def window2(self): # <===
self.w = Window2()
std_val, mean_val, max_val, min_val = data_import_func.stats(
self.df[self.content])
layout = QtWidgets.QGridLayout()
self.heading1 = QtWidgets.QLabel(self)
self.heading1.setText('Actual')
self.heading2 = QtWidgets.QLabel(self)
self.heading2.setText('Normalised')
#display statistical information about the signal
self.avg_label = QtWidgets.QLabel(self)
self.avg_label.setText('Average')
self.avg_lineedit = QtWidgets.QLineEdit(self)
self.avg_lineedit.setText('%.5s' % str(mean_val))
self.avg_norm_lineedit = QtWidgets.QLineEdit(self)
self.avg_norm_lineedit.setText('-')
self.std_label = QtWidgets.QLabel(self)
self.std_label.setText('Standard Deviation')
self.std_lineedit = QtWidgets.QLineEdit(self)
self.std_lineedit.setText('%.5s' % str(std_val))
self.std_norm_lineedit = QtWidgets.QLineEdit(self)
self.std_norm_lineedit.setText('%.4s' % str(std_val / mean_val))
self.max_label = QtWidgets.QLabel(self)
self.max_label.setText('Max')
self.max_lineedit = QtWidgets.QLineEdit(self)
self.max_lineedit.setText('%.5s' % str(max_val))
self.max_norm_lineedit = QtWidgets.QLineEdit(self)
self.max_norm_lineedit.setText('%.4s' % str(max_val / mean_val))
self.min_label = QtWidgets.QLabel(self)
self.min_label.setText('Min ')
self.min_lineedit = QtWidgets.QLineEdit(self)
self.min_lineedit.setText('%.5s' % str(min_val))
self.min_norm_lineedit = QtWidgets.QLineEdit(self)
self.min_norm_lineedit.setText('%.4s' % str(min_val / mean_val))
layout.addWidget(self.heading1, 0, 1)
layout.addWidget(self.heading2, 0, 2)
layout.addWidget(self.avg_label, 1, 0)
layout.addWidget(self.avg_lineedit, 1, 1)
layout.addWidget(self.avg_norm_lineedit, 1, 2)
layout.addWidget(self.std_label, 2, 0)
layout.addWidget(self.std_lineedit, 2, 1)
layout.addWidget(self.std_norm_lineedit, 2, 2)
layout.addWidget(self.max_label, 3, 0)
layout.addWidget(self.max_lineedit, 3, 1)
layout.addWidget(self.max_norm_lineedit, 3, 2)
layout.addWidget(self.min_label, 4, 0)
layout.addWidget(self.min_lineedit, 4, 1)
layout.addWidget(self.min_norm_lineedit, 4, 2)
self.w.setLayout(layout)
self.w.show()
def plot(self):
#Figure 1 is used to plot the time series.
self.ax1.clear()
self.content = self.combo_box.currentText()
std_val, mean_val, max_val, min_val = data_import_func.stats(
self.df[self.content])
self.l3_output.setText('%.5s' % str(mean_val))
#Add Normalisation to the statistical parameters
self.l4_output.setText('%.5s' % str(std_val) + '/' +
'%.4s' % str(std_val / mean_val))
self.l5_output.setText('%.5s' % str(max_val) + '/' +
'%.4s' % str(max_val / mean_val))
self.l6_output.setText('%.5s' % str(min_val) + '/' +
'%.4s' % str(min_val / mean_val))
mean_array = [mean_val] * len(self.df[self.content])
self.ax1.plot(self.df[self.content], label='Data')
self.ax1.plot(self.df.index, mean_array, label='Mean', linestyle='--')
self.ax1.set_ylabel('Force [N]')
self.ax1.set_xlabel('Time [s]')
self.ax1.set_title('Time Signal')
self.ax1.grid()
self.ax1.legend()
self.canvas1.draw()
#Figure 2 will be used to plot the fft
#read the bins provided for the fft operation
if self.l9_input.text() == 'Default':
self.l9_get_text = 'Default'
else:
self.l9_get_text = float(self.l9_input.text())
#allow for normalisation using the checkbox
spec, freq = data_import_func.spectral_analysis(
self.df, self.content, self.l7_get_text, self.l8_get_text,
self.l9_get_text)
self.ax2.clear()
if self.l10_checkbox.isChecked() == True:
rotor_freq = data_import_func.rotor_freq(self.df['RPM'])
freq = freq / rotor_freq
self.ax2.loglog(freq[1:int(len(freq) / 2)], spec[1:])
self.ax2.set_xlabel(r'$\frac{F}{f_0}$ [Hz/Hz]')
self.ax2.set_title('Normalised FFT')
else:
self.ax2.loglog(freq[1:int(len(freq) / 2)], spec[1:])
self.ax2.set_xlabel('Frequency [s]')
self.ax2.set_title('FFT')
self.ax2.set_ylabel('Force [N]')
self.ax2.grid(True, which='both', ls='--')
self.canvas2.draw()
#read the bins for the histogram first
self.l11_get_text = int(self.l11_input.text())
#Figure 3 will be used to plot a normal distribution of normalised loads
self.ax3.clear()
self.ax3.hist(self.df[self.content],
bins=self.l11_get_text,
density=True)
self.ax3.set_xlabel('Force [N]')
self.ax3.set_ylabel('PDF')
sorted_array = self.df.sort_values(by=self.content)[self.content]
self.ax3.plot(sorted_array, norm.pdf(sorted_array, mean_val, std_val))
self.ax3.set_title('Probability density Function')
self.ax3.grid()
self.canvas3.draw()
#Plot the polar chart with
#pass values to the function angle_turbine
t = self.df.index.to_numpy()
Fy1 = self.df['Fy1'].to_numpy()
Fy2 = self.df['Fy2'].to_numpy()
Fy3 = self.df['Fy3'].to_numpy()
fr = data_import_func.rotor_freq(self.df['RPM'])
self.theta = data_import_func.angle_turbine(t, Fy1, Fy2, Fy3, fr)
self.ax4.clear()
#half the samples are plotted to avoid clutter
self.ax4.scatter(self.theta[0:(int(len(self.theta) / 2))],
self.df[self.content][0:(int(self.l7_get_text / 2))],
s=0.01,
alpha=0.75)
self.canvas4.draw()
def plot(self):
#Figure 1 is used to plot the time series.
