def __init__(self):
QMainWindow.__init__(self)
loadUi("../ui/Continious.ui", self)
self.setWindowTitle("Wavelet Transform Data Analyzer Tool")
self.setWindowIcon(QtGui.QIcon('../icon/icon.png'))
self.setFixedSize(1087, 642)
Features.center(self)
self.signals = []
self.audio = []
self.time = []
self.load_check = False
self.analyze_check = False
self.actionClose.triggered.connect(self.close_window)
self.cont_signal_Load.triggered.connect(self.load_signal)
self.actionFolder.triggered.connect(self.load_folder)#load signal via menubar
self.addToolBar(NavigationToolbar(self.widget_1DCont_Original.canvas, self)) # embedding matplotlib to widget
self.pushButton_1DCont_Analyze.clicked.connect(self.analyze_cont) #
self.pushButton_save_db_cont.clicked.connect(self.save_to_db)
wave_types = pywt.wavelist(kind='continuous')
self.comboBox_1DCont_Type.addItems(wave_types)
self.checkBox_1D_Average.setChecked(True),
self.checkBox_1D_Entropy.setChecked(True)
self.checkBox_1D_Kurtosis.setChecked(True)
self.checkBox_1D_Max.setChecked(True) # discrete page statistic functions checks
self.checkBox_1D_Median.setChecked(True)
self.checkBox_1D_Min.setChecked(True)
self.checkBox_1D_Skewness.setChecked(True)
self.checkBox_1D_StandartDeviation.setChecked(True)
def run(self):
self.average, self.entropy, self.kurtosis, self.max_v, self.median, self.min_v, self.skewness, self.standart_dev = Features.check_statistics(
self)
#self.wavelet_type, self.sampling_per, self.min_scale, self.max_scale, self.scale_step = Features.cont_check_wavelet(self)
self.sampling_per = 1
self.min_scale = 1
self.max_scale = 8
self.scale_step = 1
wave_types = pywt.wavelist(kind='continuous')
self.scaling = np.arange(self.min_scale, self.max_scale,
self.scale_step)
for wave_func in wave_types:
self.wavelet_type = wave_func
self.wavelet_level = len(self.scaling) - 1
col, self.header, self.db_header = Features.init_table(
self, len(self.all_signals))
self.db_matrix = np.zeros(
(len(self.all_signals), 8 * (self.wavelet_level + 1)),
dtype=complex)
for iter in range(0, len(self.all_signals)):
#print(self.all_signals[iter])
self.signals.clear()
self.audio, self.sample = librosa.load(self.all_signals[iter])
self.signals.append(self.audio)
self.time = np.arange(0, len(self.audio)) / self.sample
coef, freqs = pywt.cwt(self.audio,
self.scaling, self.wavelet_type,
int(self.sampling_per))
for i in range(0, self.wavelet_level + 1):
self.signals.append(
coef[i]) # adding signals array to coeffs
self.db_matrix[iter] = Features.insertTableComplex(
self, iter, col)
w_name = str(self.wavelet_type)
table_name = "Db_GTZAN_function_" + w_name + "_Scale_1_to_8_Period_1"
Database.create_table(
Database.database_name, table_name, self.db_header,
"") # creating new table with statistic function
Database.create_table(Database.database_name, table_name,
self.db_header, "Comp_")
for index in range(0, len(self.all_signals)):
name = self.all_signals[index].split(sep='/')
Database.add_values_to_table(Database.database_name,
table_name, name[-1],
self.db_header,
self.db_matrix[index], "")
Database.add_values_to_table(Database.database_name,
table_name, name[-1],
self.db_header,
self.db_matrix[index], "Comp_")
print(table_name)
def load_signal(self):
self.all_signals, self.filter, self.load_check = Features.load_signal(self)
if (self.load_check):
if (len(self.all_signals) > 1): # Multiple file
self.label_1DCont_DataName.setText("Multiple Signals") # Data name
else:
name = self.all_signals[0].split(sep='/')
self.label_1DCont_DataName.setText(name[-1]) # Setting data name
else:
Features.message("You have to load at least 1 signal !", QMessageBox.Warning)
self.label_1DCont_DataName.setText("No signal selected")
def load_folder(self):
self.all_signals, self.load_check = Features.load_folder(self)
if (self.load_check):
Features.message(str(len(self.all_signals)) + " signals ready to be processed !", QMessageBox.Information)
if (len(self.all_signals) > 1): # Multiple file
