):
if dx[dx_ordering[N - 1 - i]] > whisker_dis:
outlier_index = np.append(outlier_index,
dx_ordering[N + 1 - i])
# identify phase extremes
distance_to_upper = np.full(N, np.inf)
distance_to_lower = np.full(N, np.inf)
out_50_CR = np.setdiff1d(np.arange(0, N), outlier_index)
for i in range(0, out_50_CR.shape[0]):
j = out_50_CR[i]
distance_to_upper[j] = np.sqrt(trapz((upper_v - v[:, j])**2, time))
distance_to_lower[j] = np.sqrt(trapz((lower_v - v[:, j])**2, time))
max_index = distance_to_upper.argmin()
min_index = distance_to_lower.argmin()
minn = gam[:, min_index]
maxx = gam[:, max_index]
min_psi = psi[:, min_index]
max_psi = psi[:, max_index]
s = np.linspace(0, 1, 100)
Fs2 = np.zeros((time.shape[0], 595))
Fs2[:, 0] = (1 - s[0]) * (minn - t) + s[0] * (Q1 - t)
for j in range(1, 100):
Fs2[:, j] = (1 - s[j]) * (minn - t) + s[j] * (Q1a - t)
Fs2[:, 98 + j] = (1 - s[j]) * (Q1a - t) + s[j] * (Q1 - t)
Fs2[:, 197 + j] = (1 - s[j]) * (Q1 - t) + s[j] * (median_x - t)
Fs2[:, 296 + j] = (1 - s[j]) * (median_x - t) + s[j] * (Q3 - t)
Fs2[:, 395 + j] = (1 - s[j]) * (Q3 - t) + s[j] * (Q3a - t)
Fs2[:, 494 + j] = (1 - s[j]) * (Q3a - t) + s[j] * (maxx - t)
d1 = np.sqrt(trapz(psi_median * Q1_psi, time))
d1a = np.sqrt(trapz(Q1_psi * Q1a_psi, time))
dl = np.sqrt(trapz(Q1a_psi * min_psi, time))
d3 = np.sqrt(trapz((psi_median * Q3_psi), time))
d3a = np.sqrt(trapz((Q3_psi * Q3a_psi), time))
du = np.sqrt(trapz((Q3a_psi * max_psi), time))
part1 = np.linspace(-d1 - d1a - dl, -d1 - d1a, 100)
part2 = np.linspace(-d1 - d1a, -d1, 100)
part3 = np.linspace(-d1, 0, 100)
part4 = np.linspace(0, d3, 100)
part5 = np.linspace(d3, d3 + d3a, 100)
part6 = np.linspace(d3 + d3a, d3 + d3a + du, 100)
allparts = np.hstack((part1, part2[1:100], part3[1:100], part4[1:100],
part5[1:100], part6[1:100]))
U, V = np.meshgrid(time, allparts)
U = np.transpose(U)
V = np.transpose(V)
self.Q1 = Q1
self.Q3 = Q3
self.Q1a = Q1a
self.Q3a = Q3a
self.minn = minn
self.maxx = maxx
self.outlier_index = outlier_index
self.median_x = median_x
self.psi_media = psi_median
plt = collections.namedtuple('plt', [
'U', 'V', 'Fs2', 'allparts', 'd1', 'd1a', 'dl', 'd3', 'd3a', 'du',
'Q1_psi', 'Q3_psi'
])
self.plt = plt(U, V, Fs2, allparts, d1, d1a, dl, d3, d3a, du, Q1a_psi,
Q3a_psi)
return
# In[21]:
sns.pairplot(data)
# In[90]:
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import numpy as np
x = np.random.randn(2000)
y = np.random.randn(2000)
plt([
go.Histogram2dContour(x=x, y=y, contours=dict(coloring='heatmap')),
go.Scatter(x=x, y=y, mode='markers', marker=dict(color='white', size=3))
],
show_link=False)
# In[5]:
import plotly.offline as offline
import plotly.graph_objs as go
offline.plot(
{
'data': [{
'y': [14, 22, 30, 44]
}],
'layout': {
'title': 'Offline Plotly',
'font': dict(size=16)
def construct_boxplot(self, alpha=.05, k_a=1):
"""
This function constructs the amplitude boxplot using the elastic
square-root slope (srsf) framework.
