value='lines') ], style=third_width) ], style=dict(width='80%', display='none'), hidden=True) graph_settings_container = html.Div(id='graph-settings-container', children=[plot_control_container]) graph_update_timer = dcc.Interval( id='update-timer', interval=2 * 1000 #ms ) graph_update_trigger = dcc.Store(id='update-trigger') visualization_container = html.Div(id='visualization-container', children=[ dashboard_graph, graph_settings_container, graph_update_timer, graph_update_trigger ], style={ 'float': 'left', 'width': '85%' }) ########################################################### # RIGHT COLUMN # ###########################################################
def shutdown(): """ Shutdown Flask server """ func = request.environ.get("werkzeug.server.shutdown") if func is None: raise RuntimeError("Not running with the Werkzeug Server") func() app.layout = html.Div( className="container-fluid", id="mainp", children=[ dcc.Store(id="memory"), html.Div( className="row", children=[ html.Div( className="col", children=[ html.H1(children="Complex Object Creator 2.0"), dcc.Dropdown( id="dropdown", options=[ {"label": "Cartesian", "value": "Cartesian"}, {"label": "Polar", "value": "Polar"}, {"label": "Exp", "value": "Exp"}, ], value="Cartesian",
# Graph html.Div( className="nine columns card-left", children=[ html.Div( className="bg-white", children=[ html.H5("Last Updated:"), dcc.Input(id='h_date', value='0', type='hidden'), html.Div(id='date', style={'marginLeft': '43px'}), dcc.Graph(id="plot"), ] ) ], ), dcc.Store(id="error", storage_type="memory"), ], ) ]), dcc.Tab(label='STOCK SELECTION', children=[html.Div([ html.Div([ html.Div([ html.H4("Filter for Stock Selection:"), dcc.RadioItems( id='filter-radio', options=[{'label': i, 'value': i} for i in ['Long', 'Short', 'Long to Short', 'Short to Long']], value='Long',
disabled=True, href="/views/dashboard3", active="exact"), ], vertical=True, pills=True, ), ], style=SIDEBAR_STYLE, ) content = html.Div(id="page-content", style=CONTENT_STYLE) app.layout = html.Div([ # Store dataframe dcc.Store(id='dataframe', storage_type='local'), dcc.Store(id='rule-set-store', storage_type='local'), dcc.Store(id='rule-dataframe', storage_type='local'), dcc.Location(id='url', refresh=False), sidebar, content ]) home_content = html.Div([ html.H2("SQIs Table"), dcc.Upload( id='upload-data', children=html.Div(['Drag and Drop or ', html.A('Select Files')]), style={ 'width': '100%',
) ]), html.Div([ html.A('Download activations', id='download-link', download='rawdata.csv', target='_blank', className='btn btn-success btn-block disabled'), html.Table( id='table-network-info', children=[], ) ], id='network-info-panel'), html.Pre(id='output-activated-nodes'), dcc.Store(id='graph-pickled'), dcc.Store(id='data-selected-nodes'), dcc.Loading(children=[ dcc.Store(id='data-activated-nodes'), ], className='loading-box'), ], className='left-panel'), html.Div([ dbc.Tabs( id='visualisation-tabs', children=[ dbc.Tab(label='Network', children=[ cyto.Cytoscape(id='cytoscape-elements', layout={
html.Button(id='analyze', n_clicks_timestamp=0, children='Analyze', title='Analyze text file to detect outliers') ], style={ 'float': 'center', 'display': 'inline-block' }), dcc.Loading(id='parse summary and compute zscore', fullscreen=True, debug=True, type='graph'), # Other dcc.Input() dcc.Store(id='df'), dcc.Store(id='subjects'), dcc.Store(id='dfcombined'), # other dcc.Store() html.Br(), html.Div( id='results', children=[ html.Div( 'Analysis complete! Now you can browse through the summary below!', id='analyze-status'), html.Br(), dcc.Link('See outliers summary', href='/summary'), html.Br(), dcc.Link('See outliers in graphs and GLM fitting', id='compare-link',
import dash_core_components as dcc import dash_html_components as html import dash_daq as daq import dash_bootstrap_components as dbc import dash_cytoscape as cyto from src import settings base = html.Div([ dcc.Location(id='url', refresh=False), html.Div(id='page-content'), dcc.Loading(type='graph', fullscreen=True, children=[ html.Div(id='container-data-store', style={'display': 'none'}, children=[dcc.Store(id='data-store')]) ]) ]) def add_help(inside, tooltip_id=None, hide=True): """Wrapper html tag to display help tooltips.""" style = {'display': 'inline-block', 'margin-right': '5px'} visibility = 'hidden' if hide else 'visible' try: inside.style except AttributeError: inside.style = {} inside.style.update(style) result = html.Span([ inside,
