def pickfiles(name=None):
root = Tk()
root.withdraw()
if name is None:
filename = filedialog.askopenfilenames(parent=root,
title="Choose DVHs ")
else:
filename = filedialog.askopenfilenames(parent=root, title=name)
filename = list(filename)
return filename
def uigetfullfile(basefilename=""):
"""Act like uigetfile in matlab, returned filename can be multiple
"""
d = GetFolderName(basefilename)
root = tk.Tk()
root.withdraw()
if len(d) > 0:
file = filedialog.askopenfilenames(initialdir=d)
else:
file = filedialog.askopenfilenames()
fns = root.tk.splitlist(file)
return fns
def get_files(self, tk_root):
if tk_root is not None:
print("""If you want to pass files directly do it by interactive_plotting(filenames = list) """)
root = tk_root
files = filedialog.askopenfilenames(parent=tk_root,title='Choose files')
root.quit()
self.filenames = files
else:
print("""If you want to pass files directly do it by interactive_plotting(filenames = list) """)
root = Tk()
root.files = filedialog.askopenfilenames(title='Choose files')
self.filenames = root.files
root.withdraw()
def __init__(self, parent=None):
self.parent = parent
self.parent.statusbar.showMessage("AOI Aggregation in process...")
QWidget.__init__(self,parent)
self.ui = gui.aoiUi()
self.ui.setupUi(self)
self.batch_files = filedialog.askopenfilenames(parent=root, title='Choose the file(s) you want to AOI aggregate')
if len(self.batch_files) < 1:
msgBox = QMessageBox()
msgBox.setGeometry(400, 400, 400, 200)
msgBox.setText("Please select one or more datasets to aggregate")
msgBox.move(QApplication.desktop().screen().rect().center()- self.rect().center())
msgBox.exec_()
self.close()
return
self.columns = []
self.parent.statusbar.showMessage("Checking column validity...")
#extract columns
for item in self.batch_files:
self.columns.append(helpers.extract_columns(item))
#check to see if all columns are equal in all the datasets
if not helpers.are_columns_same(self.columns):
if not helpers.columns_not_equal_message(self):
self.close()
return
message = ""
if self.columns != []:
if 'GazeAOI' not in self.columns[0]:
message += "'GAZE AOI' "
if 'FixationAOI' not in self.columns[0]:
message += "'FIXATION AOI'"
if message != "":
message += ": Field(s) missing from dataset - try again with appropriate data."
reply = QMessageBox.question(self, 'Message',message, QMessageBox.Ok, QMessageBox.Ok)
if reply == QMessageBox.Ok:
self.parent.statusbar.showMessage("Welcome back!")
self.close()
return
self.parent.ui.logOutput.append("AOI AGGREGATED:")
print("AOI AGGREGATED:")
self.AOI_aggregate('GazeAOI')
self.AOI_aggregate('FixationAOI')
#after job has been completed!
helpers.job_complete_message(self)
self.parent.statusbar.showMessage("Welcome back!")
self.close()
def _askfilescaller(self):
"""Event handler for pressing "Compile" button.
Open tkinter AskOpenFilenames dialog.
dialog initialdir = last accessed dir or main dir
dialog filetypes = c source types (.c, .h, or all .*)
if files selected, set last dir and call func to update
sources list.
"""
initialdir = self.lastdir if self.lastdir else self.main_dir
sources = askopenfilenames(
initialdir=initialdir,
filetypes=self.srctypes
)
'''Do nothing if user didn't select anything'''
if sources:
sources = re.findall(
self.magic_filenames_ptrn,
''.join([' ',sources, ' '])
)
self.lastdir = "/".join(sources[0].split("/")[:-1])
self.UpdateSrcList(sources)
def add_files(self):
errlog = ''
try:
open = tkinter.Tk()
open.withdraw()
newfiles = filedialog.askopenfilenames()
newfiles = self.tk.splitlist(newfiles)
except Exception:
errlog += "{0}\n".format(traceback.format_exc())
self.show_error(
"Unable to add all files.\n" \
"See Error.log for more information.",
errlog
)
finally:
filelistpaths = []
for file in self.file_list:
filelistpaths.append(file[1])
newfiles = list(newfiles)
self.delduplicates(filelistpaths, newfiles)
for file in newfiles:
self.oldname_lstbx.insert(len(self.file_list), os.path.basename(file))
self.file_list.append([os.path.basename(file), file])
self.update_newname()
open.destroy()
def main():
# Ask for CPU_usage file in text format
root = tk.Tk()
root.withdraw()
Files_sel = filedialog.askopenfilenames()
Files_l = list(Files_sel)
Files_l.sort()
file_name = input("Enter a file name:")
for files in Files_l:
tar = tarfile.open(files, mode="r")
f = tar.getnames()
for filename in f:
if "meminfo" in filename:
f = tar.extractfile(filename)
content = f.read().splitlines()
header = header_create(content)
mem_parse(content, header, file_name)
def selectImages() :
del selectedImages[:] # Clear the list of selected images
x = filedialog.askopenfilenames(filetypes=myfiletypes)
if x == ('',):
messagebox.showerror("Unable to read library location","Could not identify the location of the selected file(s). \nInstead of browsing through Windows' libraries, try selecting Desktop in the left hand pane and then open the folder that says your username. \nIdeally, you would browse through the full path of the images. E.g. C:\\Users\\[your username]\\")
else:
for y in x:
selectedImages.append(y)
# Clear the selectedPicturesFrame
for child in selectedPicturesFrame.winfo_children():
child.destroy()
# Check whether we selected images
if not selectedImages:
Label(selectedPicturesFrame,text="None",font="none 9 italic").pack(padx=75)
else:
# Create the scrollbar and add it to our frame
listboxImagesScrollbar = Scrollbar(selectedPicturesFrame,orient=VERTICAL)
listboxImagesScrollbar.pack(side=RIGHT, fill=Y)
# Create a listbox and add it to our user interface
listboxImages = Listbox(selectedPicturesFrame)
listboxImages.pack(padx=(4,3))
# Loop through the selected images and add them to the listbox
for selectedImg in selectedImages:
listboxImages.insert(END,path.basename(selectedImg))
# Configure the scrollbar to interact with the listbox
listboxImages.config(height=4,width=26,yscrollcommand=listboxImagesScrollbar.set)
listboxImagesScrollbar.config(command=listboxImages.yview)
def plot_navigation(paths: List[str] = None):
"""
Plots navigation packets in the list of files. If paths is None, a file open dialog is shown
:param paths: List of paths to plot nav data for
:return: None
"""
# TODO: Plot navigation from other packets if navigation packets not present.
# Open file dialog if paths not specified
if not paths:
root_gui = tk.Tk()
root_gui.withdraw()
paths = filedialog.askopenfilenames(
title='Select XTF files...',
filetypes= [('eXtended Triton Files (XTF)', '.xtf')]
)
nav = [] # type: List[pyxtf.XTFHeaderNavigation]
for path in paths:
(fh, p) = xtf_read(path, types=[XTFHeaderType.navigation])
if XTFHeaderType.navigation in p:
nav.extend(p[XTFHeaderType.navigation])
# Sort by time
if nav:
nav.sort(key=XTFHeaderNavigation.get_time)
x = [p.RawXcoordinate for p in nav]
y = [p.RawYcoordinate for p in nav]
plt.plot(x, y)
plt.show()
else:
warn('No navigation packets present in XTF files')
def open_mass_spectrum(self):
try:
self.progress.reset_bar()
data_buffer = []
files = filedialog.askopenfilenames(title='Select Mass '+
'Spectrum File(s)')
self.task_label.set('Opening Mass Spectra')
for index, file in enumerate(files):
self.progress.counter.set((float(index) /
len(files))*100)
self.progress.update_progress_bar()
self.filename = file
mass_spec_buffer = MassSpectrum(self)
mass_spec_buffer.open_mass_spectrum()
data_buffer.append(mass_spec_buffer)
self.mass_spectra = data_buffer
self.task_label.set('Idle')
self.progress.fill_bar()
self.task_label.set('Plotting Mass Spectra')
if self.mass_spectra:
self.axes.clear()
for index, mass_spectrum in enumerate(self.mass_spectra):
self.progress.counter.set((float(index) /
len(files))*100)
self.progress.update_progress_bar()
mass_spectrum.plot_mass_spectrum()
finalize_plot(self)
self.task_label.set('Idle')
self.progress.fill_bar()
except Exception as e:
messagebox.showinfo('Warning','The selected files could '+
'not be opened.')
self.logger.error(e)
def main():
logging_level = logging.DEBUG
logging.Formatter.converter = time.gmtime
log_format = '%(asctime)-15s %(levelname)s:%(message)s'
logging.basicConfig(format=log_format, datefmt='%Y/%m/%d %H:%M:%S UTC', level=logging_level,
handlers=[logging.FileHandler('testparsedasbin.log'), logging.StreamHandler()])
logging.info('_____ Started _____')
t_step = 5 #60
home = os.path.expanduser('~')
start_dir = home
status = []
root = Tk()
root.withdraw() # this will hide the main window
list_of_bin_files = filedialog.askopenfilenames(filetypes=('binary {*.bin}', 'Binary files'),
initialdir = start_dir, initialfile = '')
for i in list_of_bin_files:
logging.info('processing ' + i)
status.append(pdb.parse_bin_files_to_text_files(in_filename=i, verbose_flag=True, dtm_format=True,
time_step = t_step))
if status[-1] < 256:
if status[-1] > 0:
messagebox.showwarning('Warning', 'Parsing of ' + i + ' ended with warning code ' + str(status[-1])
+ '.')
else:
messagebox.showerror('Error', 'Parsing of ' + i + ' ended with error code '+ str(status[-1]) + '!')