:param alpha: quantile value (e.g.,=.05, i.e., 95\%)
:param k_a: scalar for outlier cutoff (e.g.,=1)
"""
if self.warp_data.rsamps:
ft = self.warp_data.fs
f_median = self.warp_data.fmean
qt = self.warp_data.qs
q_median = self.warp_data.mqn
time = self.warp_data.time
else:
ft = self.warp_data.fn
f_median = self.warp_data.fmean
qt = self.warp_data.qn
q_median = self.warp_data.mqn
time = self.warp_data.time
N = ft.shape[1]
lam = 0.5
# compute amplitude distances
dy = np.zeros(N)
for i in range(0, N):
dy[i] = np.sqrt(trapz((q_median - qt[:, i])**2, time))
dy_ordering = dy.argsort()
CR_50 = dy_ordering[0:np.ceil(N / 2).astype('int')]
tmp = dy[CR_50]
m = tmp.max()
# identify amplitude quartiles
angle = np.zeros((CR_50.shape[0], CR_50.shape[0]))
energy = np.zeros((CR_50.shape[0], CR_50.shape[0]))
for i in range(0, CR_50.shape[0] - 1):
for j in range(i + 1, CR_50.shape[0]):
q1 = qt[:, CR_50[i]] - q_median
q3 = qt[:, CR_50[j]] - q_median
q1 = q1 / np.sqrt(trapz(q1**2, time))
q3 = q3 / np.sqrt(trapz(q3**2, time))
angle[i, j] = trapz(q1 * q3, time)
energy[i, j] = (1 - lam) * (dy[CR_50[i]] / m + dy[CR_50[j]] /
m) - lam * (angle[i, j] + 1)
maxloc = energy.argmax()
maxloc_row, maxloc_col = np.unravel_index(maxloc, energy.shape)
Q1_index = CR_50[maxloc_row]
Q3_index = CR_50[maxloc_col]
Q1_q = qt[:, Q1_index]
Q3_q = qt[:, Q3_index]
Q1 = ft[:, Q1_index]
Q3 = ft[:, Q3_index]
# identify amplitude quantiles
dy_ordering = dy.argsort()
CR_alpha = dy_ordering[0:np.round(N * (1 - alpha)).astype('int')]
tmp = dy[CR_alpha]
m = tmp.max()
angle = np.zeros((CR_alpha.shape[0], CR_alpha.shape[0]))
energy = np.zeros((CR_alpha.shape[0], CR_alpha.shape[0]))
for i in range(0, CR_alpha.shape[0] - 1):
for j in range(i + 1, CR_alpha.shape[0]):
q1 = qt[:, CR_alpha[i]] - q_median
q3 = qt[:, CR_alpha[j]] - q_median
q1 /= np.sqrt(trapz(q1**2, time))
q3 /= np.sqrt(trapz(q3**2, time))
angle[i, j] = trapz(q1 * q3, time)
energy[i,
j] = (1 - lam) * (dy[CR_alpha[i]] / m + dy[CR_alpha[j]]
/ m) - lam * (angle[i, j] + 1)
maxloc = energy.argmax()
maxloc_row, maxloc_col = np.unravel_index(maxloc, energy.shape)
Q1a_index = CR_alpha[maxloc_row]
Q3a_index = CR_alpha[maxloc_col]
Q1a_q = qt[:, Q1a_index]
Q3a_q = qt[:, Q3a_index]
Q1a = ft[:, Q1a_index]
Q3a = ft[:, Q3a_index]
# compute amplitude whiskers
IQR = dy[Q1_index] + dy[Q3_index]
v1 = Q1_q - q_median
v3 = Q3_q - q_median
upper_q = Q3_q + k_a * IQR * v3 / np.sqrt(trapz(v3**2, time))