def start(): app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP], suppress_callback_exceptions=True) app.title = 'AutoML Benchmark' algorithms = ['autosklearn', 'h2o', 'tpot', 'autokeras', 'autogluon'] CONTENT_STYLE = { "marginLeft": "22rem", "marginRight": "2rem", "padding": "2rem 1rem", } content = html.Div(id="page-content", style=CONTENT_STYLE) app.layout = html.Div([ dcc.Location(id="url"), sidebar, dcc.Store(id="store_class_openml"), dcc.Store(id="store_reg_openml"), dcc.Store(id="store_class_kaggle"), dcc.Store(id="store_reg_kaggle"), dcc.Store(id="store_class_results_openml"), dcc.Store(id="store_reg_results_openml"), dcc.Store(id="store_class_results_kaggle"), dcc.Store(id="store_reg_results_kaggle"), dcc.Store(id="store_pipelines_class_openml"), dcc.Store(id="store_pipelines_reg_openml"), dcc.Store(id="store_pipelines_class_kaggle"), dcc.Store(id="store_pipelines_reg_kaggle"), dcc.Store(id="store_pipelines_results_class_openml"), dcc.Store(id="store_pipelines_results_reg_openml"), dcc.Store(id="store_pipelines_results_class_kaggle"), dcc.Store(id="store_pipelines_results_reg_kaggle"), content ]) app.validation_layout=html.Div([openmlbenchmark, kagglebenchmark, testbenchmark, pastresultopenml, pastresultkaggle]) @app.callback(Output("page-content", "children"), [Input("url", "pathname")]) def render_page_content(pathname): return render_page_content_function(pathname) #populiamo i 4 store @app.callback( [Output('store_class_openml', 'data'), Output('store_reg_openml', 'data'), Output('store_pipelines_class_openml', 'data'), Output('store_pipelines_reg_openml', 'data'), Output('res-bench-openml-table-class', 'children'), Output('res-bench-openml-table-reg', 'children')], [Input('submit-openml', 'n_clicks')], [State('nmore', 'value'), State('ndf', 'value'), State("autosklearn-timelife", "value"), State("h2o-timelife", "value"), State("tpot-timelife", "value"), State("autokeras-timelife", "value"), State("autogluon-timelife", "value")] ) def start_openml(n_clicks, nmore, ndf, as_tl, h2o_tl, t_tl, ak_tl, ag_tl): options = make_options(as_tl, h2o_tl, t_tl, ak_tl, ag_tl) return start_openml_function(nmore, ndf, options) #populiamo i 4 store @app.callback( [Output('store_class_kaggle', 'data'), Output('store_reg_kaggle', 'data'), Output('store_pipelines_class_kaggle', 'data'), Output('store_pipelines_reg_kaggle', 'data'), Output('res-bench-kaggle-table-class', 'children'), Output('res-bench-kaggle-table-reg', 'children')], [Input('submit-kaggle', 'n_clicks')], [State('kaggledataset', 'value'), State("autosklearn-timelife", "value"), State("h2o-timelife", "value"), State("tpot-timelife", "value"), State("autokeras-timelife", "value"), State("autogluon-timelife", "value")] ) def start_kaggle(n_clicks, kaggledataset, as_tl, h2o_tl, t_tl, ak_tl, ag_tl): options = make_options(as_tl, h2o_tl, t_tl, ak_tl, ag_tl) return start_kaggle_function(kaggledataset, options) @app.callback( [Output('res-bench-test', 'children')], [Input('submit-test', 'n_clicks')], [State('dfid', 'value'), State('algorithms', 'value'), State("autosklearn-timelife", "value"), State("h2o-timelife", "value"), State("tpot-timelife", "value"), State("autokeras-timelife", "value"), State("autogluon-timelife", "value")] ) def start_test(n_clicks, dfid, algorithms, as_tl, h2o_tl, t_tl, ak_tl, ag_tl): options = { 'autosklearn': {'time': as_tl, 'type': 'minute/s'}, 'h2o': {'time': h2o_tl, 'type': 'minute/s'}, 'tpot': {'time': t_tl, 'type': 'generation/s'}, 'autokeras': {'time': ak_tl, 'type': 'epoch/s'}, 'autogluon': {'time': ag_tl, 'type': 'minute/s'}, } return start_test_function(dfid, algorithms, options) #qui aggiorno i store di class e reg e stampo inizialmente le tabelle con i relativi risultati #OPNEML @app.callback( [Output('store_class_results_openml', 'data'), Output('store_reg_results_openml', 'data'),Output('store_pipelines_results_class_openml', 'data'), Output('store_pipelines_results_reg_openml', 'data'), Output('result-past-bench-openml-table-class', 'children'), Output('result-past-bench-openml-table-reg', 'children'), ], [Input('pastresultopenml', 'value')] ) def get_store_past_bech_openml(timestamp): return get_store_past_bech_function(timestamp, 'OpenML') #KAGGLE @app.callback( [Output('store_class_results_kaggle', 'data'), Output('store_reg_results_kaggle', 'data'),Output('store_pipelines_results_class_kaggle', 'data'), Output('store_pipelines_results_reg_kaggle', 'data'), Output('result-past-bench-kaggle-table-class', 'children'), Output('result-past-bench-kaggle-table-reg', 'children'), ], [Input('pastresultkaggle', 'value')] ) def get_store_past_bech_kaggle(timestamp): return get_store_past_bech_function(timestamp, 'Kaggle') #modfico stra scatter e histogram i risultati di classificazione @app.callback([Output('res-bench-openml-graph-class', 'children')], [Input("tabs-class", "active_tab"), Input('store_class_openml', 'data')]) def render_tab_content_class(active_tab, store_class_openml): return render_tab_content_function(active_tab, store_class_openml, ('acc', 