self.label_1DCont_DataName.setText("Multiple Signals") # Data name
else:
name = self.all_signals[0].split(sep='/')
self.label_1DCont_DataName.setText(name[-1]) # Setting data name
else:
Features.message("You have to load at least 1 signal !", QMessageBox.Warning)
self.label_1DCont_DataName.setText("No signal selected")
def __init__(self):
super().__init__()
QMainWindow.__init__(self)
loadUi("../ui/mainWindow.ui", self)
self.setWindowTitle("Wavelet Transform Toolbox")
self.setWindowIcon(QtGui.QIcon('../icon/icon.png'))
self.setFixedSize(500, 300)
Features.center(self)
self.pushButton_DiscreteWavelet1D.clicked.connect(self.openDiscretePage)
self.pushButton_ContinuousWavelet1D.clicked.connect(self.openContinuousPage)
self.pushButton_Prediction.clicked.connect(self.openClassificationPage)
def load_folder(self): #Dosya Yükleme
self.all_signals, self.load_check = Features.load_folder(self)
if (self.load_check):
Features.message(
str(len(self.all_signals)) +
" signals ready to be processed !", QMessageBox.Information)
if (len(self.all_signals) > 1):
self.label_1D_DataName.setText("Multiple Signals")
else:
name = self.all_signals[0].split(sep='/')
self.label_1D_DataName.setText(name[-1])
else:
Features.message("You have to load at least 1 signal",
QMessageBox.Warning)
def load_signal(self):
self.all_signals, self.filter, self.load_check = Features.load_signal(
self)
self.progressBar.setValue(0)
if (self.load_check):
if (len(self.all_signals) > 1): #Çoklu Veri Girişi
self.label_1D_DataName.setText("Multiple Signals") #Veri Adı
else:
name = self.all_signals[0].split(sep='/')
self.label_1D_DataName.setText(name[-1]) #Veri Adını oluşturma
else:
Features.message("You have to load at least 1 signal",
QMessageBox.Warning)
self.label_1D_DataName.setText("No signal selected")
def __init__(self):
QMainWindow.__init__(self)
loadUi("../ui/Discrete.ui", self)
self.setWindowTitle("Discrete Wavelet Analysis")
self.setWindowIcon(
QtGui.QIcon('../icon/iDiscrete Wavelet Analysiscon.png'))
self.setFixedSize(991, 830)
Features.center(self)
self.MplWidget.canvas.axes.remove() #ortada oluşan modeli siliyor
self.clear_count = 0 #currently how many canvas we have
self.load_check = False #Sinyal Yüklenmiş Mi?
self.analyze_check = False #İşlenmiş mi
self.signals = [] #Sinyaller
self.audio = [] #Ses Dosyaları
self.time = [] #Zaman
#self.addToolBar(NavigationToolbar(self.MplWidget.canvas, self)) # Sinyalleri daha iyi görmek için toolbar
self.actionClose.triggered.connect(self.close_window) #Sayfayı Kapatma
self.actionFolder.triggered.connect(
self.load_folder) #Menu Bardan Dosya Yükleme
self.actionImage.triggered.connect(
self.load_signal) #Menu Bardan Sinyal Yükleme
self.pushButton_1D_Analyze.clicked.connect(
self.analyze_signal) #Analiz butonu
self.progressBar.setValue(0) #progressBar değeri ilk başta 0
self.pushButton_save_to_db.clicked.connect(
self.save_to_db) #Veritabanına kaydetme
wave_types = pywt.wavelist(kind='discrete')
self.comboBox_1D_Type.addItems(
wave_types) #Wavelet Typeları için comboBox oluşturuluyor
for i in range(1, 20):
self.comboBox_1D_Level.addItem(str(i), i)
self.comboBox_1D_Level.currentIndexChanged.connect(
self.on_combobox_changed, self.comboBox_1D_Level.currentIndex())
self.checkBox_1D_Average.setChecked(True)
self.checkBox_1D_Entropy.setChecked(True)
self.checkBox_1D_Kurtosis.setChecked(True)
self.checkBox_1D_Max.setChecked(True)
self.checkBox_1D_Median.setChecked(True)
self.checkBox_1D_Min.setChecked(True)
self.checkBox_1D_Skewness.setChecked(True)
self.checkBox_1D_StandartDeviation.setChecked(True)
def __init__(self):
QMainWindow.__init__(self)
loadUi("../ui/Discrete.ui", self)
self.setWindowTitle("Wavelet Transform Data Analyzer Tool")
self.setWindowIcon(QtGui.QIcon('../icon/icon.png'))
self.setFixedSize(1482, 949)
Features.center(self)
self.MplWidget.canvas.axes.remove()
self.clear_count = 0 #currently how many canvas we have
self.load_check = False #işlenecek bir sinyal var mı ?
