def plot(self, name='wikipgs'):
fraction = 1
width = 512
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all',
gridspec_kw={'hspace': 0.5})
#plt.plot(self.uncertainty, color='green', alpha=0.9)
#plt.plot(self.y50,alpha=0.8, label='ofat')
plt.plot(self.y_test, alpha=0.8, label='gt')
plt.plot(self.yqcnn, alpha=0.8, label='qcnn')
plt.plot(self.ycnn, alpha=0.8, label='cnn')
plt.plot(self.yqclnn, alpha=0.8, label='qconvl')
plt.plot(self.yqlstm[1], alpha=0.8, label='qlstm')
plt.plot(self.ylstm, alpha=0.6, label='lstm')
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
plt.legend(loc='upper right',
bbox_to_anchor=(1.01, 1.05),
ncol=3,
columnspacing=0.5)
plt.xlabel('Time (days)')
#print('{} smae: {} std: {}'.format(name, self.smae, self.std_s))
plt.ylabel('Daily page visits')
plt.tight_layout()
def _save(self, name='wiki1'):
'''
Parameters
----------
name : TYPE, optional
DESCRIPTION. The default is 'wiki1'.
Returns
-------
None.
'''
fraction = 1
width = 212
fig, ax = plt.subplots(figsize=set_size(width, fraction),
sharex='all',
gridspec_kw={'hspace': 0.5})
#plt.plot(self.y10, color='green', alpha=0.9)
plt.plot(self.y50, color='red', alpha=0.8, label='OFAT')
plt.plot(self.y_test, color='olive', alpha=0.8, label='ground truth')
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
plt.legend(loc='upper right', bbox_to_anchor=(1.0, 1.05), ncol=2)
plt.xlabel('Time (days)')
#print('{} smae: {} std: {}'.format(name, self.smae, self.std_s))
plt.ylabel('Weekly prices')
plt.tight_layout()
plt.savefig('../../ofat/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def partDescriptorSize(title=False, filename=None, show=False):
smallX, smallY = splitIntoXY(data.DATA_300x300["descriptorPartSize"])
mediumX, mediumY = splitIntoXY(data.DATA_640x480["descriptorPartSize"])
largeX, largeY = splitIntoXY(data.DATA_1280x720["descriptorPartSize"])
fig, (top, upper, middle, bottom) = plt.subplots(4, 1, sharex=True, figsize=PICTURE_SIZE)
if title:
bottom.set_title("Průměrná velikost deskriptoru části", fontsize="xx-large")
fig.text(0.04, 0.55, "Průměrná velikost deskriptoru části (v tisících)", va="center", rotation="vertical", fontsize="xx-large")
setupXAxis(bottom)
for axis in [top, bottom, middle, upper]:
for tick in axis.get_yticklabels():
tick.set_fontsize("x-large")
bottom.set_ylim(0, 8000)
middle.set_ylim(10000, 30000)
middle.xaxis.tick_top()
upper.set_ylim(40000, 60000)
upper.xaxis.tick_top()
top.set_ylim(100000, 104000)
top.xaxis.tick_top()
# up to 3 bars (sizes), 0.8 is default and leaves a little room between algorithms
barWidth = 0.8 / 3
hW = barWidth / 2
bottom.yaxis.set_major_formatter(FuncFormatter(SECOND_THOUSAND_FORMATTER))
middle.yaxis.set_major_formatter(FuncFormatter(SECOND_THOUSAND_FORMATTER))
upper.yaxis.set_major_formatter(FuncFormatter(SECOND_THOUSAND_FORMATTER))
top.yaxis.set_major_formatter(FuncFormatter(SECOND_THOUSAND_FORMATTER))
bottom.yaxis.set_major_locator(plt.LinearLocator(5))
middle.yaxis.set_major_locator(plt.LinearLocator(5))
upper.yaxis.set_major_locator(plt.LinearLocator(5))
top.yaxis.set_major_locator(plt.LinearLocator(5))
drawAcross([bottom, middle], smallX - 2 * hW, smallY, barWidth, color=pickColors(COLORS_300x300, smallX), edgecolor="black")
drawAcross([bottom, middle, upper], mediumX, mediumY, barWidth, color=pickColors(COLORS_640x480, mediumX), edgecolor="black")
drawAcross([bottom, middle, upper, top], largeX + 2 * hW, largeY, barWidth, color=pickColors(COLORS_1280x720, largeX), edgecolor="black")
middle.legend(loc="center left", bbox_to_anchor=(1.0125, 1), handles=GRAY_LEGEND, fontsize='large')
bottom.grid(True, axis="y")
middle.grid(True, axis="y")
upper.grid(True, axis="y")
top.grid(True, axis="y")
drawAxisSplitters(bottom, middle, upper, top)
topMargin = TOP_MARGIN_TITLE if title else TOP_MARGIN_NO_TITLE
plt.subplots_adjust(**transformMargins(top=topMargin, pictureSize=PICTURE_SIZE, **DEFAULT_MARGINS), hspace=0.25)
if filename:
plt.savefig(os.path.abspath(f"{OUTPUT_DIR}/{filename}"))
