def plot_sentiment(df_sentiment):
""" ---------------------------------------------------------
CREDITS TO:
http://stackoverflow.com/questions/31313606/pyplot-matplotlib
-bar-chart-with-fill-color-depending-on-value
------------------------------------------------------------
This function plot the sentiment analysis for the country.
It takes as input:
@df_sentiment"""
# Set up colors : red to green
plt.figure(figsize=(17, 6))
y = np.array(df_sentiment['sentiment'])
colors = cm.YlGn(y / float(max(y)))
plot = plt.scatter(y, y, c=y, cmap='YlGn')
plt.clf()
clb = plt.colorbar(plot)
clb.ax.set_title("Sentiment")
# Display bar plot : country frequency vs. country name, with color indicating polarity score
plt.bar(range(df_sentiment.shape[0]),
df_sentiment['mention'],
tick_label=df_sentiment.index,
color=colors)
plt.xticks(rotation=45, ha='right')
plt.xlabel("Country")
plt.ylabel("Mentions")
plt.title('Sentiment analysis per country', fontsize=15)
plt.show()
def plot_tangent_one_file(bspline_course, tangent_bspline, normal_bspline,
bspline_course_tck_3, tangent_bspline_tck_3,
normal_bspline_tck_3, bspline_course_tck_5,
tangent_bspline_tck_5, normal_bspline_tck_5):
greens = cm.YlGn(np.linspace(0, 1, 10))
reds = cm.YlOrRd(np.linspace(0, 1, 10))
pyplot.figure(figsize=(8, 7))
pyplot.ylabel('x-axis component of the vector', fontsize=12)
pyplot.xlabel('x(m)', fontsize=12)
pyplot.plot(bspline_course_tck_3.x,
tangent_bspline_tck_3.x,
'-',
color=reds[9],
label='Tangent 3rd Degree B-spline')
pyplot.plot(bspline_course_tck_5.x,
tangent_bspline_tck_5.x,
'--',
color=reds[6],
label='Tangent 5th Degree B-spline')
pyplot.plot(bspline_course.x,
tangent_bspline.x,
'-',
color=reds[3],
label='Tangent Bezier curve')
pyplot.plot(bspline_course_tck_3.x,
normal_bspline_tck_3.x,
'-',
color=greens[9],
label='Normal 3rd Degree B-spline')
pyplot.plot(bspline_course_tck_5.x,
normal_bspline_tck_5.x,
'--',
color=greens[6],
label='Normal 5th Degree B-spline')
pyplot.plot(bspline_course.x,
normal_bspline.x,
'-',
color=greens[3],
label='Normal Bezier curve')
pyplot.legend(loc='upper right',
bbox_to_anchor=(2, 1.0),
shadow=False,
ncol=2,
fontsize=12)
pyplot.show()
def get_cmaps_biasCNN():
"""
Create color maps
"""
nsteps = 8
colors_all_1 = np.moveaxis(
np.expand_dims(cm.Greys(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_2 = np.moveaxis(
np.expand_dims(cm.GnBu(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_3 = np.moveaxis(
np.expand_dims(cm.YlGn(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_4 = np.moveaxis(
np.expand_dims(cm.OrRd(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all = np.concatenate((colors_all_1[np.arange(2, nsteps, 1), :, :],
colors_all_2[np.arange(2, nsteps, 1), :, :],
colors_all_3[np.arange(2, nsteps, 1), :, :],
colors_all_4[np.arange(2, nsteps, 1), :, :],
colors_all_2[np.arange(2, nsteps, 1), :, :]),
axis=1)
int_inds = [3, 3, 3, 3]
colors_main = np.asarray(
[colors_all[int_inds[ii], ii, :] for ii in range(np.size(int_inds))])
colors_main = np.concatenate((colors_main, colors_all[5, 1:2, :]), axis=0)