logging.info('______ Ended ______')
def add_files(entries, color):
path = filedialog.askopenfilenames()
for name in path:
if color == 'green':
listbox_green.insert(END, name)
else:
listbox_red.insert(END, name)
def file_chooser(): root = Tk() root.files = filedialog.askopenfilenames(title='Choose files') if len(files) == 1: files = files[0] root.withdraw() return root.files
def selectIMG(self): # function for select target image
self.imgName.configure(state="normal") # entry writable
self.imgName.delete(0, END) # clear entry area
self.text.delete(0.0, END) # clear info area
path = filedialog.askopenfilenames(
filetypes=[ # store multiple images as array to path
("image files", "*.png *.gif *.bmp"),
("All files", "*.*"),
]
)
self.selectedIMGs = {} # dict. store the image : size
for img in path:
oriSize = os.path.getsize(img) # size of target file
self.selectedIMGs[img] = oriSize # add into the dictionary
fileName = os.path.basename(img) # get the filename
# print(fileName)
self.imgName.configure(state="normal") # entry writable
self.imgName.insert(END, fileName + ";") # insert the image path
self.imgName.configure(state="readonly") # disable entry
self.text.insert(
END, fileName + ":" + str(round(oriSize / 1000 / 1000, 3)) + "MB\n"
) # display selected size
return self.selectedIMGs
def getFilenames(title, types=[], initialdir=None):
root = Tk()
root.withdraw()
filenames = filedialog.askopenfilenames(title=title, filetypes=types,
initialdir=initialdir)
root.destroy()
return root.tk.splitlist(filenames)
def load_new_tables(self):
'''Opens a file chooser dialog so that the user can choose table files
to load. Loads these into dicts.'''
file_opts = {"defaultextension": ".csv",
"title": "Choose table file(s)",
}
filenames = filedialog.askopenfilenames(**file_opts)
for f in filenames:
table_name = f[f.rfind('/')+1:f.rfind('.csv')]
try:
self.tables[table_name] = load_table(f)
except TableFormatError as err:
mbox.showwarning("Open file",
"Bad formatting: {}".format(err.err_msg))
return
except csvError:
mbox.showwarning("Open file",
"Cannot open file: Bad CSV input.")
return
except FileNotFoundError:
mbox.showwarning("Open file",
"Cannot open file: File not found.")
return
# Update the options in self.table_menu.
new_comm = tk._setit(self.chosen_table, table_name)
self.table_menu['menu'].add_command(label=table_name,
command=new_comm)
def add_files(self):
options = {}
config = self.parent.settings_frame.config
options['defaultextension'] = config["File"]["Extension"]
if options['defaultextension'] == ".txt":
options['filetypes'] = [('Text files', '.txt'), ('XML files', '.xml'),
('HTML files', '.html')]
if options['defaultextension'] == ".xml":
options['filetypes'] = [('XML files', '.xml'), ('Text files', '.txt'),
('HTML files', '.html')]
if options['defaultextension'] == ".html":
options['filetypes'] = [('HTML files', '.html'),
('Text files', '.txt'),
('XML files', '.xml')]
default_dir = config["File"]["InputDir"].strip()
if os.path.exists(default_dir):
options['initialdir'] = default_dir
else:
options['initialdir'] = ""
options['initialfile'] = ''
options['parent'] = self.setup_frame
options['title'] = 'Open corpus file(s)'
files_str = filedialog.askopenfilenames(**options)
for f in self.parent.root.splitlist(files_str):
self.files.append(f)
self.listbox.insert("end", os.path.basename(f))
def abrir_pt1(self):
self.status.configure(text = 'Abrindo arquivos...')
self.arquivos = sorted(filedialog.askopenfilenames(title='Abrir',
filetypes=[('SAC','*.sac')]))
if len(self.arquivos) > 0:
try:
for i in self.arquivos:
self.sts.append(read(i))
# for i in self.sts:
# print(i[0].stats.station)'''
self.abrir_pt2()
except:
messagebox.showerror("Sismologica","Erro na leitura do arquivo.")
self.status.configure(text = '')
del self.sts[:]
self.arquivo = None
def do_share(self):
"""Share songs."""
paths = filedialog.askopenfilenames()
if isinstance(paths, str): # http://bugs.python.org/issue5712
paths = self.master.tk.splitlist(paths)
logging.debug("paths: {}".format(paths))
for path in paths:
self.user.recommend(path)
self.update()
def showopen():
file_names = filedialog.askopenfilenames(title = "select your favourite song",filetypes=[("mp4 file","*.mp4"),("mp3 file","*.mp3")])
#askopenfilenames is basically a method of class filedialog present in tkinter module
print(type(file_names))
print((file_names))
str = ""
for x in file_names:
str+=x + "\n"
lbl.configure(text = str)
def OpenFileCommand(self):
InputfilesList = filedialog.askopenfilenames(title="Select the source input file(s)", filetypes=[("All files",".*")])
InputfilesList = self.root.tk.splitlist(InputfilesList)
for i in range ((len(InputfilesList))):
if InputfilesList[i][-3:].lower() in ('mp4','wav','avi','mov','wmv'):
self.queue_app.put(['setFileNameLIst', [InputfilesList]])
else:
messagebox.showinfo('error','choose a mp4, AVI, mov file' )
def get_filenames_UI(UIdir=None, UIcaption='', UIfilters=''):
"""
Custom function that is equivalent to Matlab's uigetfile command. Returns filename as a string.
filenamestring = GetFile(UIdir=None,UIcaption='',UIfilters='')
"""
withdraw_tkinter()
filenames = askopenfilenames()
return filenames
def Load_inputs_callback():
Load_filename = askopenfilenames()
List.delete(0,"end") #Should a load command delete the current set? With this in it will.
print(Load_filename[0])
with open(Load_filename[0]) as text_file:
print (text_file)
for i, l in enumerate(text_file):
List.insert("end", l)
def load_file(self):
if self.isAuth == 1:
fname = askopenfilenames()
size = len(fname)
for x in range(size):
piece = splitFiles(fname[x],key)
Encrypted_List.append((fname[x].rsplit('/', 1)[1]))
File_List.append((fname[x],piece))
self.init_EncryptedList()
def get_input_files(self):
"""Pop up the 'open file' dialog, assign whatever's picked to the input file entry box, and set an attribute with an iterable of the file selectors."""
options = {
"title": "Select file",
}
selectors = tkfd.askopenfilenames(**options)
self.input_selectors = selectors
self.infile.delete(0, tk.END)
self.infile.insert(0, selectors)
return None
def make_widgets(self):
MLabel(self, text='文件对话框演示')
self.label = MLabel(self, text='等待测试...')
MButton(self, text='打开目录', command=lambda: self.print_result(filedialog.askdirectory()))
MButton(self, text='打开文件', command=lambda: self.print_result(filedialog.askopenfile()))
MButton(self, text='选择文件名', command=lambda: self.print_result(filedialog.askopenfilename()))
MButton(self, text='选择多个文件名', command=lambda: self.print_result(filedialog.askopenfilenames()))
MButton(self, text='打开多个文件', command=lambda: self.print_result(filedialog.askopenfiles()))
MButton(self, text='保存为...', command=lambda: self.print_result(filedialog.asksaveasfile()))
MButton(self, text='保存为文件名...', command=lambda: self.print_result(filedialog.asksaveasfilename()))
def add(self):
file = tk.askopenfilenames(initialdir='C:/Users/babbu/Downloads')
songsTuple = root.splitlist(file) #turn user's opened filenames into tuple
songsList = list(songsTuple) #convert to list
#Add the full filename of songto playlist list, and a shortened version to the listBox
for song in songsList:
playlist.append(song);
tempArray = song.split('/')
songShort = tempArray[len(tempArray)-1]
self.playlistbox.insert(END, songShort)
def getFilenames(title, types=[], initialdir=None):
try:
root = Tk()
root.withdraw()
filenames = filedialog.askopenfilenames(title=title, filetypes=types,
initialdir=initialdir)
root.destroy()
return root.tk.splitlist(filenames)
except OSError:
print("No files selected!")
def add_file(self):
new_files = askopenfilenames(filetypes=[("Wave", "*.wav")])
if new_files:
for new_file in new_files:
try:
self.mp3_engine.add_audio_file(new_file)
except RuntimeError:
showerror("Import Error", new_file + " is already in your queue.")
self.flush_tree()
def doCopy():
global entryWidget
global statusWidget
srcPath = "c:\\temp\\src"
destPath = "c:\\temp\\dest"
readMeFilePath = "c:\\temp\\dest\\ReadMe.txt"
#prompt for file selection
options = {}
options['title'] = "Select APEX Files"
options['initialdir'] = srcPath
options['filetypes'] = [("txt files",".txt")]
selectedFiles = askopenfilenames(**options)
#split selected files string into a list
fileList = root.tk.splitlist(selectedFiles)
#delete .apex existing files
deleteFiles = entryDelete.get().strip()
if deleteFiles == "yes":
for filename in os.listdir(destPath):
deleteFile = os.path.join(destPath, filename)
if deleteFile.endswith(".txt"):
os.remove(deleteFile)
statusWidget.insert(END, "\n" + " Deleted File: " + filename)
root.update_idletasks()
#update status
statusWidget.insert(END, "\n\n" + "Selected Files")
for file in fileList:
statusWidget.insert(END, "\n" + str(file))
root.update_idletasks()
#copy the files
with open(readMeFilePath, mode = "a") as readMeFile:
now = datetime.datetime.now()
readMeFile.write("\n\n" + os.environ.get("USERNAME", "Butterfield") + " - " + now.strftime("%m-%d-%Y %H:%M %p"))
readMeFile.write("\n" + entryWidget.get().strip())
readMeFile.write("\n" + "Installed the following packages:")
for copyFile in fileList:
statusWidget.insert(END, "\n\n" + "Copying File: " + copyFile)
statusWidget.yview(MOVETO, 1.0)
root.update_idletasks()
shutil.copy(copyFile, destPath)
readMeFile.write("\n [" + os.path.split(copyFile)[1] + "]")
#update status
statusWidget.insert(END, " - DONE!")
root.update_idletasks()
statusWidget.insert(END, "\n\n" + "COMPLETE!")
