-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathGUI4.9.2.py
3327 lines (2624 loc) · 164 KB
/
GUI4.9.2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
import time
import csv
from itertools import groupby
from operator import itemgetter
from tkinter import filedialog, messagebox
import seaborn as sns
import matplotlib.pyplot as plt
import traces
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.dates import DateFormatter
from matplotlib.figure import Figure
from pandas.api.types import CategoricalDtype
from statsmodels.tsa.seasonal import seasonal_decompose
from tkinter import ttk
import math
# import datetime as dt
from functions_forecast import *
from functions_clean import *
import tkinter as tk
from tkinter import *
from sys import exit
import webbrowser
import os
pd.options.mode.chained_assignment = None
label_version = 'v.1.1.0 160822'
FORMAT_DATE = "%d/%m/%y %H:%M:%S"
# FORMAT_DATE = "%H:%M:%S"
DATE_FORM = DateFormatter(FORMAT_DATE)
NORMAL_FONT = "Helvetica 8 bold"
dictionary_keywords = pd.read_csv('dictionary.csv', header=0, index_col=0)
selected_language = 'en'
def about_dsc(dictionary_keywords, selected_language):
tk.messagebox.showinfo(dictionary_keywords.loc[14,selected_language], dictionary_keywords.loc[98,selected_language])
def manual(selected_language):
os.system('Manual.pdf')
# tk.messagebox.showinfo(dictionary_keywords.loc[97,selected_language], dictionary_keywords.loc[99,selected_language])
# MsgBox = tk.messagebox.askquestion (dictionary_keywords.loc[100,selected_language],dictionary_keywords.loc[99,selected_language],icon = 'question')
# if MsgBox == 'yes':
# webbrowser.open("https://wisdom.ips.pt/manual")
# else:
# tk.messagebox.showinfo('Return','You will now return to the application screen')
def research_paper(selected_language):
os.system('Research_paper.pdf')
def add_dataset(which_tab=None): # Criar condicao para quando ha e nao ha dataset
if which_tab == Tab1: # Quando nao ha dataset anterior meter o original
repo.set_dataset("dataset1", repo.get_dataset("original"))
elif which_tab == Tab2: # Quando nao ha dataset anterior meter o original
repo.set_dataset("dataset2", repo.get_dataset("original"))
elif which_tab == Tab3: # Quando nao ha dataset anterior meter o original
repo.set_dataset("dataset3", repo.get_dataset("original"))
elif which_tab == Tab4: # Quando nao ha dataset anterior meter o original
repo.set_dataset("dataset4", repo.get_dataset("original"))
else: # QUando houver dataset previo colocar esse como entrada
pass
def file_output(key): # Exportação do dataset
# Pergunta que dirétorio guardar o dataset
file_out = tk.filedialog.asksaveasfilename(defaultextension='.csv', filetype=(
("CSV", "*.csv"), ("Text files", "*.txt"), ("all files", "*.*")))
# Exporta dataset, com NaN
df_out = repo.get_dataset_output(key)
df_out.to_csv(file_out, index=False, header=True, na_rep="NaN")
messagebox.showinfo(dictionary_keywords.loc[20,selected_language],dictionary_keywords.loc[21,selected_language]) #exported and data exported
class Repository:
def __init__(self):
self.dataset_original = None # Dataset do input do user
self.plot_dataset = None
# Datasets de entrada para cada separador
self.datasets = {
"dataset1": None,
"dataset2": None,
"dataset3": None,
"dataset4": None
}
self.datasets_outputs = {
"dataset1": None,
"dataset2": None,
"dataset3": None,
"dataset4": None
}
self.peaks = {
"negative_peaks": None,
"high_peaks": None,
"low_peaks": None,
"flow_PH": None,
"flow_PL": None,
"slopes_PH": None,
"slopes_PL": None,
"flat_points": None
}
self.tab1_values = {
}
self.tab2_values = {
"nans": None,
"time_spacing": None,
"set_nans": None,
"set_nans_short": None,
"set_nans_long": None
}
def set_original_dataset(self, dataset): # Insere dataset do user
self.dataset_original = dataset
def set_history_dataset(self, dataset): # Insere dataset do user
self.dataset_history = dataset
def set_dataset(self, key, dataset): # Insere o dataset que vem dos separadores
if key == "plot":
self.plot_dataset = dataset
else:
self.datasets[key] = dataset
def set_dataset_output(self, key, dataset): # Insere o dataset que vem dos separadores
self.datasets_outputs[key] = dataset
def get_dataset_output(self, key): # Retorna qualquer dataset que esteja no repositório
if self.datasets_outputs[key] is None:
return self.datasets[key]
return self.datasets_outputs[key]
def get_dataset(self, key): # Retorna qualquer dataset que esteja no repositório
if key == "original":
return self.dataset_original
elif key == "plot":
return self.plot_dataset
elif key == "history":
return self.dataset_history
else:
return self.datasets[key]
def set_peaks(self, negative_peaks, high_peaks, low_peaks, flow_PH, flow_PL, slopes_PH, slopes_PL, flat_points):
self.peaks.update({
"negative_peaks": negative_peaks,
"high_peaks": high_peaks,
"low_peaks": low_peaks,
"flow_PH": flow_PH,
"flow_PL": flow_PL,
"slopes_PH": slopes_PH,
"slopes_PL": slopes_PL,
"flat_points": flat_points
})
def get_peaks(self):
return self.peaks
def set_values_tab1(self):
self.tab1_values.update({
})
def get_values_tab1(self):
return self.tab1_values
def get_values_tab2(self):
return self.tab2_values
def set_values_tab2(self, nans, time_spacing):