# plot the color map
#plt.figure();plt.imshow(np.expand_dims(colors_main,axis=0))
colors_sf = np.moveaxis(
np.expand_dims(cm.GnBu(np.linspace(0, 1, 8)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_sf = colors_sf[np.arange(2, 8, 1), :, :]
return colors_main, colors_sf
def plot(data, fname='capacity-factors-usa.png'):
labels, values = zip(*sorted(data['val'].items()))
labels = [label.capitalize() for label in labels]
values = np.array(values)
width = 0.35
index = np.arange(len(labels))
fig, ax = plt.subplots()
colors = cm.YlGn(values / 100.0)
bars = ax.bar(index, values, width, color=colors)
plt.title(data['title'])
ax.set_ylabel('Capacity factor (%)')
ax.set_xticks(index)
ax.set_xticklabels(labels)
ax.set_yticks(np.arange(0, 101, 10))
ax.grid(color='0.7', linestyle='--', axis='y')
plt.xticks(rotation=90)
plt.tight_layout()
if fname:
plt.savefig(fname)
else:
plt.show()
def plot_smoothing_tangent_normal(course):
arc_length = compute_arc_length(course)
#d_hz = arc_length / len(course.x)
d_hz = arc_length / 1000
smoothing = 0.0
b1 = bspline3Dtck(course, 3, smoothing, d_hz)
db1 = deriv_bspline3Dtck(1, course, 3, smoothing, d_hz)
tangent_b1, normal_b1, binormal_b1 = getParallelTransportVectors(db1)
b1x, error1 = compute_position_error(course, b1)
smoothing = 0.0000001
b2 = bspline3Dtck(course, 3, smoothing, d_hz)
db2 = deriv_bspline3Dtck(1, course, 3, smoothing, d_hz)
tangent_b2, normal_b2, binormal_b2 = getParallelTransportVectors(db2)
b2x, error2 = compute_position_error(course, b2)
smoothing = 0.0000003
b3 = bspline3Dtck(course, 3, smoothing, d_hz)
db3 = deriv_bspline3Dtck(1, course, 3, smoothing, d_hz)
tangent_b3, normal_b3, binormal_b3 = getParallelTransportVectors(db3)
b3x, error3 = compute_position_error(course, b3)
smoothing = 0.0000006
b4 = bspline3Dtck(course, 3, smoothing, d_hz)
db4 = deriv_bspline3Dtck(1, course, 3, smoothing, d_hz)
tangent_b4, normal_b4, binormal_b4 = getParallelTransportVectors(db4)
b4x, error4 = compute_position_error(course, b4)
smoothing = 0.0000012
b5 = bspline3Dtck(course, 3, smoothing, d_hz)
db5 = deriv_bspline3Dtck(1, course, 3, smoothing, d_hz)
tangent_b5, normal_b5, binormal_b5 = getParallelTransportVectors(db5)
b5x, error5 = compute_position_error(course, b5)
greens = cm.YlGn(np.linspace(0, 1, 10))
reds = cm.YlOrRd(np.linspace(0, 1, 10))
pyplot.figure(figsize=(8, 7))
pyplot.ylabel('Absolute position error (mm)', fontsize=12)
pyplot.xlabel('x(m)', fontsize=12)
#pyplot.plot(b1x, error1,'r-', label='s=0')
pyplot.plot(b2x, error2, 'r--', label='s=1E-7')
pyplot.plot(b3x, error3, 'y-', label='s=3E-7')
pyplot.plot(b4x, error4, 'g--', label='s=6E-7')
pyplot.plot(b5x, error5, 'b-', label='s=12E-7')
pyplot.legend(loc='upper right',
bbox_to_anchor=(1.0, 1.0),
shadow=False,
ncol=1,
fontsize=12)
pyplot.show()
pyplot.figure(figsize=(8, 7))
pyplot.ylabel('y(m)', fontsize=12)
pyplot.xlabel('x(m)', fontsize=12)
pyplot.plot(b2.x, b2.y, 'r--', label='s=1E-7')
pyplot.plot(b3.x, b3.y, 'y-', label='s=3E-7')
pyplot.plot(b4.x, b4.y, 'g--', label='s=6E-7')
pyplot.plot(b5.x, b5.y, 'b-', label='s=12E-7')
