def pass_data(file_num, alg_choice = None):
""" This function takes the data obtained from the selected menu choices above, and passes them to their respective algorithms.
*** Should be phased out after implementing GUI. ***
Args:
file_num (String): The option chosen for number of files selected, either 1, multiple or a test sinusoid is selected.
alg_choice (String, optional): Used in the case of creating a test sinusoid. Algorithm is chosen prior to . Defaults to None.
"""
if file_num == "1":
time, detrended_flux, background = data_process.get_data()
# Change values in columns to float values for later processing.
time = [float(data) for data in time]
detrended_flux = [float(data) for data in detrended_flux]
noise = [float(data) for data in background]
while(True):
alg_choice = input("Select analysis method: \n1 - Time Series \n2 - Lomb-Scargle \n3 - Autocorrelation \n4 - Morlet Wavelet \n5 - GPS\n6 - All\n0 - Exit Program\n")
alg.selection(time, detrended_flux, alg_choice)
else:
time, detrended_flux, background = data_process.get_data()
# Change values in columns to float values for later processing.
time = [float(data) for data in time]
detrended_flux = [float(data) for data in detrended_flux]
noise = [float(data) for data in background]
alg.selection(time, detrended_flux, alg_choice)
c=C,
graphType=gt,
display=False,
imgHeight=10,
imgWidth=10)
trainImgs.append(img)
# train output values
# source: https://github.com/ztgu/sorting_algorithms_py
(compsB, movesB) = ALGO.bubble(arrayCopy1d(ys))
(compsH, movesH) = ALGO.heap(arrayCopy1d(ys))
(compsI, movesI) = ALGO.insertion(arrayCopy1d(ys))
(compsM, movesM) = ALGO.merge(arrayCopy1d(ys))
(compsQ, movesQ) = ALGO.quick(arrayCopy1d(ys))
(compsS, movesS) = ALGO.selection(arrayCopy1d(ys))
# train output - comparison
trainOutputs_0.append([compsB, compsH, compsI, compsM, compsQ, compsS])
# train output - move
trainOutputs_1.append([movesB, movesH, movesI, movesM, movesQ, movesS])
# train output - total cost
trainOutputs_2.append([
compsB + movesB, compsH + movesH, compsI + movesI, compsM + movesM,
compsQ + movesQ, compsS + movesS
])
# 학습 데이터의 평균 및 표준편차 구하기
# 테스트 시에도 학습 데이터 기준으로 평균 및 표준편차 적용
menu = True
while(menu):
menu_selec = input("Select file option: \n1 - Single file \n2 - Multiple Files \n0 - Exit Program\n\n")
if(menu_selec == "1"):
file_path = input("Choose file for period analysis: ")
File_Management.read_input_file(file_path)
pass_data(menu_selec)
menu = False
elif(menu_selec == "2"):
files = File_Management.open_dir()
alg_choice = input("Select analysis method: \n1 - Time Series \n2 - Lomb-Scargle \n3 - Autocorrelation \n4 - Wavelets \n5 - All\n0 - Exit Program\n")
for path in files:
File_Management.read_input_file(path)
pass_data(menu_selec, alg_choice)
elif(menu_selec == "3"):
data_process.create_sin()
alg_choice = input("Select analysis method: \n1 - Time Series \n2 - Lomb-Scargle \n3 - Autocorrelation \n4 - Wavelets \n5 - All\n0 - Exit Program\n")
time, detrended_flux, background = data_process.get_data()
alg.selection(time, detrended_flux, alg_choice)
elif(menu_selec == "0"):
sys.exit()
else:
print("This is not a valid selection.")
def data_op(file_num=None, alg_choice=None):
""" This function takes in a file/s and an algorithm, and passes the given file data to the chosen algorithm.
Note: each of these parameters are optional in the event that the user does not select a choice from either of their
respective ComboBoxes, however if either is left as None this function will exit itself.
Args:
file_num (String): the amount of files chosen by the user. Either single or multiple files, or a test sinusoid.
alg_choice (String): the user's chosen algorithm.
"""
# Maps the algorithm choices to numbers for compatibility with algorithms.py
alg_dict = {
'Time Series': '1',
'Lomb-Scargle': '2',
'Autocorrelation': '3',
'Wavelets': '4',
'GPS': '5',
'All': '6'
}
# Prevents program from crashing in the event that the user doesn't select properly.
if file_num is None or file_num == "Select" or alg_choice is None or alg_choice == "Select":
tk.messagebox.showinfo(
"Error", "Please select both a file/folder and an algorithm")
elif file_num == "Single File":
# Prevents program from crashing in the event that the user closes the file selection window.
if not files:
tk.messagebox.showinfo("Error", "Error: No Files Selected")
return
# Also prevents program from crashing in the event that the user closes the file selection window.
elif files[0] == "" or files[0] is None:
tk.messagebox.showinfo("Error", "Error: No Files Selected")
return
else:
print(files[0])
File_Management.read_input_file(files[0])
time, detrended_flux, background = data_process.get_data()
time = [float(data) for data in time]
detrended_flux = [float(data) for data in detrended_flux]
noise = [float(data) for data in background]
alg_choice = alg_dict[alg_choice]
alg.selection(time, detrended_flux, alg_choice)
elif file_num == "Multiple Files":
# Iterates through the files in the selected folder and passes each one through the chosen algorithm.
# One potential issue with this is if the user intends to pass files through different algorithms.
for path in files:
# Prevents program from crashing in the event that the user chooses a folder containing bad file types.
if not (path.endswith('.csv') or path.endswith('.fits')):
continue
File_Management.read_input_file(path)
time, detrended_flux, background = data_process.get_data()
time = [float(data) for data in time]
detrended_flux = [float(data) for data in detrended_flux]
noise = [float(data) for data in background]
alg_new = alg_dict[alg_choice]
alg.selection(time, detrended_flux, alg_new)
# This option is not currently functional when used in sequence with a .csv file.
elif file_num == "Test Sinusoid":
data_process.create_sin()
time, detrended_flux, background = data_process.get_data()
time = [float(data) for data in time]
detrended_flux = [float(data) for data in detrended_flux]
noise = [float(data) for data in background]
alg_choice = alg_dict[alg_choice]
alg.selection(time, detrended_flux, alg_choice)