self.tab2_values.update({
"nans": nans,
"time_spacing": time_spacing
})
def set_params_tab3(self, params, key):
if key=='ARIMA':
self.params_arima = params
if key=='ES':
self.params_es = params
def get_params_tab3(self,key):
if key=='ARIMA':
return self.params_arima
if key=='ES':
return self.params_es
class MainGUI(tk.Tk):
def __init__(self, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs) # Criação da janela
#Import do dicionário de termos
# Criação do quadro da janela
self.container = tk.Frame(self)
self.container.pack(side="top", fill="both", expand=True)
self.container.grid_columnconfigure(0, weight=1)
self.container.grid_rowconfigure(0, weight=1)
# Configurações da plataforma
# self.bind("<Escape>", exit) # Para sair do programa
self.protocol('WM_DELETE_WINDOW', exit) # Para sair do programa
# Preparacao da janela
self.title(dictionary_keywords.loc[0,selected_language] + " (" + label_version + ")")
self.geometry('1100x700') # Em pixels
self.iconbitmap('icon.ico') # Logo
self.configure(background='white') # Background
# Preparação de uma grelha 100 x 100 (colunas x linhas) com grossura de 1
for x in range(100):
self.columnconfigure(x, weight=1)
for x in range(100):
self.rowconfigure(x, weight=1)
self.frames = {} # Todas os separadores da plataforma ficarão aqui
frame = Tab0(self.container) # Inicia construtor do separador
self.frames[Tab0] = frame
frame.grid(row=0, column=0, sticky="nsew")
"""Show a frame for the given page name"""
frame = self.frames[Tab0] # Apanha a frame requirida
frame.tkraise() # Coloca-a "à frente" da plataforma
menubar = frame.menubar(self) # Coloca o menu respetivo do separador
self.configure(menu=menubar)
def file_input(self, tab): # Introdução do ficheiro
# Guarda diretorio do ficheiro
file_inp = filedialog.askopenfilename(
filetype=(("CSV", "*.csv"), ("Text files", "*.txt"), ("all files", "*.*")))
# print(file_inp)
# Verificação caso o ficheiro de entrada não contenha cabeçalho
with open(file_inp, newline='') as csvfile: # Parsa CSV, retorna true ou false se tiver cabeçalho ou não
dialect = csv.Sniffer().has_header(csvfile.read(1024))
csvfile.seek(0)
if dialect is True: # Remove e coloca o cabeçalho uniforme da plataforma (para evitar cabeçalhos errados)
dataset = pd.read_csv(file_inp, header=0, names=['date', 'value'], parse_dates=['date'],
infer_datetime_format=False, dayfirst=True).sort_values(by=['date']).reset_index(drop=True)
else: # Apenas coloca cabeçalho uniforme
dataset = pd.read_csv(file_inp, names=['date', 'value'], parse_dates=['date'],
infer_datetime_format=False, dayfirst=True).sort_values(by=['date']).reset_index(drop=True)
repo.set_original_dataset(dataset) # Insere no repositório como o primeiro a entrar
tk.messagebox.showinfo(dictionary_keywords.loc[1,selected_language], dictionary_keywords.loc[2,selected_language])
if tab == Tab0: # Se o import foi feito a partir da Capa:
add_dataset(which_tab=Tab1)
self.show_frame(Tab1)
else:
add_dataset(which_tab=tab)
self.show_frame(tab) # Faz-se o show_frame para a mesma tab para fazer as funções de cada separador
def history_input(self): # Introdução do ficheiro
# Guarda diretorio do ficheiro
file_inp = filedialog.askopenfilename(
filetype=(("CSV", "*.csv"), ("Text files", "*.txt"), ("all files", "*.*")))
# print(file_inp)
# Verificação caso o ficheiro de entrada não contenha cabeçalho
# Parsa CSV, retorna true ou false se tiver cabeçalho ou não
with open(file_inp, newline='') as csvfile:
dialect = csv.Sniffer().has_header(csvfile.read(1024))
csvfile.seek(0)
# Remove e coloca o cabeçalho uniforme da plataforma (para evitar cabeçalhos errados)
if dialect is True:
dataset = pd.read_csv(file_inp, header=0, names=['date', 'value'], parse_dates=['date'],
infer_datetime_format=False, dayfirst=True).sort_values(by=['date']).reset_index(drop=True)
else: # Apenas coloca cabeçalho uniforme
dataset = pd.read_csv(file_inp, names=['date', 'value'], parse_dates=['date'],
infer_datetime_format=False, dayfirst=True).sort_values(by=['date']).reset_index(drop=True)
# Insere no repositório como o primeiro a entrar
repo.set_history_dataset(dataset)
tk.messagebox.showinfo(dictionary_keywords.loc[1,selected_language], dictionary_keywords.loc[3,selected_language])
def show_frame(self, page_name, from_menu=False, force=False):
if len(self.frames) != 5 or force is True: #cria as framesaquando da 1ª utilização. Tabs são criadas na lingua pretendida
for F in (Tab1, Tab2, Tab3, Tab4):
frame = F(self.container) # Inicia construtor do separador
self.frames[F] = frame
frame.grid(row=0, column=0, sticky="nsew")
"""Show a frame for the given page name"""
frame = self.frames[page_name] # Apanha a frame requirida
frame.tkraise() # Coloca-a "à frente" da plataforma
if from_menu is False: # Se o separador não foi selecionado pelo menubar
frame.tab_functions()
menubar = frame.menubar(self) # Coloca o menu respetivo do separador
self.configure(menu=menubar)
def clear_all(self):
# print('clear_all')
plt.close('all')
#Limpar dados de cada separador
#Tab1
repo.set_dataset("dataset1", None) # Remove o dataset do repositório
repo.set_dataset("plot", None)
repo.set_peaks(None, None, None, None, None, None, None, None)
