Visuals overview¶
An application run typically has an input (the problem) and an output (the result, or solution). When you view an application run in Nextmv Console, there are several default views that help you understand and explore this data.
However, there are often additional views into the data that are unique to your situation that are not covered by the default views. With custom run assets you can add your own custom views to a run in Nextmv Console with custom visual assets using the data from your model run or any steps in your decision workflow.
A custom visual asset is just a custom asset with an
additional visual property that defines the type of visual and how it should
be rendered. When this is defined, Nextmv Console treats the custom asset not
as something that should be packaged for download but rather as content that
should be rendered in the browser.
There are currently three supported custom visual types:
- Chart.js: Create charts, plots and more with the performant JavaScript library.
- Plotly: Create your own plots and more with the powerful JavaScript library.
- GeoJSON: Create your own interactive maps using the common standard for geospatial data exchange.
A visual asset must follow the schema so that it is recognized and rendered correctly by Nextmv Console.
You can mix and match custom visuals on a single run as well. That is, you can have a custom GeoJSON map in one tab and Chart.js charts in another tab. Also, for the two charting libraries you can multiple charts under a single tab.
Visual assets are surfaced differently depending on the content format. Please refer to each section for further details.
json: Assets are surfaced under anassetskey in the output.multi-file: Assets are surfaced under a.jsonfile, whose path is specified in theapp.yamlmanifest.
Schema¶
A custom visual asset is a custom asset with an extra
visual property that defines which type of visual it is. Like all custom
assets, the custom visualization data must be placed in a top-level assets
property of your output JSON.
This assets block must be an array of objects with the following properties:
| Property | Required | Type | Description |
|---|---|---|---|
name |
Yes | string |
Name of the asset. For custom visuals this is not currently used but it should be treated as a display item for the end user. |
content_type |
Yes | string |
Defines the content type for the custom visualization. For now, only content type json is allowed. |
content |
Yes | object |
This is where the custom data to be rendered is placed. Refer to the custom visual type’s subpage for how the data for that particular data type should be defined. |
visual |
Yes | object (see VisualSchema below) |
The visual schema defines the type of visual and its properties. |
description |
No | string |
(Optional) Any additional information you would like to show to the end user. For custom visuals, if the description is defined it is included as a tooltip next to the custom visual tab. |
The VisualSchema definition for the visual property must include the
information defined in the table below.
| Property | Type | Description |
|---|---|---|
schema |
string |
One of: geojson, plotly, chartjs. Defines how the visual data should be interpreted and rendered on the front-end: geojson tells Nextmv Console to render the custom asset data as GeoJSON on a map, plotly tells Nextmv Console to render the custom asset data with the Plotly library, and chartjs tells Nextmv Console to render the custom asset data with the Chart.js library. |
type |
string |
Defines the type of custom visual, currently there is only one type: custom-tab. This renders the visual in its own tab view of the run details. |
label |
string |
The label for your custom tab. |
Combining the schema definitions above, the definition for a custom visual that renders GeoJSON would look like this:
{
"version": {...},
"solutions": [...],
"statistics": {...},
"assets": [
{
"name": "GeoJSON Example",
"description": "Optional content displayed as a UI tooltip.",
"content_type": "json",
"visual": {
"schema": "geojson",
"type": "custom-tab",
"label": "Custom Plot"
},
"content": {...}
}
]
}
json¶
In the json content format, a JSON object is
received as output and is written to standard out (stdout). This object
must contain an assets key with the metrics.
Consider the following example from our python-hello-world community
app. This is the output produced by the application.
{
"options": {
"details": true
},
"solution": {
"message": "Hello, world"
},
"assets": [ # (1)!
{
"name": "Plotly example",
"content": [
{
"data": [...], # ... Output trimmed for brevity.
"layout": {...}, # ... Output trimmed for brevity.
