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yalmansour1998
@ -12,17 +12,13 @@ from Bscan_Cscan_trait import *
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dash.register_page(
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__name__,
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path="/ascan",
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title="A-Scan filters",
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name="A-Scan filters",
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description="Apply filters on the A-Scan",
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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, 20)
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]
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# on lit les fichiers et on les met dans un tableau
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@ -30,7 +26,7 @@ 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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dim_x, dim_y, dim_z = volume.shape
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click=None
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X, Y, Z = np.mgrid[0:dim_x, 0:dim_y, 0:dim_z]
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# on définit le thème de l'application
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pio.templates.default = "plotly_dark"
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@ -54,20 +50,20 @@ layout = html.Div(
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dbc.Select(
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id="select-ascan-filter1",
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options=[
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{"label": "Transformer du Hilbert", "value": "1"},
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{"label": "transformer du Hilbert", "value": "1"},
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],
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value=1,
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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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width=2,
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),
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dbc.Col(
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[
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dbc.Select(
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id="select-ascan-filter2",
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options=[
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{"label": "No filter ", "value": "2"},
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{"label": "sans filtre ", "value": "2"},
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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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@ -84,14 +80,14 @@ layout = html.Div(
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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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width=2,
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),
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dbc.Col(
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[
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dbc.Select(
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id="select-ascan-filter3",
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options=[
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{"label": "No filter ", "value": "2"},
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{"label": "sans filtre ", "value": "2"},
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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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@ -108,41 +104,50 @@ layout = html.Div(
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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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width=2,
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),
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dbc.Col(
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[
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html.Div(
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[
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dbc.Label(
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"Apply selection on all data",
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style={"margin": "auto 0"},
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),
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dbc.Button(
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id="button-validate-filter",
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children=dbc.Spinner(
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html.Div("Apply", id="loading"),
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show_initially=False,
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),
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color="primary",
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),
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],
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style={
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"justifyContent": "space-between",
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"display": "flex",
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},
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dbc.Label(
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"apply the filters",
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style={"marginRight": "5px"},
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),
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dbc.Button(
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id="button-validate-filter",
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children=dbc.Spinner(
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html.Div("Valider", id="loading"), show_initially=False
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),
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color="primary",
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style={"marginBottom": "15px"},
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),
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],
