### Test HoloViz Package Installations

Source: https://holoviz.org/tutorial/Setup

Imports several HoloViz libraries (datashader, panel, hvplot, param, holoviews) and uses a helper function 'check_packages' to verify their correct installation. It also sets up HoloViews extensions for Bokeh and Matplotlib.

```python
import datashader, panel, hvplot, param, holoviews as hv  # noqa
from package_checker import check_packages
hv.extension('bokeh', 'matplotlib')

packages = ['datashader', 'holoviews', 'panel', 'hvplot', 'param']
check_packages(packages)
```

--------------------------------

### Perform editable install with dependencies

Source: https://holoviz.org/contribute

Installs the project in editable mode, including specified optional dependencies like 'tests' and 'examples'. This allows for direct code changes to be reflected without reinstallation. Multiple channels can be specified for package sources.

```shell
doit develop_install -c channel1 -c channel2 -o tests -o ... -o ...
```

```shell
doit develop_install -c pyviz/label/dev -c conda-forge -o tests -o examples
```

--------------------------------

### Launch Jupyter Lab with Anaconda Project

Source: https://holoviz.org/tutorial/Setup

Launches the Jupyter Lab interface using anaconda-project. This command will download necessary packages, set up a dedicated conda environment for the tutorial, and then start Jupyter Lab.

```bash
anaconda-project run jupyter lab
```

--------------------------------

### Create and Activate Conda Environment for Anaconda Project

Source: https://holoviz.org/tutorial/Setup

Creates a new conda environment named 'project' and installs the 'anaconda-project' package, which is used to manage tutorial dependencies. This ensures a clean and isolated environment for the tutorial.

```bash
conda create -n project "anaconda-project>=0.11"
conda activate project
```

--------------------------------

### Run examples tests

Source: https://holoviz.org/contribute

Executes tests related to the project's examples, often involving notebooks. This is used to verify that example code functions correctly.

```shell
doit test_examples.
```

--------------------------------

### Download and Unzip Tutorial Project Files (macOS/Linux)

Source: https://holoviz.org/tutorial/Setup

Downloads the HoloViz tutorial ZIP archive using curl and unzips it into a 'holoviz_tutorial' directory. It then navigates into the extracted directory. This is specific to macOS and Linux environments.

```bash
curl -o holoviz_tutorial.zip "https://assets.holoviz.org/holoviz/tutorial/holoviz_tutorial.zip"
unzip holoviz_tutorial.zip -d holoviz_tutorial
cd holoviz_tutorial
```

--------------------------------

### Download and Unzip Tutorial Project Files (Windows)

Source: https://holoviz.org/tutorial/Setup

Downloads the HoloViz tutorial ZIP archive using PowerShell and extracts its contents to a 'holoviz_tutorial' directory. It then navigates into the extracted directory. This is specific to Windows environments.

```powershell
Invoke-WebRequest -Uri "https://assets.holoviz.org/holoviz/tutorial/holoviz_tutorial.zip" -OutFile "holoviz_tutorial.zip"
Expand-Archive -Path "holoviz_tutorial.zip" -DestinationPath "holoviz_tutorial"
cd holoviz_tutorial
```

--------------------------------

### Install Development Version

Source: https://holoviz.org/contribute

To test a development release, install the package using Conda from a development label. This allows for pre-release testing and verification of functionality before a final release.

```bash
conda install -c pyviz/label/dev panel
```

--------------------------------

### Create a Panel Row Layout

Source: https://holoviz.org/tutorial/Dashboards

Creates a Panel `Row` layout to arrange components horizontally. This example combines a Markdown title, a horizontal spacer, and a PNG logo.

```python
header = pn.Row(dashboard_title, pn.layout.HSpacer(), pn.pane.PNG(logo_path, height=40, align='end'))
header
```

--------------------------------

### Check Data File Accessibility

Source: https://holoviz.org/tutorial/Setup

Uses a helper function 'check_data' to verify that a specific dataset file ('../data/earthquakes-projected.parq') exists and is readable. This ensures that the tutorial can access the necessary data.

```python
from data_checker import check_data

data_path = '../data/earthquakes-projected.parq'
check_data(data_path)
```

