feat: Refactor ascan.py, filtrage.py and selection_filtre.py
merge: Merge with latest commit
This commit is contained in:
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1
3D_app/Dataset/Shear_Wave_Rot00_CSV_Data/x-values.csv
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1
3D_app/Dataset/Shear_Wave_Rot00_CSV_Data/x-values.csv
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@ -0,0 +1 @@
|
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0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,610,620,630,640,650,660,670,680,690,700,710,720,730,740,750,760,770,780,790,800,810,820,830,840,850,860,870,880,890,900,910,920,930,940,950,960,970,980,990,1000
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|
1
3D_app/Dataset/Shear_Wave_Rot00_CSV_Data/y-values.csv
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1
3D_app/Dataset/Shear_Wave_Rot00_CSV_Data/y-values.csv
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0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600
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|
1
3D_app/Dataset/Shear_Wave_Rot00_CSV_Data/z-values.csv
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3D_app/Dataset/Shear_Wave_Rot00_CSV_Data/z-values.csv
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@ -1,42 +1,49 @@
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.signal import hilbert,butter, filtfilt,wiener,hamming, convolve
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from scipy.signal import hilbert, butter, filtfilt, wiener, hamming, convolve
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#transformer du Hilbert
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# transformer du Hilbert
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def transformer_hilbert(data_input):
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data_output=np.abs(hilbert(data_input))
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data_output = np.abs(hilbert(data_input))
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return data_output
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#filtre de moyenne mobile
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def filtre_moyenne_mobile(data_input,window_size):
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data_output=pd.Series(data_input).rolling(window=window_size).mean()
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# filtre de moyenne mobile
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def filtre_moyenne_mobile(data_input, window_size):
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data_output = pd.Series(data_input).rolling(window=window_size).mean()
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return data_output
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# filtre passe bas
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def filtre_passe_bas(data_input,fs,cut_off,order):
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nyq=0.5*fs
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normal_cut_off=cut_off/nyq
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[b,a]=butter(order,normal_cut_off,btype='low', analog=False)
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data_output=filtfilt(b,a,data_input)
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def filtre_passe_bas(data_input, fs, cut_off, order):
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nyq = 0.5 * fs
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normal_cut_off = cut_off / nyq
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[b, a] = butter(order, normal_cut_off, btype="low", analog=False)
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data_output = filtfilt(b, a, data_input)
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return data_output
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#filtre adaptatif de réduction de bruit
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# filtre adaptatif de réduction de bruit
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def filtre_adaptatif(data_input):
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data_output=wiener(data_input)
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data_output = wiener(data_input)
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return data_output
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#Le filtre à réponse impulsionnelle infinie
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def filtre_RII(data_input,window_size):
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b1=hamming(window_size)/np.sum(window_size)
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data_output=filtfilt(b1,1,data_input)
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# Le filtre à réponse impulsionnelle infinie