'f1')) @app.callback([Output('res-bench-kaggle-graph-class', 'children')], [Input("tabs-class", "active_tab"), Input('store_class_kaggle', 'data')]) def render_tab_content_class(active_tab, store_class_kaggle): return render_tab_content_function(active_tab, store_class_kaggle, ('acc', 'f1')) @app.callback([Output('result-past-bench-openml-graph-class', 'children')], [Input("tabs-class", "active_tab"), Input('store_class_results_openml', 'data')]) def render_tab_content_class(active_tab, store_class_results_openml): return render_tab_content_function(active_tab, store_class_results_openml, ('acc', 'f1')) @app.callback([Output('result-past-bench-kaggle-graph-class', 'children')], [Input("tabs-class", "active_tab"), Input('store_class_results_kaggle', 'data')]) def render_tab_content_class(active_tab, store_class_results_kaggle): return render_tab_content_function(active_tab, store_class_results_kaggle, ('acc', 'f1')) #modfico stra scatter e histogram i risultati di regressione @app.callback([Output('res-bench-openml-graph-reg', 'children')], [Input("tabs-reg", "active_tab"), Input('store_reg_openml', 'data')]) def render_tab_content_reg(active_tab, store_reg_openml): return render_tab_content_function(active_tab, store_reg_openml, ('rmse', 'r2')) @app.callback([Output('res-bench-kaggle-graph-reg', 'children')], [Input("tabs-reg", "active_tab"), Input('store_reg_kaggle', 'data')]) def render_tab_content_reg(active_tab, store_reg_kaggle): return render_tab_content_function(active_tab, store_reg_kaggle, ('rmse', 'r2')) @app.callback([Output('result-past-bench-openml-graph-reg', 'children')], [Input("tabs-reg", "active_tab"), Input('store_reg_results_openml', 'data')]) def render_tab_content_reg(active_tab, store_reg_results_openml): return render_tab_content_function(active_tab, store_reg_results_openml, ('rmse', 'r2')) @app.callback([Output('result-past-bench-kaggle-graph-reg', 'children')], [Input("tabs-reg", "active_tab"), Input('store_reg_results_kaggle', 'data')]) def render_tab_content_reg(active_tab, store_reg_results_kaggle): return render_tab_content_function(active_tab, store_reg_results_kaggle, ('rmse', 'r2')) @app.callback( [Output(f"collapse-{algo}", "is_open") for algo in algorithms], [Input(f"{algo}-options", "n_clicks") for algo in algorithms], [State(f"collapse-{algo}", "is_open") for algo in algorithms], ) def collapse_alogrithms_options(n1, n2, n3, n4, n5, is_open1, is_open2, is_open3, is_open4, is_open5): return collapse_alogrithms_options_function(n1, n2, n3, n4, n5, is_open1, is_open2, is_open3, is_open4, is_open5) @app.callback( [Output(f"{algo}-options", "disabled") for algo in algorithms], [Input("algorithms", "value")], ) def disable_buttons_collapse(choice): return render_collapse_options(choice) @app.callback( [Output({"type":"modal-Pipelines", "index": MATCH}, "is_open"), Output({"type": 'body-modal-Pipelines', "index": MATCH}, 'children')], [Input({"type": "open-Pipelines", "index": MATCH}, "n_clicks"), Input({"type": "close-modal-Pipelines", "index": MATCH}, "n_clicks"), Input({"type": "open-Pipelines", "index": MATCH}, "value"), Input("url", "pathname"), Input('store_pipelines_class_openml', 'data'), Input('store_pipelines_reg_openml', 'data'), Input('store_pipelines_class_kaggle', 'data'), Input('store_pipelines_reg_kaggle', 'data'), Input('store_pipelines_results_class_openml', 'data'), Input('store_pipelines_results_reg_openml', 'data'), Input('store_pipelines_results_class_kaggle', 'data'), Input('store_pipelines_results_reg_kaggle', 'data'),], [State({"type":"modal-Pipelines", "index": MATCH}, "is_open")] ) def show_hide_pipelines(n1, n2, value, path, s1, s2, s3, s4, s5, s6, s7, s8, is_open): stores = { "/openml": [s1,s2], "/kaggle": [s3,s4], '/results-openml': [s5,s6], '/results-kaggle': [s7,s8], } s = stores.get(path, None) if s is not None: return show_hide_pipelines_function(s[0], s[1], n1, n2, value, is_open) else: return None, None app.run_server(host='0.0.0.0', port=8050, debug=True)
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.read_csv( 'https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv' ) available_countries = df['country'].unique() app.layout = html.Div([ dcc.Graph(id='clientside-graph'), dcc.Store(id='clientside-figure-store', data=[{ 'x': df[df['country'] == 'Canada']['year'], 'y': df[df['country'] == 'Canada']['pop'] }]), 'Indicator', dcc.Dropdown(id='clientside-graph-indicator', options=[{ 'label': 'Population', 'value': 'pop' }, { 'label': 'Life Expectancy', 'value': 'lifeExp' }, { 'label': 'GDP per Capita', 'value': 'gdpPercap' }], value='pop'), 'Country', dcc.Dropdown(id='clientside-graph-country',