statusWidget.yview(MOVETO, 1.0)
def clicked():
plot.cla()
plot2.cla()
file = filedialog.askopenfilenames()
input = text.get("1.0", tkinter.END)
# se l'utente non specifica il gap, ne viene settato uno di default
if text.compare("end-1c", "==", "1.0") or isinstance(input,
str) or input <= 0:
gap_value = def_gap
else:
gap_value = float(input)
selection = v.get()
instanceProblem = setting_data_problem(file)
# calcolo della soluzione ottima intera
integer_optimal_solution = solve_problem_binario(instanceProblem)
integer_optimal_solution = float(integer_optimal_solution)
canvas = FigureCanvasTkAgg(figure, window)
canvas.get_tk_widget().grid(row=10, column=3)
canvas2 = FigureCanvasTkAgg(figure2, window)
canvas2.get_tk_widget().grid(row=10, column=4)
if selection == 1:
# Gomory cuts interi
percent_limit = gap_value
iteration_manager = GomoryIterationManager(float(0), False,
percent_limit)
solve_problem_intero(instanceProblem, iteration_manager)
elif selection == 2:
# Gomory cuts frazionari
percent_limit = gap_value
iteration_manager = GomoryIterationManager(float(0), False,
percent_limit)
solve_problem_frazionario(instanceProblem, iteration_manager)
elif selection == 3:
# Gomory cuts interi multipli
percent_limit = gap_value
iteration_manager = GomoryIterationManager(float(0), False,
percent_limit)
solve_problem_multiple_interi(instanceProblem, iteration_manager)
elif selection == 4:
# Gomory cuts frazionari multipli
percent_limit = gap_value
iteration_manager = GomoryIterationManager(float(0), False,
percent_limit)
solve_problem_multiple_frazionario(instanceProblem, iteration_manager)
x1 = [0, instanceProblem.num_try]
y1 = [integer_optimal_solution, integer_optimal_solution]
plot.plot(x1, y1)
x2 = [0] * instanceProblem.num_try
y2 = [0] * instanceProblem.num_try
for i in range(instanceProblem.num_try):
x2[i] = i
y2[i] = instanceProblem.optimal_solution[i]
plot.plot(x2, y2, color="lightblue", marker=".", linestyle="")
plot2.plot(x2, y2, color="lightblue", marker=".", linestyle="")
final_lbl0 = Label(window, text=" ")
final_lbl0.grid(column=3, row=10)
final_lbl = Label(window,
text=" Numero di iterazioni : " +
str(round(instanceProblem.num_try, 6)))
final_lbl.grid(column=3, row=13)
final_lbl01 = Label(window, text=" ")
final_lbl01.grid(column=3, row=14)
final_lbl2 = Label(window,
text=" Valore iniziale : " +
str(round(instanceProblem.optimal_solution[0], 6)))
final_lbl2.grid(column=3, row=15)
final_lbl3 = Label(
window,
text=" Valore finale : " +
str(round(instanceProblem.optimal_solution[instanceProblem.num_try],
6)))
final_lbl3.grid(column=3, row=16)
incremento = instanceProblem.optimal_solution[
instanceProblem.num_try] - instanceProblem.optimal_solution[0]
final_lbl4 = Label(
window,
text=" Incremento : " + str(round(incremento, 6)) + " ( " +
str(round(100 * incremento / instanceProblem.optimal_solution[0], 6)) +
" % )")
final_lbl4.grid(column=4, row=15)
final_lbl5 = Label(
window,
text=" Distanza dall'ottimo intero : " + str(
round(
integer_optimal_solution -
instanceProblem.optimal_solution[instanceProblem.num_try], 6)))
final_lbl5.grid(column=4, row=16)
from tkinter import filedialog
#%% TKinter GUI for selecting files.
root = tk.Tk()
root.overrideredirect(True)
root.geometry('0x0+0+0')
root.focus_force()
initial_folder = "C:/" # Change the default directory here.
FT = [("All files", "*.*"), ("Abf files", "*.abf"), ("Excel files", "*.xlsx"),
("CSV files", "*.csv")]
ttl = 'Select Files'
file_paths = filedialog.askopenfilenames(parent=root,
title=ttl,
filetypes=FT,
initialdir=initial_folder)
root.withdraw()
#%% Loop for each selected file
for file_path in file_paths:
split = os.path.split(file_path)
file_extension = os.path.splitext(split[1])[1][1:]
file_name = os.path.basename(os.path.normpath(file_path))
# Extracting the signals according to type of files
if file_extension == 'abf':
# if "data" in cName and "python" in cName:
# pyColumn = i
# print("data types in python in column :", i)
if cName==hh:
# print("header #", i)
# print(cName)
# print("found it!")
return i
if i == len(Canheader)-1:
print("no column in canvas was found associated with this assignment!")
col = int(input("Enter the column to edit in CANVAS, refer to CanvasColumn.xlsx file in TA drive: "))
return col+3
root = tkinter.Tk()
root.withdraw()
filez = filedialog.askopenfilenames(parent=root,title='SELECT ALL HACKERANK FILES')
fileList = list(filez)
for i in range(0, len(fileList)):
with open(fileList[i]) as csvDataFile:
csvReader = csv.reader(csvDataFile)
Hr = list(csv.reader(csvDataFile))
head, HRtail = os.path.split(fileList[i])
print("You have successfully imported HR file ", HRtail)
numrows1 = len(Hr)
numcols1 = len(Hr[0])
col = getCol(HRtail)
print("this is the respective column : ", col)
if col < 46:
points = 100
def getDataBaseToBeUsed():
root = tk.Tk()
root.withdraw(
) # we don't want a full GUI, so keep the root window from appearing
paths = askopenfilenames(title='Choose the databases')
return paths
def btn_select_clicked(self):
self.path.insert(0, filedialog.askopenfilenames())
self.btn_upload.config(state="normal")
def findPeakPos(self):
file_path = filedialog.askopenfilenames()
folder = file_path[0].split('/')
path = ''
for i in range(0, len(folder) - 1):
path = path + folder[i] + "/"
path = path + 'Resolution.txt'
try:
with open(path, 'r') as f:
for line in f.readlines():
category, value = line.strip().split(';')
self.deltaT = float(value)
except:
self.deltaT = FileHandle.getResolution()
self.LoadCsvBar.setMaximum(len(file_path))
self.show()
diff_peaks = []
pos_array = []
stringdata = ""
for numb, i in enumerate(file_path):
val = []
self.LoadCsvBar.setValue(numb + 1)
QtGui.QGuiApplication.processEvents()
try:
with open(i) as csv_file:
reader = csv.reader(csv_file, delimiter=';')
for row in reader:
if len(row) == 1:
val.append(float(row[0]))
else:
val.append(float(row[1]))
except:
break
self.close()
return
valarray = np.asarray(val)
maxval = np.max(valarray)
peaks, _ = find_peaks(valarray,
height=(maxval * 0.8, maxval),
distance=19 / self.deltaT)
diff_peaks.append(peaks * self.deltaT)
pos_array = np.asarray(list(peaks * self.deltaT))
for i in pos_array:
stringdata = stringdata + str(i) + ";"
stringdata = stringdata + "\n"
diff_peaks = list(diff_peaks)
diff = []
for i in diff_peaks:
diff = np.append(diff, i[1:-1] - i[0:-2])
FileHandle.SaveData(time=range(len(diff)), data=diff)
try:
savefile = filedialog.asksaveasfile(mode='w',
defaultextension=".csv").name
with open(savefile, "w+") as f:
f.write(stringdata)
#for val in stringdata:
# print(val)
# f.write(val)
except:
return
self.close()
def select_file():
global log_file
log_file = filedialog.askopenfilenames(title="请选择剧本文件", filetypes=[("Text", "*.txt"), ("All Files", "*")])[0]
from paths import master_path
from functions_misc import tk_killwindow
from tkinter.filedialog import askopenfilenames
import numpy as np
from functions_gratings import calculate_dsi_osi, project_angles
import matplotlib.pyplot as plt
tk_killwindow()
# get the files
filenames = askopenfilenames(initialdir=master_path)
# for all the filenames
for files in filenames:
# load the data
contents = np.load(files, allow_pickle=True)
data = contents['data'].item()
# check if there is a DG field, otherwise skip it
if 'DG' not in data:
continue
# calculate DSI, OSI for each ROI
data = data['DG']
# get the number of ROIs
roi_number = data['data'].shape[0]
# calculate the trial average
trial_average = np.nanmean(data['data'], axis=2)
# get the angle exponentials
angle_exponentials = project_angles(np.unique(data['trial_info'][:, 1]).astype(np.int32))
# allocate memory for the indexes
dsi_osi_list = [calculate_dsi_osi(trial_average[roi, :, :], angle_exponentials) for roi in np.arange(roi_number)]
from tkinter import filedialog as fd
from tkinter import messagebox
import os
import tarfile
# import sys, and then the gdal_merge from the local file location
import sys
sys.path.append(r"C:\Program Files\QGIS 2.18\bin")
import gdal_merge
# setup TK window and hide so only dialogs are visible
root = Tk()
root.withdraw()
# user input of landsat tar.gz filenames to be extracted, abort if no files selected
fileraw = fd.askopenfilenames(initialdir = "/", title = "Select Landsat tar.gz files")
if len(fileraw) == 0:
messagebox.showwarning("File Selection", "No files selected")
quit()
# user input of output file location, abort if no folder selected
outlocation = fd.askdirectory(title="Please select an output folder")
if len(outlocation) == 0:
messagebox.showwarning("Output location", "No output folder selected")
quit()
# convert tuple of files into list, and loop through each tar file, performing extraction and merge
filelist = list(fileraw)
