black

Author: jankoslavic

docs/source/conf.py

@@ -14,6 +14,7 @@
 #
 import os
 import sys
+
 sys.path.insert(0, os.path.abspath('.'))
 sys.path.insert(0, os.path.abspath('../..'))
 
@@ -43,7 +44,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.viewcode',
     # Add support for the Google docstring format
-    'sphinx.ext.napoleon', 
+    'sphinx.ext.napoleon',
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -90,7 +91,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-#html_static_path = ['_static']
+# html_static_path = ['_static']
 
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.
@@ -115,15 +116,12 @@
     # The paper size ('letterpaper' or 'a4paper').
     #
     # 'papersize': 'letterpaper',
-
     # The font size ('10pt', '11pt' or '12pt').
     #
     # 'pointsize': '10pt',
-
     # Additional stuff for the LaTeX preamble.
     #
     # 'preamble': '',
-
     # Latex figure (float) alignment
     #
     # 'figure_align': 'htbp',
@@ -133,19 +131,21 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'pyexsi.tex', 'pyExSi project Documentation',
-     'Aleš Zorman, Domen Gorjup, Janko Slavič', 'manual'),
+    (
+        master_doc,
+        'pyexsi.tex',
+        'pyExSi project Documentation',
+        'Aleš Zorman, Domen Gorjup, Janko Slavič',
+        'manual',
+    ),
 ]
 
 
 # -- Options for manual page output ------------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'pyExSi_project', 'pyExSi Documentation',
-     [author], 1)
-]
+man_pages = [(master_doc, 'pyExSi_project', 'pyExSi Documentation', [author], 1)]
 
 
 # -- Options for Texinfo output ----------------------------------------------
@@ -154,9 +154,15 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'pyExSi', 'pyExSi Documentation',
-     author, 'pyExSi', 'Excitation signals as used in structural dynamics and vibration fatigue',
-     'Miscellaneous'),
+    (
+        master_doc,
+        'pyExSi',
+        'pyExSi Documentation',
+        author,
+        'pyExSi',
+        'Excitation signals as used in structural dynamics and vibration fatigue',
+        'Miscellaneous',
+    ),
 ]
 
 
@@ -178,4 +184,4 @@
 epub_exclude_files = ['search.html']
 
 
-# -- Extension configuration -------------------------------------------------
+# -- Extension configuration -------------------------------------------------

example/example_nonstationarity.py

@@ -1,4 +1,5 @@
 import sys, os
+
 my_path = os.path.dirname(os.path.abspath(__file__))
 sys.path.insert(0, my_path + '/../')
 
@@ -9,76 +10,102 @@
 import pyExSi as es
 
 
-N = 2**16 # number of data points of time signal
-fs = 1024 # sampling frequency [Hz]
-t = np.arange(0,N)/fs # time vector
+N = 2 ** 16  # number of data points of time signal
+fs = 1024  # sampling frequency [Hz]
+t = np.arange(0, N) / fs  # time vector
 
 # define frequency vector and one-sided flat-shaped PSD
-M = N // 2 + 1 # number of data points of frequency vector
-f = np.arange(0, M, 1) * fs / N # frequency vector
-f_min = 50 # PSD upper frequency limit  [Hz]
-f_max = 100 # PSD lower frequency limit [Hz]
-PSD = es.get_psd(f, f_min, f_max) # one-sided flat-shaped PSD
-plt.plot(f,PSD)
+M = N // 2 + 1  # number of data points of frequency vector
+f = np.arange(0, M, 1) * fs / N  # frequency vector
+f_min = 50  # PSD upper frequency limit  [Hz]
+f_max = 100  # PSD lower frequency limit [Hz]
+PSD = es.get_psd(f, f_min, f_max)  # one-sided flat-shaped PSD
+plt.plot(f, PSD)
 plt.xlabel('Frequency [Hz]')
 plt.ylabel('PSD [Unit**2/Hz]')
-plt.xlim(0,200)
+plt.xlim(0, 200)
 plt.show()
 
 
-#Random Generator seed
+# Random Generator seed
 seed = 1234
-rg = np.random.default_rng(seed) #or rng = np.random.default_rng(seed) 
+rg = np.random.default_rng(seed)  # or rng = np.random.default_rng(seed)
 
-#get gaussian stationary signal
+# get gaussian stationary signal
 gaussian_signal = es.random_gaussian(N, PSD, fs, rg=rg)
-#calculate kurtosis 
+# calculate kurtosis
 k_u_stationary = es.get_kurtosis(gaussian_signal)
 
-#get non-gaussian stationary signal, with kurtosis k_u=10
+# get non-gaussian stationary signal, with kurtosis k_u=10
 k_u_target = 10
 rng = np.random.default_rng(seed)
 nongaussian_signal = es.stationary_nongaussian_signal(N, PSD, fs, k_u=k_u_target, rg=rg)
-#calculate kurtosis
+# calculate kurtosis
 k_u_stationary_nongaussian = es.get_kurtosis(nongaussian_signal)
 