def get_create_study_div(): """ Returns the create study div :return: Create study div containing: title, input fields for study name, study duration and number of subjects and a list containing sensor + checkboxes """ return html.Div( id='create-study-div', children=[ dcc.Store(id='study-details', data=get_default_study_details_dict()), dcc.Store(id='ema-details', data=get_default_ema_details_dict()), dcc.Store(id='passive-monitoring-details', data=get_default_passive_monitoring_details_dict()), html.H2(children='Create new study'), html.Div(children=[ html.Div(children=[ html.Span(className='create-span', children='Study name*:'), dcc.Input(id='create-study-name', placeholder='Your study', type='text', debounce=True) ]), html.Div(children=[ html.Span(className='create-span', children='Study duration*:'), dcc.Input(id='create-study-duration', placeholder='Days', type='number', min='1', debounce=True) ]), html.Div(children=[ html.Span(className='create-span', children='Number of subjects*:'), dcc.Input(id='create-subject-number', placeholder='Number of subjects', type='number', min='0', debounce=True) ]), html.Div(children=[ html.Span(className='create-span', children='Study description:'), dcc.Textarea(id='create-study-description', placeholder="Enter study description", maxLength='500') ]), dcc.Checklist(id='modality-list', options=modality_list, labelStyle={'display': 'block'}), html.Div(id='data-div'), html.Button(id='create-study-button', children='Create study'), # is filled if user tries to create study, reset also other input fields dcc.Loading(children=[html.P(id='create-study-output-state')], type='circle') ]) ])
def contributions_layout(explainer, n_features=15, round=2, **kwargs): """returns layout for individual contributions tabs :param explainer: ExplainerBunch to build layout for :type explainer: ExplainerBunch :type title: str :param standalone: when standalone layout, include a a label_store, defaults to False :type standalone: bool :param hide_selector: if model is a classifier, optionally hide the positive label selector, defaults to False :type hide_selector: bool :param n_features: Default number of features to display in contributions graph, defaults to 15 :type n_features: int, optional :param round: Precision of floats to display, defaults to 2 :type round: int, optional :rtype: [dbc.Container """ if explainer.is_classifier: index_choice_form = dbc.Form([ dbc.FormGroup([ html.Div([ dbc.Label( 'Range to select from (prediction probability or prediction percentile):', html_for='prediction-range-slider'), dcc.RangeSlider(id='prediction-range-slider', min=0.0, max=1.0, step=0.01, value=[0.5, 1.0], allowCross=False, marks={ 0.0: '0.0', 0.1: '0.1', 0.2: '0.2', 0.3: '0.3', 0.4: '0.4', 0.5: '0.5', 0.6: '0.6', 0.7: '0.7', 0.8: '0.8', 0.9: '0.9', 1.0: '1.0' }, tooltip={'always_visible': False}) ], style={'margin-bottom': 25}) ]), dbc.FormGroup([ dbc.RadioItems(id='include-labels', options=[ { 'label': explainer.pos_label_str, 'value': 'pos' }, { 'label': 'Not ' + explainer.pos_label_str, 'value': 'neg' }, { 'label': 'Both/either', 'value': 'any' }, ], value='any', inline=True), dbc.RadioItems(id='preds-or-ranks', options=[ { 'label': 'Use predictions', 'value': 'preds' }, { 'label': 'Use percentiles', 'value': 'ranks' }, ], value='preds', inline=True) ]) ]) else: index_choice_form = dbc.Form([ dbc.FormGroup([ html.Div([ html.Div([ dbc.Label( 'Range of predicted outcomes to select from:', html_for='prediction-range-slider'), dcc.RangeSlider( id='prediction-range-slider', min=min(explainer.preds), max=max(explainer.preds), step=np.float_power(10, -round), value=[min(explainer.preds), max(explainer.preds)], marks={ min(explainer.preds): str(np.round(min(explainer.preds), round)), max(explainer.preds): str(np.round(max(explainer.preds), round)) }, allowCross=False, tooltip={'always_visible': False}) ], style={'margin-bottom': 25}) ]), ], style={'margin-bottom': 25}), ]) return dbc.Container([ dbc.Row([ dbc.Col([ html.H2('Display prediction for:'), dbc.Input(id='input-index', placeholder="Fill in index here...", debounce=True), index_choice_form, dbc.Button("Random Index", color="primary", id='index-button'), dcc.Store(id='index-store'), ], md=6), dbc.Col([ dcc.Loading(id="loading-model-prediction", children=[dcc.Markdown(id='model-prediction')]), ], md=6), ], align="start", justify="between"), dbc.Row([ dbc.Col([ html.H3('Contributions to prediction'), dbc.Label('Number of features to display:', html_for='contributions-size'), html.Div([ dcc.Slider(id='contributions-size', min = 1, max = len(explainer.columns), marks = {int(i) : str(int(i)) for i in np.linspace( 1, len(explainer.columns_cats), 6)}, step = 1, value=min(n_features, len(explainer.columns_cats)), tooltip = {'always_visible' : False} ), ], style={'margin-bottom':25}), dbc.Label('(click on a bar to display pdp graph)'), dcc.Loading(id="loading-contributions-graph", children=[dcc.Graph(id='contributions-graph')]), html.Div(id='contributions-size-display', style={'margin-top': 20}), html.Div(id='contributions-clickdata'), ], md=6), dbc.Col([ html.H3('Partial Dependence Plot'), dbc.Label("Plot partial dependence plot (\'what if?