if show:
plt.show()
def plotuncertanty(self, name='preduncer'):
fraction = 0.5
width = 510
timex = 2000
y = np.concatenate((self.y_train[-timex:], self.yqcnn), axis=0)
#self.y_test[91]=9.06
fig, ax = plt.subplots(figsize=set_size(width, fraction), sharey='all')
#plt.plot(self.y_train[-timex:],'grey', label='fitted model')
plt.plot(y + 0.1, 'red', label='ground truth', alpha=0.9)
ax.fill_between(range(timex,
len(self.y_test) + timex),
self.yqcnnl,
self.yqcnnu - 0.5,
color='royalblue',
alpha=0.5,
label='predictive uncertainty')
plt.plot(range(timex,
len(self.y_test) + timex),
self.y_test + 0.1,
'orange',
label='fitted model',
alpha=0.8)
ax.axvline(x=timex,
ymin=0,
ymax=5,
color='grey',
ls='--',
lw=2,
alpha=0.7)
#plt.scatter(timex+91, self.y_test[91], marker='*', s=70, color='red', label='predicted extreme event')#extreme event
#ax.grid(True, zorder=5)
#plt.plot(self.y_test, color='olive', alpha=0.8, label ='ground truth')
#plt.plot(range(timex,len(self.y_test)+timex), self.yqlstm[0],color='grey',alpha=0.3)
#plt.plot(range(timex,len(self.y_test)+timex), self.yqlstm[2],color='grey',alpha=0.3)
plt.ylim([8.8, 10.2])
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
plt.legend(loc='upper right',
bbox_to_anchor=(0.75, 1.03),
ncol=1,
columnspacing=0.05)
ax.set_xlabel('Time ')
ax.set_ylabel(r'EDF stock price (\pounds)')
plt.tight_layout()
plt.savefig('../_8th/myphd/thesis_1/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def plot(input_folder, plot_point_count, points_per_pixel,
not_normalized_gradient):
_, gray, scene = read_input(input_folder)
pyramid = calc_pyramid(gray, PYRAMID_LVL_COUNT, 2)
center_point = scene.model.vector6
deltas = [calc_translation_delta(scene.mesh.vertices)] * 3 + \
[ANGLE_DELTA_IN_DEGREES] * 3
for image in pyramid:
integral_calculator = IntegralCalculator(image, scene,
points_per_pixel,
not not_normalized_gradient)
for axis in xrange(6):
limits = (center_point[axis] - deltas[axis],
center_point[axis] + deltas[axis])
plot_data = build_plot_data(integral_calculator, center_point,
axis, limits, plot_point_count)
plt.subplot(231 + axis)
plt.grid(True)
plt.xlabel(X_LABELS[axis], fontsize='x-large')
plt.xlim(*limits)
plt.gca().xaxis.set_major_locator(plt.LinearLocator(3))
plt.plot(*plot_data)
plt.tight_layout(pad=0.1, w_pad=0.1, h_pad=0.1)
plt.show()
def plot(bench, filename):
def function(x1, x2):
vec = bench.function()
return vec(2, [x1, x2])
plotN = 100
x1 = np.linspace(bench.Lower, bench.Upper, plotN)
x2 = np.linspace(bench.Lower, bench.Upper, plotN)
x1, x2 = np.meshgrid(x1, x2)
vecFunction = np.vectorize(function)
z = vecFunction(x1, x2)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x1,
x2,
z,
rstride=1,
cstride=1,
cmap=plt.cm.rainbow,
linewidth=0,
antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=7, cmap=plt.cm.coolwarm)
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.01f'))
plt.savefig(filename)
def plot_cost_function(cost_function, x_range, y_range, resolution, point1, point2):
x = np.arange(x_range[0], x_range[1], resolution)
y = np.arange(y_range[0], y_range[1], resolution)
X, Y = np.meshgrid(x, y)
Z = np.vectorize(cost_function)(X, Y)
fig = plt.figure()
fig.suptitle(r'Cost function with $\theta = 0$')
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.RdBu, linewidth=0, antialiased=False)
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.02f'))
ax.set_xlabel('x-position')
ax.set_ylabel('y-position')
ax.set_zlabel('cost function')
ax.view_init(elev=25, azim=-120)
fig.colorbar(surf, shrink=0.5, aspect=5)
# Plot 2D
fig = plt.figure()
im = plt.imshow(Z, cmap=plt.cm.RdBu)
cset = plt.contour(Z, [cost_function(point1[0], point1[1]), cost_function(point2[0], point2[1])],
linewidths=2, cmap=plt.cm.Set2)
plt.clabel(cset, inline=True, fmt='%1.1f', fontsize=10)
plt.colorbar(im)
def plot_HessD(fig, arr, subx1, suby2, subsize, extent, interpolation, title,
xlabel, ylabel):
cm = makecmap(arr)
#cm.set_gamma(0.2)
ax = fig.add_axes([subx1, suby2, subsize, subsize])
ax.imshow(arr,