lower_q = Q1_q + k_a * IQR * v1 / np.sqrt(trapz(v1**2, time))
upper_dis = np.sqrt(trapz((upper_q - q_median)**2, time))
lower_dis = np.sqrt(trapz((lower_q - q_median)**2, time))
whisker_dis = max(upper_dis, lower_dis)
# identify amplitude outliers
outlier_index = np.array([])
for i in range(0, N):
if dy[dy_ordering[N - i - 1]] > whisker_dis:
outlier_index = np.append(outlier_index,
dy[dy_ordering[N + 1 - i]])
# identify amplitude extremes
distance_to_upper = np.full(N, np.inf)
distance_to_lower = np.full(N, np.inf)
out_50_CR = np.setdiff1d(np.arange(0, N), outlier_index)
for i in range(0, out_50_CR.shape[0]):
j = out_50_CR[i]
distance_to_upper[j] = np.sqrt(trapz((upper_q - qt[:, j])**2,
time))
distance_to_lower[j] = np.sqrt(trapz((lower_q - qt[:, j])**2,
time))
max_index = distance_to_upper.argmin()
min_index = distance_to_lower.argmin()
min_q = qt[:, min_index]
max_q = qt[:, max_index]
minn = ft[:, min_index]
maxx = ft[:, max_index]
s = np.linspace(0, 1, 100)
Fs2 = np.zeros((time.shape[0], 595))
Fs2[:, 0] = (1 - s[0]) * minn + s[0] * Q1
for j in range(1, 100):
Fs2[:, j] = (1 - s[j]) * minn + s[j] * Q1a
Fs2[:, 98 + j] = (1 - s[j]) * Q1a + s[j] * Q1
Fs2[:, 197 + j] = (1 - s[j]) * Q1 + s[j] * f_median
Fs2[:, 296 + j] = (1 - s[j]) * f_median + s[j] * Q3
Fs2[:, 395 + j] = (1 - s[j]) * Q3 + s[j] * Q3a
Fs2[:, 494 + j] = (1 - s[j]) * Q3a + s[j] * maxx
d1 = np.sqrt(trapz((q_median - Q1_q)**2, time))
d1a = np.sqrt(trapz((Q1_q - Q1a_q)**2, time))
dl = np.sqrt(trapz((Q1a_q - min_q)**2, time))
d3 = np.sqrt(trapz((q_median - Q3_q)**2, time))
d3a = np.sqrt(trapz((Q3_q - Q3a_q)**2, time))
du = np.sqrt(trapz((Q3a_q - max_q)**2, time))
part1 = np.linspace(-d1 - d1a - dl, -d1 - d1a, 100)
part2 = np.linspace(-d1 - d1a, -d1, 100)
part3 = np.linspace(-d1, 0, 100)
part4 = np.linspace(0, d3, 100)
part5 = np.linspace(d3, d3 + d3a, 100)
part6 = np.linspace(d3 + d3a, d3 + d3a + du, 100)
allparts = np.hstack((part1, part2[1:100], part3[1:100], part4[1:100],
part5[1:100], part6[1:100]))
U, V = np.meshgrid(time, allparts)
U = np.transpose(U)
V = np.transpose(V)
self.Q1 = Q1
self.Q3 = Q3
self.Q1a = Q1a
self.Q3a = Q3a
self.minn = minn
self.maxx = maxx
self.outlier_index = outlier_index
self.f_median = f_median
self.q_median = q_median
plt = collections.namedtuple('plt', [
'U', 'V', 'Fs2', 'allparts', 'd1', 'd1a', 'dl', 'd3', 'd3a', 'du',
'Q1q', 'Q3q'
])
self.plt = plt(U, V, Fs2, allparts, d1, d1a, dl, d3, d3a, du, Q1a_q,
Q3a_q)
return