pyplot.plot(course.x,
course.y,
'k*',
markersize=8,
label='Tow Course received')
pyplot.legend(loc='lower right',
bbox_to_anchor=(1.0, 0.00),
shadow=False,
ncol=1,
fontsize=12)
pyplot.show()
pyplot.figure(figsize=(8, 7))
pyplot.ylabel('x-axis component of the vector', fontsize=12)
pyplot.xlabel('x(m)', fontsize=12)
pyplot.plot(b1.x, tangent_b1.x, '-', color=reds[9], label='Tangent s=0')
pyplot.plot(b2.x,
tangent_b2.x,
'--',
color=reds[8],
label='Tangent s=1E-7')
pyplot.plot(b3.x, tangent_b3.x, '-', color=reds[7], label='Tangent s=3E-7')
pyplot.plot(b4.x,
tangent_b4.x,
'--',
color=reds[6],
label='Tangent s=6E-7')
pyplot.plot(b5.x,
tangent_b5.x,
'-',
color=reds[3],
label='Tangent s=12E-7')
pyplot.plot(b1.x, normal_b1.x, '-', color=greens[9], label='Normal s=0')
pyplot.plot(b2.x,
normal_b2.x,
'--',
color=greens[8],
label='Normal s=1E-7')
pyplot.plot(b3.x, normal_b3.x, '-', color=greens[7], label='Normal s=3E-7')
pyplot.plot(b4.x,
normal_b4.x,
'--',
color=greens[6],
label='Normal s=6E-7')
pyplot.plot(b5.x,
normal_b5.x,
'-',
color=greens[3],
label='Normal s=12E-7')
pyplot.legend(loc='upper right',
bbox_to_anchor=(1.7, 1.0),
shadow=False,
ncol=2,
fontsize=12)
pyplot.show()
def update_tree_values(self):
"""
Update the tree created in add_tree_node with values of Nsa, Ns, Ps, Qsa and Usa. Colour each of the nodes
with a colour representing their values.
Note that nodes on the tree are represented as (s_t, a_(t-1), p_t) which means that when calling Qsa, Usa
or Nsa (ones involving a) we need to use (s_(t-1), a_(t-1), p_(t-1)), as opposed to Ns and Ps, where we use
(s_t, p_t).
"""
tree_itr = self.tree.traverse()
root = next(tree_itr) # skip the first two nodes the root and the root's root (not sure why there's 2?)
root_face = TextFace(u'(s\u209C, a\u209C\u208B\u2081, agent\u209C) = ({}, {}, {}, Ns={})'.format(
[float(dim) / g_accuracy for dim in root.name],
root.action, root.agent, self.Ns[root.name]))
root_face.background.color = '#FF0000'
root.add_face(root_face, column=0, position="branch-top")
for node in tree_itr:
s, a, agent = node.up.name, node.action, node.up.agent
p_from_a = node.agent
state = [float(dim) / g_accuracy for dim in node.name]
# note that (s, a) is the same as node.name (since parent state & action taken from there = child state)
try:
delattr(node, "_faces") # need to remove previously added faces otherwise they stack
except:
pass
_, v = self.nnet.predict(np.array(state), p_from_a)
# -------- ADD ANNOTATION FOR STATE LOSS AND ACTION TAKEN ----------
loss = self.env.state_loss(state=state)
# unicode to get subscripts
loss_face = TextFace(u'(x\u209C, u\u209C\u208B\u2081, agent\u209C) = ({0}, {1}, {2}), c(x\u209C) = {3:.3f}, v_pred = {4:.3f}'.format(self.env.round_state(state=state), a, p_from_a, loss, v))
c_loss = cm.viridis(255+int(loss*255)) # viridis goes from 0-255
c_loss = "#{0:02x}{1:02x}{2:02x}".format(*[int(round(i * 255)) for i in [c_loss[0], c_loss[1], c_loss[2]]])
loss_face.background.color = c_loss # need rgb colour in hex, "#FFFFFF"=(255, 255, 255)
node.add_face(loss_face, column=0, position="branch-top")
