def cv_bp(cv_df, title, axes):
axes.grid(b=True,
which='both',
axis='both',
color='grey',
linestyle='--',
linewidth='0.3')
sns.boxplot(x='model_name',
y='roc_auc',
data=cv_df,
width=0.5,
ax=axes,
palette=current_palette).set_title(title)
sns.stripplot(x='model_name',
y='roc_auc',
data=cv_df,
size=5,
jitter=True,
edgecolor="grey",
linewidth=1,
ax=axes)
plt.ylim(0.2, 1)
plt.savefig('{}.png'.format(title), format='png')
def plot_graph(train_history, label_col, mode):
# Obtain scores from history
loss = train_history.history['loss'] #List
val_loss = train_history.history['val_loss']
#Check if binary or multiclass problem to obtain correct metrics
if mode == 0:
acc = train_history.history['binary_accuracy']
val_acc = train_history.history['val_binary_accuracy']
else:
acc = train_history.history['categorical_accuracy']
val_acc = train_history.history['val_categorical_accuracy']
# Plot loss scores
sns.set_style("whitegrid")
fig, ax = plt.subplots(1, 1)
ax.plot(loss, label = "Loss")
ax.plot(val_loss, label = "Validation Loss")
ax.set_title('Model Loss')
ax.legend(loc = "upper right")
ax.set_xlim([0, 100])
ax.set_ylabel("Loss")
ax.set_xlabel("Epochs")
ax.minorticks_on()
ax.grid(b=True, which='major')
ax.grid(b=True, which='minor')
plt.savefig(results_dir + '/' + label_col + '_loss.png')
plt.show()
# Plot accuracy scores
fig, ax = plt.subplots(1, 1)
ax.plot(acc, label = "Accuracy")
ax.plot(val_acc, label = "Validation Accuracy")
ax.set_title('Model Accuracy')
ax.legend(loc = "lower right")
ax.set_xlim([0, 100])
ax.grid(b=True, which='major')
ax.grid(b=True, which='minor')
ax.set_ylabel("Accuracy")
ax.set_xlabel("Epochs")
ax.minorticks_on()
plt.savefig(results_dir + '/' + label_col + '_acc.png')
plt.show()
return 0
if len(resY3):
plt.plot(resx3, resY3)
if len(resY4):
plt.plot(resx4, resY4)
(supX1, supY1, supX2, supY2, supX3, supY3, supX4, supY4,
supSlope) = computeSupportLines(low, timestamp)
plt.plot(supX1, supY1)
plt.plot(supX2, supY2)
if len(supY3):
plt.plot(supX3, supY3)
if len(supY4):
plt.plot(supX4, supY4)
sys, resYs = [supY1, supY3, supY4], [resY1, resY3, resY4]
(supYmVal, ascDescVal, riseFallVal,
avgTouches) = detectTriangle([supY1, supY3, supY4], [resY1, resY3, resY4],
high, low)
mins, maxs = computePivotPoints(high, low, timestamp)
for x, y in maxs:
ax.plot(x, y)
for x, y in mins:
ax.plot(x, y)
ax.xaxis.set_major_locator(mticker.MaxNLocator(10))
plt.ylim(ymin=min(low), ymax=max(high) + max(high) * 0.05)
ax.grid(True)
plt.yscale('log')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title(args.symbol)
plt.show()
def run(data):
# update the data
t,y = data
if t>-1:
xdata.append(t)
ydata.append(y)
#if t>xsize: # Scroll to the left.
# ax.set_xlim(t-xsize, t)
line.set_data(xdata, ydata)
return line,
def on_close_figure(event):
sys.exit(0)
data_gen.t = -1
fig = plt.figure()
fig.canvas.mpl_connect('close_event', on_close_figure)
ax = fig.add_subplot(111)
line, = ax.plot([], linestyle='-.', lw=7, color = 'red')
ax.set_ylim(0, 250)
ax.set_xlim(0, 400)
ax.grid()
xdata, ydata = [], []