#Tab2
repo.set_dataset("dataset2", None)
repo.set_values_tab2(None, None)
#Tab3
repo.set_dataset("dataset3", None)
#Tab4
repo.set_dataset("dataset4", None)
repo.set_history_dataset(None)
repo.set_dataset_output("dataset4", None)
#Criar nova janela de cada separador
for F in (Tab1, Tab2, Tab3, Tab4):
frame = F(self.container) # Inicia construtor do separador
self.frames[F] = frame
frame.grid(row=0, column=0, sticky="nsew")
#puxa tab0
frame = self.frames[Tab0] # Apanha a frame requirida
frame.tkraise() # Coloca-a "à frente" da plataforma
menubar = frame.menubar(self) # Coloca o menu respetivo do separador
self.configure(menu=menubar)
def change_language(self, language):
global selected_language
selected_language = language
# print('change_language')
frame = Tab0(self.container) # Inicia construtor do separador
self.frames[Tab0] = frame
frame.grid(row=0, column=0, sticky="nsew")
"""Show a frame for the given page name"""
frame = self.frames[Tab0] # Apanha a frame requirida
frame.tkraise() # Coloca-a "à frente" da plataforma
menubar = frame.menubar(self) # Coloca o menu respetivo do separador
self.configure(menu=menubar)
return
class Tab0(tk.Frame): # Capa
def __init__(self, parent):
tk.Frame.__init__(self, parent)
self.canvas = Canvas(self, bg="white")
self.canvas.pack(fill="both", expand=True)
self.background = PhotoImage(file="background_cover.png")
self.canvas.create_image(0, 0, image=self.background, anchor="nw")
self.img0 = PhotoImage(file="Wisdom_logo.png")
self.canvas.create_image(1100/2, 600/2, image=self.img0, anchor="center")
self.img1 = PhotoImage(file="ips.png")
self.canvas.create_image(1100/5 * 1, 600, image=self.img1, anchor="center")
self.img2 = PhotoImage(file="ist.png")
self.canvas.create_image(1100/5 * 2, 600, image=self.img2, anchor="center")
self.img3 = PhotoImage(file="inesc.png")
self.canvas.create_image(1100/5 * 3, 600, image=self.img3, anchor="center")
self.img4 = PhotoImage(file="fct.png")
self.canvas.create_image(1100/5 * 4, 600, image=self.img4, anchor="center")
self.canvas_txt_id1 = self.canvas.create_text(1100/2, 400, fill="white", font="Helvetica 14 bold",
text=dictionary_keywords.loc[4,selected_language] + ' ')
self.canvas.create_text(1050, 20, fill="white", font="Helvetica 8",
text=label_version)
def menubar(self, root): # Menu - Semelhante em cada separador, diferenciando na visualização dos gráficos (WIP)
menubar = tk.Menu(root) # Criação do menu
fileMenu = tk.Menu(menubar, tearoff=False) # Ficheiro
fileMenu.add_command(label=dictionary_keywords.loc[5,selected_language], command=lambda: root.file_input(Tab0))
fileMenu.add_separator()
fileMenu.add_command(label=dictionary_keywords.loc[6,selected_language], command=self.quit)
menubar.add_cascade(label=dictionary_keywords.loc[7,selected_language], menu=fileMenu)
methodMenu = tk.Menu(menubar, tearoff=False) # Métodos
methodMenu.add_command(label=dictionary_keywords.loc[8,selected_language], command=lambda: root.show_frame(Tab1, from_menu=True))
methodMenu.add_command(label=dictionary_keywords.loc[9,selected_language], command=lambda: root.show_frame(Tab2, from_menu=True))
methodMenu.add_command(label=dictionary_keywords.loc[10,selected_language], command=lambda: root.show_frame(Tab3, from_menu=True))
methodMenu.add_command(label=dictionary_keywords.loc[11,selected_language], command=lambda: root.show_frame(Tab4, from_menu=True))
menubar.add_cascade(label=dictionary_keywords.loc[12,selected_language], menu=methodMenu)
helpMenu = tk.Menu(menubar, tearoff=False) # Ajuda e Sobre
helpMenu.add_command(label=dictionary_keywords.loc[97,selected_language], command=lambda: manual(selected_language))
helpMenu.add_command(label=dictionary_keywords.loc[103,selected_language], command=lambda: research_paper(selected_language))
helpMenu.add_command(label=dictionary_keywords.loc[14,selected_language ], command=lambda: about_dsc(dictionary_keywords, selected_language))
menubar.add_cascade(label=dictionary_keywords.loc[13,selected_language], menu=helpMenu)
languageMenu = tk.Menu(menubar, tearoff=False) # Ajuda e Sobre
languageMenu.add_command(label="English", command=lambda: root.change_language("en"))
languageMenu.add_command(label="Portuguese", command=lambda: root.change_language("pt"))
menubar.add_cascade(label=dictionary_keywords.loc[94,selected_language], menu=languageMenu)
return menubar
def get_data(self):
# Na capa não tem dados para ir buscar ao repositório
pass
class Tab1(tk.Frame): # 1- Identificação de falhas
def __init__(self, parent):
tk.Frame.__init__(self, parent) # Criação do separador
self.configure(background='white') # Background
# Preparação de uma grelha 100 x 100 (colunas x linhas) com grossura de 1
for x in range(100):
self.columnconfigure(x, weight=1)
for x in range(100):
self.rowconfigure(x, weight=1)
# # Formato das horas para graphs
# self.format_date = "%d/%m/%y %H:%M:%S"
# DATE_FORM = DateFormatter(self.format_date) # Labels nos gráficos
# Separadores - linhas que dividem o programa
ttk.Separator(self, orient=tk.HORIZONTAL).grid(column=2, row=12, columnspan=96, sticky='ew')
ttk.Separator(self, orient=tk.HORIZONTAL).grid(column=2, row=60, columnspan=96, sticky='ew')