--------------------------------

### Python Warnings Module: warn Function Examples

Source: https://holoviz.org/about/heps/hep2

Demonstrates the usage of Python's `warnings.warn` function and the impact of the `stacklevel` parameter on the warning output. Shows how to correctly identify the source of a warning in larger codebases.

```python
# mod1.py
import warnings

def foo():
    warnings.warn('The function foo is deprecated.')

foo()

```

```python
# mod1.py
import warnings

def foo():
    warnings.warn('The function foo is deprecated.', stacklevel=2)

foo()

```

--------------------------------

### Install Final Release Version

Source: https://holoviz.org/contribute

After a final release is made, install the package using Conda to perform final checks. This ensures the stable version is functioning as expected and free of critical issues.

```bash
conda install -c pyviz panel
```

--------------------------------

### Create a Panel Float Slider Widget

Source: https://holoviz.org/tutorial/Interactive_Pipelines

Demonstrates creating a Panel FloatSlider widget for selecting a minimum earthquake magnitude. The slider has a name, a start value, an end value, and an initial value.

```python
mag_slider = pn.widgets.FloatSlider(name='Minimum Magnitude', start=0, end=9, value=6)
mag_slider
```

--------------------------------

### Get hvplot Help for Scatter Plots

Source: https://holoviz.org/tutorial/Plotting

Demonstrates how to use the `hvplot.help()` function to retrieve documentation for specific plot types, such as 'scatter'. This function displays a documentation pane within the notebook, detailing available options and parameters.

```python
# hvplot.help('scatter')

```

--------------------------------

### Load and Prepare Earthquake Data (Python)

Source: https://holoviz.org/tutorial/exercises/Advanced_Dashboarding

Loads earthquake data from a Parquet file into a Pandas DataFrame and times it. This is a starting point for building dashboards, allowing for subsequent filtering and visualization.

```Python
%%time
df = pd.read_parquet(pathlib.Path('../../data/earthquakes-projected.parq'))

```

--------------------------------

### Apply Options to HoloViews Points Element with Specific Options

Source: https://holoviz.org/tutorial/Custom_Interactivity

Applies specific visual options, such as marker style, to a HoloViews `Points` element. This example shows how to use `.opts()` with parameters like `marker`.

```python
import holoviews as hv
import numpy as np
hv.extension("bokeh")

# Assuming 'esri' and 'most_severe' are defined elsewhere
# esri * hv.Points(most_severe, ['easting', 'northing'], 'mag').opts(color='mag', size=8, aspect='equal', marker='+')
```

--------------------------------

### Get hvplot Style Options Help

Source: https://holoviz.org/tutorial/Plotting

Retrieves specific documentation for the 'style' options of a 'scatter' plot using `hvplot.help()`. By setting `style=True` and `generic=False`, it filters the help output to show only style-related parameters that are directly passed to the Bokeh plotting backend.

```python
# hvplot.help('scatter', style=True, generic=False)

```

--------------------------------

### DeprecationWarning in Panel Apps Served with 'panel serve'

Source: https://holoviz.org/about/heps/hep2

When serving a Panel application using `panel serve`, the module namespace of the served files is altered (e.g., `bokeh_app_<uuid>`), not `__main__`. Consequently, `DeprecationWarning`s are typically not displayed. This example demonstrates a warning that won't be shown by default when served.

```python
# app.py
import warnings
import panel as pn

warnings.warn('Not displayed :(', DeprecationWarning)
pn.panel('Hello world!').servable()

```

--------------------------------

### Apply Visual Options to HoloViews Points Element

Source: https://holoviz.org/tutorial/Custom_Interactivity

Customizes the appearance of a HoloViews `Points` element using the `.opts()` method. This example sets the color based on a dimension, point size, enables a color bar, and sets the aspect ratio.

```python
import holoviews as hv
import numpy as np
hv.extension("bokeh")

xs = np.random.randn(100)
ys = np.random.randn(100)
fitness = np.random.randn(100)
height_v_weight = hv.Points((xs, ys, fitness), ['weight', 'height'], 'fitness')
height_v_weight.opts(color='fitness', size=8, colorbar=True, aspect='square')
```