self.analyze_check = False #işlenmiş mi
self.signals = []
self.audio = []
self.time = []
self.addToolBar(NavigationToolbar(
self.MplWidget.canvas, self)) # embedding matplotlib to widget
self.actionClose.triggered.connect(self.close_window)
self.actionFolder.triggered.connect(self.load_folder)
self.actionImage.triggered.connect(
self.load_signal) #load signal via menubar
self.pushButton_1D_Analyze.clicked.connect(
self.analyze_signal) #analyze button
#self.pushButton_1D_Analyze.clicked.connect(self.run) #bütün dataya bütün fonksiyonlar
self.pushButton_save_to_db.clicked.connect(self.save_to_db)
wave_types = pywt.wavelist(kind='discrete')
self.comboBox_1D_Type.addItems(wave_types) #initialize comboBox
for i in range(1, 20):
self.comboBox_1D_Level.addItem(str(i), i)
self.comboBox_1D_Level.currentIndexChanged.connect(
self.on_combobox_changed, self.comboBox_1D_Level.currentIndex())
self.checkBox_1D_Average.setChecked(True)
self.checkBox_1D_Entropy.setChecked(True)
self.checkBox_1D_Kurtosis.setChecked(True)
self.checkBox_1D_Max.setChecked(
True) # discrete page statistic functions checks
self.checkBox_1D_Median.setChecked(True)
self.checkBox_1D_Min.setChecked(True)
self.checkBox_1D_Skewness.setChecked(True)
self.checkBox_1D_StandartDeviation.setChecked(True)
def analyze_signal(self): #Veriyi Analiz Etme
self.average, self.entropy, self.kurtosis, self.max_v, self.median, self.min_v, self.skewness, self.standart_dev = Features.check_statistics(
self)
self.wavelet_type, self.wavelet_level = Features.discrete_check_wavelet(
self)
if (self.load_check and
(self.average or self.entropy or self.kurtosis or self.max_v
or self.median or self.min_v or self.skewness
or self.standart_dev)): #işlenecek müzik olması durumu
self.analyze_check = True
col, self.header, self.db_header = Features.init_table(
self, len(self.all_signals))
self.db_matrix = np.zeros(
(len(self.all_signals), 8 * (self.wavelet_level + 1)))
for iter in range(0, len(self.all_signals)):
print(self.all_signals[iter])
self.signals.clear()
self.audio, self.sample = librosa.load(self.all_signals[iter])
self.signals.append(self.audio)
self.time = np.arange(0, len(self.audio)) / self.sample
coeffs = pywt.wavedec(
self.audio, self.wavelet_type,
level=self.wavelet_level) #Wavelet Analiz
for i in range(0, self.wavelet_level + 1):
self.signals.append(coeffs[i]) #Sinyalleri Coef içine atma
if (iter == 0
and self.wavelet_level < 8): #İlk sinyali yazdırma
Features.discrete_plot_signal(self)
self.db_matrix[iter] = Features.insertTable(self, iter, col)
self.progressBar.setValue(
((iter + 1) / len(self.all_signals)) * 100)
#level = 3 ise signals içinde 5 (4 analiz edilmiş + 1 source)
else:
if not self.load_check:
Features.message("You have to load at least 1 signal",
QMessageBox.Warning)
else:
Features.message(
"You have to pick at least 1 statistic function",
QMessageBox.Warning)
def save_to_db(self):
if (self.load_check and self.analyze_check):
w_name = str(self.wavelet_type)
table_name = "Db_GTZAN_function_" + w_name + "_Scale_" + self.min_scale + "_to_" + self.max_scale + "_Period_" + self.sampling_per
Database.create_table(
Database.database_name, table_name, self.db_header,
"") #İstatistiksel fonksiyonlar ile yeni tablo oluşturma
Database.create_table(Database.database_name, table_name,
self.db_header, "Comp_")
for index in range(0, len(self.all_signals)):
name = self.all_signals[index].split(sep='/')
Database.delete_row(Database.database_name, table_name,
name[-1], "")
Database.add_values_to_table(Database.database_name,
table_name, name[-1],
self.db_header,
self.db_matrix[index], "")