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width=3,
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width=2,
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),
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dbc.Col(
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[
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dbc.Label(
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"Save data",
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style={"marginRight": "5px"},
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),
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dbc.Button(
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id="button-save-data",
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children=dbc.Spinner(
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html.Div("save", id="save"), show_initially=False
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),
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color="primary",
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style={"marginBottom": "15px"},
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),
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],
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width=2,
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),
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]
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),
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dbc.Row(
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[
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dbc.Col(
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html.B(" 1st filter settings "),
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[html.Br(), html.B(" paramètre du 1er filtre ")],
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width=2,
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style={"textAlign": "center", "padding": "3.5vh"},
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style={"textAlign": "center"},
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),
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dbc.Col(
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[
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@ -151,7 +156,7 @@ layout = html.Div(
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id="input-ascan-solo-fs",
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type="number",
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placeholder="Fs",
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value=1,
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value=100,
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step=0.1,
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),
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],
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@ -159,11 +164,11 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Cut Off ", html_for="cut off"),
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dbc.Label("cut off ", html_for="cut off"),
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dbc.Input(
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id="input-ascan-solo-cutoff",
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type="number",
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placeholder="Cut Off",
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placeholder="cut_off",
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value=1,
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step=0.1,
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),
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@ -172,11 +177,11 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Order ", html_for="order"),
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dbc.Label("order ", html_for="order"),
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dbc.Input(
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id="input-ascan-solo-order",
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type="number",
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placeholder="Order",
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placeholder="order",
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value=1,
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step=1,
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),
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@ -185,11 +190,11 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Window size ", html_for="window size"),
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dbc.Label("window size ", html_for="window size"),
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dbc.Input(
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id="input-ascan-solo-windowsize",
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type="number",
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placeholder="Window_size",
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placeholder="window_size",
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value=1,
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step=1,
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),
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@ -197,9 +202,9 @@ layout = html.Div(
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width=1,
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),
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dbc.Col(
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html.B(" 2nd filter settings "),
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[html.Br(), html.B(" paramètre du 2e filtre ")],
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width=2,
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style={"textAlign": "center", "padding": "3.5vh"},
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style={"textAlign": "center"},
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),
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dbc.Col(
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[
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@ -208,7 +213,7 @@ layout = html.Div(
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id="input-ascan-solo-fs-2",
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type="number",
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placeholder="Fs",