--------------------------------

### Run Panel Dashboard via Command Line

Source: https://holoviz.org/tutorial/Dashboards

Illustrates how to run the Panel dashboard application from the command line. This includes instructions for serving a Jupyter notebook as an app and for serving a Python script file. It also shows how to use `anaconda-project` to run the dashboard.

```bash
# Serve a Jupyter notebook as an app
panel serve --port 5067 06_Dashboards.ipynb

# Serve a Python script file
panel serve file.py

# Using anaconda-project
anaconda-project run dashboard

```

--------------------------------

### Install pyctdev using Conda

Source: https://holoviz.org/contribute

Installs the pyctdev tool, a developer utility for HoloViz projects, using Conda. It specifies a minimum version requirement for pyctdev.

```shell
conda install -c pyviz/label/dev "pyctdev>0.5.0"
```

--------------------------------

### Create Panels using pn.panel

Source: https://holoviz.org/tutorial/Dashboards

Demonstrates using the `pn.panel` function to automatically create appropriate Panel pane objects (Markdown and PNG) from given inputs (string and file path).

```python
dashboard_title = pn.panel('## Earthquakes')
usgs_logo = pn.panel(logo_path, height=130)
```

--------------------------------

### Declare TextInput Widget

Source: https://holoviz.org/tutorial/Custom_Dashboards

Declares a Panel TextInput widget for filtering data by place name. It includes a placeholder to guide user input.

```python
place_filter = pn.widgets.TextInput(placeholder='Enter a placename')
```

--------------------------------

### Import Libraries and Initialize Panel Extension

Source: https://holoviz.org/tutorial/Dashboards

Imports necessary libraries like pathlib, pandas, panel, xarray, holoviews, colorcet, and hvplot extensions. It also initializes the Panel extension with a 'bootstrap' template and 'tabulator' functionality.

```python
import pathlib
import pandas as pd
import panel as pn
import xarray as xr
import holoviews as hv

pn.extension('tabulator', template='bootstrap')

import colorcet as cc
import hvplot.xarray # noqa
import hvplot.pandas # noqa
```

--------------------------------

### HoloViews Object Notation

Source: https://holoviz.org/tutorial/Composing_Plots

Example of HoloViews notation for a Curve element. It indicates the element type ('Curve'), key dimension ('time'), and value dimension ('count').

```text
:Curve   [time]   (count)
```

--------------------------------

### Serve Dashboard from Notebook

Source: https://holoviz.org/tutorial/Dashboards

Provides the command to serve the Panel dashboard as a standalone web application. This command is executed from the terminal.

```bash
$ panel serve 06_Dashboards.ipynb
```

--------------------------------

### Create a PNG Pane

Source: https://holoviz.org/tutorial/Dashboards

Creates a Panel PNG pane to display a PNG image. It takes a path to the image file as input and allows specifying the height.

```python
logo_path = pathlib.Path('../assets/usgs_logo.png')
pn.pane.PNG(logo_path, height=130)
```

--------------------------------

### HoloViews Magnitude Plot Notation

Source: https://holoviz.org/tutorial/Composing_Plots

Example of HoloViews notation for the weekly mean magnitude plot. It specifies a 'Curve' element with 'time' as the key dimension and 'mag' as the value dimension.

```text
:Curve   [time]   (mag)
```

--------------------------------

### Declare RangeSlider Widget

Source: https://holoviz.org/tutorial/Custom_Dashboards

Declares a Panel RangeSlider widget for filtering data by magnitude. The widget's name, start, and end values are configured based on the loaded DataFrame.

```python
mag_filter = pn.widgets.RangeSlider(name='Magnitude', start=0, end=df.mag.max())
```

--------------------------------

### Serve Panel Application

Source: https://holoviz.org/tutorial/Dashboards

Provides code snippets for serving the Panel application. The first snippet makes the widgets available in the sidebar, and the second serves the main dashboard layout with a specified title. These are typically used when running the application with `panel serve`.

```python
pn.param.ReactiveExpr(filtered_table).widgets.servable(area='sidebar')

```

```python
column.servable(title='Earthquake Interactive Demo')