for infile in filelist:
# if file is not a tar.gz file, skip
from tkinter import filedialog
from os import path
empty_set = set()
empty_list = list()
aggr_list = list()
cnt = 0
temp = 0
sheet_name_array = []
hoge = []
select_file = tk.Tk()
select_file.withdraw()
full_path = filedialog.askopenfilenames()
for f in full_path:
drive, tail = path.split(f)
root, ext = path.splitext(f)
wb = xlrd.open_workbook(f)
sheet_name_array = wb.sheet_names()
sheet = wb.sheet_by_name('解析結果')
for row in range(3, sheet.nrows):
hoge = str(sheet.cell(row, 3).value).split('\n')
for hogehoge in hoge:
empty_set.add(hogehoge)
empty_set.remove('')
empty_set.remove('42')
import os
import openpyxl
import tkinter as tk
import tkinter.filedialog as tkdialog
import tkinter.messagebox as tkmbox
import module_spfi_csv_cov_pd_rev01 as csvconv
root = tk.Tk() #ウィンドウオブジェクトを作成
root.withdraw() #ウィンドウを表示しない設定
fTyp = [("", "csv")] #ファイルタイプ
iDir = os.path.abspath(os.path.dirname(__file__)) #ファイルのdir名からdirパスを取得
tkmbox.showinfo('csv変換', '変換するCSVファイルを選択してください(複数可)')
files = tkdialog.askopenfilenames(filetypes=fTyp,
initialdir=iDir) #ファイルのフルパスのタプルを作成
for file in files:
csvconv.spfi_csv_conv(file)
from PIL import Image
from tkinter import Tk, filedialog
root = Tk()
root.withdraw()
alpha = Image.open(
filedialog.askopenfilename(filetypes=[("Alpha Image",
" *.png;*.bmp;*.jpg")]))
alpha = alpha.getchannel("A")
inputs = filedialog.askopenfilenames(filetypes=[("Input Images",
" *.png;*.bmp;*.jpg")])
for image in inputs:
im = Image.open(image)
im = Image.merge(
"RGBA",
(im.getchannel("R"), im.getchannel("G"), im.getchannel("B"), alpha))
im.save(image)
quit()
def MakeAvg(self, printer):
file_path = filedialog.askopenfilenames()
folder = file_path[0].split('/')
path = ''
for i in range(0, len(folder) - 1):
path = path + folder[i] + "/"
path = path + 'Resolution.txt'
try:
with open(path, 'r') as f:
for line in f.readlines():
category, value = line.strip().split(';')
self.deltaT = float(value)
except:
self.deltaT = FileHandle.getResolution()
cutval = []
self.LoadCsvBar.setMaximum(len(file_path))
self.show()
for numb, i in enumerate(file_path):
val = []
self.LoadCsvBar.setValue(numb + 1)
QtGui.QGuiApplication.processEvents()
try:
with open(i) as csv_file:
reader = csv.reader(csv_file, delimiter=';')
for row in reader:
if len(row) == 1:
val.append(float(row[0]))
else:
val.append(float(row[1]))
except:
break
self.close()
return
valarray = np.asarray(val)
maxval = np.max(valarray)
peaks, _ = find_peaks(valarray,
height=(maxval * 0.6, maxval),
distance=19 / self.deltaT)
distance = peaks[1:-1] - peaks[0:-1 - 1]
try:
if np.min(distance) * self.deltaT < 18 or np.max(
distance) * self.deltaT > 25:
continue
except:
continue
if (min(peaks) < 20):
continue
print(i)
valarray = valarray[peaks[0] + 700:-1]
cutval.append(valarray)
#############################################
#self.close()
plt.subplot(2, 1, 1)
plt.grid(which='both', axis='both')
print("/////////////////")
length = []
for i in cutval:
length.append(len(i))
try:
minlength = np.min(length)
except:
self.close()
return
Timeaxis = np.arange(0, minlength, 1) * self.deltaT
timemask = Timeaxis < 210
Timeaxis = Timeaxis[timemask]
avg = np.zeros(len(Timeaxis))
for number, i in enumerate(cutval):
cutval[number] = i[0:minlength]
cutval[number] = cutval[number][timemask]
avg = avg + cutval[number]
plt.plot(Timeaxis, cutval[number])
avg = np.true_divide(avg, len(cutval))
print(len(cutval))
plt.xlabel(r"Zeit t [ps]")
plt.ylabel(r"V$_{\rm{TIA}}$")
###################################################################################
plt.subplot(2, 1, 2)
plt.plot(Timeaxis, avg, label='Mittelwert')
plt.grid(which='both', axis='both')
plt.xlabel(r"Zeit t [ps]")
plt.ylabel(r"V$_{\rm{TIA}}$")
plt.xlabel(r"Zeit t [ps]")
plt.legend()
self.close()
FileHandle.SaveData(Timeaxis, avg)
plt.show()
def main(argv):
config = read_parser(argv, Inputs, InputsOpt_Defaults)
from Main_Analysis import invoke_signal
if config['mode'] == 'avg_spectrum':
n_avg = config['n_avg']
magX_list = []
for i in range(n_avg):
dict_int = invoke_signal(config)
x = dict_int['signal']
t = dict_int['time']
f = dict_int['freq']
magX = dict_int['mag_fft']
filename = dict_int['filename']
if i == 0:
magX_avg = np.zeros(len(magX))
magX_avg = magX_avg + magX
magX_avg = magX_avg / float(n_avg)
fig, ax = plt.subplots()
ax.plot(f, magX_avg)
ax.set_xlabel('Frequency [Hz]')
plt.show()
if config['save'] == 'ON':
mydict = {}
mydict['freq_array'] = f
mydict['avg_spectrum'] = magX_avg
if config['name'] == 'auto':
import datetime
stamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
else:
stamp = config['name']
save_pickle('avg_spectrum_' + config['channel'] + stamp + '.pkl', mydict)
else:
print('no save')
# plt.plot(f/1000., magX_avg)
# plt.xlabel('Frequenz [kHz]')
# params = {'mathtext.default': 'regular' }
# plt.rcParams.update(params)
# plt.ylabel('Amplitude [mV]')
# plt.show()
elif config['mode'] == 'plot_spectrum':
magX_avg_list = []
f_list = []
root = Tk()
root.withdraw()
root.update()
Filenames = filedialog.askopenfilenames()
root.destroy()
for filename in Filenames:
print(os.path.basename(filename))
mydict = read_pickle(filename)
f_list.append(mydict['freq_array'])
magX_avg_list.append(mydict['avg_spectrum'])
channel = 'AE_1'
element = 'Mag-FFT'
fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True, sharey=True)
# ax[].set_title(channel + ' ' + element + '\n' + os.path.basename(filename), fontsize=10)
ax[0].set_title('BMB am Punkt D', fontsize=10, fontweight='bold')
ax[1].set_title('Drahtbuerste am Punkt F', fontsize=10, fontweight='bold')
ax[2].set_title('Kugelfall Maximale Hoehe', fontsize=10, fontweight='bold')
ax[0].title.set_position([0.5, 1.0])
ax[1].title.set_position([0.5, 0.8])
ax[2].title.set_position([0.5, 0.8])
ax[0].plot(f_list[0]/1000., magX_avg_list[0], 'r')
ax[1].plot(f_list[1]/1000., magX_avg_list[1], 'r')
ax[2].plot(f_list[2]/1000., magX_avg_list[2], 'r')
ax[2].set_xlabel('Frequenz kHz')
ax[0].ticklabel_format(axis='y', style='sci', scilimits=(-1, 1))
ax[1].ticklabel_format(axis='y', style='sci', scilimits=(-1, 1))
ax[2].ticklabel_format(axis='y', style='sci', scilimits=(-1, 1))
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
ax[0].set_ylabel('Amplitude (m$V_{in}$)')
ax[1].set_ylabel('Amplitude (m$V_{in}$)')
ax[2].set_ylabel('Amplitude (m$V_{in}$)')
plt.show()
elif config['mode'] == 'plot_avg_spectrum':
magX_avg_list = []
f_list = []
root = Tk()
root.withdraw()
root.update()
Filenames = filedialog.askopenfilenames()
root.destroy()
for filename in Filenames:
print(os.path.basename(filename))
mydict = read_pickle(filename)
f_list.append(mydict['freq_array'])
magX_avg_list.append(mydict['avg_spectrum'])
channel = config['channel']
element = 'Mag-FFT'
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True)
# ax[].set_title(channel + ' ' + element + '\n' + os.path.basename(filename), fontsize=10)
# ax.set_title(config['channel'] + ' FFT-Average WB-Source at F', fontsize=10, fontweight='bold')
# title = config['channel'] + ' FFT-Mittelwert Hsu-Nielsen-Bursts'
title = config['channel'] + ' FFT-Mittelwert'
title = title.replace('_', '-')
ax[0].set_title('Mit Lack', fontsize=12)
ax[1].set_title('Ohne Lack', fontsize=12)
# ax.ticklabel_format(axis='y', style='sci', scilimits=(-1, 1))
ax[0].plot(np.array(f_list[0])/1000., magX_avg_list[0]*1000., 'r')
ax[1].plot(np.array(f_list[1])/1000., magX_avg_list[1]*1000., 'darkred')
# ax.set_xlabel('Frequency (kHz)')
ax[1].set_xlabel('Frequenz [kHz]', fontsize=12)
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
ax[1].set_ylabel('Amplitude [mV]', fontsize=12)
ax[0].set_ylabel('Amplitude [mV]', fontsize=12)
ax[0].tick_params(axis='both', labelsize=11)
ax[1].tick_params(axis='both', labelsize=11)
plt.tight_layout()
plt.show()
else:
print('unknown mode')
return
def LoadPLot(self):
file_path = filedialog.askopenfilenames()
try:
folder = file_path[0].split('/')
except:
return
path = ''
for i in range(0, len(folder) - 1):
path = path + folder[i] + "/"
path = path + 'Resolution.txt'
try:
with open(path, 'r') as f:
for line in f.readlines():
category, value = line.strip().split(';')
self.deltaT = float(value)
print(self.deltaT)
except:
self.deltaT = FileHandle.getResolution()
print(self.deltaT)
self.LoadCsvBar.setMaximum(len(file_path))
self.show()
try:
for numb, i in enumerate(file_path):
avg = []
Timearray = []
oneax = True
self.LoadCsvBar.setValue(numb + 1)
QtGui.QGuiApplication.processEvents()
with open(i) as csv_file:
reader = csv.reader(csv_file, delimiter=';')
for row in reader:
if len(row) == 2:
Timearray.append(float(row[0]))
avg.append(float(row[1]))
oneax = False
else:
avg.append(float(row[0]))