# Botões
# Remover os eventos anómalos todos
tk.Button(self, text=dictionary_keywords.loc[15,selected_language], command=self.remove_all, font=NORMAL_FONT,
relief=GROOVE).grid(row=68, column=75, columnspan=10, sticky='NSEW')
# Avançar
tk.Button(self, text=dictionary_keywords.loc[16,selected_language], command=self.next_tab, font=NORMAL_FONT,
relief=GROOVE).grid(row=90, column=75, columnspan=10, sticky='NSEW')
# Inputs de parâmetros
#### Picos altos ####
# Tamanho da janela
tk.Label(self, text=dictionary_keywords.loc[17,selected_language], bg='white',
font=NORMAL_FONT, anchor='e').grid(column=5, row=56, columnspan=10, sticky='NSWE')
self.wPH = tk.Entry(self, textvariable=tk.StringVar(self, None))
self.wPH.grid(column=15, row=56, columnspan=1, sticky='NSWE')
# Threshold
tk.Label(self, text=dictionary_keywords.loc[95,selected_language], bg='white', #threshold label
font=NORMAL_FONT, anchor='e').grid(column=5, row=58, columnspan=10, sticky='NSWE')
self.tPH = tk.Entry(self, textvariable=tk.StringVar(self, None))
self.tPH.grid(column=15, row=58, columnspan=1, sticky='NSWE')
#### Picos baixos ####
# Tamanho da janela
tk.Label(self, text=dictionary_keywords.loc[17,selected_language], bg='white',
font=NORMAL_FONT, anchor='e').grid(column=45, row=56, columnspan=10, sticky='NSWE')
self.wPL = tk.Entry(self, textvariable=tk.StringVar(self, None))
self.wPL.grid(column=55, row=56, columnspan=1, sticky='NSWE')
# Threshold
tk.Label(self, text=dictionary_keywords.loc[95,selected_language], bg='white', #threshold label
font=NORMAL_FONT, anchor='e').grid(column=45, row=58, columnspan=10, sticky='NSWE')
self.tPL = tk.Entry(self, textvariable=tk.StringVar(self, None))
self.tPL.grid(column=55, row=58, columnspan=1, sticky='NSWE')
#### Patamares ####
# Tamanho da janela
tk.Label(self, text=dictionary_keywords.loc[17,selected_language], bg='white',
font=NORMAL_FONT, anchor='e').grid(column=70, row=56, columnspan=10, sticky='NSWE')
self.wFL = tk.Entry(self, textvariable=tk.StringVar(self, None))
self.wFL.grid(column=80, row=56, columnspan=2, sticky='NSWE')
# Threshold
tk.Label(self, text=dictionary_keywords.loc[102,selected_language], bg='white', #threshold label
font=NORMAL_FONT, anchor='e').grid(column=70, row=58, columnspan=10, sticky='NSWE')
self.tFL = tk.Entry(self, textvariable=tk.StringVar(self, None))
self.tFL.grid(column=80, row=58, columnspan=2, sticky='NSWE')
# Funções dos gráficos em branco
self.graph_original() # Série original
self.table_original() # Análise do espaçamento/Caudal
self.histograms_original() # Histograma Espaçamento/Caudal
self.peak_high_original() # Picos anómalos altos
self.peak_low_original() # Picos anómalos baixos
self.flat_original() # Patamares estáticos
self.final_graph() # Série com anómalos identificados
self.table_results_original() # Contagem
def tab_functions(self):
df = repo.get_dataset("dataset1")
values = df['value']
dates = df['date']
values2 = df['value'].values.copy()
dates2 = df['date'].values.copy()
# Create the diffs
flow = np.diff(values2)
flow = np.insert(flow, 0, np.nan, axis=0)
time = np.diff(dates2).tolist()
time = np.divide(time, np.power(10, 9))
slopes = np.divide(abs(flow[1:]), time)
slopes = np.insert(slopes, 0, 0, axis=0)
slopes = pd.DataFrame(slopes)
slopes = slopes.describe(percentiles=[0.05, 0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.97, 0.995]) # Estatisticas do espacamento
slopes = slopes.iloc[:, 0]
# print(slopes)
plt.close('all')
self.upd_graph_original(values, dates) # Atualiza Série original
self.upd_table_original(values, dates) # Atualiza Análise do espaçamento/caudal
self.upd_histograms_original(values) # Atualiza histograma do espaçamento/caudal
# print(self.spacing_detail)
# Para limpar os gráficos
self.wPH.delete(0, "end")
self.tPH.delete(0, "end")
self.wPH.insert(0, int(self.spacing_detail[0][5] * 3))
self.tPH.insert(0, round(float(slopes[12]), 3))
self.wPL.delete(0, "end")
self.tPL.delete(0, "end")
self.wPL.insert(0, int(self.spacing_detail[0][5] * 3))
self.tPL.insert(0, round(float(slopes[12]), 3))
self.wFL.delete(0, "end")
self.tFL.delete(0, "end")
self.wFL.insert(0, max(int(self.spacing_detail[0][5] * 2.5),300))
self.tFL.insert(0, round(float(self.value_detail[2] * 0.03), 3))
# Para limpar os gráficos que já estavam preenchidos
self.peak_high_original()
self.peak_low_original()
self.flat_original()
self.final_graph()
self.table_results_original()
def menubar(self, root): # O mesmo com o Tab0, adicionando os gráficos respetivos
menubar = tk.Menu(root)
fileMenu = tk.Menu(menubar, tearoff=False) # Ficheiro
fileMenu.add_command(label=dictionary_keywords.loc[5,selected_language], command=lambda: root.file_input(Tab1))
fileMenu.add_command(label=dictionary_keywords.loc[18,selected_language], command=self.clear)
fileMenu.add_command(label=dictionary_keywords.loc[101,selected_language], command=lambda: root.clear_all()) #Clear data
fileMenu.add_command(label=dictionary_keywords.loc[19,selected_language], command=lambda: file_output("dataset1")) #export data
fileMenu.add_separator()
fileMenu.add_command(label=dictionary_keywords.loc[6,selected_language], command=self.quit)
menubar.add_cascade(label=dictionary_keywords.loc[7,selected_language], menu=fileMenu)
