html.Hr(),
generate_prop_info('RangeSlider')
])
# Checklist
Checklist = html.Div(children=[
html.H3('Checklist Properties'),
generate_prop_info('Checklist')
])
# Input
Input = html.Div(children=[
html.H1('Input Examples and Reference'),
html.Hr(),
html.H3('Supported Input Types'),
Syntax(examples['input-all-types'][0]),
Example(examples['input-all-types'][1]),
html.Br(),
html.H3('Debounce delays the Input processing'),
Syntax(examples['input-basic'][0]),
Example(examples['input-basic'][1]),
html.Br(),
html.H3('Number Input'),
dcc.Markdown("""
*fixed and enhanced in Dash v1.1*
Number type is now close to native HTML5 `input` behavior across
browsers. We also apply a strict number casting in callbacks:
valid number converts into corresponding number types, and invalid number
converts into None. E.g.
dcc.Markdown('''***
Dash apps are composed of two parts. The first part is the "`layout`" of
the app and it describes what the application looks like.
The second part describes the interactivity of the application and will be
covered in the [next chapter](/getting-started-part-2).
Dash provides Python classes for all of the visual components of
the application. We maintain a set of components in the
`dash_core_components` and the `dash_html_components` library
but you can also [build your own](https://github.com/plotly/dash-components-archetype)
with JavaScript and React.js.
'''.replace(' ', '')),
Syntax(examples[0][0],
summary='''
To get started, create a file named `app.py` with the following code:
'''),
dcc.Markdown('''
Run the app with
```
$ python app.py
...Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
```
and visit [http:127.0.0.1:8050/](http:127.0.0.1:8050/)
in your web browser. You should see an app that looks like this.
'''.replace(' ', '')),
Example(examples[0][1]),
dcc.Markdown('''
Note:
''')),
Syntax('''df = pd.DataFrame({
'a': [1, 2, 3],
'b': [4, 1, 4],
'c': ['x', 'y', 'z'],
})
app.layout = html.Div([
dcc.Dropdown(
id='dropdown',
options=[{'label': i, 'value': i} for i in df['c'].unique()],
value='a'
),
html.Div(id='output'),
])
@app.callback(Output('output', 'children'),
[Input('dropdown', 'value')])
def update_output_1(value):
# Here, `df` is an example of a variable that is
# "outside the scope of this function".
# *It is not safe to modify or reassign this variable
# inside this callback.*
global df = df[df['c'] == value] # do not do this, this is not safe!
return len(df)
''',
summary='''
Here is a sketch of an app with a callback that modifies data
out of it's scope. This type of pattern *will not work reliably*
for the reasons outlined above.'''),
Syntax('''df = pd.DataFrame({
### Memoization
Since Dash's callbacks are functional in nature (they don't contain any state),
it's easy to add memoization caching. Memoization stores the results of a
function after it is called and re-uses the result if the function is called
with the same arguments.
To better understand how memoization works, let's start with a simple example.
'''),
Syntax('''
import time
import functools32
@functools32.lru_cache(maxsize=32)
def slow_function(input):
time.sleep(10)
return 'Input was {}'.format(input)
'''),
dcc.Markdown('''
Calling `slow_function('test')` the first time will take 10 seconds.
Calling it a second time with the same argument will take almost no time
since the previously computed result was saved in memory and reused.
***
Dash apps are frequently deployed across multiple processes or threads.
In these cases, each process or thread contains its own memory, it doesn't
share memory across instances. This means that if we were to use `lru_cache`,
- **`persisted_props`** (list of strings; optional): These are the props
whose values will persist. Typically this defaults to all user-editable
props, which in many cases is just one (ie `'value'`). `dash-table` has
many props users can edit, and all of them except `'data'` are included
here by default. Sometimes only a *part* of a prop is saved; this happens
with table headers, which are only part of the `columns` prop.
The corresponding `persisted_props` value is `'columns.name'`.
In the following example, notice that a callback that depends on persisted
values receives those persisted values, even if the layout initially
provided something else. Also the city is persisted in `localStorage` so
will be saved indefinitely, but the neighborhood - which remembers a
different value for each city - resets if you open the page in a new tab.