cmap=cm,
aspect='auto',
extent=extent,
interpolation=interpolation)
ax.set_title(title, fontsize=11)
ax.set_xlabel(xlabel, fontsize=11)
ax.set_ylabel(ylabel, fontsize=11)
ax.set_xticklabels(ax.get_xticks(), size=11)
ax.set_yticklabels(ax.get_yticks(), size=11)
#cax = fig.add_axes([subx1+0.13, suby2+0.35, subsize-0.15, subsize-0.365])
cax = fig.add_axes(
[subx1 + 0.03, suby2 + 0.35, subsize - 0.20, subsize - 0.365])
cb_arr = np.linspace(arr.min(), arr.max(), 1e2).reshape(1, 1e2)
cax_limits = [np.floor(arr.min()), np.ceil(arr.max()), 0, 1]
cax.imshow(cb_arr,
cmap=cm,
aspect='auto',
extent=cax_limits,
interpolation='nearest')
cax.plot([arr.min(), arr.min()], [0, 1], 'k-')
cax.plot([arr.max(), arr.max()], [0, 1], 'w-')
cax.axis(cax_limits)
cax.yaxis.set_visible(False)
cax.xaxis.tick_bottom()
cax.xaxis.set_major_locator(plt.LinearLocator(5))
cax.set_xticklabels(cax.get_xticks(), size=8)
#cax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
return ax, cax
def plot_function_countur3D(min, max, action, episode):
x = np.arange(min, max, 0.025)
y = np.arange(min, max, 0.025)
# meshgrid create a matrix von min to max with 0.1 as distance
# the output is 2 matrix where one is the transport of the other one
X, Y = pyl.meshgrid(x, y)
#Z=z_function(X,np.transpose(X))
Z = action
print Y
#==========
# 3D
#==========
fig = plt.figure()
#plt.ion()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(Y,
X,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.RdBu,
linewidth=0,
antialiased=False)
ax.set_xlim([min, max])
ax.set_ylim([min, max])
ax.zaxis.set_major_locator(plt.LinearLocator(3))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.02f'))
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show(False)
plt.draw()
name_file = 'exp/episode3D' + str(episode) + '.png'
print name_file.__str__()
plt.savefig(name_file.__str__())
plt.close()
def plot_prediction(y, predictions, title):
total_days = [datetime.date(2021, 1, 1) + datetime.timedelta(days=int(i)) for i in range(int(y.shape[0]) + predictions.shape[0])]
if config['time'] >= config['update_data']:
today = str(datetime.date.today())
last_day = str(datetime.date.today() + datetime.timedelta(days=config['days_to_forecast']))
else:
today = str(datetime.date.today() - datetime.timedelta(1))
last_day = str(datetime.date.today() - datetime.timedelta(1) + datetime.timedelta(days=config['days_to_forecast']))
final_dates = []
for i in total_days:
i = str(i)
final_dates.append(i[5:])
y = np.array(y)
y = y.reshape((y.shape[0]), 1)
predictions = np.array(predictions)
predictions = predictions.reshape((predictions.shape[0]), 1)
series = np.concatenate([y, predictions], axis=0)
#old = load_predictions(title)
fig, ax = plt.subplots(figsize=(15, 8))
ax.plot(final_dates, series, label='Predicted cases')
ax.plot(y, color='red', label='Verified cases')
fig.autofmt_xdate()
plt.gca().xaxis.set_major_locator(plt.LinearLocator(numticks=30))
ax.axvspan(today[5:], last_day[5:], alpha=0.25)
plt.title(title)
plt.legend()
plt.grid()
plt.show()
fig.savefig(path_img + title.replace(" ", "") + ".png")
def plot_figs(fig_num, elev, azim, a):
fig = plt.figure(fig_num, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, elev=elev, azim=azim)
ax.scatter(body_mass, work_level, heat_output, c='k', marker='+')
X = np.arange(55, 85, 0.5)
Y = np.arange(90, 180, 0.5)
X, Y = np.meshgrid(X, Y)
Z = a[0] + a[1]*X + +(Y/(a[2]+a[3]*X))
ax.plot_surface(X, Y, Z,alpha=.5, antialiased=True,rstride=200, cstride=100, cmap=plt.cm.coolwarm)
ax.set_xlabel('BODY_MASS', color='b')
ax.set_ylabel('WORK_LEVEL', color='b')
ax.set_zlabel('HEAT_OUTPUT', color='b')
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.f'))
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.f'))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.f'))
def plot_HessD(fig, arr, subx1, suby2, subsize, extent, cmin, cmax,
interpolation, title, xlabel, ylabel, col_bool):
if col_bool:
cm = matplotlib.cm.jet #makecmap(arr)
nm = None
cmap_ofs = 0.