methodMenu = tk.Menu(menubar, tearoff=False) # Métodos
methodMenu.add_command(label=dictionary_keywords.loc[8,selected_language], command=lambda: root.show_frame(Tab1, from_menu=True))
methodMenu.add_command(label=dictionary_keywords.loc[9,selected_language], command=lambda: root.show_frame(Tab2, from_menu=True))
methodMenu.add_command(label=dictionary_keywords.loc[10,selected_language], command=lambda: root.show_frame(Tab3, from_menu=True))
methodMenu.add_command(label=dictionary_keywords.loc[11,selected_language], command=lambda: root.show_frame(Tab4, from_menu=True))
menubar.add_cascade(label=dictionary_keywords.loc[12,selected_language], menu=methodMenu)
graphMenu = tk.Menu(menubar, tearoff=False) # Gráficos de visualização
graphMenu.add_command(label=dictionary_keywords.loc[22,selected_language], command=self.G1)
graphMenu.add_command(label=dictionary_keywords.loc[23,selected_language], command=self.G2)
graphMenu.add_command(label=dictionary_keywords.loc[24,selected_language], command=self.G3)
graphMenu.add_command(label=dictionary_keywords.loc[25,selected_language], command=self.G4)
graphMenu.add_command(label=dictionary_keywords.loc[26,selected_language], command=self.G5)
menubar.add_cascade(label=dictionary_keywords.loc[27,selected_language], menu=graphMenu)
helpMenu = tk.Menu(menubar, tearoff=False) # Ajuda e sobre
helpMenu.add_command(label=dictionary_keywords.loc[97,selected_language], command=lambda: manual(selected_language))
helpMenu.add_command(label=dictionary_keywords.loc[103,selected_language], command=lambda: research_paper(selected_language))
helpMenu.add_command(label=dictionary_keywords.loc[14,selected_language], command=lambda: about_dsc(dictionary_keywords, selected_language))#about
menubar.add_cascade(label=dictionary_keywords.loc[13,selected_language], menu=helpMenu)
return menubar
def clear(self): # Limpar dados
repo.set_dataset("dataset1", None) # Remove o dataset do repositório
repo.set_dataset("plot", None)
repo.set_peaks(None, None, None, None, None, None, None, None)
plt.close('all')
# Limpa todas as labels das estatísticas
for x in range(self.spacing_detail.shape[0]):
tk.Label(self, text='', bg='white').grid(column=50, row=1 + x, columnspan=5, sticky='NSWE')
for x in range(self.value_detail.shape[0]):
tk.Label(self, text='', bg='white').grid(column=55, row=1 + x, columnspan=5, sticky='NSWE')
# Remove todos os parâmetros
self.wPH.delete(0, "end")
self.tPH.delete(0, "end")
self.wPL.delete(0, "end")
self.tPL.delete(0, "end")
self.wFL.delete(0, "end")
self.tFL.delete(0, "end")
for x in range(8):
tk.Label(self, text='', bg='white').grid(column=80, row=74 + x, sticky='NSWE')
# Funcoes dos gráficos em branco
self.graph_original()
self.histograms_original()
self.peak_high_original()
self.peak_low_original()
self.flat_original()
self.final_graph()
def remove_all(self): # Função de remover todos os anómalos, em sequência
plt.close(2)
plt.close(3)
plt.close(4)
plt.close(5)
df = repo.get_dataset("dataset1").copy()
df, dup_equal = remove_duplicates_exact(df)
# = len(repo.get_dataset("dataset1")) - len(df) # Duplicates exatos
nans = [] # nans originais
# array com ID's dos nans
for row in range(len(df)):
if np.isnan(df['value'][row]):
nans.append(row)
# Usa o dataset e vai passando por uma sucessão de funções, onde no fim irá ter todos os valores colocados a nan
# os respetivos ìndices, para visualização
df, dup_diffs = remove_duplicates_dif(df) # Duplicates diferentes
temp_df = df.copy() # Para utilizar nos gráficos a posteriori
repo.set_dataset("plot", temp_df)
df, negative_peaks = remove_negatives(df) # negativos
# Picos altos
df, high_peaks, flow_PH, slopes_PH = remove_pontuals_high(df, win_size=int(self.wPH.get()),
threshold=float(self.tPH.get()))
self.upd_peak_high(temp_df, high_peaks, flow_PH, slopes_PH)
# Picos baixos
df, low_peaks, flow_PL, slopes_PL = remove_pontuals_low(df, win_size=int(self.wPL.get()),
threshold=float(self.tPL.get()))
self.upd_peak_low(temp_df, low_peaks, flow_PL, slopes_PL)
# Patamares
df, flat_points = new_remove_flat_lines(df, win_size=int(self.wFL.get()), threshold=float(self.tFL.get()))
self.upd_flat(df, temp_df, flat_points)
# atualizar todos os gráficos
self.upd_final_graph(df, temp_df, negative_peaks, high_peaks, low_peaks, flat_points)
self.upd_table_results(dup_equal, dup_diffs, nans, negative_peaks, high_peaks, low_peaks, flat_points)
repo.set_dataset_output("dataset1", df)
repo.set_peaks(negative_peaks, high_peaks, low_peaks, flow_PH, flow_PL, slopes_PH, slopes_PL, flat_points)
def next_tab(self): # Botão avançar
# O que sai dos métodos é colocado no dataset de saída
# E no de entrada para ser usado no método (Separador) seguinte
repo.set_dataset("dataset2", repo.get_dataset_output("dataset1"))
app.show_frame(Tab2) # Muda de separador
def graph_original(self): # Gráfico da Série original, em branco
self.fig = Figure(figsize=(1, 1), dpi=50)
self.ax = self.fig.add_subplot(111)
self.ax.set_title(dictionary_keywords.loc[22,selected_language])
# Label das horas
self.ax.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[28,selected_language])
self.ax.legend()
