import dash
import dash_html_components as html
import imageio
from dash_slicer import VolumeSlicer

app = dash.Dash(__name__, update_title=None)

vol = imageio.volread("imageio:stent.npz")
slicer0 = VolumeSlicer(app, vol, axis=0)
slicer1 = VolumeSlicer(app, vol, axis=1)
slicer2 = VolumeSlicer(app, vol, axis=2)

slicer0.graph.config["scrollZoom"] = False
slicer1.graph.config["scrollZoom"] = False
slicer2.graph.config["scrollZoom"] = False

app.layout = html.Div(
    style={
        "display": "grid",
        "gridTemplateColumns": "33% 33% 33%",
    },
    children=[
        html.Div([slicer0.graph, html.Br(), slicer0.slider, *slicer0.stores]),
        html.Div([slicer1.graph, html.Br(), slicer1.slider, *slicer1.stores]),
        html.Div([slicer2.graph, html.Br(), slicer2.slider, *slicer2.stores]),
    ],
)

if __name__ == "__main__":
    app.run_server(debug=True, dev_tools_props_check=False)

pip install dash_slicer

import dash
import dash_html_components as html
import imageio
from dash_slicer import VolumeSlicer

app = dash.Dash(__name__, update_title=None)

vol = imageio.volread("imageio:stent.npz")
slicer = VolumeSlicer(app, vol)
slicer.graph.config["scrollZoom"] = False

app.layout = html.Div([slicer.graph, slicer.slider, *slicer.stores])

if __name__ == "__main__":
    app.run_server(debug=True, dev_tools_props_check=False)

import dash
from dash import dcc, html
import plotly.express as px
import dask.dataframe as dd

app = dash.Dash(__name__)

df = dd.read_csv('./gdp-life-exp-2007.csv')

def filter_df(pop_thresh):
    filt_df = df[df["population"] > pop_thresh].compute()
    return filt_df

def build_graphs():
    pop_thresh = 5 * 10 ** 6
    filt_df = filter_df(pop_thresh)

    fig_out = px.scatter(filt_df, x="gdp per capita", y="life expectancy",
                         size="population", color="continent", hover_name="country",
                         log_x=True, size_max=60)
    return fig_out

fig = build_graphs()

app.layout = html.Div([
    dcc.Graph(
        id='life-exp-vs-gdp',
        figure=fig
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

# -*- coding: utf-8 -*-
import dash
from dash import html

import pandas as pd
import numpy as np
import holoviews as hv
from holoviews import streams
from holoviews.plotting.plotly.dash import to_dash

n = 200
xs = np.linspace(0, 1, n)
ys = np.cumsum(np.random.randn(n))
df = pd.DataFrame({'x': xs, 'y': ys})
curve = hv.Scatter(df)

def make_from_boundsx(boundsx):
    sub = df.set_index('x').loc[boundsx[0]:boundsx[1]]
    return hv.Table(sub.describe().reset_index().values, 'stat', 'value')

dmap = hv.DynamicMap(
    make_from_boundsx, streams=[streams.BoundsX(source=curve, boundsx=(0, 0))]
)

layout = curve + dmap

# Create App
app = dash.Dash(__name__)

# Dash display
components = to_dash(
    app, [layout], reset_button=True
)

app.layout = html.Div(components.children)

if __name__ == '__main__':
    app.run_server(debug=True)

import dash
from dash import html
from plotly.data import iris

import holoviews as hv
from holoviews.plotting.plotly.dash import to_dash
from holoviews.operation.datashader import datashade

import numpy as np
import pandas as pd

# Load iris dataset and replicate with noise to create large dataset
df_original = iris()[["sepal_length", "sepal_width", "petal_length", "petal_width"]]
df = pd.concat([df_original + np.random.randn(*df_original.shape) * 0.1 for i in range(10000)])

dataset = hv.Dataset(df)

scatter = datashade(
hv.Scatter(dataset, kdims=["sepal_length"], vdims=["sepal_width"])).opts(title="Datashader with %d points" % len(dataset))

app = dash.Dash(__name__)
components = to_dash(
    app, [scatter], reset_button=True
)

app.layout = html.Div(components.children)

if __name__ == "__main__":
    app.run_server(debug=True)

import dash
from dash import html
import holoviews as hv
from holoviews.plotting.plotly.dash import to_dash
from holoviews.element.tiles import CartoDark
from plotly.data import carshare

# Convert from lon/lat to web-mercator easting/northing coordinates
df = carshare()
df["easting"], df["northing"] = hv.Tiles.lon_lat_to_easting_northing(
    df["centroid_lon"], df["centroid_lat"]
)

points = hv.Points(df, ["easting", "northing"]).opts(color="crimson")
tiles = CartoDark()
overlay = tiles * points

app = dash.Dash(__name__)
components = to_dash(app, [overlay])

app.layout = html.Div(
    components.children
)

if __name__ == '__main__':
    app.run_server(debug=True)

# -*- coding: utf-8 -*-
import dash
from dash import html
from plotly.data import iris

import holoviews as hv
from holoviews import opts
from holoviews.plotting.plotly.dash import to_dash

# Load dataset
df = iris()
dataset = hv.Dataset(df)

# Build selection linking object
selection_linker = hv.selection.link_selections.instance()
scatter = selection_linker(
    hv.Scatter(dataset, kdims=["sepal_length"], vdims=["sepal_width"])
)
hist = selection_linker(
    hv.operation.histogram(dataset, dimension="petal_width", normed=False)
)

# Use plot hook to set the default drag mode to box selection
def set_dragmode(plot, element):
    fig = plot.state
    fig['layout']['dragmode'] = "select"
    if isinstance(element, hv.Histogram):
        # Constrain histogram selection direction to horizontal
        fig['layout']['selectdirection'] = "h"

scatter.opts(opts.Scatter(hooks=[set_dragmode]))
hist.opts(opts.Histogram(hooks=[set_dragmode]))

app = dash.Dash(__name__)
components = to_dash(
    app, [scatter, hist], reset_button=True
)

app.layout = html.Div(components.children)

if __name__ == "__main__":
    app.run_server(debug=True)

pip install holoviews