```

--------------------------------

### Initialize Geo Data Visualization Components

Source: https://holoviz.org/learn/presentations/Overview

Sets up components for visualizing large geographic datasets, including map tiles, earthquake data loading with Dask, and colormap definitions. It uses EsriImagery for map tiles and reads parquet data for earthquakes.

```python
import dask.dataframe as dd
from colorcet import palette
from holoviews.element.tiles import EsriImagery

topts = hv.opts.Tiles(width=700, height=600, bgcolor='black', 
                      xaxis=None, yaxis=None, show_grid=False)
tiles = EsriImagery().opts(topts) 
earthquakes  = dd.read_parquet(Path('../../data/earthquakes-projected.parq'), engine='pyarrow').persist()
colormaps = {n: palette[n] for n in ['fire','bgy','bgyw','bmy','gray','kbc']}

```

--------------------------------

### Create a Markdown Pane

Source: https://holoviz.org/tutorial/Dashboards

Creates a Panel Markdown pane to display text formatted in Markdown. This is useful for adding titles or descriptions to dashboards.

```python
pn.pane.Markdown('## Earthquake Dashboard')
```

--------------------------------

### Inspect HoloViews Element Options with hv.help

Source: https://holoviz.org/tutorial/Custom_Interactivity

Demonstrates how to use `hv.help` to inspect the available style options for a HoloViews element type, such as `hv.Scatter`. This is useful for discovering customization possibilities.

```python
# Commented as there is a lot of help output!
# import holoviews as hv
# hv.extension("bokeh")
# hv.help(hv.Scatter)
```

--------------------------------

### Assemble Dashboard Layout with Panel

Source: https://holoviz.org/tutorial/Dashboards

Constructs the final dashboard layout using Panel. It combines the interactive map visualization with widgets and other plots like histograms for magnitude and depth. It also sets up linked selections for interactive data exploration and configures the Panel template for serving the application.

```python
pn.state.template.sidebar_width = 250
pn.state.template.title = 'Earthquake Interactive Demo'

ls = hv.link_selections.instance(unselected_alpha=0.02)

# Table is not yet dynamically linked to the linked selection
filtered_table = filtered_subrange.pipe(ls.filter, selection_expr=ls.param.selection_expr)[['time', 'place', 'mag', 'depth']]
table = pn.widgets.Tabulator(
    filtered_table, pagination='remote', page_size=10, show_index=False
)

mag_hist = filtered_subrange.hvplot(
    y='mag', kind='hist', responsive=True, min_height=300, max_height=600, grid=True)

depth_hist = filtered_subrange.hvplot(
    y='depth', kind='hist', responsive=True, min_height=300, max_height=600, grid=True)

geo = filtered_subrange.hvplot(
    'easting', 'northing', color='mag', kind='points',
    xaxis=None, yaxis=None, responsive=True, min_height=500,
    data_aspect=1, framewise=False, clim=(4, 10), line_color='black'
)

column = pn.Column(
    pn.Row(
        hv.element.tiles.EsriImagery() * ls(hv.DynamicMap(geo)),
    ),
    pn.Row(
        table,
        ls(hv.DynamicMap(depth_hist)),
        ls(hv.DynamicMap(mag_hist)),
    )
)

```

--------------------------------

### Import Libraries for HoloViz Plotting

Source: https://holoviz.org/tutorial/Interlinked_Plots

Imports necessary libraries including pathlib for path manipulation, holoviews for plotting, pandas for data handling, hvplot.pandas for plotting integration, and colorcet for colormaps. This setup is crucial for generating interactive plots.

```python
import pathlib
import holoviews as hv
import pandas as pd
import hvplot.pandas  # noqa
import colorcet as cc
```

--------------------------------

### Run unit tests

Source: https://holoviz.org/contribute

Executes the unit tests for the project. This command is typically run after making code changes to ensure functionality.

```shell
doit test_unit.
```

--------------------------------

### Create GitHub Release

Source: https://holoviz.org/contribute

After a successful release, create a corresponding GitHub Release. This involves navigating to the repository's Releases section, selecting the most recent tag, and adding release notes that mirror the changelog.

```github
Go to the Github repository
Click _Releases_
Click _Tags_
Click the most recent tag that you just added
Click _Create a new release_
Add release notes and publish the release
```