# Important: Although blit=True makes graphing faster, we need blit=False to prevent
# spurious lines to appear when resizing the stripchart.
ani = animation.FuncAnimation(fig, run, data_gen, blit=False, interval=100, repeat=False)
plt.show()
def makeClocPlot(info_list, name):
data = []
code_lines = []
comment_lines = []
comment_percentage_lines = []
comment_percentage_lines_max = []
comment_percentage_lines_min = []
debug_lines = []
debug_percentage_lines = []
debug_percentage_lines_max = []
debug_percentage_lines_min = []
#[print(cloc[x][0]) for x in range(0,5)]
try:
[
data.append(DT.datetime.strptime(info_list[index][0], "%Y_%m_%d"))
for index in range(0, n_cloc)
]
#[print(cloc[x][2]) for x in range(0,5)]
except Exception as ex:
print(ex)
[code_lines.append(info_list[index][2]) for index in range(0, n_cloc)]
[comment_lines.append(info_list[index][3]) for index in range(0, n_cloc)]
[
comment_percentage_lines.append(info_list[index][5])
for index in range(0, n_cloc)
]
[
comment_percentage_lines_max.append(info_list[index][6])
for index in range(0, n_cloc)
]
[
comment_percentage_lines_min.append(info_list[index][8])
for index in range(0, n_cloc)
]
[debug_lines.append(info_list[index][4]) for index in range(0, n_cloc)]
[
debug_percentage_lines.append(info_list[index][10])
for index in range(0, n_cloc)
]
[
debug_percentage_lines_max.append(info_list[index][11])
for index in range(0, n_cloc)
]
[
debug_percentage_lines_min.append(info_list[index][13])
for index in range(0, n_cloc)
]
lines = []
labels_fig = []
#dates = mpdates.date2num(data)
dates = []
[dates.append(index) for index in range(0, n_cloc)]
gs_top = plt.GridSpec(5, 1, top=0.95)
fig, ((ax1, ax4), (ax2, ax5),
(ax3, ax6)) = plt.subplots(3, 2, sharex=True,
facecolor=light_grey_c) #, sharey=True)
ax1.plot_date(dates,
code_lines,
'r-',
label="Code lines",
lw=2,
color=red_c)
ax1.set_title("Code lines:", weight="bold")
ax2.plot_date(dates,
comment_lines,
'g-',
label="Comment lines",
lw=2,
color=yell_c)
ax2.set_title("Comment lines:", weight="bold")
ax3.plot_date(dates,
comment_percentage_lines,
'b-',
label="Comment % lines",
color=blue_c)
ax3.plot_date(dates,
comment_percentage_lines_max,
'b--',
label="Comment % lines (max)",
color=blue_c)
ax3.plot_date(dates,
comment_percentage_lines_min,
'b--',
label="Comment % lines (min)",
color=blue_c)
ax4.axis('off')
ax5.plot_date(dates,
debug_lines,
'm-',
label="Debug lines",
lw=2,
color=grey_c)
ax5.set_title("Debug lines:", weight="bold")
ax6.plot_date(dates,
debug_percentage_lines,
'c-',
label="Debug % lines",
color=green_c)
ax6.plot_date(dates,
debug_percentage_lines_max,
'c--',
label="Debug % lines (max)",
color=green_c)
ax6.plot_date(dates,
debug_percentage_lines_min,
'c--',
label="Debug % lines (min)",
color=green_c)
handles_all = []
labels_all = []
for ax in ax1, ax2, ax3, ax4, ax5, ax6:
ax.grid(True)
ax.margins(0.08) # 5% padding in all directions
#ax.set_facecolor(light_grey_c)
handles, labels = ax.get_legend_handles_labels()
handles_all += handles
labels_all += labels
ax1.set_ylabel('Number of lines', weight="bold")
ax2.set_ylabel('Number of lines', weight="bold")
ax3.set_ylabel('Percentage', weight="bold")
ax3.set_xlabel(' --> Time --> ', weight="bold")
ax6.set_xlabel(' --> Time --> ', weight="bold")
ax4.legend(handles_all[::1],
labels_all[::1],
loc="upper left",
bbox_to_anchor=[0.15, 1.1],
ncol=1,
shadow=True,
fancybox=True,
fontsize=8)
#fontsize : int or float or {‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’}
#title="Legend",
#fig.suptitle(name)
#fig.autofmt_xdate()
fig.subplots_adjust(left=0.13,
bottom=0.11,
right=0.93,
top=0.92,
wspace=0.15,
hspace=0.25)
plt.xticks([])
plt.show()
img_name = name
img_name.replace(" ", "")
img_name += ".png"
fig.savefig(img_name, bbox_inches='tight')
#q_1 = np.mean(dec_1_q[a:b])
mag_0 = np.sqrt(i_0[s]**2 + q_0[s]**2)
mag_1 = np.sqrt(i_1[s]**2 + q_1[s]**2)
#mean_0_i.append(i_0)
#mean_0_q.append(q_0)
offset_0.append(mag_0)
#mean_1_i.append(i_1)
#mean_1_q.append(q_1)
offset_1.append(mag_1)
offset.append(mag_1 - mag_0)
nsubs = 4
ax.grid(which='major')
plt.subplot(nsubs, 1, 1)
plt.plot(data)
plt.title("Time domain audio wave")
#data_fft = np.fft.fft(np.array(data))
#frequencies = np.abs(data_fft)
#plt.subplot(nsubs,1,2)
#plt.plot(frequencies)
#plt.title("Frequencies found")
#plt.xlim(0,20000)
#plt.subplot(nsubs,1,2)
#plt.title("IQ Decode")
#plt.plot(range(nframes), dec_0_i, 'r', dec_0_q, 'g', dec_1_i, 'c', dec_1_q, 'b')