else:
cm = makecmap(arr)
nm = None
cmap_ofs = 0.05
#cm.set_gamma(0.2)
ax = fig.add_axes([subx1, suby2, subsize, subsize])
#Change cm to gist_yarg if you want grayscale
ax.imshow(arr,
cmap=cm,
aspect='auto',
extent=extent,
interpolation=interpolation,
norm=nm,
clim=(cmin, cmax))
ax.set_title(title, fontsize=8)
ax.set_xlabel(xlabel, fontsize=8)
ax.set_ylabel(ylabel, fontsize=8)
ax.get_xticks()
#ax.set_xticklabels(ax.get_xticks(), size=8)
#ax.set_yticklabels(ax.get_yticks(), size=8)
lowx = extent[0] * 0.9
highx = extent[1] * 0.7
#ax.set_xlim(np.array(extent[0])-lowx, np.array(extent[1])-highx)
ax.set_ylim(maxdepth + .1, mindepth + 1)
#ax.set_xlim(lowx, highx)
ax.set_xlim(minshift + .3, maxshift - 1)
#ax.set_xticks(np.array([-1, 0, 1, 2, 3]))
#ax.set_xticklabels(np.array([-1.0, 0, 1.0, 2.0, 3.0]), size = 8)
ax.set_yticklabels(ax.get_yticks(), size=8)
ax.set_xticklabels(ax.get_xticks(), size=8)
#cax = fig.add_axes([subx1+0.13, suby2+0.35, subsize-0.15, subsize-0.365])
cax = fig.add_axes([
subx1 + 0.07 + cmap_ofs, suby2 + 0.225, subsize - 0.10 - cmap_ofs,
subsize - 0.235
])
cb_arr = np.linspace(arr.min(), arr.max(), 100).reshape(1, 100)
cax_limits = [np.floor(arr.min()), np.ceil(arr.max()), 0, 1]
#Change cmap = cm to cmap = 'gist_yarg' if you want grayscale
cax.imshow(cb_arr,
cmap=cm,
aspect='auto',
extent=cax_limits,
interpolation='nearest',
norm=nm)
cax.plot([arr.min(), arr.min()], [0, 1], 'k-')
cax.plot([arr.max(), arr.max()], [0, 1], 'w-')
cax.axis(cax_limits)
cax.yaxis.set_visible(False)
cax.xaxis.tick_bottom()
cax.xaxis.set_major_locator(plt.LinearLocator(5))
cax.set_xticklabels(cax.get_xticks(), size=5)
#cax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
return ax, cax
def imageDescriptorTime(title=False, filename=None, show=False):
smallX, smallY = splitIntoXY(data.DATA_300x300["descriptorImage"])
mediumX, mediumY = splitIntoXY(data.DATA_640x480["descriptorImage"])
largeX, largeY = splitIntoXY(data.DATA_1280x720["descriptorImage"])
fig, (upper, bottom) = plt.subplots(2, 1, sharex=True, figsize=PICTURE_SIZE)
if title:
bottom.set_title("Délka výpočtu deskriptoru obrázku", fontsize="xx-large")
setupXAxis(bottom)
fig.text(0.04, 0.55, "Délka výpočtu deskriptoru obrázku [ms]", va="center", rotation="vertical", fontsize="xx-large")
for axis in [bottom, upper]:
for tick in axis.get_yticklabels():
tick.set_fontsize("x-large")
bottom.set_ylim(0, 90)
upper.set_ylim(100, 475)
upper.xaxis.tick_top()
# up to 3 bars (sizes), 0.8 is default and leaves a little room between algorithms
barWidth = 0.8 / 3
hW = barWidth / 2
bottom.yaxis.set_major_locator(plt.LinearLocator(7))
upper.yaxis.set_major_locator(plt.LinearLocator(6))
bottom.bar(smallX - 2 * hW, smallY, barWidth, color=pickColors(COLORS_300x300, smallX), edgecolor="black")