-#get non-gaussian non-stationary signal, with kurtosis k_u=10
-#a) amplitude modulation, modulating signal defined by PSD
+# get non-gaussian non-stationary signal, with kurtosis k_u=10
+# a) amplitude modulation, modulating signal defined by PSD
 rng = np.random.default_rng(seed)
-PSD_modulating = es.get_psd(f, f_low=1, f_high=k_u_target) 
-plt.plot(f,PSD, label='PSD, carrier signal')
-plt.plot(f,PSD_modulating, label='PSD, modulating signal')
+PSD_modulating = es.get_psd(f, f_low=1, f_high=k_u_target)
+plt.plot(f, PSD, label='PSD, carrier signal')
+plt.plot(f, PSD_modulating, label='PSD, modulating signal')
 plt.xlabel('Frequency [Hz]')
 plt.ylabel('PSD [Unit**2/Hz]')
-plt.xlim(0,200)
+plt.xlim(0, 200)
 plt.legend()
 plt.show()
-#define array of parameters delta_m and p
-delta_m_list = np.arange(.1,2.1,.5) 
-p_list = np.arange(.1,2.1,.5)
-#get signal 
-nongaussian_nonstationary_signal_psd = es.nonstationary_signal(N,PSD,fs,k_u=k_u_target,modulating_signal=('PSD',PSD_modulating),
-                                                        param1_list=delta_m_list,param2_list=p_list,seed=seed)
-#calculate kurtosis 
-k_u_nonstationary_nongaussian_psd = es.get_kurtosis(nongaussian_nonstationary_signal_psd)
+# define array of parameters delta_m and p
+delta_m_list = np.arange(0.1, 2.1, 0.5)
+p_list = np.arange(0.1, 2.1, 0.5)
+# get signal
+nongaussian_nonstationary_signal_psd = es.nonstationary_signal(
+    N,
+    PSD,
+    fs,
+    k_u=k_u_target,
+    modulating_signal=('PSD', PSD_modulating),
+    param1_list=delta_m_list,
+    param2_list=p_list,
+    seed=seed,
+)
+# calculate kurtosis
+k_u_nonstationary_nongaussian_psd = es.get_kurtosis(
+    nongaussian_nonstationary_signal_psd
+)
 
-#b) amplitude modulation, modulating signal defined by cubis spline intepolation. Points are based on beta distribution
-#Points are separated by delta_n = 2**8 samples (at fs=2**10)
-delta_n = 2**10
-#define array of parameters alpha and beta
-alpha_list = np.arange(1,10,1)
-beta_list = np.arange(1,10,1)
-#get signal 
-nongaussian_nonstationary_signal_beta = es.nonstationary_signal(N,PSD,fs,k_u=k_u_target,modulating_signal=('CSI',delta_n),
-                                                        param1_list=alpha_list,param2_list=beta_list,seed=seed)
-#calculate kurtosis 
-k_u_nonstationary_nongaussian_beta = es.get_kurtosis(nongaussian_nonstationary_signal_beta)
+# b) amplitude modulation, modulating signal defined by cubis spline intepolation. Points are based on beta distribution
+# Points are separated by delta_n = 2**8 samples (at fs=2**10)
+delta_n = 2 ** 10
+# define array of parameters alpha and beta
+alpha_list = np.arange(1, 10, 1)
+beta_list = np.arange(1, 10, 1)
+# get signal
+nongaussian_nonstationary_signal_beta = es.nonstationary_signal(
+    N,
+    PSD,
+    fs,
+    k_u=k_u_target,
+    modulating_signal=('CSI', delta_n),
+    param1_list=alpha_list,
+    param2_list=beta_list,
+    seed=seed,
+)
+# calculate kurtosis
+k_u_nonstationary_nongaussian_beta = es.get_kurtosis(
+    nongaussian_nonstationary_signal_beta
+)
 
-#Plot
-plt.plot(gaussian_signal[:200], label = 'Gaussian')
-plt.plot(nongaussian_signal[:200], label = 'non-Gaussian stationary')
-plt.plot(nongaussian_nonstationary_signal_psd[:200], label = 'non-Gaussian non-stationary (PSD)')
-plt.plot(nongaussian_nonstationary_signal_beta[:200], label = 'non-Gaussian non-stationary (CSI)')
+# Plot
+plt.plot(gaussian_signal[:200], label='Gaussian')
+plt.plot(nongaussian_signal[:200], label='non-Gaussian stationary')
+plt.plot(
+    nongaussian_nonstationary_signal_psd[:200],
+    label='non-Gaussian non-stationary (PSD)',
+)
+plt.plot(
+    nongaussian_nonstationary_signal_beta[:200],
+    label='non-Gaussian non-stationary (CSI)',
+)
 plt.xlabel('Sample [n]')
 plt.ylabel('Signal [Unit]')
 plt.legend()
@@ -87,5 +114,9 @@
 print(f'kurtosis of stationary Gaussian signal:{k_u_stationary:.3f}')
 print(f'Desired kurtosis of non-Gaussian signals:{k_u_target:.3f}')
 print(f'kurtosis of stationary non-Gaussian signal:{k_u_stationary_nongaussian:.3f}')
-print(f'kurtosis of non-stationary non-Gaussian signal (psd):{k_u_nonstationary_nongaussian_psd:.3f}')
-print(f'kurtosis of non-stationary non-Gaussian signal (beta):{k_u_nonstationary_nongaussian_beta:.3f}')
+print(
+    f'kurtosis of non-stationary non-Gaussian signal (psd):{k_u_nonstationary_nongaussian_psd:.3f}'
+)
+print(
+    f'kurtosis of non-stationary non-Gaussian signal (beta):{k_u_nonstationary_nongaussian_beta:.3f}'
+)