self.ax1.clear()
self.content = self.combo_box.currentText()
std_val, mean_val, max_val, min_val = data_import_func.stats(
self.df[self.content])
#self.l3_output.setText('%.5s' % str(mean_val))
#Add Normalisation to the statistical parameters
#self.l4_output.setText('%.5s' % str(std_val) + '/' + '%.4s' % str(std_val/mean_val))
#self.l5_output.setText('%.5s' % str(max_val) + '/' + '%.4s' % str(max_val/mean_val))
#self.l6_output.setText('%.5s' % str(min_val) + '/' + '%.4s' % str(min_val/mean_val))
mean_array = [mean_val] * len(self.df[self.content])
upper_std = [(mean_val + std_val)] * len(self.df[self.content])
lower_std = [(mean_val - std_val)] * len(self.df[self.content])
self.ax1.plot(self.df[self.content], label='Data')
self.ax1.plot(self.df.index, mean_array, label='Mean', linestyle='--')
self.ax1.plot(self.df.index,
upper_std,
label='Upper Standard Deviation',
linestyle='--')
self.ax1.plot(self.df.index,
lower_std,
label='Lower Standard Deviation',
linestyle='--')
self.ax1.set_ylabel('Force [N]')
self.ax1.set_xlabel('Time [s]')
self.ax1.set_title('Time Signal')
self.ax1.grid()
self.ax1.legend()
self.canvas1.draw()
#Figure 2 will be used to plot the fft
#read the bins provided for the fft operation
if self.fft_input.text() == 'Default':
self.fft_get_text = 'Default'
else:
self.fft_get_text = float(self.fft_input.text())
#allow for normalisation using the checkbox
spec, freq = data_import_func.spectral_analysis(
self.df, self.content, self.fft_get_text)
self.ax2.clear()
if self.fft_checkbox.isChecked() == True:
rotor_freq = data_import_func.rotor_freq(self.df['RPM'])
freq = freq / rotor_freq
self.ax2.loglog(freq[1:int(len(freq) / 2)], spec[1:])
self.ax2.set_xlabel(r'$\frac{F}{f_0}$ [Hz/Hz]')
self.ax2.set_title('Normalised FFT')
else:
self.ax2.loglog(freq[1:int(len(freq) / 2)], spec[1:])
self.ax2.set_xlabel('Frequency [Hz]')
self.ax2.set_title('FFT')
self.ax2.set_ylabel('Force [N]')
self.ax2.grid(True, which='both', ls='--')
self.canvas2.draw()
#read the bins for the histogram first
self.bin_input = int(self.bin_lineedit.text())
#Figure 3 will be used to plot a normal distribution of normalised loads
self.ax3.clear()
self.ax3.hist(self.df[self.content], bins=self.bin_input, density=True)
self.ax3.set_xlabel('Force [N]')
self.ax3.set_ylabel('PDF')
sorted_array = self.df.sort_values(by=self.content)[self.content]
self.ax3.plot(sorted_array, norm.pdf(sorted_array, mean_val, std_val))
self.ax3.set_title('Probability density Function')
self.ax3.grid()
self.canvas3.draw()
#Plot the polar chart with
self.ax4.clear()
plt.autoscale(axis='both', tight='bool')
self.ax4.set_rlabel_position(90)
self.ax4.set_theta_zero_location("N") # theta=0 at the top
self.ax4.set_theta_direction(-1) # theta increasing clockwise
self.ax4.yaxis.set_major_locator(plt.MaxNLocator(3))
#pass values to the function angle_turbine
angle, fit = data_import_func.angle_theta(self.df, self.content)
self.ax4.scatter(angle['theta'], angle['signal'], s=0.01)
self.ax4.plot(fit['theta_s'], fit['Average'], color='red')
#half the samples are plotted to avoid clutter
#self.ax4.scatter(self.theta, self.y_new, s = 0.5, alpha = 1)
self.ax4.set_title('Varation against Blade Angle')
self.canvas4.draw()