Create main.py
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mathur04
268
3D_app/main.py
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268
3D_app/main.py
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import dash
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import dash_core_components as dcc
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import dash_html_components as html
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from dash.dependencies import Input, Output
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import plotly.graph_objects as go
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import numpy as np
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from util import *
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import dash_bootstrap_components as dbc
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import plotly.express as px
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#initialisation de figure
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dossier = "Dataset/Shear_transform"
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fichiers_selectionnes = ['Shear_x001-x101_y{:03d}_Rot00_transform.csv'.format(i) for i in range(10, 62)]
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#dossier = "Dataset/Shear_Wave_Rot00_CSV_Data"
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#fichiers_selectionnes = ['Shear_x001-x101_y{:03d}_Rot00.csv'.format(i) for i in range(10, 62)]
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print(fichiers_selectionnes)
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pre_volume = np.array(lire_fichier_csv(dossier,fichiers_selectionnes))
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volume = pre_volume[:, ::32, :]
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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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fig = go.Figure(data=go.Volume(
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x=X.flatten(),
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y=Y.flatten(),
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z=Z.flatten(),
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value=volume.flatten(),
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#isomin=volume.min(),
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isomin=5000,
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isomax=volume.max(),
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opacity=0.1, # needs to be small to see through all surfaces
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surface_count=20, # needs to be a large number for good volume rendering
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colorscale='Jet'
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))
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# Initialize the Dash app
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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# Define the app layout
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#3D_card = dbc.Card(
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#----------------definition layout----------------
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mesh_card = dbc.Card([
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dbc.CardBody([
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dcc.Graph(id='3dplot', figure=fig), # 'fig' is your 3D plotly figure
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dcc.Slider(
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id='iso-slider',
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min=volume.min(),
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max=volume.max() / 2,
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value=volume.min(),
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marks={str(i): str(i) for i in range(int(volume.min()), int(volume.max() / 2) + 1, int((volume.max() / 2 - volume.min()) / 10))},
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step=1
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),
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dcc.RangeSlider(
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id='y-slider',
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min=0,
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max=dim_y,
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value=[0, dim_y / 2],
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marks={str(i): str(i) for i in range(0, int(dim_y) + 1, max(1, int(dim_y / 20)))},
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step=1
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)
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])
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])
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Ascan_card = dbc.Card([
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dbc.CardBody([
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dcc.Graph(id='heatmap-ascan'), # 'fig' is your 2D plotly figure
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dcc.Slider(
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id='layer-slider-ascan',
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min=0,
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max=dim_x - 1,
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value=0,
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step=1
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)
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])
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])
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Bscan_card_xy = dbc.Card([
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dbc.CardBody([
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dcc.Graph(id='heatmap-bscan-xy'), # 'fig' is your 2D plotly figure
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dcc.Slider(
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id='layer-slider-bscan-xy',
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min=0,
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max=dim_x - 1,
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value=0,
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step=1,
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marks={}
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)
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])
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])
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Bscan_card_zx = dbc.Card([
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dbc.CardBody([
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dcc.Graph(id='heatmap-bscan-zx'), # 'fig' is your 2D plotly figure
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dcc.Slider(
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id='layer-slider-bscan-zx',
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min=0,
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max=dim_z - 1,
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value=0,
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step=1
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)
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])
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])
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with open("assets/modal.md", "r") as f:
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howto_md = f.read()
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modal_overlay = dbc.Modal(
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[
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dbc.ModalBody(html.Div([dcc.Markdown(howto_md)], id="howto-md")),
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dbc.ModalFooter(dbc.Button("Close", id="howto-close", className="howto-bn")),
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],
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id="modal",
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size="lg",
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)
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# Buttons
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button_gh = dbc.Button(
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"Learn more",
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id="howto-open",
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outline=True,
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color="secondary",
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# Turn off lowercase transformation for class .button in stylesheet
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style={"textTransform": "none"},
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)
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button_howto = dbc.Button(
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"View Code on github",
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outline=True,
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color="primary",
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href="https://github.com/mathur04/stage_IJL/tree/main",
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id="gh-link",
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style={"text-transform": "none"},
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)
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nav_bar = dbc.Navbar(
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dbc.Container(
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[
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dbc.Row(
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[
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dbc.Col(
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dbc.Row(
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[
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dbc.Col(
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html.A(
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html.Img(
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src=app.get_asset_url("logo_IJL couleur.png"),
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height="30px",
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),
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href="https://ijl.univ-lorraine.fr/",
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),
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style={"width": "min-content"},
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),
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dbc.Col(
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html.Div(
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[
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html.H3("3D app"),
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html.P(
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"IJL - Institut Jean Lamour / project stage M2 EEA 2023-2024",
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),
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],
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id="app_title",
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)
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),
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],
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align="center",
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style={"display": "inline-flex"},
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)
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),
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dbc.Col(
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[
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dbc.NavbarToggler(id="navbar-toggler"),
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dbc.Collapse(
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dbc.Nav(
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[dbc.NavItem(button_howto), dbc.NavItem(button_gh)],
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className="ml-auto",
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navbar=True,
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),
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id="navbar-collapse",
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navbar=True,
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),
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]
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),
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modal_overlay,
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],
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align="center",
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style={"width": "100%"},
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),
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],
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fluid=True,
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),
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color="white",
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dark=True,
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)
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# main layout
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app.layout = dbc.Container([
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nav_bar,
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dbc.Row([dbc.Col(Ascan_card, width=6), dbc.Col(mesh_card, width=6)]),
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dbc.Row([dbc.Col(Bscan_card_xy, width=6), dbc.Col(Bscan_card_zx, width=6)])
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], fluid=True)
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#----------------definition interaction----------------
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@app.callback(
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Output('3dplot', 'figure'),
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[Input('iso-slider', 'value'),Input('y-slider', 'value')]
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)
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def update_3dplot(iso_value, y_values):
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y_min, y_max = y_values
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selected_volume = volume[0:dim_x, int(y_min):int(y_max), 0:dim_z]
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X, Y, Z = np.mgrid[0:selected_volume.shape[0], 0:selected_volume.shape[1], 0:selected_volume.shape[2]]
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fig = go.Figure(data=go.Volume(
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x=X.flatten(),
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y=Y.flatten(),
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z=Z.flatten(),
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value=selected_volume.flatten(),
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isomin=iso_value,
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isomax=selected_volume.max(),
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opacity=0.1,
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surface_count=20,
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colorscale='Jet'
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))
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return fig
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@app.callback(
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Output('heatmap-ascan', 'figure'),
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[Input('layer-slider-ascan', 'value')]
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)
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def update_heatmap_ascan(layer):
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fig = px.line(y=volume[layer, :, 5], title='A-scan')
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return fig
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return update_heatmap_ascan(volume, layer)
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@app.callback(
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Output('heatmap-bscan-xy', 'figure'),
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[Input('layer-slider-bscan-xy', 'value')]
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)
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def update_heatmap_bscan_xy(layer):
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fig = px.imshow(volume[layer, :, :], color_continuous_scale='Jet', aspect='auto')
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return fig
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@app.callback(
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Output('heatmap-bscan-zx', 'figure'),
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[Input('layer-slider-bscan-zx', 'value')]
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)
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def update_heatmap_bscan_zx(layer):
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fig = go.Figure(data=go.Heatmap(
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z=volume[:, :, layer],
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colorscale='Jet'
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))
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return fig
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# Run the app
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if __name__ == '__main__':
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app.run_server(debug=True, port=8051)
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