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value=1,
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value=100,
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step=0.1,
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),
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],
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@ -216,11 +221,11 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Cut Off ", html_for="cut off"),
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dbc.Label("cut off ", html_for="cut off"),
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dbc.Input(
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id="input-ascan-solo-cutoff-2",
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type="number",
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placeholder="Cut Off",
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placeholder="cut_off",
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value=1,
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step=0.1,
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),
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@ -229,11 +234,11 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Order ", html_for="order"),
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dbc.Label("order ", html_for="order"),
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dbc.Input(
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id="input-ascan-solo-order-2",
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type="number",
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placeholder="Order",
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placeholder="order",
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value=1,
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step=1,
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),
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@ -242,11 +247,11 @@ layout = html.Div(
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),
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dbc.Col(
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[
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dbc.Label("Window size ", html_for="window size"),
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dbc.Label("window size ", html_for="window size"),
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dbc.Input(
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id="input-ascan-solo-windowsize-2",
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type="number",
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placeholder="Window_size",
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placeholder="window_size",
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value=1,
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step=1,
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),
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@ -283,7 +288,7 @@ layout = html.Div(
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),
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]
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),
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dbc.Label("X"),
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dbc.Label("x"),
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dcc.Slider(
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id="layer-slider-ascan-solo-x",
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min=1,
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@ -294,7 +299,7 @@ layout = html.Div(
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str(i): str(i) for i in range(1, dim_z + 1, max(1, int(dim_z / 20)))
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},
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),
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dbc.Label("Y"),
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dbc.Label("y"),
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dcc.Slider(
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id="layer-slider-ascan-solo-y",
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min=1,
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@ -305,7 +310,7 @@ layout = html.Div(
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str(i): str(i) for i in range(1, dim_x + 1, max(1, int(dim_x / 20)))
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},
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),
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dbc.Label("Z"),
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dbc.Label("z"),
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dcc.RangeSlider(
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id="layer-slider-ascan-solo-z",
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min=1,
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@ -316,12 +321,10 @@ layout = html.Div(
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str(i): str(i) for i in range(1, dim_y + 1, max(1, int(dim_y / 20)))
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},
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),
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html.Div(id="loading-fullscreen"),
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],
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style={"padding": "20px"},
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)
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@callback(
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Output("store-filters", "data"),
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[
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@ -416,13 +419,65 @@ def update_filter_values(select_filtre_1, select_filtre_2):
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]
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@callback(
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Output("loading", "children"),
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Input("button-validate-filter", "n_clicks"),
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[
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State("select-ascan-filter1", "value"),
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State("select-ascan-filter2", "value"),
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State("select-ascan-filter3", "value"),