drawAcross([bottom, upper], mediumX, mediumY, barWidth, color=pickColors(COLORS_640x480, mediumX), edgecolor="black")
drawAcross([bottom, upper], largeX + 2 * hW, largeY, barWidth, color=pickColors(COLORS_1280x720, largeX), edgecolor="black")
bottom.legend(loc="center left", bbox_to_anchor=(1.0125, 1.02), handles=GRAY_LEGEND, fontsize='large')
bottom.grid(True, axis="y")
upper.grid(True, axis="y")
drawAxisSplitters(bottom, upper)
top = TOP_MARGIN_TITLE if title else TOP_MARGIN_NO_TITLE
plt.subplots_adjust(**transformMargins(top=top, pictureSize=PICTURE_SIZE, **DEFAULT_MARGINS), hspace=0.15)
if filename:
plt.savefig(os.path.abspath(f"{OUTPUT_DIR}/{filename}"))
if show:
plt.show()
def _generate_bar_chart_with_line_twinx(df, title, xaxis_rotation,
xaxis_df_name, xaxis_label,
yaxis1_df_name, yaxis2_df_name,
yaxis1_label, yaxis2_label):
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(19, 6))
# define the number of ticks
NUM_TICKS = 11
ax.yaxis.set_major_locator(plt.LinearLocator(numticks=NUM_TICKS))
ax.set_title(title)
ax2 = ax.twinx()
sns.barplot(ax=ax, x=xaxis_df_name, y=yaxis1_df_name, data=df)
sns.scatterplot(ax=ax2, x=xaxis_df_name, y=yaxis2_df_name, data=df)
ax.set_xticklabels(labels=df[xaxis_df_name], rotation=xaxis_rotation)
ax.set(xlabel=xaxis_label, ylabel=yaxis1_label)
vals = ax.get_yticks()
ax.set_yticklabels(
['${:,.0f}'.format(x).replace('$-', '-$') for x in vals])
ax2.set(ylabel=yaxis2_label)
ax2.set_yticks(
np.linspace(ax2.get_yticks()[0],
ax2.get_yticks()[-1], len(ax.get_yticks())))
ax2.yaxis.set_major_locator(plt.LinearLocator(numticks=NUM_TICKS))
i = 0
for line in df.index.values.tolist():
ax2.text(i + 0.1,
df[yaxis2_df_name][line],
df[yaxis2_df_name][line],
horizontalalignment='left',
size='medium',
color='black',
weight='bold')
i = i + 1
plt.show()
def plot_points(self, A: np.ndarray, size=(10, 10), title: str = None):
import matplotlib.pyplot as plt
plot_main_title_font = {
'family': 'sans serif',
'color': 'black',
'weight': 'bold',
'size': 18
}
fig, ax = plt.subplots(figsize=size)
# Set title.
if title != None:
ax.set_title(label=title,
loc='center',
fontdict=plot_main_title_font,
pad=20)
# Axis ticks.
ax.get_xaxis().set_ticks([0, A.shape[0]])
ax.get_yaxis().set_ticks([np.min(A), np.max(A)])
ax.get_xaxis().set_major_locator(plt.MultipleLocator(len(A)))
ax.get_xaxis().set_minor_locator(
plt.LinearLocator(numticks=len(A) // 500))
ax.get_yaxis().set_major_locator(plt.MultipleLocator(np.max(A)))
ax.get_yaxis().set_minor_locator(plt.LinearLocator(numticks=10))
ax.grid(b=True, which='both', axis='both')
ax.plot(A,
color='grey',
marker='.',
markerfacecolor='orange',
markersize=10,
alpha=0.5)
return ax
def scatterplot_fpi_results(frame, atlas, task, suffix=''):
frame = frame.sort_values(by='pi_avg',
axis=0,
ascending=True,
inplace=False)
dpi = 100
h = max(0.22 * len(frame.index), 14)