self.ax.set_ylabel(dictionary_keywords.loc[28,selected_language]) # Label do Y (caudal m^3/h)
FigureCanvasTkAgg(self.fig, master=self).get_tk_widget().grid(
column=0, row=0, columnspan=45, rowspan=10, sticky='NSEW')
def upd_graph_original(self, values, dates): # Atualiza gráfico da série original
# Limpa gráfico prévio
self.ax.cla()
# Gráfico da série original
self.ax.plot(dates, values, '#4772FF', label=dictionary_keywords.loc[28,selected_language])
# Formatação do gráfico
self.ax.xaxis.set_major_formatter(DATE_FORM) # Para a label do x, data no formato estipulado no main
self.ax.set_ylabel(dictionary_keywords.loc[28,selected_language]) # Label do Y (caudal m^3/h)
self.ax.set_title(dictionary_keywords.loc[22,selected_language])
self.ax.legend()
self.fig.canvas.draw()
def table_original(self): # Estatisticas do Input
tk.Label(self, text=dictionary_keywords.loc[29,selected_language], bg='white', font=NORMAL_FONT,
wraplength=100).grid(column=50, row=0, columnspan=5, rowspan=1, sticky='NSWE')#Title
tk.Label(self, text=dictionary_keywords.loc[30,selected_language], bg='white', font=NORMAL_FONT,
wraplength=80).grid(column=55, row=0, columnspan=5, rowspan=1, sticky='NSWE')
tk.Label(self, text=dictionary_keywords.loc[31,selected_language], bg='white', font=NORMAL_FONT).grid(
column=45, row=1, columnspan=5, sticky='NSWE')
tk.Label(self, text=dictionary_keywords.loc[32,selected_language], bg='white', font=NORMAL_FONT).grid(
column=45, row=2, columnspan=5, sticky='NSWE')
tk.Label(self, text=dictionary_keywords.loc[33,selected_language], bg='white', font=NORMAL_FONT).grid(
column=45, row=3, columnspan=5, sticky='NSWE')
tk.Label(self, text=dictionary_keywords.loc[34,selected_language], bg='white', font=NORMAL_FONT).grid(
column=45, row=4, columnspan=5, sticky='NSWE')
tk.Label(self, text="P25", bg='white', font=NORMAL_FONT).grid(
column=45, row=5, columnspan=5, sticky='NSWE')
tk.Label(self, text="P50", bg='white', font=NORMAL_FONT).grid(
column=45, row=6, columnspan=5, sticky='NSWE')
tk.Label(self, text="P75", bg='white', font=NORMAL_FONT).grid(
column=45, row=7, columnspan=5, sticky='NSWE')
tk.Label(self, text=dictionary_keywords.loc[35,selected_language], bg='white', font=NORMAL_FONT).grid(
column=45, row=8, columnspan=5, sticky='NSWE')
for x in range(8):
tk.Label(self, text='', bg='white').grid(column=50, row=1 + x, columnspan=5, sticky='NSWE')
for x in range(8):
tk.Label(self, text='', bg='white').grid(column=55, row=1 + x, columnspan=5, sticky='NSWE')
def upd_table_original(self, values, dates): # Atualiza estatistica de input
# Preparacao das variaveis
sum = 0
time_spacing = np.zeros(len(values) - 1)
# Cálculo do spacamento temporal
for line in range(1, len(values), 1):
# Conversao ns para s
time_spacing[line - 1] = float((dates[line] - dates[line - 1]) / np.power(10, 9))
sum += float((dates[line] - dates[line - 1]) / np.power(10, 9))
time_spacing = pd.DataFrame(time_spacing)
self.spacing_detail = time_spacing.describe() # Estatisticas do espacamento
self.espacamento = time_spacing.iloc[:, 0]
# print('detail',self.spacing_detail)
# print('espacamento',self.espacamento)
# Escrever ao longo da "tabela"
for x in range(self.spacing_detail.shape[0]):
tk.Label(self, text=round(self.spacing_detail[0][x], 2), bg='white').grid(
column=50, row=1 + x, columnspan=5, sticky='NSWE')
self.value_detail = values.describe(include=[np.number]) # Estatisticas do caudal
# Escrever ao longo da "tabela"
for x in range(self.value_detail.shape[0]):
if isinstance(self.value_detail[x], str):
tk.Label(self, text=self.value_detail[x], bg='white').grid(column=55, row=1 + x, columnspan=5,
sticky='NSWE')
else:
tk.Label(self, text=round(self.value_detail[x], 2), bg='white').grid(column=55, row=1 + x, columnspan=5,
sticky='NSWE')
tk.Label(self, text=self.spacing_detail[0][0] + 1, bg='white').grid(column=55, row=1 + 0, columnspan=5,
sticky='NSWE')
def histograms_original(self): # Histogramas do Input
# Espaçamento temporal
self.fig2 = Figure(figsize=(1, 1), dpi=50)
self.hist = self.fig2.add_subplot(111)
self.hist.set_title(dictionary_keywords.loc[36,selected_language]) #Histogram spacing title
FigureCanvasTkAgg(self.fig2, master=self).get_tk_widget().grid(
column=60, row=0, columnspan=20, rowspan=10, sticky='NSEW')
# Caudal
self.fig3 = Figure(figsize=(1, 1), dpi=50)
self.hist2 = self.fig3.add_subplot(111)
self.hist2.set_title(dictionary_keywords.loc[37,selected_language]) #Histogram Flow title
FigureCanvasTkAgg(self.fig3, master=self).get_tk_widget().grid(
column=80, row=0, columnspan=20, rowspan=10, sticky='NSEW')
def upd_histograms_original(self, caudal): # Atualiza histograma do input
self.hist.cla()
self.hist.hist(self.espacamento, bins=20, alpha=0.5)
self.hist.set_title(dictionary_keywords.loc[36,selected_language]) #Histogram spacing title
self.fig2.canvas.draw()
self.hist2.cla()
self.hist2.hist(caudal, bins=20, alpha=0.5)
self.hist2.set_title(dictionary_keywords.loc[37,selected_language]) #Histogram Flow title
self.fig3.canvas.draw()
### Tratamento dos dados ###
def peak_high_original(self): # Picos altos
self.fig5 = Figure(figsize=(1, 1), dpi=50)
self.deltaq_pho = self.fig5.add_subplot(111)