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State("input-ascan-solo-fs", "value"),
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State("input-ascan-solo-cutoff", "value"),
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State("input-ascan-solo-order", "value"),
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State("input-ascan-solo-windowsize", "value"),
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State("input-ascan-solo-fs-2", "value"),
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State("input-ascan-solo-cutoff-2", "value"),
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State("input-ascan-solo-order-2", "value"),
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State("input-ascan-solo-windowsize-2", "value"),
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]
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)
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def load_button(n_clicks,
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selec_transforme_hilbert,
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select_filtre_1,select_filtre_2,
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fs_filtre_1,cutoff_filtre_1,
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order_filtre_1,windowsize_filtre_1,
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fs_filtre_2,cutoff_filtre_2,order_filtre_2,windowsize_filtre_2,
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):
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bouton = "Valider"
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global data_traits,click
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if n_clicks != click:
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data_traits=Cscant(data_input=data_traits,
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sel1=int(selec_transforme_hilbert),sel2=int(select_filtre_1),
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sel3=int(select_filtre_2),
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fs_1=int(fs_filtre_1),cut_off_1=float(cutoff_filtre_1),
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order_1=int(order_filtre_1),window_size_1=int(windowsize_filtre_1),
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fs_2=int(fs_filtre_2),cut_off_2=float(cutoff_filtre_2),
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order_2=int(order_filtre_2),window_size2=int(windowsize_filtre_2))
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bouton = "Valider"
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click=n_clicks
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return bouton
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@callback(
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Output("save", "children"),
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Input("button-save-data","n_clicks"),
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)
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def save_data(n_clicks):
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bouton = "save"
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global data_traits
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if n_clicks!=None:
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np.save('Dataset/saves/datat.npy',data_traits)
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bouton = "save"
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return bouton
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# callback to update the heatmap
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@callback(
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[
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Output("heatmap-ascan-solo", "figure"),
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Output("heatmap-bscan-solo", "figure"),
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Output("heatmap-fft-solo", "figure"),
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Output("loading", "children"),
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],
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[
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Input("layer-slider-ascan-solo-x", "value"),
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@ -442,7 +497,7 @@ def update_filter_values(select_filtre_1, select_filtre_2):
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State("input-ascan-solo-cutoff-2", "value"),
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State("input-ascan-solo-order-2", "value"),
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State("input-ascan-solo-windowsize-2", "value"),
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],
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]
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)
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def update_heatmap_ascan(
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select_ascan_x,
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@ -462,6 +517,7 @@ def update_heatmap_ascan(
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windowsize_filtre_2,
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):
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# TODO: implement the filter
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print("debut du traitement")
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data_avec_traitement = volume[
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int(select_ascan_y) - 1,
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@ -498,23 +554,7 @@ def update_heatmap_ascan(
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int(windowsize_filtre_2),
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)
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print("fin du traitement")
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bouton = "Apply"
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if n_clicks != None:
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data_traits = Cscant(
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volume,
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int(selec_transforme_hilbert),
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int(select_filtre_1),
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int(select_filtre_2),
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float(fs_filtre_1),
