Widgets

Each widget functions as a Python class and can be assigned to a variable. By calling .value on the variable, you can access the actual widget value. For example:

from lplots.widgets.select import w_select

options = w_select(
  label="Select an item from the dropdown",
  options=["alpha", "bravo", "charlie"],
)


print(options.value)
# prints `"alpha", "bravo" or "charlie"` depending on user selection

Widgets are rendered in the order that they are called.

Below is a comprehensive list of supported widget types.

File selector widgets allow users to select a file in their Latch Data and return a latch path that can be downloaded and used in a layout.

from lplots.widgets.ldata import w_ldata_picker
import pandas as pd

csv = w_ldata_picker(
  label="Condition CSV",
  default="latch:///welcome/deseq2/conditions.csv"
)

csv_path = csv.value
# ex. "latch:///welcome/deseq2/conditions.csv"

# Read the selected file into a Pandas DataFrame
df = pd.read_csv(csv_path.download())

Widget Parameters

label: string string value (required)

default: string latch data path string used as the default widget value

required: boolean a boolean that sets the input as requiring input by user and errored when empty

appearance: dict containing widget appearance attributes:

"placeholder": string placeholder value displayed before a set value

"detail": string secondary label text displayed after thet label

"help_text": string informative text displayed below input

"error_text": string error text displayed below input that replaces help_text and sets the input state as errored

"description": string longer description text displayed in a hoverable tooltip next to the label

Registry Table Select inputs allow users to select a table from their workspaces registry. The input returns a table id that can be used to fetch table data using the Table class.

from lplots.widgets.registry import w_registry_table_picker
from latch.registry.table import Table

# Get the unique ID of the Registry table
table = w_registry_table_picker(label="Select a Registry table")

table_id = table.value
# ex. "3092"

if table_id is not None:
  # Get a dataframe from the table ID
  df = Table(
    id=table_id
  ).get_dataframe()

Widget Parameters

label: string label (required)

default: string id of a registry table

required: boolean that sets the input as requiring input by user and errored when empty

appearance: dict containing widget appearance attributes:

"placeholder": string placeholder value displayed before a set value

"detail": string secondary label text displayed after thet label

"help_text": string informative text displayed below input

"error_text": string error text displayed below input that replaces help_text and sets the input state as errored

"description": string longer description text displayed in a hoverable tooltip next to the label

Table Class Parameters & Methods

Parameters

id: string id of registry table

Methods

Table.get_dataframe(): returns a pandas dataframe for the provided registry table id

Text inputs allow users to specify a custom text string.

from lplots.widgets.text import w_text_input, w_text_output

name = w_text_input(
  label="Your name",
  default="Barnaby Jones"
)

name_value = name.value
# ex. "Barnaby Jones"

label: string label (required)

default: default string value for input

required: a boolean that sets the input as requiring input by user and errored when empty

appearance: a dict containing widget appearance attributes:

"placeholder": placeholder value displayed before a set value

"detail": secondary label text displayed after thet label

"help_text": informative text displayed below input

"error_text": error text displayed below input that replaces help_text and sets the input state as errored

"description": longer description text displayed in a hoverable tooltip next to the label

Select inputs allow users to select a single item from a list of string or number options.

from lplots.widgets.select import w_select

option = w_select(
  label="Select an item from the dropdown",
  options=[
    "alpha",
    "bravo",
    "charlie"
  ],
)

option_value = option.value
# ex. "alpha"

label: string label (required)

options: list of values for the select

default: default option from options

required: a boolean that sets the input as requiring input by user and errored when empty

appearance: a dict containing widget appearance attributes:

"placeholder": placeholder value displayed before a set value

"detail": secondary label text displayed after thet label

"help_text": informative text displayed below input

"error_text": error text displayed below input that replaces help_text and sets the input state as errored

"description": longer description text displayed in a hoverable tooltip next to the label

Multiselect inputs allow users to select a list of items from a list of string or number options.