self.deltaq_pho.set_title(dictionary_keywords.loc[23,selected_language]) #abnormal high peaks title
# Labels e gráfico default para gráfico do caudal
self.deltaq_pho.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[39,selected_language])
self.deltaq_pho.set_ylabel(dictionary_keywords.loc[39,selected_language]) # Label do Y (difference)
self.deltaq_pho.legend()
FigureCanvasTkAgg(self.fig5, master=self).get_tk_widget().grid(
column=0, row=13, columnspan=38, rowspan=20, sticky='NSEW')
# Labels e gráfico default para gráfico do gradiente do caudal
self.fig6 = Figure(figsize=(1, 1), dpi=50)
self.gradq_pho = self.fig6.add_subplot(111)
self.gradq_pho.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[38,selected_language])
self.gradq_pho.set_ylabel(dictionary_keywords.loc[38,selected_language]) # Label do Y rate of change
self.gradq_pho.legend()
FigureCanvasTkAgg(self.fig6, master=self).get_tk_widget().grid(
column=0, row=33, columnspan=38, rowspan=18, sticky='NSEW')
def upd_peak_high(self, df, high_peaks, flow_PH, slopes_PH): # Atualiza gráficos dos picos altos
# Limpa graficos existentes
self.deltaq_pho.cla()
self.gradq_pho.cla()
#
# # Divide em dois arrays para simplificar a manipulação nos gráficos
# self.values = [float(i[1]) for i in self.df2] # Valores de Caudal, em float
# self.values = np.array(self.values)
#
# self.dates = [i[0].to_pydatetime() for i in self.df2] # Datas, em pydatetime
# self.dates = np.array(self.dates)
# Gráfico do caudal
self.deltaq_pho.plot(df['date'], flow_PH, "#6F8EAF", label=dictionary_keywords.loc[39,selected_language]) #flowrate lable
self.deltaq_pho.plot(df['date'][high_peaks], flow_PH[high_peaks], "^r", label=dictionary_keywords.loc[50,selected_language]) #abnormal high peaks lable
# self.deltaq_pho.xaxis.set_major_formatter(DATE_FORM)
self.deltaq_pho.set_title(dictionary_keywords.loc[23,selected_language]) #abnormal high values title
self.deltaq_pho.set_ylabel(dictionary_keywords.loc[39,selected_language]) # Label do Y (difference)
self.deltaq_pho.legend()
self.fig5.canvas.draw()
# Gráfico do gradiente do caudal
self.gradq_pho.plot(df['date'], slopes_PH, "#6F8EAF", label=dictionary_keywords.loc[38,selected_language]) #label of rate of change plot
self.gradq_pho.plot(df['date'][high_peaks], slopes_PH[high_peaks],
"^r", label=dictionary_keywords.loc[50,selected_language]) #label of Abnormally high values plot
self.gradq_pho.axhline(y=float(self.tPH.get()), linestyle="--", label="Threshold")
self.gradq_pho.axhline(y=-float(self.tPH.get()), linestyle="--")
# self.gradq_pho.xaxis.set_major_formatter(DATE_FORM)
self.gradq_pho.set_ylabel(dictionary_keywords.loc[38,selected_language]) # Label do Y rate of change
self.gradq_pho.legend()
self.fig6.canvas.draw()
def peak_low_original(self): # Picos Baixos, Igual aos picos altos
self.fig8 = Figure(figsize=(1, 1), dpi=50)
self.deltaq_plo = self.fig8.add_subplot(111)
self.deltaq_plo.set_title(dictionary_keywords.loc[23,selected_language]) #abnormal high values title
self.deltaq_plo.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[39,selected_language]) #label of difference in plot
self.deltaq_plo.set_ylabel(dictionary_keywords.loc[39,selected_language]) # Label do Y (difference)
self.deltaq_plo.legend()
FigureCanvasTkAgg(self.fig8, master=self).get_tk_widget().grid(
column=38, row=13, columnspan=32, rowspan=20, sticky='NSEW')
self.fig9 = Figure(figsize=(1, 1), dpi=50)
self.gradq_plo = self.fig9.add_subplot(111)
self.gradq_plo.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[38,selected_language]) #label of rate of change in plot
self.gradq_plo.set_ylabel(dictionary_keywords.loc[38,selected_language]) # Label do Y rate of change
self.gradq_plo.legend()
FigureCanvasTkAgg(self.fig9, master=self).get_tk_widget().grid(
column=38, row=33, columnspan=32, rowspan=18, sticky='NSEW')
def upd_peak_low(self, df, low_peaks, flow_PL,
slopes_PL): # Atualiza gráficos dos picos baixos, igual aos picos altos
self.deltaq_plo.cla()
self.gradq_plo.cla()
self.deltaq_plo.plot(df['date'], flow_PL, "#6F8EAF", label=dictionary_keywords.loc[39,selected_language]) #label of difference in plot
self.deltaq_plo.plot(df['date'][low_peaks], flow_PL[low_peaks], "^m", label=dictionary_keywords.loc[51,selected_language]) #low peaks label in plot
# self.deltaq_plo.xaxis.set_major_formatter(DATE_FORM)
self.deltaq_plo.set_title(dictionary_keywords.loc[24,selected_language]) #low peaks title
self.deltaq_plo.set_ylabel(dictionary_keywords.loc[39,selected_language]) # Label do Y (difference)
self.deltaq_plo.legend()
self.fig8.canvas.draw()
self.gradq_plo.plot(df['date'], slopes_PL, "#6F8EAF", label=dictionary_keywords.loc[38,selected_language]) #rate of change plot lable
self.gradq_plo.plot(df['date'][low_peaks], slopes_PL[low_peaks], "^m", label=dictionary_keywords.loc[51,selected_language]) #low peaks label in plot
self.gradq_plo.axhline(y=float(self.tPL.get()), linestyle="--", label=dictionary_keywords.loc[96,selected_language]) #X label threshold
self.gradq_plo.axhline(y=-float(self.tPL.get()), linestyle="--")
# self.gradq_plo.xaxis.set_major_formatter(DATE_FORM)
self.gradq_plo.set_ylabel(dictionary_keywords.loc[38,selected_language]) # Label Y rate of change
self.gradq_plo.legend()