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float(cutoff_filtre_1),
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int(order_filtre_1),
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int(windowsize_filtre_1),
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float(fs_filtre_2),
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float(cutoff_filtre_2),
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int(order_filtre_2),
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int(windowsize_filtre_2),
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)
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bouton = "Apply"
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fig = px.line(title="A-scan")
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new_trace = go.Scatter(y=data_avec_traitement, mode="lines", name=" Ascan trait ")
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fig.add_trace(new_trace)
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@ -524,21 +564,10 @@ def update_heatmap_ascan(
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fig.add_trace(new_trace)
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fig.update_layout(xaxis_title="indix", yaxis_title="amplitude")
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data_bscan = Bscant(
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volume[select_ascan_y - 1, select_ascan_z[0] : select_ascan_z[1], :],
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int(selec_transforme_hilbert),
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int(select_filtre_1),
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int(select_filtre_2),
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float(fs_filtre_1),
|
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float(cutoff_filtre_1),
|
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int(order_filtre_1),
|
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int(windowsize_filtre_1),
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float(fs_filtre_2),
|
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float(cutoff_filtre_2),
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int(order_filtre_2),
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int(windowsize_filtre_2),
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)
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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),
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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),)
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fig2 = px.imshow(
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data_bscan,
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color_continuous_scale="Jet",
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@ -549,14 +578,14 @@ def update_heatmap_ascan(
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data_sans_traitement_fft = np.fft.fft(
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volume[
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int(select_ascan_y) - 1,
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select_ascan_z[0] : select_ascan_z[1],
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int(select_ascan_z[0]) : int(select_ascan_z[1]),
|
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int(select_ascan_x) - 1,
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]
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)
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fig3 = px.line(title="FFT")
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trace3 = go.Scatter(y=np.abs(data_sans_traitement_fft), mode="lines", name=" FFT ")
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trace3 = go.Scatter(y=np.abs(data_sans_traitement_fft[int(select_ascan_z[0]) :int(int(select_ascan_z[1])/2)]), mode="lines", name=" FFT ")
|
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fig3.add_trace(trace3)
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fig3.update_layout(
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xaxis_title="FFT indix", yaxis_title="FFT of signal (Mangnitude)"
|
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)
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return [fig, fig2, fig3, bouton]
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return [fig, fig2, fig3]
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|
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@ -6,29 +6,20 @@ import numpy as np
|
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import plotly.express as px
|
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import plotly.io as pio
|
||||
from util import *
|
||||
from selection_filtre import *
|
||||
from os.path import join
|
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import diskcache
|
||||
from pages.ascan import data_traits, pre_volume
|
||||
|
||||
dash.register_page(__name__, path="/", description="The home page of the web app")
|
||||
|
||||
# on définit le dossier et les fichiers à lire
|
||||
dossier = "Dataset/Shear_transform"
|
||||
fichiers_selectionnes = [
|
||||
"Shear_x001-x101_y{:03d}_Rot00_transform.csv".format(i) for i in range(10, 14)
|
||||
]
|
||||
|
||||
cache = diskcache.Cache("./cache")
|
||||
background_callback_manager = DiskcacheManager(cache)
|
||||
|
||||
# on charge le fichier numpy
|
||||
# fichiers = np.load("Dataset/npy/export.npy")
|
||||
dash.register_page(__name__, path="/")
|
||||
|
||||
# valeurs d'échantillonage
|
||||
echantillonage_x = 1
|
||||
echantillonage_y = 32
|
||||
echantillonage_z = 1
|
||||
|
||||
pre_volume = np.array(lire_fichier_csv(dossier, fichiers_selectionnes))
|
||||
volume = pre_volume[::echantillonage_x, ::echantillonage_y, ::echantillonage_z]
|
||||
dim_x, dim_y, dim_z = volume.shape
|
||||
|
||||
@ -263,7 +254,7 @@ Ascan_card = dbc.Fade(
|
||||
dcc.Slider(
|
||||
id="layer-slider-ascan-fullscreen",
|
||||
min=0,
|
||||
max=dim_x - 1,
|
||||
max=dim_x,
|
||||
value=0,
|
||||
step=1,
|
||||
marks={
|
||||
@ -345,7 +336,7 @@ Bscan_card_xy = dbc.Fade(
|
||||
dcc.Slider(
|
||||
id="layer-slider-bscan-zx-fullscreen",
|
||||
min=0,
|
||||
max=dim_x - 1,
|
||||
max=dim_x,
|
||||
value=0,
|
||||
step=1,
|
||||
marks={
|
||||
@ -470,14 +461,18 @@ layout = html.Div(
|
||||
# on défini les callbacks
|
||||
# callback pour le plot 3D
|
||||
@callback(
|
||||
[Output("3dplot", "figure"), Output("fade-3dplot", "is_in")],