# h = max(0.20 * np.unique(frame.))
fig, ax = plt.subplots(figsize=(10, h), dpi=dpi)
sns.scatterplot('pi_avg',
frame.index,
ax=ax,
data=frame,
size=frame.pi_freq,
size_norm=(5**2, 20**2),
label='pi_avg',
markers='o')
sns.scatterplot('pi_high',
frame.index,
ax=ax,
data=frame,
label='pi_high',
markers='o')
ax.legend(title='Legend',
loc='lower right') # , markerscale=0.8)#, fontsize='xx-large')
ax.yaxis.set_major_locator(plt.LinearLocator())
if task == gbl.clf:
# ax.xaxis.set_major_locator(plt.MultipleLocator(base=0.05))
ax.xaxis.set_major_locator(plt.MaxNLocator())
elif task == gbl.reg:
ax.xaxis.set_major_locator(plt.MaxNLocator())
ax.autoscale(enable=True)
ax.set_aspect('auto')
plt.title('Feature Importance') # , fontsize='large')
plt.ylabel('feature') # , fontsize='large')
plt.xlabel('permutation importance') # , fontsize='large')
plt.yticks(frame.index,
weight='bold') # , rotation='vertical')#, fontsize='x-small')
# plt.tick_params(axis='x', width=2.0, grid_alpha=0.7)
plt.grid(True)
plt.tight_layout() # prevent clipping
plt.savefig('{}_{}_fpi{}.png'.format(atlas, task, suffix))
def plot_all_spectrum(data_dict):
for key, value in data_dict.items():
plt.figure()
x = value['wavelength']
y = value['intensity']
spectrum = value['spectrum']
x, y = cut_lower_bound(380, x, y)
if spectrum == 'Transmission Spectrum':
x, y = cut_lower_bound(420, x, y)
else:
h = (max(y)) * (0.6)
peaks, props = find_peaks(y, height=h, width=10, prominence=1)
x_peaks = []
y_peaks = []
for peak in peaks:
x_peaks.append(round(x[peak]))
y_peaks.append(round(y[peak]))
string = '(' + str(x_peaks) + ' , ' + str(y_peaks) + ')'
plt.text(x[peak] + 0.3, y[peak] + 0.3, string, fontsize=8)
plt.plot(x_peaks, y_peaks, 'x')
plt.legend(['peaks', 'signals'])
plt.plot(x, y)
#plt.semilogy(x,y)
plt.xlabel('wavelength (nm)')
plt.ylabel('intensity (counts)')
ax = plt.gca()
ax.xaxis.set_major_locator(ticker.AutoLocator())
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_locator(plt.LinearLocator(4))
#ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
if spectrum == 'Absorbance Spectrum':
plt.legend(['peaks', 'signals'])
fileName = './Result/' + key + '.png'
plt.title(key)
mp.savefig(fileName)
value['peaks'] = [x_peaks, y_peaks]
# print(x,y)
return 0
def plot(self, name='testing'):
'''
Parameters
----------
name : TYPE, optional
DESCRIPTION. The default is 'testing'.
Returns
-------
None.
'''
fraction = 0.5
width = 510
fig, ax = plt.subplots(figsize=set_size(width, fraction), sharex='all')
plt.plot(self.y50, 'red')
plt.plot(self.y_test, 'orange')
ax.fill_between(range(0, len(self.y50)),
self.y10,
self.y90,
color='grey',
alpha=0.5)
#ax.grid(True, zorder=5)
plt.plot(self.y50, color='red', alpha=0.8, label='OFAT')
plt.plot(self.y_test, color='olive', alpha=0.8, label='ground truth')
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f'))
ax.xaxis.set_major_locator(plt.LinearLocator(6))
ax.yaxis.set_major_locator(plt.LinearLocator(6))
#plt.legend(loc='upper right', bbox_to_anchor=(1.0, 1.05), ncol=2)
ax.set_xlabel('Time (weeks)')
ax.set_ylabel('Average price')
plt.tight_layout()
plt.savefig('../../ofat/figures/' + name + '.pdf',
format='pdf',
bbox_inches='tight')
def plot_HessD(fig, arr, subx1, suby2, subsize, extent, interpolation, title,
xlabel, ylabel, col_bool):
if col_bool:
cm = matplotlib.cm.jet #makecmap(arr)
nm = cl.SymLogNorm(vmin=-0.1,
vmax=arr.max(),
linthresh=0.03,
linscale=0.03)
cmap_ofs = 0.
else:
cm = makecmap(arr)
nm = None
cmap_ofs = 0.05
#cm.set_gamma(0.2)
ax = fig.add_axes([subx1, suby2, subsize, subsize])
ax.imshow(arr,
cmap=cm,
aspect='auto',
extent=extent,
interpolation=interpolation,
norm=nm)
ax.set_title(title, fontsize=8)
ax.set_xlabel(xlabel, fontsize=8)
ax.set_ylabel(ylabel, fontsize=8)
ax.set_xticklabels(ax.get_xticks(), size=8)
ax.set_yticklabels(ax.get_yticks(), size=8)
#cax = fig.add_axes([subx1+0.13, suby2+0.35, subsize-0.15, subsize-0.365])
cax = fig.add_axes([
subx1 + 0.07 + cmap_ofs, suby2 + 0.225, subsize - 0.10 - cmap_ofs,
subsize - 0.235
])
cb_arr = np.linspace(arr.min(), arr.max(), 1e2).reshape(1, 1e2)
cax_limits = [np.floor(arr.min()), np.ceil(arr.max()), 0, 1]
cax.imshow(cb_arr,
cmap=cm,
aspect='auto',
extent=cax_limits,
interpolation='nearest',
norm=nm)
cax.plot([arr.min(), arr.min()], [0, 1], 'k-')
cax.plot([arr.max(), arr.max()], [0, 1], 'w-')
cax.axis(cax_limits)
cax.yaxis.set_visible(False)
cax.xaxis.tick_bottom()
cax.xaxis.set_major_locator(plt.LinearLocator(5))
cax.set_xticklabels(cax.get_xticks(), size=5)
#cax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
return ax, cax
def year_avg_timeseries(df, height=1.5, aspect=1, col_wrap=None):
df2 = df.groupby(["site_no", df.lev_dt.dt.year]).lev_va.mean()
df2 = df2.reset_index()
g = sns.FacetGrid(df2,
col="site_no",
col_wrap=col_wrap,
height=height,
aspect=aspect,
sharex=False,
sharey=False)
g = g.map(plt.plot, "lev_dt", "lev_va", marker=".")