'''),
Syntax(examples['persistence'][0]),
Example(examples['persistence'][1]),
dcc.Markdown('''
## Explicitly clearing saved data
Persistence continues (subject to `persistence_type`) as long as the
component `id`, the `persistence` value, and the prop provided by the
server when creating or updating the *entire* component has the same value
as it had before the user changed it. But if you have a callback whose
`Output` is the specific persisted prop itself, that takes precedence over
any saved value. This lets you reset user edits, using `PreventUpdate` or
`dash.no_update` until you detect the reset condition.
'''),
Syntax(examples['persistence_clear'][0]),
Example(examples['persistence_clear'][1]),
dcc.Markdown('''
**A:** *New in v0.38.0!* In addition to event properties like `n_clicks`
that change whenever an event happens (in this case a click), there is a
global variable `dash.callback_context`, available only inside a callback.
It has properties:
- `triggered`: list of changed properties. This will be empty on initial
load, unless an `Input` prop got its value from another initial callback.
After a user action it is a length-1 list, unless two properties of a
single component update simultaneously, such as a value and a timestamp
or event counter.
- `inputs` and `states`: allow you to access the callback params
by id and prop instead of through the function args. These have the form
of dictionaries `{ 'component_id.prop_name': value }`
Here's an example of how this can be done:''')),
Syntax(examples['last_clicked_button'][0]),
Example(examples['last_clicked_button'][1]),
dcc.Markdown(
s('''
Prior to v0.38.0, you needed to compare timestamp properties like
`n_clicks_timestamp` to find the most recent click. While existing uses of
`*_timestamp` continue to work for now, this approach is deprecated, and
may be removed in a future update. The one exception is
`modified_timestamp` from `dcc.Store`, which is safe to use, it is NOT
deprecated.
------------------------
**Q:** *Can I use Jinja2 templates with Dash?*
Dash components are described declaratively by a set of attributes.
All of these attributes can be updated by callback functions, but only
a subset of these attributes are updated through user interaction, such as
when you click on an option in a `dcc.Dropdown` component and the
`value` property of that component changes.
The `dcc.Graph` component has four attributes that can change
through user-interaction: `hoverData`, `clickData`, `selectedData`,
`relayoutData`.
These properties update when you hover over points, click on points, or
select regions of points in a graph.
'''.replace(' ', '')),
Syntax(examples['simple-graph-events'][0],
summary="""
Here's an simple example that
prints these attributes in the screen.
"""),
Example(examples['simple-graph-events'][1]),
html.Hr(),
html.H3('Update Graphs on Hover'),
Syntax(examples['world-indicators'][0],
summary="""
Let's update our world indicators example from the previous chapter
by updating time series when we hover over points in our scatter plot.
"""),
Example(examples['world-indicators'][1]),
dcc.Markdown(
s('''
Try mousing over the points in the scatter plot on the left.
Notice how the line graphs on the right update based off of the point that
dcc.Markdown(s('''
The Dash upload component allows your app's viewers to upload files,
like excel spreadsheets or images, into your application.
Your Dash app can access the contents of an upload by listening to
the `contents` property of the `dcc.Upload` component.
`contents` is a base64 encoded string that contains the files contents,
no matter what type of file: text files, images, zip files,
excel spreadsheets, etc.
''')),
Syntax(examples['upload-datafile'][0], summary=dcc.Markdown(s('''
Here's an example that parses CSV or Excel files and displays
the results in a table. Note that this example uses the
`DataTable` prototype from the
[dash-table-experiments](https://github.com/plotly/dash-table-experiments)
project.
'''))),
Example(examples['upload-datafile'][1]),
html.Hr(),
Syntax(examples['upload-image'][0], summary=dcc.Markdown(s('''
This next example responds to image uploads by displaying them
in the app with the `html.Img` component.
'''))),
Example(examples['upload-image'][1]),
Syntax(examples['upload-gallery'][0], summary=dcc.Markdown(s('''
dcc.Markdown(s('''
> This is the *4th* chapter of the [Dash Tutorial](/).