Database.delete_row(Database.database_name, table_name,
name[-1], "Comp_")
Database.add_values_to_table(Database.database_name,
table_name, name[-1],
self.db_header,
self.db_matrix[index], "Comp_")
Features.message("Your Data Saved Succesfully !",
QMessageBox.Warning)
else:
if (self.load_check):
Features.message("You have to analyze the signals first !",
QMessageBox.Warning)
else:
Features.message("You have to load at least 1 signal !",
QMessageBox.Warning)
def save_to_db(self):
if (self.load_check and self.analyze_check):
w_name = str(self.wavelet_type)
table_name = "Db_GTZAN_function_" + w_name + "_Degree_" + str(
self.wavelet_level)
Database.create_table(
Database.database_name, table_name, self.db_header,
"") # creating new table with statistic function
for index in range(0, len(self.all_signals)):
name = self.all_signals[index].split(sep='/') #name of signal
Database.delete_row(Database.database_name, table_name,
name[-1], "")
Database.add_values_to_table(Database.database_name,
table_name, name[-1],
self.db_header,
self.db_matrix[index],
"") #adding db to values
Features.message("Your Data Saved Succesfully",
QMessageBox.Information)
else:
if (self.load_check):
Features.message("You have to analyze the signals first",
QMessageBox.Warning)
else:
Features.message("You have to load at least 1 signal",
QMessageBox.Warning)
def analyze_cont(self):
self.average, self.entropy, self.kurtosis, self.max_v, self.median, self.min_v, self.skewness, self.standart_dev = Features.check_statistics(self)
self.wavelet_type, self.sampling_per, self.min_scale, self.max_scale, self.scale_step = Features.cont_check_wavelet(self)
if (self.load_check and (
self.average or self.entropy or self.kurtosis or self.max_v or self.median or self.min_v or self.skewness or self.standart_dev)): # işlenecek müzik olması durumu
self.scaling = np.arange(int(self.min_scale), int(self.max_scale), int(self.scale_step))
self.analyze_check = True
self.wavelet_level = len(self.scaling) - 1
col, self.header, self.db_header = Features.init_table(self, len(self.all_signals))
self.db_matrix = np.zeros((len(self.all_signals), 8 * (self.wavelet_level + 1)), dtype=complex)
for iter in range(0, len(self.all_signals)):
print(self.all_signals[iter])
self.signals.clear()
self.audio, self.sample = librosa.load(self.all_signals[iter])
self.signals.append(self.audio)
self.time = np.arange(0, len(self.audio)) / self.sample
coef, freqs = pywt.cwt(self.audio, self.scaling, self.wavelet_type, int(self.sampling_per))
for i in range(0, self.wavelet_level + 1):
self.signals.append(coef[i]) # adding signals array to coeffs
if (iter == 0): # plotting first signal only
self.plot_original_signal()
firstau = self.audio
time = self.time
self.db_matrix[iter] = Features.insertTableComplex(self, iter, col)
self.plot_wavelet(firstau,time)
else:
if not self.load_check:
Features.message("You have to load at least 1 signal !", QMessageBox.Warning)
else:
Features.message("You have to pick at least 1 statistic function !", QMessageBox.Warning)
def save_to_db(self):
if (self.load_check and self.analyze_check):
w_name = str(self.wavelet_type)
table_name = "Db_GTZAN_function_" + w_name + "_Degree_" + str(
self.wavelet_level)
Database.create_table(
Database.database_name, table_name, self.db_header,
"") # İstatistiksel fonksiyonlar için tablo oluşturma
for index in range(0, len(self.all_signals)):
name = self.all_signals[index].split(sep='/') #Sinyalin adı
Database.delete_row(Database.database_name, table_name,
name[-1], "")
Database.add_values_to_table(Database.database_name,
table_name, name[-1],
self.db_header,
self.db_matrix[index],