\') for column:", html_for='pdp-col'), dcc.Dropdown(id='pdp-col', options=[{'label': col, 'value':col} for col in explainer.mean_abs_shap_df(cats=True)\ .Feature.tolist()], value=explainer.mean_abs_shap_df(cats=True).Feature[0]), dcc.Loading(id="loading-pdp-graph", children=[dcc.Graph(id='pdp-graph')]), ], md=6) ]), dbc.Row([ dbc.Col([ html.H3('Contributions to prediction'), dash_table.DataTable( id='contributions_table', style_cell={'fontSize':20, 'font-family':'sans-serif'}, columns=[{'id': c, 'name': c} for c in ['Reason', 'Effect']], ), ], md=6), ]), ], fluid=True)
) fig = go.Figure(data=mydata, layout=mylayout) ## Confusion Matrix cm = pd.read_csv('analysis/conf_matrix.csv') ########### Initiate the app external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) server = app.server app.title=tabtitle ########### Layout app.layout = html.Div(children=[ dcc.Store(id='tmdb-store', storage_type='session'), dcc.Store(id='summary-store', storage_type='session'), html.Div([ html.H1(['Horror Movie Predictor']), html.Div([ html.Div([ html.Div('Randomly select a movie summary'), html.Button(id='eek-button', n_clicks=0, children='EEK!', style={'color': 'rgb(255, 255, 255)'}), html.Div(id='movie-title', children=[]), html.Div(id='movie-release', children=[]), html.Div(id='movie-overview', children=[]), ], style={ 'padding': '12px', 'font-size': '22px', # 'height': '400px', 'border': 'thick red solid',
sort_action='custom', sort_mode='multi', sort_by=[], style_table={}, tooltip={i: { 'value': i, 'use_with': 'both' } for i in df0.keys()}, ) app = dash.Dash(__name__) app.layout = html.Div(className="row", children=[ dcc.Store(id='filter_df_store'), dcc.Link(id='clicking', children=["dfdf"], href='https://www.baidu.com'), html.H4( 'factor分析', style={ "left": 5, "top": 5, "position": "absolute" }, ), html.Div( id='factors_table-container', style={ "width": 800,
dbc.Col(dcc.Graph(id='pca-variance-bar')), dbc.Col(dcc.Graph(id='pca-components-2d')) ] ) num_viz_layout = dbc.Col( [ html.Br(), num_viz_header, num_viz_3d_dropdown, num_viz_3d_plot, html.Br(), num_viz_dropdown, num_viz_2d_plots ] ) ## Bring it all together layout = dbc.Container( [ dcc.Store(id='memory-output', storage_type='memory'), dbc.Col( [ header, top_tabs, content ], align = 'stretch' ) ], )
# Satellite L12-5 data df_non_gps_h_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "non_gps_data_h_1.csv"))) df_non_gps_m_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "non_gps_data_m_1.csv"))) df_gps_m_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "gps_data_m_1.csv"))) df_gps_h_1 = pd.read_csv( os.path.join(APP_PATH, os.path.join("data", "gps_data_h_1.csv"))) # Root root_layout = html.Div( id="root", children=[ dcc.Store(id="store-placeholder"), dcc.Store( id="store-data", data={ "hour_data_0": { "elevation": [df_non_gps_h_0["elevation"][i] for i in range(60)], "temperature": [df_non_gps_h_0["temperature"][i] for i in range(60)], "speed": [df_non_gps_h_0["speed"][i] for i in range(60)], "latitude": [ "{0:09.4f}".format(df_gps_h_0["lat"][i]) for i in range(60) ], "longitude": [ "{0:09.4f}".format(df_gps_h_0["lon"][i])
), id='navbar-collapse2', navbar=True, ), )) ], ), # color='dark', # dark=True, className='mb-5', ) ################################################################################################################### body1 = html.Div([ dbc.Container( [ dbc.Row(dbc.Col(dcc.Store(id='memory-output'))), dbc.Row(dbc.Col(navbar)), dbc.Row( dbc.Col( html.H3('Financial Dashboard for Euronext Stock Exchange', style={'text-align': 'center'}))), # dbc.Row(dbc.Col(html.Br())), dbc.Row([ dbc.Col( html.Div(dbc.Input(id="input", value='ABI', debounce=True))), dbc.Col(html.Div(id='output', style={'text-align': 'center'})), dbc.Col(html.Div(id='output2', style={'text-align': 'center' })), dbc.Col(html.Div(id='output3', style={'text-align': 'center' })),
app.config.suppress_callback_exceptions = True server = app.server df = pd.read_csv('game_set_outcome.csv', delimiter=';') #function to find bulletpoints and seperate lines based on a bulletpoint def textDecoder(text): lines = text.split(u'\u2022') return ([html.Li(i) for i in lines if i is not ' ' and i is not '']) body = html.Div([ dcc.Store(id='memory'), html.Div([ html.H1("Beat the 'rithm.", className="title title--adjusted"), html.Div([ html.Div('Your bonus: $'), html.Div(children="0", id="bonus", className="bonus__count"), ], className="bonus flex"), ], className="header"), html.Div( [ html.Div([ html.Div([ html.Div('', className="circle"), html.Div('Job vacancy'),