self.fig9.canvas.draw()
def flat_original(self): # Patamares
self.fig10 = Figure(figsize=(1, 1), dpi=50)
self.flat = self.fig10.add_subplot(111)
self.flat.set_title(dictionary_keywords.loc[25,selected_language]) #flat lines title
# we plot y as a function of a, which parametrizes x
self.flat.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[28,selected_language]) #flowrate lable
self.flat.legend()
self.flat.set_ylabel(dictionary_keywords.loc[28,selected_language]) # Y lable of flowrate
FigureCanvasTkAgg(self.fig10, master=self).get_tk_widget().grid(
column=70, row=13, columnspan=35, rowspan=42, sticky='NSEW')
def upd_flat(self, df, temp_df, flat_points): # Atualiza gráficos dos patamares
self.flat.cla() # Limpa gráficos prévios
# Plot original com os picos assinalados
self.flat.plot(df['date'], df['value'], "#6F8EAF", label=dictionary_keywords.loc[28,selected_language]) # Plot original flowrate
self.flat.plot(temp_df['date'][flat_points], temp_df['value'][flat_points], "^g", label=dictionary_keywords.loc[40,selected_language]) #flat lines values plot
# self.flat.xaxis.set_major_formatter(DATE_FORM)
self.flat.set_title(dictionary_keywords.loc[25,selected_language]) #flat lines title
self.flat.set_ylabel(dictionary_keywords.loc[28,selected_language]) # Label y - flowrate
self.flat.legend()
self.fig10.canvas.draw()
def final_graph(self): # Série de Output
self.fig11 = Figure(figsize=(1, 1), dpi=65)
self.final_fig = self.fig11.add_subplot(111)
self.final_fig.set_title(dictionary_keywords.loc[26,selected_language]) #Time series with identified anomalous values
# we plot y as a function of a, which parametrizes x
self.final_fig.plot(['00:00:00', '06:00:00', '12:00:00', '18:00:00', '24:00:00'],
['N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'white', label=dictionary_keywords.loc[28,selected_language]) # plot label of - flowrate
self.final_fig.legend()
self.final_fig.set_ylabel(dictionary_keywords.loc[28,selected_language]) # Label y - flowrate
FigureCanvasTkAgg(self.fig11, master=self).get_tk_widget().grid(
column=0, row=61, columnspan=75, rowspan=40, sticky='NSEW')
def upd_final_graph(self, df, temp_df, negative_peaks, high_peaks, low_peaks,
flat_points): # Atualiza série de output
self.final_fig.cla() # Limpa gráficos prévios
# Gráfico original
self.final_fig.plot(df['date'], df['value'], '#4772FF', label=dictionary_keywords.loc[28,selected_language]) # plot label - flowrate
# Negativos
self.final_fig.plot(temp_df['date'][negative_peaks], temp_df['value'][negative_peaks],
linestyle='None', marker="x", color='#D95221', label=dictionary_keywords.loc[41,selected_language])
# Picos altos
self.final_fig.plot(temp_df['date'][high_peaks], temp_df['value'][high_peaks], "xr", label=dictionary_keywords.loc[23,selected_language])#Abnormally high values
# Picos Baixos
self.final_fig.plot(temp_df['date'][low_peaks], temp_df['value'][low_peaks], "xm", label=dictionary_keywords.loc[24,selected_language])#Abnormally low values
# Patamares
self.final_fig.plot(temp_df['date'][flat_points], temp_df['value'][flat_points], "xg", label=dictionary_keywords.loc[44,selected_language]) #Values in flat line
# Formatação dos gráficos
self.final_fig.xaxis.set_major_formatter(DATE_FORM)
self.final_fig.set_ylabel(dictionary_keywords.loc[28,selected_language]) # Label y - flowrate
self.final_fig.set_title(dictionary_keywords.loc[26,selected_language]) #Time series with identified anomalous values
self.final_fig.legend()
self.fig11.canvas.draw()
def table_results_original(self): # Tabela resultados finais
#count:
tk.Label(self, text=dictionary_keywords.loc[59,selected_language], bg='white', font=NORMAL_FONT).grid(column=80, row=73, sticky='NSWE')
#Dup. equal:
tk.Label(self, text=dictionary_keywords.loc[46,selected_language], bg='white', font=NORMAL_FONT).grid(column=79, row=74, sticky='NSWE')
#dup different:
tk.Label(self, text=dictionary_keywords.loc[47,selected_language], bg='white', font=NORMAL_FONT).grid(column=79, row=75, sticky='NSWE')
#negative:
tk.Label(self, text=dictionary_keywords.loc[41,selected_language], bg='white', font=NORMAL_FONT).grid(
column=79, row=76, sticky='NSWE')
#Abnormally high values
tk.Label(self, text=dictionary_keywords.loc[23,selected_language], bg='white', font=NORMAL_FONT).grid(
column=79, row=77, sticky='NSWE')
#Abnormally low values:
tk.Label(self, text=dictionary_keywords.loc[24,selected_language], bg='white', font=NORMAL_FONT).grid(
column=79, row=78, sticky='NSWE')
#Values in flat line
tk.Label(self, text=dictionary_keywords.loc[44,selected_language], bg='white', font=NORMAL_FONT).grid(
column=79, row=79, sticky='NSWE')
#null measurments:
tk.Label(self, text=dictionary_keywords.loc[48,selected_language], bg='white', font=NORMAL_FONT).grid(
column=79, row=80, sticky='NSWE')
#total:
tk.Label(self, text=dictionary_keywords.loc[49,selected_language], bg='white', font=NORMAL_FONT).grid(
column=79, row=81, sticky='NSWE')
for x in range(8):
tk.Label(self, text='', bg='white').grid(column=80, row=74 + x, sticky='NSWE')
def upd_table_results(self, dup_equal, dup_diffs, nans, negative_peaks, high_peaks, low_peaks,
flat_points): # Atualiza a tabela de resultados final