# -------- ADD ANNOTATION FOR ACTION VALUE WRT agent, Q --------
#print("s={}, a={}, agent={}".format(s, a, agent))
if (s, a) in self.Qsa:
#print(self.Ns[s])
#print(self.Nsa[(s, a)])
#print(self.Ps[s][a])
q = self.Qsa[(s, a)] if agent == 0 else -self.Qsa[(s, a)]
ucb = self.args.cpuct * self.Ps[s][a]*np.sqrt(self.Ns[s])/(1+self.Nsa[(s, a)])
u = self.Usa[(s, a)]
else:
q = 0
ucb = self.args.cpuct * self.Ps[node.name][a]*np.sqrt(self.Ns[s] + EPS)
u = ucb
q_formula = '(Q(x))' if agent == 0 else '(-Q(x))'
QA_face = TextFace("U\u209C = {:.3f}{} + {:.3f}(ucb) = {:.3f}".format(q, q_formula, ucb, u))
c_value = cm.viridis(255+int((q+ucb)*255)) # plasma goes from 0-255
c_value = "#{0:02x}{1:02x}{2:02x}".format(
*[int(round(i * 255)) for i in [c_value[0], c_value[1], c_value[2]]])
QA_face.background.color = c_value
node.add_face(QA_face, column=0, position="branch-bottom")
# -------- ADD ANNOTATION FOR NUMBER OF VISITS -------
# have to use node.name for printing Ns
ns = 0 if node.name not in self.Ns else self.Ns[node.name]
N_face = TextFace(" Nsa(x\u209C\u208B\u2081, u\u209C)={}, Ns(x)={}".format(self.Nsa[(s, a)], ns))
c_vis = cm.YlGn(int(255*(1-self.Nsa[(s, a)]/(self.args.numMCTSSims*2)))) # YlGn goes from 0-255
c_vis = "#{0:02x}{1:02x}{2:02x}".format(*[int(round(i * 255)) for i in [c_vis[0], c_vis[1], c_vis[2]]])
N_face.background.color = c_vis
node.add_face(N_face, column=1, position="branch-bottom")
freq = [0.0] * 10
#print (bins)
for idx in range(len(freq_list)):
bins_ = bins_list[idx]
freq_ = freq_list[idx]
for i in range(len(bins_)):
if (bins_[i] <= bins[i + 1] and bins_[i] >= bins[i]):
freq[i] += freq_[i]
repr_bins = [] # 10 bins for plotting, take average of each period
for i in range(len(bins) - 1):
repr_bins.append((bins[i] + bins[i + 1]) / 2.0)
return repr_bins, freq
colors = cm.YlGn(np.linspace(0, 1, 25))
a = np.loadtxt("act_d_bins.txt")
b = np.loadtxt("act_d_freq.txt")
a = a[:15640]
b = b[:15640]
a = a[:, :
-1] # temporary remove last point of bins list. match dimension for plotting.
print("weight shapes")
print(a.shape)
print(b.shape)
a = np.array(np.vsplit(a, 20))
b = np.array(np.vsplit(b, 20))
bins_g_1 = []
freq_g_1 = []
for i in range(25):
bins,freq = merg_hist(epochs_d_1_1[i], epochs_d_1_2[i] )
bins_d_1.append(bins)
freq_d_1.append(freq)
bins,freq = merg_hist(epochs_d_2_1[i], epochs_d_2_2[i] )
bins_d_2.append(bins)
freq_d_2.append(freq)
bins,freq = merg_hist(epochs_g_1_1[i], epochs_g_1_2[i] )
bins_g_1.append(bins)
freq_g_1.append(freq)
#print (bins_d_1)
print ("plotting d1 ")
colors = cm.YlGn(np.linspace(0, 1, len(bins_d_1)))
for i in range(25):
if (i<=2 or i >= 22):
plt.plot(bins_d_1[i], freq_d_1[i],marker="+", color=colors[i], label="epoch_"+str(i))
else :
plt.plot(bins_d_1[i], freq_d_1[i],marker="+", color=colors[i])
plt.legend(loc="upper left", ncol=1)
plt.savefig ("d_1.pdf")
plt.clf()
print ("plotting d2 ")
colors = cm.YlGn(np.linspace(0, 1, len(bins_d_2)))
for i in range(25):
if (i<=2 or i >= 22):
plt.plot(bins_d_2[i], freq_d_2[i],marker="+", color=colors[i], label="epoch_"+str(i))
else :