avgarray = np.asarray(avg)
if oneax:
Timearray = np.arange(0, len(avgarray), 1) * self.deltaT
plt.plot(Timearray, avgarray)
plt.xlabel(r"Zeit t [ps]")
plt.ylabel(r"V$_{\rm{TIA}}$")
del Timearray
del avgarray
del avg
gc.collect()
except:
self.close()
return
self.close()
plt.grid()
plt.show()
gc.collect()
return
def plotSelectedGraph(self):
selectedFiles = askopenfilenames()
x = []
y = []
x2 = []
y2 = []
x3 = []
y3 = []
x4 = []
y4 = []
x5 = []
y5 = []
x6 = []
y6 = []
xall = []
yall = []
if len(selectedFiles) == 1:
with open(selectedFiles[0]) as f:
for i, line in enumerate(f):
if i > 8:
x.append(int(line.split()[0])/ 1000.0)
y.append(float(line.split()[1]))
elif len(selectedFiles) == 2:
with open(selectedFiles[0]) as f:
for i, line in enumerate(f):
if i > 8:
x.append(int(line.split()[0])/ 1000.0)
y.append(float(line.split()[1]))
with open(selectedFiles[1]) as f:
for i, line in enumerate(f):
if i > 8:
x2.append(int(line.split()[0])/ 1000.0)
y2.append(float(line.split()[1]))
elif len(selectedFiles) == 3:
with open(selectedFiles[0]) as f:
for i, line in enumerate(f):
if i > 8:
x.append(int(line.split()[0])/ 1000.0)
y.append(float(line.split()[1]))
with open(selectedFiles[1]) as f:
for i, line in enumerate(f):
if i > 8:
x2.append(int(line.split()[0])/ 1000.0)
y2.append(float(line.split()[1]))
with open(selectedFiles[2]) as f:
for i, line in enumerate(f):
if i > 8:
x3.append(int(line.split()[0])/ 1000.0)
y3.append(float(line.split()[1]))
elif len(selectedFiles) == 6:
with open(selectedFiles[0]) as f:
for i, line in enumerate(f):
if i > 8:
x.append(int(line.split()[0])/ 1000.0)
y.append(float(line.split()[1]))
with open(selectedFiles[1]) as f:
for i, line in enumerate(f):
if i > 8:
x4.append(int(line.split()[0])/ 1000.0)
y4.append(float(line.split()[1]))
with open(selectedFiles[2]) as f:
for i, line in enumerate(f):
if i > 8:
x2.append(int(line.split()[0])/ 1000.0)
y2.append(float(line.split()[1]))
with open(selectedFiles[3]) as f:
for i, line in enumerate(f):
if i > 8:
x5.append(int(line.split()[0])/ 1000.0)
y5.append(float(line.split()[1]))
with open(selectedFiles[4]) as f:
for i, line in enumerate(f):
if i > 8:
x3.append(int(line.split()[0])/ 1000.0)
y3.append(float(line.split()[1]))
with open(selectedFiles[5]) as f:
for i, line in enumerate(f):
if i > 8:
x6.append(int(line.split()[0])/ 1000.0)
y6.append(float(line.split()[1]))
# 4 files selected
else:
with open(selectedFiles[0]) as f:
for i, line in enumerate(f):
if i > 8:
x.append(int(line.split()[0])/ 1000.0)
y.append(float(line.split()[1]))
with open(selectedFiles[1]) as f:
for i, line in enumerate(f):
if i > 8:
x2.append(int(line.split()[0])/ 1000.0)
y2.append(float(line.split()[1]))
with open(selectedFiles[2]) as f:
for i, line in enumerate(f):
if i > 8:
x3.append(int(line.split()[0])/ 1000.0)
y3.append(float(line.split()[1]))
with open(selectedFiles[3]) as f:
for i, line in enumerate(f):
if i > 8:
xall.append(int(line.split()[0]) / 1000.0)
yall.append(float(line.split()[1]))
plt.plot(x, y, 'r.-')
plt.plot(x2, y2, 'y.-')
plt.plot(x3, y3, 'g.-')
plt.plot(x4, y4, 'y.-')
plt.plot(x5, y5, 'kx')
plt.plot(x6, y6, 'cx')
# plt.plot(xall,yall, 'c')
# plt.plot(xall,yall, linestyle = '-')
#plt.ylim(0, 5000)
plt.title('Voltage Sweep Over Time')
plt.xlabel('Time(ms)')
plt.ylabel('Voltage(mV)')
plt.show()
def get_img_paths(self):
self.img_paths = list(
filedialog.askopenfilenames(initialdir="C:/", title="Images"))
self.img_paths_field.delete(0, len(self.img_paths_field.get()))
self.img_paths_field.insert(0, str(self.img_paths))
# plot_trial_feature.py
from tkinter.filedialog import askopenfilenames
import matplotlib.pyplot as plt
from scipy import signal
import numpy as np
import pandas as pd
import sys,os
import re
files = askopenfilenames()
print(files)
primitives = ['x-corr','y-corr']
derivatives = ['x-vel','y-vel','speed']
for file in files:
df = pd.read_csv(file)
FPS = float(input('Enter FPS for this feature timeseries: '))
fig, axes = plt.subplots(len(primitives)+len(derivatives)+1,1,squeeze=False,sharex=True,figsize=(16,10))
b,a = signal.butter(5,0.2)
for i,feature in enumerate(primitives):
# convert mm to m
if ( ( feature == 'x-corr') | ( feature == 'y-corr' ) ):
df[feature] = df[feature] / 1000
unit = 'm'
df['{}-filtered'.format(feature)] = signal.filtfilt(b,a,df[feature].fillna(value=0.0).to_numpy())
axes[i,0].plot(df['time'],df[feature],color='dimgray',label='original')
axes[i,0].plot(df['time'],df['{}-filtered'.format(feature)],color='k',linestyle='--',label='filtered')
axes[i,0].set_ylabel('{} ({})'.format(feature,unit))
def add_files(self) -> None:
self.listbox.insert('end',
*map(try_relpath, tkfiledialog.askopenfilenames()))
def filename_multi(self):
""" Get multiple existing file locations """
logger.debug("Popping Filename browser")
return filedialog.askopenfilenames(**self.kwargs)
def import_file():
filelist = tkfd.askopenfilenames(**self.file_opt)
global files
files = [i.split('/')[-1] for i in filelist]
filestuple = tuple(files)
pcr_text.insert(INSERT, filestuple)
def loadSettings(self):
settingFilePath = filedialog.askopenfilenames(
parent=self,
initialdir=self.home,
title='Choose the settings file to load',
filetypes=[("Setting files", ".fcntm"), ("All files", ".*")],
multiple=False)
if not settingFilePath:
return
with open(settingFilePath[0], 'r') as f:
settings = f.read().split('\n')
settings = list(map(lambda s: s.strip('\t'), settings))
orgTest = [
settings[0] == 'Output folder:',
settings[2] == "Analysis ('voxel' or 'region'):",
settings[4] == 'Number of parallel processes:',
settings[6] == "Priors stored as ('h5' or 'nii'):",
settings[8] == 'Position of the subjects ID in their path:',
settings[10] == 'Mask the output:',
settings[12] == 'Number of subjects:',
settings[14] == 'Number of masks:',
settings[16] == "Subject's BOLD paths:",
settings[16 + int(settings[13]) +
2] == "Masks for voxelwise analysis:"
]
if not all(orgTest):
messagebox.showwarning('Bad file',
'The file is not properly organised.',
parent=self)
return
try:
self.outDir.set(settings[1])
self.maskOutput.set(int(settings[11]))
if settings[3] in ('region', 'voxel'):
self.ana_type.set(settings[3])
else:
messagebox.showwarning(
'Bad value',
f"Bad analysis type, should be 'region' or 'voxel', but is '{settings[3]}'",
parent=self)
return
try:
int(settings[5])
except:
messagebox.showwarning(
'Bad value',
'The number of processes should be an integer',
parent=self)
return
self.nb_parallel_proc.set(settings[5])
if settings[7] in ('h5', 'nii'):
self.priors.set(settings[7])
else:
messagebox.showwarning(
'Bad value',
f"Bad prior type, should be 'h5' or 'nii', but is '{settings[7]}'",
parent=self)
return
try:
int(settings[9])
except:
messagebox.showwarning(
'Bad value',
"The posistion of the subject's ID should be an integer",
parent=self)
return
self.bold_paths = []
self.nbFiles.set(int(settings[13]))
for isub in range(17, 17 + int(settings[13])):
self.bold_paths.append(settings[isub])
if settings[9] == "-1":
self.subInPath.set('0')
self.exSubName.set('Ex: ')
self.exPath = ()
else:
self.subInPath.set(settings[9])
self.exPath = tuple(
filter(
None,
os.path.normpath(self.bold_paths[0]).split(
os.path.sep)))
self.posSubName.config(to=len(self.exPath) - 1)
self.exSubName.set('Ex: ' +
self.exPath[int(self.subInPath.get())])
self.mask_paths = []
self.nbMasks.set(int(settings[15]))
startline = 19 + int(settings[13])
stopline = startline + int(settings[15])
for isub in range(startline, stopline):
self.mask_paths.append(settings[isub])
if len(settings) > 19 + int(settings[13]) + int(settings[15]):
sett_tail = "".join(
settings[19 + int(settings[13]) +
int(settings[15]):]) # convert to 1 long string
if "###" in sett_tail:
indStop = sett_tail.find("###")
sett_tail = sett_tail[:indStop]
sett_tail = "".join(
sett_tail.split()) # remove all whitespaces
if sett_tail: # Check if there is anything left
messagebox.showwarning(
'More data than expected in the file',
('There are non-empty lines trailing after the last considered line '
'in the settings file.\n'
'Check if the number of files announced is the same as the actual '
'number of paths written in the file.'),
parent=self)
except:
messagebox.showwarning(
'Bad file',
'There is a problem with the setting file. Check if its syntax is correct.',
parent=self)
def open_filenames(self):
# 调用askopenfilenames方法获取多个文件的文件名
print(filedialog.askopenfilenames(title='打开多个文件',
filetypes=[("文本文件", "*.txt"), ('Python源文件', '*.py')], # 只处理的文件类型
initialdir='g:/')) # 初始目录
def get_source_video_path(self):
# 获取到需要添加片头的视频
source_video_path = filedialog.askopenfilenames(title="选择视频")
self.source_entry.delete(0, END)
self.source_entry.insert(0, source_video_path)