example/example_signals.py

@@ -1,42 +1,57 @@
 import sys, os
+
 my_path = os.path.dirname(os.path.abspath(__file__))
 sys.path.insert(0, my_path + '/../')
 
 import numpy as np
 import matplotlib.pyplot as plt
 import pyExSi as es
 
-#impact pulse 
-width = 300 
-N = 2*width
+# impact pulse
+width = 300
+N = 2 * width
 n_start = 90
 amplitude = 3
-#pulse_sine = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window='sine')
-pulse_sine = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window='sine')
-pulse_rectangular = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window='boxcar')
-pulse_triangular = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window='triang')
-pulse_exponential = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window=('exponential',None,10))
-pulse_sawtooth = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window='sawtooth')
+# pulse_sine = es.impulse(N=N, n_start=n_start, width=width, amplitude=amplitude, window='sine')
+pulse_sine = es.impulse(
+    N=N, n_start=n_start, width=width, amplitude=amplitude, window='sine'
+)
+pulse_rectangular = es.impulse(
+    N=N, n_start=n_start, width=width, amplitude=amplitude, window='boxcar'
+)
+pulse_triangular = es.impulse(
+    N=N, n_start=n_start, width=width, amplitude=amplitude, window='triang'
+)
+pulse_exponential = es.impulse(
+    N=N,
+    n_start=n_start,
+    width=width,
+    amplitude=amplitude,
+    window=('exponential', None, 10),
+)
+pulse_sawtooth = es.impulse(
+    N=N, n_start=n_start, width=width, amplitude=amplitude, window='sawtooth'
+)
 plt.plot(pulse_sine, '-', label='sine')
 plt.plot(pulse_rectangular, '-', label='rectangular')
-plt.plot(pulse_triangular, '-', label = 'triangular')
-plt.plot(pulse_exponential, '-', label = 'exponential')
-plt.plot(pulse_sawtooth, '-', label = 'sawtooth')
+plt.plot(pulse_triangular, '-', label='triangular')
+plt.plot(pulse_exponential, '-', label='exponential')
+plt.plot(pulse_sawtooth, '-', label='sawtooth')
 plt.xlabel('Sample [n]')
 plt.ylabel('Impact pulse [Unit]')
 plt.legend(loc='upper right')
 plt.show()
 
 
-#Random Generator 
+# Random Generator
 seed = 1234
-rg = np.random.default_rng(seed) #or rng = np.random.default_rng(seed) 
+rg = np.random.default_rng(seed)  # or rng = np.random.default_rng(seed)
 
-#uniform random, normal random and pseudo random
+# uniform random, normal random and pseudo random
 N = 1000
-uniform_random = es.uniform_random(N=N,rg=rg)
-normal_random = es.normal_random(N=N,rg=rg)
-pseudo_random = es.pseudo_random(N=N,rg=rg)
+uniform_random = es.uniform_random(N=N, rg=rg)
+normal_random = es.normal_random(N=N, rg=rg)
+pseudo_random = es.pseudo_random(N=N, rg=rg)
 plt.plot(uniform_random, label='uniform random')
 plt.plot(normal_random, label='normal random')
 plt.plot(pseudo_random, label='pseudo random')
@@ -47,20 +62,22 @@
 print(uniform_random[:5])
 
 
-#burst random
+# burst random
 N = 1000
 amplitude = 5
-burst_random = es.burst_random(N, A=amplitude, ratio=0.1, distribution='normal', n_bursts=3, rg=rg)
+burst_random = es.burst_random(
+    N, A=amplitude, ratio=0.1, distribution='normal', n_bursts=3, rg=rg
+)
 plt.plot(burst_random)
 plt.xlabel('Sample [n]')
 plt.ylabel('Burst [Unit]')
 plt.show()
 
 
-#sweep
-t = np.linspace(0,10,1000)
+# sweep
+t = np.linspace(0, 10, 1000)
 sweep = es.sine_sweep(time=t, f_start=0, f_stop=5)
 plt.plot(t, sweep)
 plt.xlabel('Time [s]')
 plt.ylabel('Sine sweep [Unit]')
-plt.show()
+plt.show()

pyExSi/__init__.py

@@ -1,2 +1,2 @@
 __version__ = '0.1'
-from .signals import *
+from .signals import *