> The [previous chapter](/getting-started-part-2) covered Dash Callbacks
> and the [next chapter](/interactive-graphing) covers interactive
> graphing and crossfiltering.
> Just getting started? Make sure to
> [install the necessary dependencies](/installation).
''')),
dcc.Markdown(s('''
In the previous chapter on
[basic Dash callbacks](/getting-started-part-2),
our callbacks looked something like:
''')),
Syntax(examples['basic-input'][0]),
Example(examples['basic-input'][1]),
dcc.Markdown(s('''
In this example, the callback function is fired whenever any of the
attributes described by the `dash.dependencies.Input` change.
Try it for yourself by entering data in the inputs above.
`dash.dependencies.State` allows you to pass along extra values without
firing the callbacks. Here's the same example as above but with the
`dcc.Input` as `dash.dependencies.State` and a button as
`dash.dependencies.Input`.
''')),
Syntax(examples['basic-state'][0]),
Example(examples['basic-state'][1]),
html.H1('Dash State and PreventUpdate'),
dcc.Markdown('''
## Dash State
> This is the *4th* chapter of the [Dash Tutorial](/).
> The [previous chapter](/getting-started-part-2) covered Dash Callbacks
> and the [next chapter](/interactive-graphing) covers interactive
> graphing and crossfiltering.
> Just getting started? Make sure to
> [install the necessary dependencies](/installation).
'''),
dcc.Markdown('''
In the previous chapter on
[basic Dash callbacks](/getting-started-part-2),
our callbacks looked something like:
'''),
Syntax(examples['basic-input'][0]),
Example(examples['basic-input'][1]),
dcc.Markdown('''
In this example, the callback function is fired whenever any of the
attributes described by the `dash.dependencies.Input` change.
Try it for yourself by entering data in the inputs above.
`dash.dependencies.State` allows you to pass along extra values without
firing the callbacks. Here's the same example as above but with the
`dcc.Input` as `dash.dependencies.State` and a button as
`dash.dependencies.Input`.
'''),
Syntax(examples['basic-state'][0]),
Example(examples['basic-state'][1]),
dcc.Markdown('''
In this example, changing text in the `dcc.Input` boxes won't fire
s('''
The Dash upload component allows your app's viewers to upload files,
like excel spreadsheets or images, into your application.
Your Dash app can access the contents of an upload by listening to
the `contents` property of the `dcc.Upload` component.
`contents` is a base64 encoded string that contains the files contents,
no matter what type of file: text files, images, zip files,
excel spreadsheets, etc.
''')),
Syntax(examples['upload-datafile'][0],
summary=dcc.Markdown(
s('''
Here's an example that parses CSV or Excel files and displays
the results in a table. Note that this example uses the
`DataTable` prototype from the
[dash-table-experiments](https://github.com/plotly/dash-table-experiments)
project.
'''))),
Example(examples['upload-datafile'][1]),
html.Hr(),
Syntax(examples['upload-image'][0],
summary=dcc.Markdown(
s('''
This next example responds to image uploads by displaying them
in the app with the `html.Img` component.
'''))),
Example(examples['upload-image'][1]),
Syntax(examples['upload-gallery'][0],
summary=dcc.Markdown(
### Memoization
Since Dash's callbacks are functional in nature (they don't contain any state),
it's easy to add memoization caching. Memoization stores the results of a
function after it is called and re-uses the result if the function is called
with the same arguments.
To better understand how memoization works, let's start with a simple example.
'''),
Syntax('''import time
import functools32
@functools32.lru_cache(maxsize=32)
def slow_function(input):
time.sleep(10)
return 'Input was {}'.format(input)
'''),
dcc.Markdown('''
Calling `slow_function('test')` the first time will take 10 seconds.
Calling it a second time with the same argument will take almost no time
since the previously computed result was saved in memory and reused.
***
Dash apps are frequently deployed across multiple processes or threads.
In these cases, each process or thread contains its own memory, it doesn't
share memory across instances. This means that if we were to use `lru_cache`,
