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yalmansour1998
2024-05-28 16:11:05 +02:00
committed by GitHub
parent 9a3149d19f
commit 2159638d6d
1 changed files with 116 additions and 36 deletions
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152
3D_app/pages/ascan.py
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@ -6,27 +6,24 @@ import numpy as np
import plotly.express as px
import plotly.io as pio
from util import *
from filtrage import *
from selection_filtre import *
dash.register_page(
__name__, path="/ascan", title="A-Scan filters", name="A-Scan filters"
)
dash.register_page(__name__, path="/ascan", title='A-Scan filters', name='A-Scan filters')
# on définit le dossier et les fichiers à lire
dossier = "Dataset/Shear_transform"
dossier = "Dataset/Shear_Wave_Rot00_CSV_Data"
fichiers_selectionnes = [
"Shear_x001-x101_y{:03d}_Rot00_transform.csv".format(i) for i in range(10, 14)
"Shear_x001-x101_y{:03d}_Rot00.csv".format(i) for i in range(10, 11)
]
# on charge le fichier numpy
fichiers = np.load("Dataset/npy/3D_Dataset_Long_Wave_Rot00.npy")
# on lit les fichiers et on les met dans un tableau
pre_volume = np.array(lire_fichier_csv(dossier, fichiers_selectionnes))
volume = pre_volume[:, ::32, :]
dim_z, dim_x, dim_y = volume.shape
volume = pre_volume[:, :, :]
dim_x, dim_y, dim_z = volume.shape
X, Y, Z = np.mgrid[0:dim_x, 0:dim_y, 0:dim_z]
# on définit le thème de l'application
pio.templates.default = "plotly_dark"
@ -40,7 +37,6 @@ configAScan = {
},
"displaylogo": False,
}
layout = html.Div(
[
dbc.Row(
@ -48,13 +44,46 @@ layout = html.Div(
dbc.Col(
[
dbc.Select(
id="select-ascan-filter",
id="select-ascan-filter1",
options=[
{"label": "Option 1", "value": "1"},
{"label": "Option 2", "value": "2"},
{"label": "transformer du Hilbert", "value": "1"},
],
style={"margin-bottom": "15px"},
value=1,
style={"margin-bottom": "15px"},
),
]
),
dbc.Col(
[
dbc.Select(
id="select-ascan-filter2",
options=[
{"label": "sans filtre ", "value": "2"},
{"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"},
],
value=2,
style={"margin-bottom": "15px"},
),
]
),
dbc.Col(
[
dbc.Select(
id="select-ascan-filter3",
options=[
{"label": "sans filtre ", "value": "2"},
{"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"},
],
value=2,
style={"margin-bottom": "15px"},
),
]
),
@ -70,13 +99,6 @@ layout = html.Div(
),
]
),
dbc.Checklist(
options=[
{"label": "Valeurs absolues", "value": 1},
],
value=[1],
id="absolute-values-ascan",
),
dcc.Graph(
id="heatmap-ascan-solo",
config=configAScan,
@ -92,12 +114,53 @@ layout = html.Div(
str(i): str(i) for i in range(1, dim_x + 1, max(1, int(dim_x / 20)))
},
),
dbc.Input(
id="input-ascan-solo",
type="number",
placeholder="Valeurs",
step=0.1,
style={"marginTop": "15px"},
dbc.Col(
[
dbc.Input(
id="input-ascan-solo-fs",
type="number",
placeholder="Fs",
step=0.1,
style={"marginTop": "15px"},
),
],
width=3,
),
dbc.Col(
[
dbc.Input(
id="input-ascan-solo-cutoff",
type="number",
placeholder="cut_off",
step=0.1,
style={"marginTop": "15px"},
),
],
width=3,
),
dbc.Col(
[
dbc.Input(
id="input-ascan-solo-order",
type="number",
placeholder="order",
step=0.1,
style={"marginTop": "15px"},
),
],
width=3,
),
dbc.Col(
[
dbc.Input(
id="input-ascan-solo-windowsize",
type="number",
placeholder="window_size",
step=1,
style={"marginTop": "15px"},
),
],
width=3,
),
],
style={"padding": "20px"},
@ -108,15 +171,32 @@ layout = html.Div(
@callback(
Output("heatmap-ascan-solo", "figure"),
[
Input("select-ascan-filter", "value"),
Input("select-ascan-filter1", "value"),
Input("select-ascan-filter2", "value"),
Input("select-ascan-filter3", "value"),
Input("layer-slider-ascan-solo", "value"),
Input("absolute-values-ascan", "value"),
Input("button-validate-filter", "n_clicks"),
Input("input-ascan-solo", "value"),
Input("input-ascan-solo-fs", "value"),
Input("input-ascan-solo-cutoff", "value"),
Input("input-ascan-solo-order", "value"),
Input("input-ascan-solo-windowsize", "value"),
],
)
def update_heatmap_ascan(filter, layer, absolute_values, n_clicks, input_value):
# TODO: implement the filter
fig = px.line(y=volume[layer - 1, :, 5], title="A-scan")
return fig
def update_heatmap_ascan(value1,value2,value3,value,n_clicks,fs,cutoff,order,windowsize):
# TODO: implement the filter
if(n_clicks!=None and n_clicks>=1):
print("L12")
data_avec_traitement=volume[0,:,1]
data_sans_traitement=volume[0,:,1]
data_avec_traitement=switch_case(data_avec_traitement,value1)
data_sans_traitement=switch_case(data_sans_traitement,value1)
data_avec_traitement=switch_case(data_avec_traitement,value2,fs,cutoff,order,windowsize)
data_avec_traitement=switch_case(data_avec_traitement,value3,fs,cutoff,order,windowsize)
fig = px.line( title="A-scan")
new_trace = go.Scatter(y=data_avec_traitement, mode='lines', name=' Ascan traits ')
fig.add_trace(new_trace)
new_trace = go.Scatter(y=data_sans_traitement, mode='lines', name=' Ascan (hilbert) ')
fig.add_trace(new_trace)
return fig