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yalmansour1998
2024-06-05 13:10:44 +02:00
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import dash
from dash import html, callback, Input, Output, dcc
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
import numpy as np
import plotly.express as px
import plotly.io as pio
from util import *
from filtrage import *
from selection_filtre import *
from Bscan_Cscan_trait import *
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_Wave_Rot00_CSV_Data"
fichiers_selectionnes = [
"Shear_x001-x101_y{:03d}_Rot00.csv".format(i) for i in range(10, 13)
]
# on lit les fichiers et on les met dans un tableau
pre_volume = np.array(lire_fichier_csv(dossier, fichiers_selectionnes))
volume = pre_volume[:, :, :]
data_traits = 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"
configAScan = {
"toImageButtonOptions": {
"format": "svg", # one of png, svg, jpeg, webp
"filename": "A-Scan",
"height": 1000,
"width": 1400,
"scale": 1, # Multiply title/legend/axis/canvas sizes by this factor
},
"displaylogo": False,
}
layout = html.Div(
[
dbc.Row(
[
dbc.Col(
[
dbc.Select(
id="select-ascan-filter1",
options=[
{"label": "transformer du Hilbert", "value": "1"},
],
value=1,
style={"margin-bottom": "15px"},
),
],
width=3,
),
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"},
),
],
width=3,
),
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"},
),
],
width=3,
),
dbc.Col(
[
dbc.Label(
"applique les filtres selections sur tous les data",
style={"marginRight": "5px"},
),
dbc.Button(
id="button-validate-filter",
children=dbc.Spinner(
html.Div("Valider", id="loading"), show_initially=False
),
color="primary",
style={"marginBottom": "15px"},
),
],
width=3,
),
]
),
dbc.Row(
[
dbc.Col(
[html.Br(), html.B(" paramètre du 1er filtre ")],
width=2,
style={"textAlign": "center"},
),
dbc.Col(
[
dbc.Label("Fs ", html_for="Fs "),
dbc.Input(
id="input-ascan-solo-fs",
type="number",
placeholder="Fs",
value=1,
step=0.1,
),
],
width=1,
),
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,
),
],
width=1,
),
dbc.Col(
[
dbc.Label("order ", html_for="order"),
dbc.Input(
id="input-ascan-solo-order",
type="number",
placeholder="order",
value=1,
step=1,
),
],
width=1,
),
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,
),
],
width=1,
),
dbc.Col(
[html.Br(), html.B(" paramètre du 2e filtre ")],
width=2,
style={"textAlign": "center"},
),
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,
),
],
width=1,
),
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,
),
],
width=1,
),
dbc.Col(
[
dbc.Label("order ", html_for="order"),
dbc.Input(
id="input-ascan-solo-order-2",
type="number",
placeholder="order",
value=1,
step=1,
),
],
width=1,
),
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,
),
],
width=1,
),
]
),
dbc.Row(
[
dbc.Col(
dcc.Graph(
id="heatmap-ascan-solo",
config=configAScan,
style={"marginBottom": "15px"},
), # 'fig' is your 2D plotly figure
width=12,
),
dbc.Col(
dcc.Graph(
id="heatmap-fft-solo",
config=configAScan,
style={"marginBottom": "15px"},
), # 'fig' is your 2D plotly figure
width=8,
),
dbc.Col(
dcc.Graph(
id="heatmap-bscan-solo",
config=configAScan,
style={"marginBottom": "15px"},
), # 'fig' is your 2D plotly figure
width=4,
),
]
),
dbc.Label("x"),
dcc.Slider(
id="layer-slider-ascan-solo-x",
min=1,
max=dim_z,
value=1,
step=1,
marks={
str(i): str(i) for i in range(1, dim_z + 1, max(1, int(dim_z / 20)))
},
),
dbc.Label("y"),
dcc.Slider(
id="layer-slider-ascan-solo-y",
min=1,
max=dim_x,
value=1,
step=1,
marks={
str(i): str(i) for i in range(1, dim_x + 1, max(1, int(dim_x / 20)))
},
),
dbc.Label("z"),
dcc.RangeSlider(
id="layer-slider-ascan-solo-z",
min=1,
max=dim_y,
value=[dim_y / dim_y, dim_y],
step=1,
marks={
str(i): str(i) for i in range(1, dim_y + 1, max(1, int(dim_y / 20)))
},
),
],
style={"padding": "20px"},
)
# callback to update filter values
@callback(
[
Output("input-ascan-solo-fs", "disabled"),
Output("input-ascan-solo-cutoff", "disabled"),
Output("input-ascan-solo-order", "disabled"),
Output("input-ascan-solo-windowsize", "disabled"),
Output("input-ascan-solo-fs-2", "disabled"),
Output("input-ascan-solo-cutoff-2", "disabled"),
Output("input-ascan-solo-order-2", "disabled"),
Output("input-ascan-solo-windowsize-2", "disabled"),
],
[
Input("select-ascan-filter2", "value"),
Input("select-ascan-filter3", "value"),
],
)
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,
]
# callback to update the heatmap
@callback(
[
Output("heatmap-ascan-solo", "figure"),
Output("heatmap-bscan-solo", "figure"),
Output("heatmap-fft-solo", "figure"),
Output("loading", "children"),
],
[
Input("select-ascan-filter1", "value"),
Input("select-ascan-filter2", "value"),
Input("select-ascan-filter3", "value"),
Input("layer-slider-ascan-solo-x", "value"),
Input("layer-slider-ascan-solo-y", "value"),
Input("layer-slider-ascan-solo-z", "value"),
Input("button-validate-filter", "n_clicks"),
Input("input-ascan-solo-fs", "value"),
Input("input-ascan-solo-cutoff", "value"),
Input("input-ascan-solo-order", "value"),
Input("input-ascan-solo-windowsize", "value"),
Input("input-ascan-solo-fs-2", "value"),
Input("input-ascan-solo-cutoff-2", "value"),
Input("input-ascan-solo-order-2", "value"),
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,
):
# 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),
)
print("fin du traitement")
bouton = "Valider"
if n_clicks != None:
data_traits= Cscant(volume,int(selec_transforme_hilbert),int(select_filtre_1),int(select_filtre_2),
float(fs_filtre_1),float(cutoff_filtre_1),int(order_filtre_1),int(windowsize_filtre_1),float(fs_filtre_2),float(cutoff_filtre_2),int(order_filtre_2),int(windowsize_filtre_2))
bouton = "Valider"
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) "
)
fig.add_trace(new_trace)
fig.update_layout(xaxis_title="indix", yaxis_title="amplitude")
data_bscan=Bscant(volume[select_ascan_y - 1, select_ascan_z[0] : select_ascan_z[1], :],int(selec_transforme_hilbert),int(select_filtre_1),int(select_filtre_2),float(fs_filtre_1),
float(cutoff_filtre_1),int(order_filtre_1),int(windowsize_filtre_1),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 XZ",
)
fig2.update_layout(xaxis_title="Z", yaxis_title=" X")
data_sans_traitement_fft = np.fft.fft(
volume[
int(select_ascan_y) - 1,
select_ascan_z[0] : select_ascan_z[1],
int(select_ascan_x) - 1,
]
)
fig3 = px.line(title="FFT")
trace3 = go.Scatter(y=np.abs(data_sans_traitement_fft), mode="lines", name=" FFT ")
fig3.add_trace(trace3)
fig3.update_layout(
xaxis_title="FFT indix", yaxis_title="FFT of signal (Mangnitude)"
)
return [fig, fig2, fig3, bouton]