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def filtre_RII(data_input, window_size):
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b1 = hamming(window_size) / np.sum(window_size)
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data_output = filtfilt(b1, 1, data_input)
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return data_output
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# Le filtre à réponse impulsionnelle finie
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def filtre_RIF(data_input,window_size):
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h=hamming(window_size)/np.sum(window_size)
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data_output=convolve(data_input, h, mode='same')
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def filtre_RIF(data_input, window_size):
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h = hamming(window_size) / np.sum(window_size)
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data_output = convolve(data_input, h, mode="same")
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return data_output
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@ -10,18 +10,20 @@ from filtrage import *
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from selection_filtre import *
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dash.register_page(__name__, path="/ascan", title='A-Scan filters', name='A-Scan filters')
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dash.register_page(
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__name__, path="/ascan", title="A-Scan filters", name="A-Scan filters"
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)
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# on définit le dossier et les fichiers à lire
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dossier = "Dataset/Shear_Wave_Rot00_CSV_Data"
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fichiers_selectionnes = [
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"Shear_x001-x101_y{:03d}_Rot00.csv".format(i) for i in range(10,13)
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"Shear_x001-x101_y{:03d}_Rot00.csv".format(i) for i in range(10, 13)
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]
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# on lit les fichiers et on les met dans un tableau
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pre_volume = np.array(lire_fichier_csv(dossier, fichiers_selectionnes))
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volume = pre_volume[:, :, :]
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data_traits=volume
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data_traits = volume
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dim_x, dim_y, dim_z = volume.shape
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X, Y, Z = np.mgrid[0:dim_x, 0:dim_y, 0:dim_z]
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@ -64,8 +66,14 @@ layout = html.Div(
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||||
{"label": "filtre passe bas ", "value": "3"},
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||||
{"label": "filtre de moyenne mobile", "value": "4"},
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||||
{"label": "filtre adaptatif (wiener)", "value": "5"},
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||||
{"label": "filtre à réponse impulsionnelle infinie", "value": "6"},
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||||
{"label": "filtre à réponse impulsionnelle finie", "value": "7"},
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||||
{
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||||
"label": "filtre à réponse impulsionnelle infinie",
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||||
"value": "6",
|
||||
},
|
||||
{
|
||||
"label": "filtre à réponse impulsionnelle finie",
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||||
"value": "7",
|
||||
},
|
||||
],
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||||
value=2,
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||||
style={"margin-bottom": "15px"},
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||||
@ -82,13 +90,18 @@ layout = html.Div(
|
||||
{"label": "filtre passe bas ", "value": "3"},
|
||||
{"label": "filtre de moyenne mobile", "value": "4"},
|
||||
{"label": "filtre adaptatif (wiener)", "value": "5"},
|
||||
{"label": "filtre à réponse impulsionnelle infinie", "value": "6"},
|
||||
{"label": "filtre à réponse impulsionnelle finie", "value": "7"},
|
||||
{
|
||||
"label": "filtre à réponse impulsionnelle infinie",
|
||||
"value": "6",
|
||||
},
|
||||
{
|
||||
"label": "filtre à réponse impulsionnelle finie",