def AnalyzeCMMspectralvar(self):
file_path = filedialog.askopenfilenames(
title="Please select the cmm summary files")
directory = str(Path(file_path[0]).parent.parent) + '\\resultFilesCMM'
if not os.path.exists(directory):
os.makedirs(directory)
os.chdir(directory)
else:
os.chdir(directory)
for pathtoread in file_path:
name = os.path.split(pathtoread)[-1].split(".")[
0] #sample name is the file name
with open(pathtoread) as f:
reader = csv.reader(f, delimiter="\t")
d = list(reader)
VocFor = []
VocRev = []
JscFor = []
JscRev = []
FFFor = []
FFRev = []
PmppFor = []
PmppRev = []
VmppFor = []
VmppRev = []
JmppFor = []
JmppRev = []
RocFor = []
RocRev = []
RscFor = []
RscRev = []
illumination = []
for i in range(
1, len(d)
): #we suppose here that the cell was scanned in both directions (forward and reverse) and that both data will be found for all illumination settings
if '_rev' in d[i][2]:
VocRev.append(float(d[i][3]))
JscRev.append(float(d[i][4]))
FFRev.append(float(d[i][5]))
PmppRev.append(float(d[i][6]))
RocRev.append(float(d[i][7]))
RscRev.append(float(d[i][8]))
VmppRev.append(float(d[i][9]))
JmppRev.append(float(d[i][10]))
else:
illumination.append(d[i][2])
VocFor.append(float(d[i][3]))
JscFor.append(float(d[i][4]))
FFFor.append(float(d[i][5]))
PmppFor.append(float(d[i][6]))
RocFor.append(float(d[i][7]))
RscFor.append(float(d[i][8]))
VmppFor.append(float(d[i][9]))
JmppFor.append(float(d[i][10]))
txtfile = [
"illumination" + '\t' + "VocFor" + '\t' + "VocRev" + '\t' +
"JscFor" + '\t' + "JscRev" + '\t' + "FFFor" + '\t' + "FFRev" +
'\t' + "PmppFor" + '\t' + "PmppRev" + '\t' + "RocFor" + '\t' +
"RscRev" + '\t' + "VmppFor" + '\t' + "VmppRev" + '\t' +
"JmppFor" + '\t' + "JmppRev" + '\n'
]
for i in range(len(illumination)):
txtfile.append(illumination[i] + '\t' + str(VocFor[i]) + '\t' +
str(VocRev[i]) + '\t' + str(JscFor[i]) + '\t' +
str(JscRev[i]) + '\t' + str(FFFor[i]) + '\t' +
str(FFRev[i]) + '\t' + str(PmppFor[i]) + '\t' +
str(PmppRev[i]) + '\t' + str(RocFor[i]) + '\t' +
str(RscRev[i]) + '\t' + str(VmppFor[i]) + '\t' +
str(VmppRev[i]) + '\t' + str(JmppFor[i]) +
'\t' + str(JmppRev[i]) + '\n')
file = open(name + '_FR.txt', 'w')
file.writelines("%s" % item for item in txtfile)
file.close()
fig = plt.figure(figsize=(10, 12))
Vocsubfig = fig.add_subplot(421)
Jscsubfig = fig.add_subplot(422)
FFsubfig = fig.add_subplot(423)
Effsubfig = fig.add_subplot(424)
Rocsubfig = fig.add_subplot(425)
Rscsubfig = fig.add_subplot(426)
Vmppsubfig = fig.add_subplot(427)
Jmppsubfig = fig.add_subplot(428)
Vocsubfig.set_ylabel('Voc (mV)')
Vocsubfig.plot(illumination, VocFor, 'ro')
Vocsubfig.plot(illumination, VocRev, 'bo')
Jscsubfig.set_ylabel('Jsc (mA/cm2)')
Jscsubfig.plot(illumination, JscFor, 'ro')
Jscsubfig.plot(illumination, JscRev, 'bo')
FFsubfig.set_ylabel('FF (%)')
FFsubfig.plot(illumination, FFFor, 'ro')
FFsubfig.plot(illumination, FFRev, 'bo')
Effsubfig.set_ylabel('Eff (%)')
Effsubfig.plot(illumination, PmppFor, 'ro')
Effsubfig.plot(illumination, PmppRev, 'bo')
Rocsubfig.set_ylabel('Roc ()')
Rocsubfig.plot(illumination, RocFor, 'ro')
Rocsubfig.plot(illumination, RocRev, 'bo')
Rscsubfig.set_ylabel('Rsc ()')
Rscsubfig.plot(illumination, RscFor, 'ro')
Rscsubfig.plot(illumination, RscRev, 'bo')
Vmppsubfig.set_ylabel('Vmpp (mV)')
Vmppsubfig.plot(illumination, VmppFor, 'ro')
Vmppsubfig.plot(illumination, VmppRev, 'bo')
Jmppsubfig.set_ylabel('Jmpp (mA/cm2)')
Jmppsubfig.plot(illumination, JmppFor, 'ro')
Jmppsubfig.plot(illumination, JmppRev, 'bo')
Vocsubfig.annotate(name + " - red=Forward; blue=Reverse",
xy=(0.6, 1.08),
xycoords='axes fraction',
fontsize=12,
horizontalalignment='left',
verticalalignment='bottom')
plt.savefig(name + ".png", dpi=300, transparent=False)
plt.close()
def TruePhaseAvg(self):
file_path = filedialog.askopenfilenames()
try:
folder = file_path[0].split('/')
except:
return
path = ''
for i in range(0, len(folder) - 1):
path = path + folder[i] + "/"
path = path + 'Resolution.txt'
try:
with open(path, 'r') as f:
for line in f.readlines():
category, value = line.strip().split(';')
self.deltaT = float(value)
print(self.deltaT)
except:
self.deltaT = FileHandle.getResolution()
print(self.deltaT)
self.LoadCsvBar.setMaximum(len(file_path))
self.show()
arraylist = []
try:
for numb, i in enumerate(file_path):
avg = []
Timearray = []
oneax = True
self.LoadCsvBar.setValue(numb + 1)
QtGui.QGuiApplication.processEvents()
with open(i) as csv_file:
reader = csv.reader(csv_file, delimiter=';')
for row in reader:
if len(row) == 2:
Timearray.append(float(row[0]))
avg.append(float(row[1]))
oneax = False
else:
avg.append(float(row[0]))
arraylist.append(np.asarray(avg))
except:
None
length = 0
cnt = 0
for i in arraylist:
if length == 0:
length = len(i)
if length > len(i):
length = len(i)
cnt = cnt + 1
avg = np.zeros(length)
for i in arraylist:
avg = avg + i[0:length]
self.close()
print(cnt)
avg = np.true_divide(avg, cnt)
Timearray = np.arange(0, length, 1) * self.deltaT
FileHandle.SaveData(Timearray, avg)
plt.plot(Timearray, avg)
plt.show()
def main(argv):
config = read_parser(argv, Inputs, InputsOpt_Defaults)
if config['mode'] == 'normal_modulation':
print('caca')
fs = 1000000.
dt = 1. / fs
tr = 1.
thr = 0.5
alpha_bursts = 130.
dura_bursts = 0.001
frec_bursts = 92000.
amort_bursts = 7887.
std_add_noise = 0.1
# std_phase_noise = 0.0
std_mult_noise = 0.0001
max_rint = 2
Noise = np.random.RandomState(seed=1)
t = np.arange(0, tr, dt)
# phase_noise = 0.5*Noise.normal(1,std_phase_noise,len(t))
# phase_noise = np.sin(2 * np.pi * t)
# xc = signal.square(2*np.pi*alpha_bursts*t, duty=phase_noise)
# rect = signal.square(2*np.pi*alpha_bursts*t, duty=phase_noise)
xc = signal.square(2 * np.pi * alpha_bursts * t)
rect = signal.square(2 * np.pi * alpha_bursts * t)
# plt.plot(xc)
# plt.show()
n = len(xc)
add_noise = Noise.normal(0, std_add_noise, n)
mult_noise = Noise.normal(1, std_mult_noise, n)
index = []
for i in range(n - 1):
if xc[i] < thr and xc[i + 1] >= thr:
index.append(i)
index = np.array(index)
nidx = len(index)
# rand_int = Noise.randint(0,max_rint,nidx)
tb = np.arange(0, dura_bursts, dt)
burst = np.sin(2 * np.pi * frec_bursts * tb) * np.exp(
-amort_bursts * tb)
nb = len(burst)
# print(rand_int)
# count = 0
for idx in index:
# print(count)
xc[idx:idx + nb] += burst
# if count != nidx -1:
# xc[idx + rand_int[idx] : idx + rand_int[idx] + nb] += burst[0:]
# else:
# xc[idx:idx+nb] += burst
# count += 1
x = xc - rect
# add_noise = np.random.normal(0,std_add_noise,n)
# mult_noise = np.random.normal(1,std_mult_noise,n)
x += add_noise
x = x * mult_noise
X, f, df = mag_fft(x, fs)
# print(signal_rms(x))
plt.plot(t, x)
plt.show()
plt.plot(f, X)
plt.show()
elif config['mode'] == 'normal_modulation_2':
print('caca')
fs = 1000000.
dt = 1. / fs
tr = 2.
thr = 0.5
alpha_bursts = 300.
dura_bursts = 0.001
frec_bursts = 92000.
amort_bursts = 7887.
planets = 3.
carrier = 5.
std_add_noise = 0.2
std_phase_noise = 0.01
std_mult_noise = 0.001
# std_add_noise = 0.1
# std_phase_noise = 0.01
# std_mult_noise = 0.001
max_rint = 2
Noise = np.random.RandomState(seed=1)
t = np.arange(0, tr, dt)
phase_noise = 0.5 * Noise.normal(1, std_phase_noise, len(t))
# phase_noise = Noise.uniform(0.5,0.5+0.05,len(t))
# plt.plot(phase_noise, 'k')
# plt.show()
# phase_noise = np.sin(2 * np.pi * t)
xc = signal.square(2 * np.pi * alpha_bursts * t, duty=phase_noise)
rect = signal.square(2 * np.pi * alpha_bursts * t, duty=phase_noise)
# xc = signal.square(2*np.pi*alpha_bursts*t)
# rect = signal.square(2*np.pi*alpha_bursts*t)
# plt.plot(xc)
# plt.show()
n = len(xc)
add_noise = Noise.normal(0, std_add_noise, n)
mult_noise = Noise.normal(1, std_mult_noise, n)
index = []
for i in range(n - 1):
if xc[i] < thr and xc[i + 1] >= thr:
index.append(i)
index = np.array(index)
nidx = len(index)
# rand_int = Noise.randint(0,max_rint,nidx)
tb = np.arange(0, dura_bursts, dt)
burst = np.sin(2 * np.pi * frec_bursts * tb) * np.exp(
-amort_bursts * tb)
nb = len(burst)
# print(rand_int)
# count = 0
for idx in index:
# print(count)
xc[idx:idx + nb] += burst
# if count != nidx -1:
# xc[idx + rand_int[idx] : idx + rand_int[idx] + nb] += burst[0:]
# else:
# xc[idx:idx+nb] += burst
# count += 1
x = xc - rect
# add_noise = np.random.normal(0,std_add_noise,n)
# mult_noise = np.random.normal(1,std_mult_noise,n)
x += add_noise
x = x * mult_noise
mod = np.ones(n) + 0.3 * np.cos(2 * np.pi * planets * carrier * t)
x = x * mod
X, f, df = mag_fft(x, fs)
x_env = hilbert_demodulation(x)
X_env, f_env, df_env = mag_fft(x_env, fs)
# print(signal_rms(x))
plt.plot(t, x)
plt.show()
# plt.plot(f, X)
# plt.show()
plt.plot(f_env, X_env, 'g-o')
plt.show()
elif config['mode'] == 'gearbox_simulator':
#+++++++++Simulation Data
fs = 1000000.