--------------------------------

### Declare Panel Widgets for Filtering

Source: https://holoviz.org/tutorial/Dashboards

Defines Panel widgets, specifically a DatetimeRangeSlider and a FloatSlider, to allow users to interactively filter the earthquake data by date and magnitude. The sliders are configured with appropriate start, end, and initial values derived from the data.

```python
date_subrange = pn.widgets.DatetimeRangeSlider(
    name='Date', 
    start=subset_df.time.iloc[0], 
    end=subset_df.time.iloc[-1],
    max_width=400
)
mag_subrange = pn.widgets.FloatSlider(name='Magnitude', start=3, end=9, value=3, max_width=400)

```

--------------------------------

### Render Plot with Matplotlib xkcd Style using hvplot.render

Source: https://holoviz.org/tutorial/Plotting

Renders a plot object and returns the underlying Matplotlib figure, allowing for advanced customization. This example applies Matplotlib's `xkcd` context manager to render the plot in a sketch-style.

```python
import matplotlib.pyplot as plt
with plt.xkcd():
    mpl_fig = hvplot.render(plot)

mpl_fig
```

--------------------------------

### Create Interactive Geo-Spatial Visualization with hvplot

Source: https://holoviz.org/tutorial/Dashboards

Generates an interactive map visualization of earthquake data using hvplot. The visualization is linked to the defined Panel widgets, allowing real-time filtering of earthquakes based on magnitude and time. The map displays 'easting' and 'northing' coordinates, colored by magnitude, with ESRI tiles.

```python
subset = pn.rx(subset_df)

filtered_subrange = subset[
    (subset['mag']   > mag_subrange) &
    (subset['time'] >= date_subrange.param.value_start) &
    (subset['time'] <= date_subrange.param.value_end)
]

geo = filtered_subrange.hvplot(
    'easting', 'northing', color='mag', kind='points', framewise=False,
    xaxis=None, yaxis=None, responsive=True, min_height=500, tiles='ESRI')

```

--------------------------------

### Display Dashboard in a New Tab

Source: https://holoviz.org/tutorial/Dashboards

Shows the created Panel dashboard in a new browser tab. This method is used for quick viewing during development.

```python
# mini_dashboard.show()
```

--------------------------------

### Tagging and Pushing a Git Commit for Release

Source: https://holoviz.org/contribute

This snippet demonstrates the Git commands required to tag a commit for a new package version and push that tag to the remote repository. The tag format is crucial for triggering automated build processes.

```bash
git tag -m "Version 1.9.6 alpha1" v1.9.6a1 main
git push origin v1.9.6a1
```

--------------------------------

### Create Matplotlib Plot with HoloViews

Source: https://holoviz.org/learn/presentations/Overview

Generates a plot using HoloViews with the Matplotlib backend. This plot includes a vertical line and text annotation, similar to the Bokeh example, but results in a static image without interactivity. The `hv.output` function explicitly sets the backend.

```python
mpl = by_state * hv.VLine(1963).opts(color="black") * \
      hv.Text(1963, 1000, "  Vaccine introduced", halign='left')
hv.output(mpl, backend='matplotlib')
```

--------------------------------

### Combine HoloViews Plots for Linked Visualization

Source: https://holoviz.org/tutorial/Custom_Interactivity

Combines multiple HoloViews plot components, including scatter plots, histograms, and vertical lines, into a single, linked visualization. This example demonstrates how to arrange these elements using HoloViews layout options like `+` for overlay and `.cols()` for arrangement.

```python
((esri * high_mag_quakes.opts(tools=['tap']) * labeller * quake_marker)
 + histogram + temporal_distribution * temporal_vline).cols(1)
```

--------------------------------

### DeprecationWarning Display Based on Module Namespace and stacklevel

Source: https://holoviz.org/about/heps/hep2

This example illustrates how DeprecationWarnings are displayed differently based on the module's namespace (`__main__` vs. others) and the `stacklevel` parameter. When a warning originates from a module not in `__main__` (like `mod2.py`), it might be filtered. `stacklevel` determines which calling line the warning is attributed to.

```python
# mod1.py
from mod2 import bottom, top

print('Call top:')
top()

print('\nCall bottom:')
bottom()

```

```python
# mod2.py
import warnings

def top():
    return bottom()

def bottom():
    warnings.warn('bottom is deprecated', DeprecationWarning, stacklevel=2)