|
||||
"value": "7",
|
||||
},
|
||||
],
|
||||
value=2,
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||||
style={"margin-bottom": "15px"},
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||||
),
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||||
|
||||
],
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||||
width=3,
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||||
),
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||||
@ -97,15 +110,15 @@ layout = html.Div(
|
||||
dbc.Label("applique les filtres selections sur tous les data"),
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||||
dbc.Button(
|
||||
id="button-validate-filter",
|
||||
children="Valider",
|
||||
children=dbc.Spinner(
|
||||
html.Div(id="loading"), show_initially=False
|
||||
),
|
||||
color="primary",
|
||||
style={"marginBottom": "15px"},
|
||||
),
|
||||
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
|
||||
]
|
||||
),
|
||||
dbc.Row(
|
||||
@ -136,7 +149,7 @@ layout = html.Div(
|
||||
value=1,
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||||
step=1,
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||||
marks={
|
||||
str(i): str(i) for i in range(1, dim_z+1,1)
|
||||
str(i): str(i) for i in range(1, dim_z + 1, max(1, int(dim_z / 20)))
|
||||
},
|
||||
),
|
||||
dbc.Label("y"),
|
||||
@ -146,145 +159,146 @@ layout = html.Div(
|
||||
max=dim_x,
|
||||
value=1,
|
||||
step=1,
|
||||
marks={
|
||||
str(i): str(i) for i in range(1, dim_x+1,1)
|
||||
},
|
||||
marks={str(i): str(i) for i in range(1, dim_x + 1, 1)},
|
||||
),
|
||||
dbc.Label("z"),
|
||||
dcc.RangeSlider(
|
||||
id="layer-slider-ascan-solo-z",
|
||||
min=1,
|
||||
max=dim_y,
|
||||
value=[dim_y/dim_y,dim_y],
|
||||
value=[dim_y / dim_y, dim_y],
|
||||
step=1,
|
||||
marks={
|
||||
str(i): str(i) for i in range(1, dim_y+1,1)
|
||||
},
|
||||
str(i): str(i) for i in range(0, dim_x + 1, max(1, int(dim_x / 20)))
|
||||
},
|
||||
),
|
||||
dbc.Row(
|
||||
[
|
||||
dbc.Label(" paramètre du 1er filtre ", html_for="Fs "),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("Fs ", html_for="Fs "),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-fs",
|
||||
type="number",
|
||||
placeholder="Fs",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
dbc.Label(" paramètre du 1er filtre ", html_for="Fs "),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("Fs ", html_for="Fs "),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-fs",
|
||||
type="number",
|
||||
placeholder="Fs",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("cut off ", html_for="cut off"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-cutoff",
|
||||
type="number",
|
||||
placeholder="cut_off",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("order ", html_for="order"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-order",
|
||||
type="number",
|
||||
placeholder="order",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("window size ", html_for="window size"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-windowsize",
|
||||
type="number",
|
||||
placeholder="window_size",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
]
|
||||
),
|
||||
dbc.Col(
|
||||
dbc.Row(
|
||||
[
|
||||
dbc.Label("cut off ", html_for="cut off"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-cutoff",
|
||||
type="number",
|
||||
placeholder="cut_off",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
dbc.Label("paramètre de 2eme filtre ", html_for="Fs "),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("Fs ", html_for="Fs "),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-fs-2",
|
||||
type="number",
|
||||
placeholder="Fs",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("cut off ", html_for="cut off"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-cutoff-2",
|
||||
type="number",
|
||||
placeholder="cut_off",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("order ", html_for="order"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-order-2",
|
||||
type="number",
|
||||
placeholder="order",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("window size ", html_for="window size"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-windowsize-2",
|
||||
type="number",
|
||||
placeholder="window_size",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
]
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("order ", html_for="order"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-order",
|
||||
type="number",
|
||||
placeholder="order",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("window size ", html_for="window size"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-windowsize",
|
||||
type="number",
|
||||