"") #Db'ye değerleri girme
Features.message("Your Data Saved Succesfully",
QMessageBox.Information)
else:
if (self.load_check):
Features.message("You have to analyze the signals first",
QMessageBox.Warning)
else:
Features.message("You have to load at least 1 signal",
QMessageBox.Warning)
def __init__(self):
QMainWindow.__init__(self)
loadUi("../ui/Prediction.ui", self)
self.setWindowTitle("Wavelet Transform Data Analyzer Tool")
self.setWindowIcon(QtGui.QIcon('../icon/icon.png'))
Features.center(self)
list_tables = Database.get_table_names(Database.database_name)
Features.list_db_tables(self, list_tables)
for i in range(2, 21):
self.comboBox_kfold.addItem(str(i), i)
self.selected_table_name = ""
self.checkBox_knn.setChecked(True)
self.checkBox_randomForest.setChecked(True)
self.checkBox_svcLinear.setChecked(True)
self.checkBox_svcPoly.setChecked(True) # prediction page checks
self.checkBox_gaussianNB.setChecked(True)
self.checkBox_svcRBF.setChecked(True)
self.pushButton.clicked.connect(self.analyze_signal)
self.dbTables_listWidget.itemSelectionChanged.connect(self.selectionChanged)
def analyze_signal(self):
if(self.selected_table_name != ""):
table_name = self.selected_table_name
if(table_name.find('Comp_') != -1) :
table_name = table_name.replace('Comp_','')
matrix = ClassifyMusic.run(table_name, int(self.comboBox_kfold.currentText()), Features.checked_functions(self), self.checkBox_printConf.isChecked())
Features.init_results_table(self)
Features.fill_result_table(self, matrix)
else :
Features.message("You have to choose a table", QMessageBox.Warning)
def run(self):
wave_types = pywt.wavelist(kind='discrete')
levels = [1, 2, 3]
signals = []
self.average, self.entropy, self.kurtosis, self.max_v, self.median, self.min_v, self.skewness, self.standart_dev = Features.check_statistics(
self)
table_names = Database.get_table_names(Database.database_name)
for wave_func in wave_types:
if wave_func.find("sym") == -1:
for level in levels:
self.wavelet_level = level
self.wavelet_type = wave_func
col, self.header, self.db_header = Features.init_table(
self, len(self.all_signals))
self.db_matrix = np.zeros(
(len(self.all_signals), 8 * (self.wavelet_level + 1)))
if not any("Db_GTZAN_function_" + str(wave_func) +
"_Degree_" + str(level) in s
for s in table_names):
for iter in range(0, len(self.all_signals)):
self.signals.clear()
self.audio, self.sample = librosa.load(
self.all_signals[iter])
self.signals.append(self.audio)
self.time = np.arange(0, len(
self.audio)) / self.sample
coeffs = pywt.wavedec(
self.audio,
self.wavelet_type,
level=self.wavelet_level) # wavelet analyze
for i in range(0, self.wavelet_level + 1):
self.signals.append(
coeffs[i]
) # adding signals array to coeffs
self.db_matrix[iter] = Features.insertTable(
self, iter, col
) # level = 3 ise signals içinde 5 (4 analiz edilmiş + 1 kaynak)
w_name = str(wave_func)
table_name = "Db_GTZAN_function_" + w_name + "_Degree_" + str(
level)
Database.create_table(Database.database_name,
table_name, self.db_header, "")
for index in range(0, len(self.all_signals)):
name = self.all_signals[index].split(
sep='/') # name of signal
Database.delete_row(Database.database_name,
table_name, name[-1], "")
Database.add_values_to_table(
Database.database_name, table_name, name[-1],
self.db_header, self.db_matrix[index],
"") # adding db to values
print(table_name)
else:
print("Db_GTZAN_function_" + str(wave_func) +
"_Degree_" + str(level) + ' passed')
else:
print(wave_func + " passed")
def on_combobox_changed(self, value):
if (value >= 7):
Features.message(
"Plotting is not supported on levels higher than 5",
QMessageBox.Information)