g.set_xticklabels(rotation=75)
for ax in g.axes.flatten():
ax.xaxis.set_major_locator(plt.LinearLocator(5))
plt.show()
def show_histogram_2d(x, y, z):
fig = plt.figure(figsize=(10, 7))
ax = fig.gca(projection='3d')
X, Y = np.meshgrid(x, y)
Z = z
surf = ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.coolwarm,
linewidth=0,
antialiased=False)
ax.set_zlim(0, np.max(Z))
#ax.set_zlim(0, 0.005)
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.06f'))
fig.colorbar(surf, shrink=0.5, aspect=7, cmap=plt.cm.coolwarm)
plt.show()
def plot3d(self, fig, ax, points=100):
x = np.linspace(self.Lower, self.Upper, points)
y = np.linspace(self.Lower, self.Upper, points)
X, Y = np.meshgrid(x, y)
solution = [X, Y]
Z = self.Fun(solution)
surf = ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
linewidth=0.1,
edgecolors='k',
alpha=0.5,
antialiased=True,
cmap=cm.RdPu_r,
zorder=1)
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.01f'))
def line_plot(results, figsize=None):
df = pd.DataFrame(results.records, columns=results.columns)
ax = df.plot(figsize=figsize, legend=False)
ax.set_ylim(-100, 100)
trend_name = results.metadata[1]['name']
ax.fill_between(df["Datestamp"], df[trend_name].tolist(), color='#eeefff')
ax.margins(0)
ax.xaxis.set_major_locator(plt.LinearLocator(3))
y_tick_labels = []
for y in ax.get_yticks():
if str(y) == "0.0":
y_tick_labels.append("baseline")
else:
y_tick_labels.append(str(y)[:-2] + "%")
ax.set_yticklabels(y_tick_labels)
plt.close()
return ax
def test_twin_axis_locaters_formatters():
vals = np.linspace(0, 1, num=5, endpoint=True)
locs = np.sin(np.pi * vals / 2.0)
majl = plt.FixedLocator(locs)
minl = plt.FixedLocator([0.1, 0.2, 0.3])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot([0.1, 100], [0, 1])
ax1.yaxis.set_major_locator(majl)
ax1.yaxis.set_minor_locator(minl)
ax1.yaxis.set_major_formatter(plt.FormatStrFormatter('%08.2lf'))
ax1.yaxis.set_minor_formatter(plt.FixedFormatter(['tricks', 'mind', 'jedi']))
ax1.xaxis.set_major_locator(plt.LinearLocator())
ax1.xaxis.set_minor_locator(plt.FixedLocator([15, 35, 55, 75]))
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%05.2lf'))
ax1.xaxis.set_minor_formatter(plt.FixedFormatter(['c', '3', 'p', 'o']))
ax2 = ax1.twiny()
ax3 = ax1.twinx()
def buildPlot(gMat):
fig = plt.figure(figsize=(16, 4))
ax1 = fig.add_subplot(121, projection='3d')
ax2 = fig.add_subplot(122)
X = np.arange(0, YDIM)
Y = np.arange(0, XDIM)
X, Y = np.meshgrid(X, Y)
surf = ax1.plot_surface(X,
Y,
gMat,
rstride=1,
cstride=1,
cmap=plt.cm.coolwarm,
linewidth=0,
antialiased=False)
ax1.set_zlim(-0.5, 1.5)
fig.colorbar(surf, shrink=0.5, aspect=5)
ax1.zaxis.set_major_locator(plt.LinearLocator(10))
ax1.zaxis.set_major_formatter(plt.FormatStrFormatter('%.02f'))
imgObj = ax2.imshow(gMat, vmin=0.0, vmax=1.0)
return fig, ax1, ax2, X, Y, surf, imgObj
def setup(self, axes, x):
self.axes = axes
self.net.setup()
self.keys = self.keys or self.net.keys
self.labels = []
for key in self.keys:
self.labels += [f'{key} RX', f'{key} TX']
self.ys = [common.none_array(x.size) for _ in self.labels]
self.lines = [
axes.plot(x, y, label=label)[0]
for label, y in zip(self.labels, self.ys)
]
axes.set_title('Network')
axes.set_ylabel('Network IO')
axes.set_xlim(x.min(), x.max())
axes.set_ylim(0, self.ylim)
axes.tick_params('x', bottom=False, labelbottom=False)
axes.grid(True, axis='y')
axes.legend()
axes.yaxis.set_major_locator(plt.LinearLocator(5))
axes.yaxis.set_major_formatter(plt.FuncFormatter(self._format_ylabel))