placeholder="window_size",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),]),
|
||||
dbc.Row(
|
||||
[
|
||||
dbc.Label("paramètre de 2eme filtre ", html_for="Fs "),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("Fs ", html_for="Fs "),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-fs-2",
|
||||
type="number",
|
||||
placeholder="Fs",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("cut off ", html_for="cut off"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-cutoff-2",
|
||||
type="number",
|
||||
placeholder="cut_off",
|
||||
value=1,
|
||||
step=0.1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("order ", html_for="order"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-order-2",
|
||||
type="number",
|
||||
placeholder="order",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
dbc.Col(
|
||||
[
|
||||
dbc.Label("window size ", html_for="window size"),
|
||||
dbc.Input(
|
||||
id="input-ascan-solo-windowsize-2",
|
||||
type="number",
|
||||
placeholder="window_size",
|
||||
value=1,
|
||||
step=1,
|
||||
style={"marginTop": "15px"},
|
||||
),
|
||||
],
|
||||
width=3,
|
||||
),
|
||||
]),
|
||||
],
|
||||
|
||||
style={"padding": "20px"},
|
||||
)
|
||||
|
||||
|
||||
# callback to update filter values
|
||||
@callback(
|
||||
[
|
||||
@ -303,24 +317,45 @@ layout = html.Div(
|
||||
],
|
||||
)
|
||||
def update_filter_values(select_filtre_1, select_filtre_2):
|
||||
fs_1=True;cutoff_1=True;ordre_1=True;windowsize_1=True
|
||||
fs_2=True;cutoff_2=True;ordre_2=True;windowsize_2=True
|
||||
if (int(select_filtre_1)==3):
|
||||
fs_1=False;cutoff_1=False;ordre_1=False
|
||||
if(int(select_filtre_2)==3):
|
||||
fs_2=False;cutoff_2=False;ordre_2=False
|
||||
if(int(select_filtre_1) in (4, 5, 6, 7)):
|
||||
windowsize_1=False
|
||||
if(int(select_filtre_2) in (4, 5, 6, 7)):
|
||||
windowsize_2=False
|
||||
return [fs_1, cutoff_1, ordre_1, windowsize_1, fs_2, cutoff_2, ordre_2, windowsize_2]
|
||||
|
||||
|
||||
fs_1 = True
|
||||
cutoff_1 = True
|
||||
ordre_1 = True
|
||||
windowsize_1 = True
|
||||
fs_2 = True
|
||||
cutoff_2 = True
|
||||
ordre_2 = True
|
||||
windowsize_2 = True
|
||||
if int(select_filtre_1) == 3:
|
||||
fs_1 = False
|
||||
cutoff_1 = False
|
||||
ordre_1 = False
|
||||
if int(select_filtre_2) == 3:
|
||||
fs_2 = False
|
||||
cutoff_2 = False
|
||||
ordre_2 = False
|
||||
if int(select_filtre_1) in (4, 5, 6, 7):
|
||||
windowsize_1 = False
|
||||
if int(select_filtre_2) in (4, 5, 6, 7):
|
||||
windowsize_2 = False
|
||||
return [
|
||||
fs_1,
|
||||
cutoff_1,
|
||||
ordre_1,
|
||||
windowsize_1,
|
||||
fs_2,
|
||||
cutoff_2,
|
||||
ordre_2,
|
||||
windowsize_2,
|
||||
]
|
||||
|
||||
|
||||
# callback to update the heatmap
|
||||
@callback(
|
||||
[Output("heatmap-ascan-solo", "figure"), Output("heatmap-bscan-solo", "figure")],
|
||||
[
|
||||
Output("heatmap-ascan-solo", "figure"),
|
||||
Output("heatmap-bscan-solo", "figure"),
|
||||
Output("loading", "children"),
|
||||
],
|
||||
[
|
||||
Input("select-ascan-filter1", "value"),
|
||||
Input("select-ascan-filter2", "value"),
|
||||
@ -339,36 +374,110 @@ def update_filter_values(select_filtre_1, select_filtre_2):
|
||||
Input("input-ascan-solo-windowsize-2", "value"),
|
||||
],
|
||||
)
|
||||
|
||||
def update_heatmap_ascan(selec_transforme_hilbert,select_filtre_1,select_filtre_2,select_ascan_x,select_ascan_y,select_ascan_z,n_clicks,fs_filtre_1,cutoff_filtre_1,order_filtre_1,windowsize_filtre_1,fs_filtre_2,cutoff_filtre_2,order_filtre_2,windowsize_filtre_2):
|
||||
def update_heatmap_ascan(
|
||||
selec_transforme_hilbert,
|
||||
select_filtre_1,
|
||||
select_filtre_2,
|
||||
select_ascan_x,
|
||||
select_ascan_y,
|
||||
select_ascan_z,
|
||||
n_clicks,
|
||||
fs_filtre_1,
|
||||
cutoff_filtre_1,
|
||||
order_filtre_1,
|
||||
windowsize_filtre_1,
|
||||
fs_filtre_2,
|
||||
cutoff_filtre_2,
|
||||
order_filtre_2,
|
||||
windowsize_filtre_2,
|
||||
):
|
||||
# TODO: implement the filter
|
||||
print("debut du traitement")
|
||||
data_avec_traitement=volume[int(select_ascan_y)-1,select_ascan_z[0]:select_ascan_z[1],int(select_ascan_x)-1]
|
||||
data_sans_traitement=volume[int(select_ascan_y)-1,select_ascan_z[0]:select_ascan_z[1],int(select_ascan_x)-1]
|
||||
|
||||
data_avec_traitement=switch_case(data_avec_traitement,int(selec_transforme_hilbert))
|
||||
data_sans_traitement=switch_case(data_sans_traitement,int(selec_transforme_hilbert))
|
||||
|
||||
data_avec_traitement=switch_case(data_avec_traitement,int(select_filtre_1),float(fs_filtre_1),float(cutoff_filtre_1),int(order_filtre_1),int(windowsize_filtre_1))
|
||||
data_avec_traitement=switch_case(data_avec_traitement,int(select_filtre_2),float(fs_filtre_2),float(cutoff_filtre_2),int(order_filtre_2),int(windowsize_filtre_2))