from lplots.widgets.multiselect import w_multi_select

options = w_multi_select(
  label="Select an item from the dropdown",
  options=[
    "alpha",
    "bravo",
    "charlie"
  ],
)
options_values = options.value
# ex. ["alpha", "Bravo"]

label: string label (required)

options: list of values for the select

default: list of default options from options

required: a boolean that sets the input as requiring input by user and errored when empty

appearance: a dict containing widget appearance attributes:

"placeholder": placeholder value displayed before a set value

"detail": secondary label text displayed after thet label

"help_text": informative text displayed below input

"error_text": error text displayed below input that replaces help_text and sets the input state as errored

"description": longer description text displayed in a hoverable tooltip next to the label

Radio group inputs allow users to select a single item from a list of string or number options.

from lplots.widgets.radio import w_radio_group

option = w_radio_group(
  label="Select an item from the dropdown",
  options=[
    "alpha",
    "bravo",
    "charlie"
  ],
)

option_value = option.value
# ex. "alpha"

Checkbox inputs allow users to select a boolean true or false value

from lplots.widgets.checkbox import w_checkbox

conditional = w_checkbox(
  label="Checkbox Input",
)

conditional_value = conditional.value
# ex. true

Each widget allows you to specify appearance and input discriptions.

select = w_multi_select(
  label="Multiselect Input",
  options=["Alpha", "Bravo", "Charlie"],
  appearance={
    "placeholder": "Placeholder…",
    "detail": "(details)",
    "help_text": "Help text",
    "error_text": "Error text",
    "description": "Hover description",
  }
)

appearance: a dict containing widget appearance attributes:

"placeholder": placeholder value displayed before a set value

"detail": secondary label text displayed after thet label

"help_text": informative text displayed below input

"error_text": error text displayed below input that replaces help_text and sets the input state as errored

"description": longer description text displayed in a hoverable tooltip next to the label

Widgets Reactivity

Widgets are reactive, meaning that they automatically update in response to changes in upstream widgets they depend on. In other words, the downstream data transformation will just run, without you having to click the “Run” button explicitly.

For instance, consider a scenario where you use a file selector widget to load a file. If you have a subsequent dropdown widget designed to display values from a specific column in that file, it will dynamically update its list of options whenever the file in the file selector widget changes. This reactive behavior ensures that the widgets remain synchronized and relevant to the current data context.

Example:

# Data Transformation cell 1
from lplots.widgets.ldata import w_ldata_picker
import pandas as pd

# Select a CSV file using the widget
csv = w_ldata_picker(label="Condition CSV", default="latch:///welcome/deseq2/conditions.csv")

# Data Transformation cell 2

# Read the selected file into a Pandas DataFrame
df = pd.read_csv(csv.value.download())

options = w_select(label="Select the condition of interest:", options=list(df["Condition"]))

Important: To enforce the reactive behavior, observe how csv.value is used in the second data transformation block, rather than in the initial block. This setup is crucial because it allows changes in csv.value to trigger the automatic execution of the subsequent data transformation cell.

With Rows you’re able to stack widgets horizontally, in a row. Widgets in a Row will automatically wrap to the next line when there is insufficient space.

from lplots.widgets.row import w_row
from lplots.widgets.select import w_select

option1 = w_select(
  label="Select 1",
  options=[
    "alpha",
    "bravo",
    "charlie"
  ],
)

option2 = w_select(
  label="Select 2",
  options=[
    "alpha",
    "bravo",
    "charlie"
  ],
)

w_row(
  items=[option1, option2]
)

Plots

Supported Widget Types

Widget Parameters

Widget Parameters

Table Class Parameters & Methods

Widget Appearance

Widgets Reactivity

Widget Rows

Plots

​Supported Widget Types

Widget Parameters

Widget Parameters

Table Class Parameters & Methods

​Widget Appearance

​Widgets Reactivity

​Widget Rows

Supported Widget Types

Widget Appearance

Widgets Reactivity

Widget Rows