|
||||
[Output("3dplot", "figure")],
|
||||
[
|
||||
Input("iso-slider", "value"),
|
||||
Input("y-slider", "value"),
|
||||
Input("store-settings", "data"),
|
||||
],
|
||||
[dash.dependencies.State("fade-3dplot", "is_in")],
|
||||
running=[
|
||||
(Output("fade-3dplot", "is_in"), False, True),
|
||||
],
|
||||
)
|
||||
### a revoir###
|
||||
def update_3dplot(iso_value, y_values, settings, is_in):
|
||||
if settings["use_real_values"]:
|
||||
y_min, y_max = y_values
|
||||
@ -508,7 +503,7 @@ def update_3dplot(iso_value, y_values, settings, is_in):
|
||||
)
|
||||
)
|
||||
|
||||
return [fig, True]
|
||||
return [fig]
|
||||
|
||||
|
||||
# callback pour le plot 3D en plein écran
|
||||
@ -516,6 +511,7 @@ def update_3dplot(iso_value, y_values, settings, is_in):
|
||||
Output("3dplot-fullscreen", "figure"),
|
||||
[Input("iso-slider-fullscreen", "value"), Input("y-slider-fullscreen", "value")],
|
||||
)
|
||||
### à revoir ###
|
||||
def update_3dplot_fullscreen(iso_value, y_values):
|
||||
y_min, y_max = y_values
|
||||
selected_volume = volume[0:dim_x, int(y_min) : int(y_max), 0:dim_z]
|
||||
@ -544,15 +540,18 @@ def update_3dplot_fullscreen(iso_value, y_values):
|
||||
|
||||
# callback pour le A-scan
|
||||
@callback(
|
||||
[Output("heatmap-ascan", "figure"), Output("fade-ascan", "is_in")],
|
||||
[Output("heatmap-ascan", "figure")],
|
||||
[Input("layer-slider-bscan-zx", "value"), Input("layer-slider-bscan-xy", "value")],
|
||||
[dash.dependencies.State("fade-ascan", "is_in")],
|
||||
running=[
|
||||
(Output("fade-ascan", "is_in"), False, True),
|
||||
],
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_heatmap_ascan(layer, layer1, is_in):
|
||||
fig = px.line(y=volume[layer - 1, :, layer1], title="A-scan")
|
||||
|
||||
return [fig, True]
|
||||
return [fig]
|
||||
|
||||
|
||||
# callback pour le A-scan en plein écran
|
||||
@ -570,10 +569,12 @@ def update_heatmap_ascan_fullscreen(layer):
|
||||
[
|
||||
Output("heatmap-bscan-zx", "figure"),
|
||||
Output("store-bscan-zx-layer", "data"),
|
||||
Output("fade-bscan-xy", "is_in"),
|
||||
],
|
||||
[Input("layer-slider-bscan-zx", "value")],
|
||||
[dash.dependencies.State("fade-bscan-zx", "is_in")],
|
||||
running=[
|
||||
(Output("fade-bscan-xy", "is_in"), False, True),
|
||||
],
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_heatmap_bscan_zx(layer, is_in):
|
||||
@ -584,7 +585,7 @@ def update_heatmap_bscan_zx(layer, is_in):
|
||||
title="B-scan ZX",
|
||||
)
|
||||
|
||||
return [fig, layer, True]
|
||||
return [fig, layer]
|
||||
|
||||
|
||||
# callback pour les B-scan ZX en plein écran
|
||||
@ -605,20 +606,19 @@ def update_heatmap_bscan_zx_fullscreen(layer):
|
||||
|
||||
# callback pour les B-scan ZX
|
||||
@callback(
|
||||
[
|
||||
Output("heatmap-bscan-xy", "figure"),
|
||||
Output("store-bscan-xy-layer", "data"),
|
||||
Output("fade-bscan-zx", "is_in"),
|
||||
],
|
||||
[Output("heatmap-bscan-xy", "figure"), Output("store-bscan-xy-layer", "data")],
|
||||
[Input("layer-slider-bscan-xy", "value")],
|
||||
[dash.dependencies.State("fade-bscan-xy", "is_in")],
|
||||
running=[
|
||||
(Output("fade-bscan-zx", "is_in"), False, True),
|
||||
],
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_heatmap_bscan_xy(layer, is_in):
|
||||
fig = go.Figure(data=go.Heatmap(z=volume[:, :, layer], colorscale="Jet"))
|
||||
fig.update_layout(title="B-scan XY")
|
||||
|
||||
return [fig, layer, True]
|
||||
return [fig, layer]
|
||||
|
||||
|
||||
# callback pour les B-scan ZX en plein écran
|
||||
@ -823,7 +823,7 @@ def update_settings(
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def redef_data(data):
|
||||
global volume, pre_volume, dim_x, dim_y, dim_z, X, Y, Z
|
||||
global volume, dim_x, dim_y, dim_z, X, Y, Z
|
||||
volume = pre_volume[
|
||||
:: data["echantillonage_x"],
|
||||
:: data["echantillonage_y"],
|
||||
@ -852,7 +852,7 @@ def redef_data(data):
|
||||
"Apply",
|
||||
]
|
||||
|
||||
|
||||
"""
|
||||
@callback(
|
||||
[Input("store-filters", "data"), Input("store-settings", "data")],
|
||||
)
|
||||
@ -903,45 +903,37 @@ def apply_filters(data, settings):
|
||||
:: settings["echantillonage_z"],
|
||||
]
|
||||
return None
|
||||
|
||||
"""
|
||||
|
||||
@callback(
|
||||
Output("store-files", "data", allow_duplicate=True),
|
||||
Input("store-files", "data"),
|
||||
[
|
||||
State("store-settings", "data"),
|
||||
State("layer-slider-bscan-zx", "value"),
|
||||
State("layer-slider-bscan-xy", "value"),
|
||||
State("iso-slider", "value"),
|
||||
State("y-slider", "value"),
|
||||
],
|
||||
State("store-settings", "data"),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_files(data, settings, layer_zx, layer_xy, iso, y):
|
||||
global pre_volume, dim_y
|
||||
def update_files(data, settings):
|
||||
global pre_volume, volume, dim_x, dim_y, dim_z, X, Y, Z
|
||||
if data is None or data == "":
|
||||
return None
|
||||
|
||||
# Charger le nouveau volume
|
||||
pre_volume = np.load(join("Dataset/saves", data))
|
||||
redef_data(settings)
|
||||
update_3dplot(iso, y, settings, False)
|
||||
update_heatmap_ascan(layer_zx, layer_xy, False)
|
||||
update_heatmap_bscan_zx(layer_zx, False)
|
||||
update_heatmap_bscan_xy(layer_xy, False)
|
||||
print("New volume loaded:", pre_volume.shape)
|
||||
|
||||
# Appliquer les nouveaux paramètres d'échantillonnage
|
||||
volume = pre_volume[
|
||||
::settings["echantillonage_x"],
|
||||
::settings["echantillonage_y"],
|
||||
::settings["echantillonage_z"],
|
||||
]
|
||||
dim_x, dim_y, dim_z = volume.shape
|
||||
X, Y, Z = np.mgrid[0:dim_x, 0:dim_y, 0:dim_z]
|
||||
print("Volume updated with new dimensions:", volume.shape)
|
||||
|
||||
# Mettre à jour les graphiques
|
||||
update_3dplot(0, [0, dim_y // 2], settings, False)
|
||||
update_heatmap_ascan(0, 0, False)
|
||||
update_heatmap_bscan_zx(0, False)
|
||||
update_heatmap_bscan_xy(0, False)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
@callback(
|
||||
Output("save-toast", "is_open"),
|
||||
Input("save-save", "n_clicks"),
|
||||
[
|
||||
State("save-input", "value"),
|
||||
State("save-format", "value"),
|
||||
],
|
||||
)
|
||||
def save_data(n_clicks, filename, format):
|
||||
if n_clicks is None:
|
||||
return False
|
||||
if format == "raw":
|
||||
np.save(join("Dataset/saves", filename), pre_volume)
|
||||
else:
|
||||
np.save(join("Dataset/saves", filename), volume)
|
||||
return True
|
||||
|
||||
Reference in New Issue
Block a user