|
||||
data_avec_traitement = volume[
|
||||
int(select_ascan_y) - 1,
|
||||
select_ascan_z[0] : select_ascan_z[1],
|
||||
int(select_ascan_x) - 1,
|
||||
]
|
||||
data_sans_traitement = volume[
|
||||
int(select_ascan_y) - 1,
|
||||
select_ascan_z[0] : select_ascan_z[1],
|
||||
int(select_ascan_x) - 1,
|
||||
]
|
||||
|
||||
data_avec_traitement = switch_case(
|
||||
data_avec_traitement, int(selec_transforme_hilbert)
|
||||
)
|
||||
data_sans_traitement = switch_case(
|
||||
data_sans_traitement, int(selec_transforme_hilbert)
|
||||
)
|
||||
|
||||
data_avec_traitement = switch_case(
|
||||
data_avec_traitement,
|
||||
int(select_filtre_1),
|
||||
float(fs_filtre_1),
|
||||
float(cutoff_filtre_1),
|
||||
int(order_filtre_1),
|
||||
int(windowsize_filtre_1),
|
||||
)
|
||||
data_avec_traitement = switch_case(
|
||||
data_avec_traitement,
|
||||
int(select_filtre_2),
|
||||
float(fs_filtre_2),
|
||||
float(cutoff_filtre_2),
|
||||
int(order_filtre_2),
|
||||
int(windowsize_filtre_2),
|
||||
)
|
||||
print("fin du traitement")
|
||||
if(n_clicks!=None):
|
||||
data_traits=switch_case(data_traits,int(selec_transforme_hilbert))
|
||||
data_traits=switch_case(data_traits,int(select_filtre_1),float(fs_filtre_1),float(cutoff_filtre_1),int(order_filtre_1),int(windowsize_filtre_1))
|
||||
data_traits=switch_case(data_traits,int(select_filtre_2),float(fs_filtre_2),float(cutoff_filtre_2),int(order_filtre_2),int(windowsize_filtre_2))
|
||||
fig = px.line( title="A-scan")
|
||||
new_trace = go.Scatter(y=data_avec_traitement, mode='lines', name=' Ascan trait ')
|
||||
if n_clicks != None:
|
||||
data_traits = switch_case(volume, int(selec_transforme_hilbert))
|
||||
data_traits = switch_case(
|
||||
data_traits,
|
||||
int(select_filtre_1),
|
||||
float(fs_filtre_1),
|
||||
float(cutoff_filtre_1),
|
||||
int(order_filtre_1),
|
||||
int(windowsize_filtre_1),
|
||||
)
|
||||
data_traits = switch_case(
|
||||
data_traits,
|
||||
int(select_filtre_2),
|
||||
float(fs_filtre_2),
|
||||
float(cutoff_filtre_2),
|
||||
int(order_filtre_2),
|
||||
int(windowsize_filtre_2),
|
||||
)
|
||||
fig = px.line(title="A-scan")
|
||||
new_trace = go.Scatter(y=data_avec_traitement, mode="lines", name=" Ascan trait ")
|
||||
fig.add_trace(new_trace)
|
||||
new_trace = go.Scatter(y=data_sans_traitement, mode='lines', name=' Ascan (hilbert) ')
|
||||
new_trace = go.Scatter(
|
||||
y=data_sans_traitement, mode="lines", name=" Ascan (hilbert) "
|
||||
)
|
||||
fig.add_trace(new_trace)
|
||||
data_bscan=switch_case(volume[select_ascan_y - 1, select_ascan_z[0]:select_ascan_z[1], :],int(selec_transforme_hilbert))
|
||||
data_bscan=switch_case(data_bscan,int(select_filtre_1),float(fs_filtre_1),float(cutoff_filtre_1),int(order_filtre_1),int(windowsize_filtre_1))
|
||||
data_bscan=switch_case(data_bscan,int(select_filtre_2),float(fs_filtre_2),float(cutoff_filtre_2),int(order_filtre_2),int(windowsize_filtre_2))
|
||||
data_bscan = switch_case(
|
||||
volume[select_ascan_y - 1, select_ascan_z[0] : select_ascan_z[1], :],
|
||||
int(selec_transforme_hilbert),
|
||||
)
|
||||
data_bscan = switch_case(
|
||||
data_bscan,
|
||||
int(select_filtre_1),
|
||||
float(fs_filtre_1),
|
||||
float(cutoff_filtre_1),
|
||||
int(order_filtre_1),
|
||||
int(windowsize_filtre_1),
|
||||
)
|
||||
data_bscan = switch_case(
|
||||
data_bscan,
|
||||
int(select_filtre_2),
|
||||
float(fs_filtre_2),
|
||||
float(cutoff_filtre_2),
|
||||
int(order_filtre_2),
|
||||
int(windowsize_filtre_2),
|
||||
)
|
||||
fig2 = px.imshow(
|
||||
data_bscan,
|
||||
color_continuous_scale="Jet",
|
||||
aspect="auto",
|
||||
title="B-scan ZX",
|
||||
)
|
||||
|
||||
return [fig, fig2]
|
||||
|
||||
return [fig, fig2, "Valider"]
|
||||
|
||||
@ -1,19 +1,21 @@
|
||||
from filtrage import *
|
||||
def switch_case(data_input,select,fs=1,cut_off=1,order=1,window_size=1):
|
||||
|
||||
|
||||
def switch_case(data_input, select, fs=1, cut_off=1, order=1, window_size=1):
|
||||
match select:
|
||||
case 1:
|
||||
return transformer_hilbert(data_input)
|
||||
case 2:
|
||||
return data_input
|
||||
case 3:
|
||||
return filtre_passe_bas(data_input,fs,cut_off,order)
|
||||
return filtre_passe_bas(data_input, fs, cut_off, order)
|
||||
case 4:
|
||||
return filtre_moyenne_mobile(data_input,window_size)
|
||||
return filtre_moyenne_mobile(data_input, window_size)
|
||||
case 5:
|
||||
return filtre_adaptatif(data_input)
|
||||
case 6:
|
||||
return filtre_RII(data_input,window_size)
|
||||
return filtre_RII(data_input, window_size)
|
||||
case 7:
|
||||
return filtre_RIF(data_input,window_size)
|
||||
return filtre_RIF(data_input, window_size)
|
||||
case _:
|
||||
return data_input
|
||||
return data_input
|
||||
|
||||
Reference in New Issue
Block a user