return self.lines
def plot_x_y(x_values, y_values, x_name, y_name, graph_name, line_type,
**kwargs):
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
# plt.suptitle(graph_name, y=1.0, fontsize=17)
#
# plt.xlabel(x_name, fontsize=16)
# plt.ylabel(y_name, fontsize=16)
plt.suptitle(graph_name)
plt.xlabel(x_name)
plt.ylabel(y_name)
# plt.xlim(min(x_values) - 0.1 * min(x_values),
# max(x_values) + 0.1 * max(x_values))
# plt.ylim(min(y_values), max(y_values) + 0.1 * max(y_values))
# plt.ylim(min(y_values), max(y_values) + 0.05 * max(y_values))
plt.ticklabel_format(axis="x", style="plain")
ax = plt.gca()
plt.plot(x_values, y_values, line_type, **kwargs)
ax.yaxis.set_major_locator(plt.LinearLocator(numticks=6))
sns.set(style="white", palette="Set2")
# plot Adjusted R2 against mu and d
fig = plt.figure(figsize=(10, 7))
ax = fig.gca(projection='3d')
x = np.arange(-5., 5., 0.3)
y = np.arange(-5., 5., 0.3)
X, Y = np.meshgrid(x, y)
Z = np.maximum(X, Y)
surf = ax.plot_wireframe(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.coolwarm,
linewidth=0.5,
antialiased=True)
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('MaxPool(X1, X2)')
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.02f'))
ax.vieW_init(30, -100)
# fig.colorbar(surf, shrink=0.5, aspect=7, cmap=plt.cm.coolwarm)
# matplotlib.rcParams['lines.linewidth'] = 2
plt.tight_layout()
# plt.show()
plt.savefig("./maxpool.pdf")
df['hm'] = df['heure minutes'].apply(lambda x: int(100 * int(
x.split(':')[0]) + (5 / 3) * int(x.split(':')[1])))
#save file for use in ML algorithm UNCOMMENT BELOW IF NEVER RUN BEFORE
# df.to_csv(r'begles_3month_Data.csv',sep = ';')
#%% plots
#start with 4*4 grid for initial view
fig_sig, ax_sig = plt.subplots(2, 2, figsize=(18, 10))
plt.suptitle('Données 01/09 - 20/12 2018')
ax_sig[0, 0].plot(df_original)
ax_sig[0, 0].set_title('Courbe de Charge')
ax_sig[0, 0].set_ylabel('P / kW', rotation=90)
ax_sig[0, 0].xaxis.set_major_locator(plt.LinearLocator(4))
ax_sig[0, 1].plot(df.groupby('heure minutes').mean()['PS ATTEINTE'])
ax_sig[0, 1].set_title('PS ATTEINTE Moyenne Journalière')
ax_sig[0, 1].set_ylabel('P / kW', rotation=90)
ax_sig[0, 1].xaxis.set_major_locator(plt.LinearLocator(9))
ax_sig[1, 0].plot(df.groupby('weekday').mean()['PS ATTEINTE'])
ax_sig[1, 0].set_title('PS ATTEINTE Moyenne Semaine')
ax_sig[1, 0].set_ylabel('P / kW', rotation=90)
ax_sig[1, 0].set_xlabel('Jour de la Semaine')
ax_sig[1, 1].scatter(df['Temperature'], df['PS ATTEINTE'])
ax_sig[1, 1].set_title('Distribution PS ATTEINTE - Temperature')
ax_sig[1, 1].set_ylabel('P / kW', rotation=90)
print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))
# Explained variance score: 1 is perfect prediction
print("Variance score: %.2f" % r2_score(y_test, y_pred))
"""
plot actual high and predict high from 2018-02-01 to 2018-02-28
"""
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
fig, ax = plt.subplots()
predictions = reg.predict(scaler.transform(data[:cut_point]))
ax.plot(dates[:cut_point].astype('O'), data[:cut_point, 1], 'b-',
dates[:cut_point].astype('O'), predictions, 'r-')
blue_patch = mpatches.Patch(color='blue', label='actual')
red_patch = mpatches.Patch(color='red', label='predict')
plt.legend(handles=[blue_patch, red_patch])
# format tricker
import matplotlib.dates as mdates
ax.xaxis.set_major_locator(mdates.DayLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
ax.yaxis.set_major_locator(plt.LinearLocator())
plt.show()