history_list = dict(zip(["ramped", "sinusoidal", "dcv"], [[], [], []])) max_plots = 2 history_dict = {str(key): {} for key in range(0, max_plots)} for e_type in ["ramped", "sinusoidal", "dcv"]: for exp in [ "current_time", "current_voltage", "voltage_time", "fft", "harms" ]: for history in ["", "_history"]: if e_type != "dcv": if exp == "harms": for i in range(0, num_harms): id_str = ("_").join([e_type, exp, str(i), "store"]) + history if history == "": store_list[e_type].append(id_str) storage_array.append(dcc.Store(id=id_str)) else: history_list[e_type].append(id_str) storage_array.append( dcc.Store(id=id_str, data=history_dict)) else: id_str = ("_").join([e_type, exp, "store"]) + history if history == "": store_list[e_type].append(id_str) storage_array.append(dcc.Store(id=id_str)) else: history_list[e_type].append(id_str) storage_array.append( dcc.Store(id=id_str, data=history_dict)) elif e_type == "dcv":
], className="six columns"), html.Div([ html.H3('Column 2'), dcc.Graph(id='g2', figure={'data': [{ 'y': [1, 2, 3] }]}) ], className="six columns"), ], className="row"), ]) day_wise_layout = html.Div( [ dcc.Store(id="aggregate_data"), # empty Div to trigger javascript file for graph resizing html.Div(id="output-clientside"), html.Div([ html.P("Select Cotegory:", className="control_label"), dcc.Dropdown( id="category", options=[{ 'label': i, 'value': i } for i in day_wise.columns.tolist()[1:]], value=f"{day_wise.columns.tolist()[1:][0]}", className="dcc_control", ), ], className="pretty_container",
}, ) controls = dbc.InputGroup(children=[ dbc.Input( id="user-input", placeholder="Write to the chatbot...", type="text"), dbc.InputGroupAddon(dbc.Button("Submit", id="submit"), addon_type="append"), ]) app.layout = dbc.Container( fluid=False, children=[ Header("Digital Legal and Compliance Officer", app), html.Hr(), dcc.Store(id="store-conversation", data=""), conversation, controls, dbc.Spinner(html.Div(id="loading-component")), ], ) @app.callback(Output("display-conversation", "children"), [Input("store-conversation", "data")]) def update_display(chat_history): return [ textbox(x, box="user") if i % 2 == 0 else textbox(x, box="AI") for i, x in enumerate(chat_history.split("<split>")[:-1]) ]
dbc.NavLink("Page 1", href="/page-1", id="page-1-link"), dbc.NavLink("Page 2", href="/page-2", id="page-2-link"), dbc.NavLink("Page 3", href="/page-3", id="page-3-link"), ], vertical=True, pills=True, ), ], id="sidebar", style=SIDEBAR_STYLE, ) content = html.Div(id="page-content", style=CONTENT_STYLE) app.layout = html.Div([ dcc.Store(id='side_click'), dcc.Location(id="url"), navbar, sidebar, content, ], ) @app.callback([ Output("sidebar", "style"), Output("page-content", "style"), Output("side_click", "data"), ], [Input("btn_sidebar", "n_clicks")], [ State("side_click", "data"), ]) def toggle_sidebar(n, nclick):
dbc.FormGroup([dbc.Button('Atualizar portfólio', id='btn-portfolio')], **_styles), # gerar recomendações dbc.FormGroup([dbc.Button('Gerar recomendações', id='btn-update')], **_styles) ]) view_portfolio = dbc.Row([html.Div(id='table-portfolio')]) view_recommends = dbc.Row([html.Div(id='table-recommends')]) app.layout = dbc.Container([ # barra superior navbar, # data stores dcc.Store('ids-store', storage_type='session'), dcc.Store('recommends-store', storage_type='session'), # alertas # dbc.Alert("Dados carregados com sucesso!", color="success", fade=True, duration=3000), # dbc.Alert("Erro na carga dos dados!", color="danger", fade=True, duration=3000), # inicio da pagina welcome, dbc.Row([ # barra lateral dbc.Col([options], md=3), # corpo da pagina dbc.Col([ dbc.Row([dcc.Loading([view_portfolio])]), dbc.Row([dcc.Loading([view_recommends])]) ], md=9)
#Add click data here ), html.Div( #add relayout data here ), html.Div([ html.A( "Code developed using the example Dash App Repositiory on Github", href=GITHUB_LINK, target="_blank", ) ]), ], className="one-third column app__right__section", ), dcc.Store(id="annotation_storage"), ]) @app.callback( Output("cube-graph", "figure"), [ Input("cube-graph", "clickData"), Input("radio-options", "value"), ], [State("cube-graph", "figure")], ) def cube_graph_handler(click_data, val, figure): if figure["data"][0]["name"] != val: figure["data"] = create_mesh_data(val)