```

--------------------------------

### Create Interactive App with Panel Interact

Source: https://holoviz.org/learn/presentations/Overview

Builds an interactive application using Panel's interact function, linking it to the earthquake visualization function. It creates widgets for colormap, alpha, and reverse colormap, and arranges them in a layout with a title.

```python
import panel as pn
explorer = pn.interact(view, cmap=colormaps, alpha=(0, 1.), reverse_colormap=False)

pn.Row(pn.Column('# Earthquake Explorer', explorer[0]), explorer[1]).servable()

```

--------------------------------

### Create Interactive Plots with hvPlot

Source: https://holoviz.org/tutorial/Dashboards

Generates two interactive plots using hvPlot: a scatter plot of earthquake magnitudes and a rasterized image plot of population density. These plots are configured to be responsive and have specific visual options.

```python
sample_points = small_df.hvplot.points(x='longitude', y='latitude', c='mag', cmap=cc.CET_L4, responsive=True)

rasterized_pop = cleaned_ds.hvplot.image(rasterize=True, logz=True, clim=(1, None), responsive=True, min_height=400).opts(bgcolor='black')

earthquake_example = rasterized_pop * sample_points
```

--------------------------------

### Create and Display a Date Range Slider Widget

Source: https://holoviz.org/tutorial/Interactive_Pipelines

Initializes and displays a Panel DatetimeRangeSlider widget, allowing users to select a date range. The widget's current value, represented as a tuple of start and end timestamps, can be accessed directly or via `value_start` and `value_end` parameters.

```python
import panel as pn

# Assuming df is a Pandas DataFrame with a DatetimeIndex
date_widget = pn.widgets.DatetimeRangeSlider(name='Date', start=df.index[0], end=df.index[-1])
date_widget
```

--------------------------------

### Create a Basic HoloViews Points Element

Source: https://holoviz.org/tutorial/Custom_Interactivity

Demonstrates the creation of a simple `Points` element in HoloViews. It generates random data for x and y coordinates and uses them to instantiate the `Points` element.

```python
import holoviews as hv
import numpy as np
hv.extension("bokeh")

xs = np.random.randn(100)
ys = np.random.randn(100)
hv.Points((xs, ys))
```

--------------------------------

### Filter and Visualize Large Datasets with Datashader Scatter Plot

Source: https://holoviz.org/tutorial/Plotting

Demonstrates filtering a large dataset before visualizing it with Datashader. This example selects earthquakes with magnitudes greater than 5 and plots them using a specific colormap ('Reds'). It utilizes `rasterize=True` for Datashader aggregation and `cnorm='eq_hist'` for colormap equalization.

```python
df[df.mag>5].hvplot.scatter(x='longitude', y='latitude', rasterize=True, cnorm='eq_hist', cmap='Reds')
```

--------------------------------

### Bind Population Calculation and Display with Panel

Source: https://holoviz.org/tutorial/Advanced_Dashboards

Connects the `affected_population` function to Panel streams and widgets using `pn.bind`. It also creates a `hv.DynamicMap` for `bounds` and combines these with other HoloViews elements for display. This setup allows for a dynamic visualization that updates population estimates and geographic bounds based on user interaction with streams and widgets.

```python
dynamic_bounds = hv.DynamicMap(bounds, streams=[index_stream, dist_slider.param.value])
bound_affected_population = pn.bind(affected_population, index=index_stream.param.index, distance=dist_slider)
pn.Column(pn.panel(bound_affected_population, width=400), 
          rasterized_pop * high_mag_points * dynamic_bounds, dist_slider)

```

--------------------------------

### Load and Sample Earthquake Data

Source: https://holoviz.org/tutorial/Dashboards

Loads earthquake data from a Parquet file into a pandas DataFrame and resets the index. It then samples a small fraction of the data for plotting.

```python
%%time
df = pd.read_parquet(pathlib.Path('../data/earthquakes-projected.parq'))
df.index = df.index.tz_localize(None)
df = df.reset_index()

small_df = df.sample(frac=.01)
```

--------------------------------

### Initialize Panel Extension

Source: https://holoviz.org/tutorial/Custom_Dashboards

Initializes the Panel extension for use in a Jupyter environment. This is a prerequisite for most Panel applications.

```python
import pathlib
import panel as pn

pn.extension()
```