dt = 1. / fs
tr = 1.
thr = 0.5
alpha_bursts = 300.
dura_bursts = 0.001
frec_bursts = 92000.
amort_bursts = 7887.
planets = 3.
carrier = 5.
std_add_noise = 0.2 #0.2
std_phase_noise = 0.03 #0.02
std_mult_noise = 0.001 #0.001
amp_mod_1 = 0.35 #0.3
amp_mod_2 = 0.95 #0.5
#+++++++++++Time Array
t = np.arange(0, tr, dt)
n = len(t)
#Noise Generator
Noise = np.random.RandomState(seed=1)
phase_noise = 0.5 * Noise.normal(1, std_phase_noise, n)
add_noise = Noise.normal(0, std_add_noise, n)
mult_noise = Noise.normal(1, std_mult_noise, n)
#+++++++++++++Parent Signals
#Train Pulses
xc = signal.square(2 * np.pi * alpha_bursts * t, duty=phase_noise)
rect = signal.square(2 * np.pi * alpha_bursts * t, duty=phase_noise)
index = []
for i in range(n - 1):
if xc[i] < thr and xc[i + 1] >= thr:
index.append(i)
index = np.array(index)
nidx = len(index)
#Bursts
tb = np.arange(0, dura_bursts, dt)
burst = np.sin(2 * np.pi * frec_bursts * tb) * np.exp(
-amort_bursts * tb)
nb = len(burst)
for idx in index:
xc[idx:idx + nb] += burst
#No modulated signal
x = xc - rect
plt.plot(x, 'k')
plt.show()
x += add_noise
x = x * mult_noise
#+++++++++++++Operational Modulations
#Distance Pitch-Sensor
mod_1 = np.ones(n) + amp_mod_1 * np.cos(
2 * np.pi * planets * carrier * t)
x = x * mod_1
#Local Fault Ring Gear
mod_2 = np.ones(n) + amp_mod_2 * signal.square(
2 * np.pi * planets * carrier * t, duty=0.0075)
# mod_2 = np.ones(n) + amp_mod_2*signal.sawtooth(2*np.pi*planets*carrier*t, width=0.25)
plt.plot(mod_2, 'k')
plt.show()
x = x * mod_2
#+++++++++++++Signal Processing
#Spectrum
X, f, df = mag_fft(x, fs)
#Envelope
x_env = hilbert_demodulation(x)
#Envelope spectrum
X_env, f_env, df_env = mag_fft(x_env, fs)
#+++++++++++++Plots
plt.plot(t, x)
plt.show()
plt.plot(f_env, X_env, 'g')
plt.show()
elif config['mode'] == 'gearbox_simulator_2':
#+++++++++Simulation Data
fs = 1000000.
dt = 1. / fs
tr = 0.25
thr = 0.5
alpha_bursts = 300.
dura_bursts = 0.001
frec_bursts = 92000.
amort_bursts = 7887.
planets = 3.
carrier = 5.
std_add_noise = 0.15 #0.15
std_phase_noise = 0.0005 #0.0005
std_mult_noise = 0.001 #0.001
amp_mod_1 = 0.4 #0.4 sinus
amp_mod_2 = 0.4 #0.4 local
#+++++++++++Time Array
t = np.arange(0, tr, dt)
n = len(t)
#Noise Generator
Noise = np.random.RandomState(seed=1)
phase_noise = 0.5 * Noise.normal(1, std_phase_noise, n)
add_noise = Noise.normal(0, std_add_noise, n)
mult_noise = Noise.normal(1, std_mult_noise, n)
#+++++++++++++Parent Signals
#Train Pulses
xc = signal.square(2 * np.pi * alpha_bursts * t, duty=phase_noise)
rect = signal.square(2 * np.pi * alpha_bursts * t, duty=phase_noise)
index = []
for i in range(n - 1):
if xc[i] < thr and xc[i + 1] >= thr:
index.append(i)
index = np.array(index)
nidx = len(index)
#Bursts
tb = np.arange(0, dura_bursts, dt)
burst = np.sin(2 * np.pi * frec_bursts * tb) * np.exp(
-amort_bursts * tb)
nb = len(burst)
for idx in index:
xc[idx:idx + nb] += burst
# if idx%15 != 0:
# xc[idx:idx+nb] += burst
# else:
# xc[idx:idx+nb] += burst*10
#No modulated signal
x = xc - rect
#+++++++++++++Operational Modulations
# #Distance Pitch-Sensor Sinusoidal
mod_s = np.ones(n) + amp_mod_1 * np.cos(
2 * np.pi * planets * carrier * t)
#Local Fault Ring Gear Pulse
mod_p = np.ones(n) + amp_mod_2 * signal.square(
2 * np.pi * planets * carrier * t, duty=0.0095)
x = x * mod_p
add_noise = add_noise * mod_p
# x = x*mod_s
add_noise = add_noise * mod_s
#+++++++++++++Noise
x += add_noise
x = x * mult_noise
# plt.plot(x)
# plt.show()
#+++++++++++++Special Burst
thr = 1.
index = []
for i in range(n - 1):
if x[i] < thr and x[i + 1] >= thr:
print('special burst!')
index.append(i)
index = np.array(index)
nidx = len(index)
#Bursts
tb = np.arange(0, dura_bursts, dt)
burst = np.sin(2 * np.pi * frec_bursts * 2 * tb + np.pi / 8) * np.exp(
-amort_bursts * 1.5 * tb)
nb = len(burst)
# print('!!', nb)
# print(len(x[100-42:100-42+nb]))
# print(type(x[25-42:25-42+nb]))
# print(type(burst))
# a = x[25-42:25-42+nb] + x[25-42:25-42+nb]
# x[1:11] = np.arange(10)
# sys.exit()
for idx in index:
# print(idx)
# print(len(x[idx-42:idx-42+nb]))
# print(len(1.3*burst))
# aaa = x[idx-42:idx-42+nb] + 1.3*burst
x[idx - 42:idx - 42 + nb] += 1.3 * burst
# x[idx-42:idx-42+nb] = aaa
plt.plot(t, x, 'r')
plt.show()
#+++++++++++++Signal Processing
#Spectrum
X, f, df = mag_fft(x, fs)
X = 20 * np.log10(X / 1.e-6)
# plt.plot(X)
# plt.show()
#Envelope
x_env = hilbert_demodulation(x)
#Envelope spectrum
X_env, f_env, df_env = mag_fft(x_env, fs)
X_env = 20 * np.log10(X_env / 1.e-6)
# # save_pickle('simulated_AE_mod_fault_frec.pkl', x)
# # x = list(x)
# # x = np.array(x)
# mydict = {config['channel']:x}
# scipy.io.savemat(filename[:-5] + '.mat', mydict)
# print('Signal was saved as .mat. Analysis finalizes')
# sys.exit()
#+++++++++++++Plots
name = 'SigProcessing_FftEnv_FftEnv_Mod_OnlyNoise'
path_1 = 'C:\\Felix\\29_THESIS\\MODEL_A\\ChapterX_SigProcessing\\03_Figures\\'
path_2 = 'C:\\Felix\\29_THESIS\\MODEL_A\\LATEX_Diss_FLn\\bilder\\Figures_SigProcessing\\'
path_1b = path_1 + name + '.svg'
path_2b = path_2 + name + '.pdf'
style = {
'xlabel': ['Frequency [Hz]', 'Frequency [Hz]'],
'ylabel': ['Magnitude [dB$_{AE}$]', 'Magnitude [dB$_{AE}$]'],
'legend': None,
'title': ['Envelope spectrum', 'Envelope spectrum'],
'customxlabels': None,
'xlim': [[0., 50], [200, 400]],
'ylim': [[20, 120.], [20, 120.]],
'color': [None],
'loc_legend': 'upper left',
'legend_line': 'OFF',
'vlines': None,
'range_lines': None,
'output': config['output'],
'path_1': path_1b,
'path_2': path_2b
}
#+++Envelope spectrum
# 'xlabel':['Frequency [Hz]', 'Frequency [Hz]'], 'ylabel':['Magnitude [dB$_{AE}$]', 'Magnitude [dB$_{AE}$]'], 'legend':None, 'title':['Envelope spectrum', 'Envelope spectrum'], 'customxlabels':None, 'xlim':[[0.,50], [200,400]], 'ylim':None
#+++Wfm?