style_table={'overflowY': 'auto'}, style_cell={'textAlign': 'left', 'minWidth': '100px', 'width': '100px', 'maxWidth': '100px'}, dropdown={ 'Class': { 'options': diagnosises }, }, ) ]), html.Hr(className="my-2"), html.Div([ html.Button("Export Table to Excel", id='excel_btn', n_clicks=0), html.Button("Submit Table", id='submit_btn', n_clicks=0), # for notification when saving to excel html.Div(id='excel_notification_placeholder', children=[]), dcc.Store(id="excel_notification_store", data=0), dcc.Interval(id='excel_notification_nterval', interval=1000), Download(id="download"), # for notification when saving to database html.Div(id='db_notification_placeholder', children=[]), dcc.Store(id="db_notification_store", data=0), dcc.Interval(id='db_notification_interval', interval=1000), ]), ]) @app.callback( [Output('table', 'data'), Output('canvas', 'image_content')], [Input('canvas', 'json_data')],
from Callbacks.FIgPca import PcaFirst from Pages.SearchTweets import parse_contents from Components.Table3 import generate_table2 # from API.Cleaning_Tweets import df_with_clean_text from API.Cleaning_Tweets import df_with_clean_text2 from API.Cleaning_Tweets import Give_The_Graph, Give_The_Graph_Word # app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP]) app.config.suppress_callback_exceptions = True len_data = 3500 app.layout = html.Div([ # Save the tweets Searched by hashtag or name dcc.Store(id='session', data=[]), # Save the tweets Searched by the name of the owner of the count dcc.Store(id='session2', data=[]), dcc.Store(id='DataFrameUploaded', data=[]), # Save the Data Cleaned dcc.Store(id="clean_df", data=[]), dcc.Store(id="NMF_Topic_word", data=[]), # Save the Confusion Matrix dcc.Store(id="Confusion_Matrix", data=[]), dcc.Location(id='url', refresh=False), html.Div(id='page-content') ]) page_1 = layout1
meta_tags=[{ "name": "viewport", "content": "width=device-width, initial-scale=1" }], ) server = app.server app.config["suppress_callback_exceptions"] = True key = 'RGAPI-9724b32a-f354-408c-8cde-8fdcc35e01fa' champions = aq.championsid(key) queues = aq.get_queuesid(key) app.layout = html.Div( id="big-app-container", children=[ dcc.Store(id="summoner-name"), dcc.Store(id="account-id"), dcc.Store(id="match-list"), dcc.Store(id="game-id"), dcc.Store(id="match-info"), dcc.Store(id="players-info"), dcc.Store(id="timeline"), dcc.Store(id="frames"), dcc.Store(id="events"), dcc.Store(id="golddiff"), dcc.Store(id="players_stats_df"), t1.build_banner(), #dcc.Interval(id="interval-component", interval=2 * 1000, n_intervals=50, disabled=True,), html.Div( id="app-container", children=[
def layout(self): return html.Div( [ html.Div( style={"marginLeft": "20%"}, children=[ html.Label( "Tornado Plot", style={"textAlign": "center", "font-weight": "bold"}, ), html.Div( style=self.set_grid_layout("1fr 1fr"), children=[html.Label("Reference:"), html.Label("Scale:"),], ), html.Div( style=self.set_grid_layout("1fr 1fr"), children=[ dcc.Dropdown( id=self.ids("reference"), options=[ {"label": r, "value": r} for r in self.sensnames ], value=self.initial_reference, clearable=False, ), dcc.Dropdown( id=self.ids("scale"), options=[ {"label": r, "value": r} for r in ["Percentage", "Absolute"] ], value="Percentage", clearable=False, ), ], ), html.Div( style=self.set_grid_layout("1fr 1fr"), children=[ html.Label( style={"marginTop": "10px"}, children="Cut by reference:", ) ], ), html.Div( style=self.set_grid_layout("1fr 1fr"), children=[ dcc.RadioItems( labelStyle={"display": "inline-block"}, id=self.ids("cut-by-ref"), options=[ {"label": "Off", "value": False}, {"label": "On", "value": True}, ], value=False, ), html.Button( style={ "position": "relative", "top": "-50%", "fontSize": "10px", }, id=self.ids("reset"), children="Clear selected", ), ], ), wcc.Graph( id=self.ids("tornado-graph"), config={"displayModeBar": False}, ), dcc.Store(id=self.ids("storage")), dcc.Store(id=self.ids("click-store")), ], ) ] )
"espacios de reflexión e investigación. Partiendo de una experiencia personal en un piso compartido cubierto " \ "de humedades, surge una idea de relación entre dos eventos que, pese a representar una razonable contraposición, " \ "acogen en su naturaleza comportamientos similares." message2 = " Lo que a primera vista puede parecer insignificante, como " \ "una pequeña gota de agua, si su reproducción se repite de forma constante puede llegar a desarrollar el desbordamiento " \ "de un caudal irrefrenable, desdibujando y construyendo al tiempo la imagen de un nuevo paisaje en tránsito. " \ "Todo este proceso de transformación puede verse no sólo en la vivienda y su imagen, sino en la corporalidad y " \ "en los hábitos del individuo que la ocupa." app.title="Paisajes en zonas de tránsito" # Create app layout app.layout = html.Div( [ dcc.Store(id="current-gray"), # empty Div to trigger javascript file for graph resizing html.Div(id="output-clientside"), html.Div( [ html.Div( [ html.H2( "Paisajes en zonas de tránsito", style={"margin-bottom": "0px"}, ), html.H4( "Orografía emocional de la vivienda", style={"margin-top": "0px"} ), ] )