--------------------------------

### Create Panel Number Indicator from Reactive Expression

Source: https://holoviz.org/tutorial/Interactive_Pipelines

This example shows how to pass a reactive expression directly to a Panel component. Here, the maximum value of the 'mag' column from a filtered dataset (a reactive expression) is computed and passed as the 'value' to a 'pn.indicators.Number' widget. This allows Panel components to efficiently re-render when the underlying expression's value changes.

```python
pn.indicators.Number(value=filtered_subrange.mag.max(), name='Magnitude')

```

--------------------------------

### Create Interactive Adder Component

Source: https://holoviz.org/tutorial/Custom_Dashboards

Declares two IntInput widgets and a function that adds their values. It then lays out the widgets and the result of the addition using pn.bind, creating a simple interactive calculator.

```python
w1 = pn.widgets.IntInput(value=1, width=60)
w2 = pn.widgets.IntInput(value=1, width=60)

def adder(v1, v2):
    return pn.panel(v1 + v2, width=50)

pn.Row(w1, '+', w2, '=', pn.bind(adder, v1=w1, v2=w2))
```

--------------------------------

### Load and Prepare Earthquake Data

Source: https://holoviz.org/tutorial/exercises/Building_a_Dashboard

Loads the earthquake dataset from a Parquet file, selects relevant columns, and filters for the most severe earthquakes (magnitude >= 7). This prepares the data for visualization.

```python
%%time
df = pd.read_parquet(pathlib.Path('../../data/earthquakes-projected.parq'))
columns = ['mag', 'depth', 'latitude', 'longitude', 'place', 'type']
df = df[columns]
most_severe = df[df.mag >= 7]

```

--------------------------------

### Import Libraries for HoloViz Dashboard

Source: https://holoviz.org/tutorial/exercises/Building_a_Dashboard

Imports necessary libraries including pathlib, numpy, pandas, and panel for building interactive dashboards. It also initializes Panel extensions and imports hvplot for plotting.

```python
import pathlib
import numpy as np
import pandas as pd
import panel as pn
pn.extension('katex')

import hvplot.pandas # noqa

```

--------------------------------

### Create Richter Scale Equation Panel Component

Source: https://holoviz.org/tutorial/exercises/Building_a_Dashboard

Creates a Panel component to display the mathematical equation for the Richter scale using LaTeX formatting. This ensures the equation is rendered correctly with proper mathematical notation.

```python
equation_string = '$M_L = log_{10}A - log_{10} A_0(delta)$'

## Define a panel component containing the equation (Hint: Use the LaTeX pane)
equation = ...

## Display it

equation = pn.pane.LaTeX(equation_string)
equation

```

--------------------------------

### Compose Static Dashboard Layout

Source: https://holoviz.org/tutorial/exercises/Building_a_Dashboard

Arranges previously defined Panel components (logo, equation, strongest_earthquakes, gmap, plot) into a static dashboard layout using `pn.Row` and `pn.Column`. Includes headers and spacing for a structured presentation.

```python
# Static Dashboard combining all the elements above.

year = 2000

logo = pn.panel(logo_url, width=200)
equation = pn.pane.LaTeX(equation_string)
strongest_earthquakes = strongest_earthquakes_fn(year)
gmap = pn.pane.HTML(gmap_fn(year), height=300, width=300)
year_df = df[(df.index.index.year == year) & (df.mag > 7)]
plot = year_df.hvplot.line(x='time', y='mag')

title = pn.panel('# Strongest Earthquakes in the Year %d' % year, width=400)
header = pn.Row(title, pn.layout.HSpacer(), logo)

body_row1 = pn.Row(
    pn.Column('### Richter scale definition', equation, '### Strongest Earthquake', gmap),
    pn.Column('### Strongest Earthquakes', strongest_earthquakes),
)

pn.Column(header, pn.Column(body_row1,pn.Column('### Magnitude Plot', plot)))

```

--------------------------------

### Initialize hvplot Explorer with DataFrame

Source: https://holoviz.org/tutorial/Plotting

This code snippet shows how to initialize the hvPlot Explorer by passing a DataFrame (e.g., 'small_df') to the `hvplot.explorer` function. This action generates an interactive Panel layout for data exploration.

```python
hvplot.explorer(small_df)
```