# 'xlabel':['Time [s]', 'Time [s]'], 'ylabel':['Amplitude [V]', 'Amplitude [V]'], 'legend':None, 'title':['Waveform', 'Envelope'], 'customxlabels':None, 'xlim':[[0.,0.25], [0.,0.25]], 'ylim':[[-3,3], [0,3]]
#+++Wfm-Env Zoom
# 'xlabel':['Time [ms]', 'Time [ms]'], 'ylabel':['Amplitude [V]', 'Amplitude [V]'], 'legend':None, 'title':['Waveform', 'Envelope'], 'customxlabels':None, 'xlim':[[66.5, 67.0], [66.5, 67.0]], 'ylim':[[-3,3], [0,3]]
# +++Spectrum
# ['xlabel':['Time [s]', 'Frequency [kHz]'], 'ylabel':['Amplitude [V]', 'Magnitude [dB$_{AE}$]'], 'legend':None, 'title':['Waveform', 'Spectrum'], 'customxlabels':None, 'xlim':[[0.,0.25], [0.,500]], 'ylim':[[-3,3], [0,100]]
data = {'x': [f_env, f_env], 'y': [X_env, X_env]}
# data = {'x':[t, t], 'y':[x, x_env]}
# data = {'x':[t*1000, t*1000], 'y':[x, x_env]}
# data = {'x':[t, f/1000.], 'y':[x, X]}
plot2_thesis_new(data, style)
# plt.plot(t, x)
# plt.show()
# plt.plot(f_env, X_env, 'g')
# plt.show()
elif config['mode'] == 'only_plot':
tr = 1
dt = 1. / 1000000.
fs = 1 / dt
#+++++++++++Time Array
t = np.arange(0, tr, dt)
root = Tk()
root.withdraw()
root.update()
filepath = filedialog.askopenfilename()
root.destroy()
x = load_signal(filepath, channel='none')
plt.plot(t, x, 'r')
plt.show()
#+++++++++++++Signal Processing
#Spectrum
X, f, df = mag_fft(x, fs)
X = 20 * np.log10(X / 1.e-6)
# plt.plot(X)
# plt.show()
#Envelope
x_env = hilbert_demodulation(x)
#Envelope spectrum
X_env, f_env, df_env = mag_fft(x_env, fs)
X_env = 20 * np.log10(X_env / 1.e-6)
# # save_pickle('simulated_AE_mod_fault_frec.pkl', x)
# # x = list(x)
# # x = np.array(x)
# mydict = {config['channel']:x}
# scipy.io.savemat(filename[:-5] + '.mat', mydict)
# print('Signal was saved as .mat. Analysis finalizes')
# sys.exit()
#+++++++++++++Plots
name = 'SigProcessing_Wfm_Env_Mod_Zoom_Fault'
path_1 = 'C:\\Felix\\29_THESIS\\MODEL_A\\ChapterX_SigProcessing\\03_Figures\\'
path_2 = 'C:\\Felix\\29_THESIS\\MODEL_A\\LATEX_Diss_FLn\\bilder\\Figures_SigProcessing\\'
path_1b = path_1 + name + '.svg'
path_2b = path_2 + name + '.pdf'
style = {
'xlabel': ['Time [ms]', 'Time [ms]'],
'ylabel': ['Amplitude [V]', 'Amplitude [V]'],
'legend': None,
'title': ['Waveform', 'Envelope'],
'customxlabels': None,
'xlim': [[66.5 * 2, 67.0 * 2], [66.5 * 2, 67.0 * 2]],
'ylim': [[-4, 4], [0, 4]],
'color': [None],
'loc_legend': 'upper left',
'legend_line': 'OFF',
'vlines': None,
'range_lines': None,
'output': config['output'],
'path_1': path_1b,
'path_2': path_2b
}
#+++Envelope spectrum
# 'xlabel':['Frequency [Hz]', 'Frequency [Hz]'], 'ylabel':['Magnitude [dB$_{AE}$]', 'Magnitude [dB$_{AE}$]'], 'legend':None, 'title':['Envelope spectrum', 'Envelope spectrum'], 'customxlabels':None, 'xlim':[[0.,50], [200,400]], 'ylim':None
#+++Wfm?
# 'xlabel':['Time [s]', 'Time [s]'], 'ylabel':['Amplitude [V]', 'Amplitude [V]'], 'legend':None, 'title':['Waveform', 'Envelope'], 'customxlabels':None, 'xlim':[[0.,0.25], [0.,0.25]], 'ylim':[[-3,3], [0,3]]
#+++Wfm-Env Zoom
# 'xlabel':['Time [ms]', 'Time [ms]'], 'ylabel':['Amplitude [V]', 'Amplitude [V]'], 'legend':None, 'title':['Waveform', 'Envelope'], 'customxlabels':None, 'xlim':[[66.5, 67.0], [66.5, 67.0]], 'ylim':[[-3,3], [0,3]]
# +++Spectrum
# ['xlabel':['Time [s]', 'Frequency [kHz]'], 'ylabel':['Amplitude [V]', 'Magnitude [dB$_{AE}$]'], 'legend':None, 'title':['Waveform', 'Spectrum'], 'customxlabels':None, 'xlim':[[0.,0.25], [0.,500]], 'ylim':[[-3,3], [0,100]]
# data = {'x':[f_env, f_env], 'y':[X_env, X_env]}
# data = {'x':[t, t], 'y':[x, x_env]}
data = {'x': [t * 1000, t * 1000], 'y': [x, x_env]}
# data = {'x':[t, f/1000.], 'y':[x, X]}
plot2_thesis_new(data, style)
# plt.plot(t, x)
# plt.show()
# plt.plot(f_env, X_env, 'g')
# plt.show()
elif config['mode'] == 'emd_simulation':
print('Select IMFs')
root = Tk()
root.withdraw()
root.update()
Filepaths = filedialog.askopenfilenames()
root.destroy()
#+++++++++Simulation Signals
fs = 1000000.
Signals = []
Spectra = []
Names = []
for filepath in Filepaths:
print(filepath)
Names.append('IMF ' + os.path.basename(filepath)[1])
x = load_signal(filepath, channel=None)
fft, f, df = mag_fft(x, fs)
Signals.append(x)
fft = 20 * np.log10(fft / 1.e-6) #dbAE
Spectra.append(fft)
n = len(Signals[0])
dt = 1. / fs
tr = n / fs
Names[0] = 'Signal'
print(Names)
#+++++++++++Time Array
t = np.arange(0, tr, dt)
#+++++++++++++Plots
name = 'SigProcessing_Wfm_Burst_EMD_Mod_Fault'
path_1 = 'C:\\Felix\\29_THESIS\\MODEL_A\\ChapterX_SigProcessing\\03_Figures\\'
path_2 = 'C:\\Felix\\29_THESIS\\MODEL_A\\LATEX_Diss_FLn\\bilder\\Figures_SigProcessing\\'
path_1b = path_1 + name + '.svg'
path_2b = path_2 + name + '.pdf'
ymax_ = -3
ymin_ = 3
style = {
'xlabel':
'Time [ms]',
'ylabel':
'[V]',
'legend':
None,
'title':
Names,
'customxlabels':
None,
'xlim': [66.5, 67],
'ylim': [[ymin_, ymax_], [ymin_, ymax_], [ymin_, ymax_],
[ymin_, ymax_], [ymin_, ymax_], [ymin_, ymax_]],
'color': [None],
'loc_legend':
'upper left',
'legend_line':
'OFF',
'vlines':
None,
'range_lines':
None,
'output':
config['output'],
'path_1':
path_1b,
'path_2':
path_2b
}
# [0.05,0.08]
# 'xlabel':'Time [ms]'
# 'Amplitude [V]'
# [[0,100], [0,100], [0,100], [0,100], [0,100], [0,100]]
# 'Frequency [kHz]'
# '[dB$_{AE}$]'
# data = {'x':[t, t, t, t, t, t], 'y':[Signals[0], Signals[1], Signals[2], Signals[3], Signals[4], Signals[5]]}
data = {
'x': [t * 1000, t * 1000, t * 1000, t * 1000, t * 1000, t * 1000],
'y': [
Signals[0], Signals[1], Signals[2], Signals[3], Signals[4],
Signals[5]
]
}
# data = {'x':[f/1000., f/1000., f/1000., f/1000., f/1000., f/1000.], 'y':[Spectra[0], Spectra[1], Spectra[2], Spectra[3], Spectra[4], Spectra[5]]}
plot6_thesis_new_emd(data, style)
# plt.plot(t, x)
# plt.show()
# plt.plot(f_env, X_env, 'g')
# plt.show()
else:
print('unknown mode')
sys.exit()
return
def generate_master(img_list):
master = np.zeros((SIZE, SIZE))
for i in trange(SIZE):
for j in range(SIZE):
L = [array[i, j] for array in img_list]
master[i, j] = np.median(L)
return master
# Liste des images à étudier
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
# show an "Open" dialog box and return the path to the selected files
raw_list = askopenfilenames(title='select RAW files')
flat_list = askopenfilenames(title='select FLAT files')
offset_list = askopenfilenames(title='select OFFSET files')
# Extraire la taille des images
hdul = fits.open(raw_list[0])
SIZE = hdul[0].header['NAXIS1']
# Concatenation des données de chacune des images
raw_concat = [fits.getdata(image) for image in raw_list]
flat_concat = [fits.getdata(image) for image in flat_list]
offset_concat = [fits.getdata(image) for image in offset_list]
# Générer les master images
master_flat = generate_master(flat_concat)
master_offset = generate_master(offset_concat)
# print instances #
###################
print(f'{"#"*(5+len(str(scan)))}\nScan {scan}\n{"#"*(5+len(str(scan)))}')
print('\n##############\nSetup instance\n##############')
print(setup)
print('\n#################\nDetector instance\n#################')
print(detector)
################
# preload data #
################
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilenames(initialdir=detector.scandir,
filetypes=[("NPZ", "*.npz"),
("NPY", "*.npy"),
("CXI", "*.cxi"),
("HDF5", "*.h5")])
nbfiles = len(file_path)
plt.ion()
obj, extension = util.load_file(file_path[0])
if extension == '.h5':
comment = comment + '_mode'
print('\n###############\nProcessing data\n###############')
nz, ny, nx = obj.shape
print("Initial data size: (", nz, ',', ny, ',', nx, ')')
if len(original_size) == 0:
original_size = obj.shape
print("FFT size before accounting for phasing_binning", original_size)
#root = easygui
#root.iconbitmap(r'c:\Python32\DLLs\py.ico')
#root.mainloop()
#print(filename)
easygui.msgbox("The task was completed" + pathToGet,
"diropenbox",
ok_button="Continue")
sys.exit(0)
def path_leaf(path):
head, tail = ntpath.split(path)
return tail or ntpath.basename(head)
if __name__ == '__main__':
window = Tk()
window.title("Welcome to LikeGeeks app")
#window.mainloop()
filez = fd.askopenfilenames(parent=window, title='Choose a file')
pathToGet = r"C:\Users\jsebastia\Pictures\Saved Pictures\imagenCompressor\imagenesPrueba\saturnohd.jpg"
print(filez)
#main()