#{'label': '6', 'value': 6} ], placeholder='select max degree' #,value=3 ),html.Button('Network', id='network-button', n_clicks=0, className='six columns') ]) ] ) ] ), html.Div(children=[ dcc.Store(id='channel-items-store'), dcc.Store(id='graph-dict-store'), html.Div(id='results-section'), html.Div(id='graph_network', style = dict(visibility='hidden'), children=[ dcc.Graph( id='plotly', style={'height': '100vh','width': '100%','textAlign': 'center'}, figure=BLANK_FIG, responsive=True ) ]) ]) ])
def server_layout(mode=None): #return the layout of the GUI session_id = str(uuid.uuid4()) #variable, distribution selection panel selection_panel = html.Div(id='selection-panel',children=[ html.Div(id='selected-column-panel',children=[ html.Div(id='selected-column-sec1-panel',children=[ drc.NamedDropdown( name='Select series', id='selected-series', searchable=False, clearable=False ), dcc.Checklist( id='apply-log-transform', options=[{'label':' Apply log transform','value':'logtransform'} ]), ]), drc.NamedTextarea( id='series-characteristics', name='Series characteristics', cols='50', rows='2', readOnly='readOnly' ), ]), html.Div(id='apply-panel',children=[ html.Div(id='apply-sec1-panel',children=[ drc.NamedDropdown( id='fitted-distributions', name='Fitted distribution', options=[ {'label': ' '+ v[0], 'value': k} for k,v in dist_par_template.items() if k != 'native' and v[2] ], value='normal', searchable=False, clearable=False ), ]), # html.Div(id='apply-sec1b-panel',children=[ # drc.NamedDropdown( # id='plotting-distributions', # name='Probability scale', # options=[ # {'label': ' '+ v[0], 'value': k} # for k,v in dist_par_template.items() if v[2] # ], # value='native', # searchable=False, # clearable=False # ), # ]), html.Div(id='apply-sec2a-panel',children=[ html.Button('Fit',id='fit-button',n_clicks=0), ]), #dcc.Checklist( # id='show-y-log', # options=[{'label':' Show y-axis in log scale','value':'true'} #]), ]), html.Label(id='graph-refresh'), dcc.Store( id='graph-refresh-hidden', data=0 ), html.Div(id='message-panel',children=[""]), ]) #variable, distribution selection panel par_panel = html.Div(id='fitted-panel',children=[ html.Div(id='fitted-sec1-panel',children=[ drc.NamedTextarea( id='fitted-par', name='Estimated distribution parameters', readOnly='readOnly', cols= 40, rows= 3 ), drc.NamedTextarea( id='estimated-quantiles', name='Estimated quantiles', readOnly='readOnly', cols= 40, rows= 3 ), ]) ]) upload_panel = html.Div(id='last-card',children=[ dcc.Upload( id='upload-data', children=html.Div([ 'Drag and Drop or ', html.A('Select File') ]), style={ 'lineHeight': '60px', 'borderWidth': '1px', 'borderStyle': 'dashed', 'borderRadius': '5px', 'textAlign': 'center', } ), html.Label('''File upload limited to < %0.0f Kb, containing no more than %d rows and %d columns'''%(configs['max_file_size']/1024,configs['max_file_rows'],configs['max_file_cols'])), html.Label('',id='data-filename'), html.Label('',id='data-description'), html.Div(id='table-panel',children=[ dash_table.DataTable( id='attribute-table', columns=[], row_selectable='multi', editable=False, style_header={ 'textAlign': 'center', 'fontWeight': 'bold', 'color': 'black', }, style_cell={ 'padding': '2px', '--selected-background': 'grey', 'color': 'black', }, style_data_conditional=[ { 'if': {'row_index': 'odd'}, 'backgroundColor': 'rgb(248, 248, 248)' } ], #locale_format= '0.3', ) ],style={'visibility':'none','display':'none'}), ]) chart_panel = html.Div(id='chart-panel',children=[ dcc.Loading(id = "loading-icon", children=[ dcc.Graph( id='graph', figure={ }, responsive=True, config=graph_config, style={'display':'none'} ) ]), ], style={'visibility':'none'}) layout = html.Div( id="body", className="container scalable", children=[ visdcc.Run_js(id = 'chart-updated-js'), visdcc.Run_js(id = 'chart-unupdated-js'), visdcc.Run_js(id = 'error-display-js'), dcc.Store(id='error-display',data=''), html.Title(title=configs['title']), #hidden session id #row 1 for title html.Div(id='top-panel',children=[ html.H5(id='app-title',children= '''Distribution Fitting and Analysis Tool. This tool is only for demostration porpuses''', style={"margin-bottom": "0px"}, ), ],className="row flex-display"), #second row layout html.Div(id='main-panel',children=[ html.Div(id='left-panel',children=[ upload_panel ]), html.Div(id='output-panel',children=[ selection_panel, par_panel, chart_panel ],style={'visibility':'none','display':'none'}), ]), #row 4 is the footer html.Div(id='footer-panel',children=[ ''' A web application built on top of Dash (v%s) (framework for Python) by Exequiel Sepúlveda and Dmitri Kavetski.'''%(dash.__version__) ]), ], ) return layout