"title": {
"text": "Radius and Distance by Planet"
},
"xaxis": {
"title": {
"text": "Planet"
}
},
"yaxis": {
"title": {
"text": "Values"
}
},
"barmode": "group"
}
}
],
"content_type": "json",
"visual": {
"schema": "plotly",
"label": "Charts",
"type": "custom-tab"
}
}
],
"metrics": {
"value": 1.23,
"message": "Hello, world"
}
}
- Visual assets are surfaced under an
assetskey in the output.
multi-file¶
In the multi-file content format one or more
files are written to a location specified in the app.yaml
manifest. A .json file must be written to the path specified in
the configuration.content.multi-file.output.assets property in the
app.yaml. The default value for this property is outputs/assets/assets.json.
Consider the same python-hello-world community app example
as shown in the json section. We are going to modify it to convert it
to the multi-file content format.
type: python
runtime: ghcr.io/nextmv-io/runtime/python:3.11
python:
pip-requirements: pyproject.toml
files:
- main.py
configuration:
content:
format: multi-file # (1)!
multi-file:
input:
path: inputs
output:
solutions: .
metrics: ./metrics.json
assets: ./assets.json # (2)!
options:
strict: false
items:
- name: details
option_type: bool
default: true
description: Print details to logs. Default true.
required: false
ui:
control_type: toggle
display_name: Details
- Converted the application to
multi-file. - You can customize the path to the assets file.
import json
import nextmv
import plotly.graph_objects as go
def main():
"""Main function that runs the model."""
# Read the input.
data_file = nextmv.json_data_file(name="input", input_data_key="input")
loaded_input = nextmv.load(data_files=[data_file])
input_data = loaded_input.data["input"]
name = input_data["name"]
options = loaded_input.options
##### Insert model here
# Print logs that render in the run view in Nextmv Console.
message = f"Hello, {name}"
nextmv.log(message)
if options.details:
detail = f"You are {input_data['distance']} million km from the sun"
nextmv.log(detail)
assets = _create_visuals(name, input_data["radius"], input_data["distance"])
solution_file = nextmv.json_solution_file(name="solution", data={"message": message})
# Write output and metrics.
nextmv.write(
options=options,
solution_files=[solution_file],
metrics={
"value": 1.23,
"message": message,
},
assets=assets, # (1)!
)
def _create_visuals(name: str, radius: float, distance: float) -> list[nextmv.Asset]:
"""Create a Plotly bar chart with radius and distance for a planet."""
fig = go.Figure()
fig.add_trace(
go.Bar(x=[name], y=[radius], name="Radius (km)", marker_color="red", opacity=0.5),
)
fig.add_trace(
go.Bar(x=[name], y=[distance], name="Distance (Millions km)", marker_color="blue", opacity=0.5),
)
fig.update_layout(
title="Radius and Distance by Planet", xaxis_title="Planet", yaxis_title="Values", barmode="group"
)
fig = fig.to_json()
assets = [ # (2)!
nextmv.Asset(
name="Plotly example",
content_type="json",
visual=nextmv.Visual(
visual_schema=nextmv.VisualSchema.PLOTLY,
visual_type="custom-tab",
label="Charts",
),
content=[json.loads(fig)],
)
]
return assets
if __name__ == "__main__":
main()
- Visual assets are written to the path specified by the
app.yamlmanifest. - Assets are created using the Python SDK helpers.
Example¶
Follow the steps below to create a quick demo app that does nothing but print the input as the output. You can then use this custom app to quickly test out the different visualizations by pasting the demo code in the input, running and viewing the result.
Keep in mind that this is just a non-functional app that echos the input as output and therefore can be used to mimick what the output would look like for an with custom visualization as part of its standard output.
- Install the Nextmv CLI. Please see the Nextmv CLI installation guide.
-
Clone the
python-hello-worldcommunity app. -
This will create a
python-hello-worlddirectory. In this directory you will find a file namedmain.py. Open that file and replace its entire contents with the following: -
Make sure you are standing in the
python-hello-worlddirectory and run the following command to create a new Cloud application and push our code: -
To quickly test any of the custom visuals, choose from the tabs below and then copy and paste the input into the
JSONeditor for a new run and then run the app.
{
"assets": [
{
"name": "Chart.js example",
"content_type": "json",
"visual": {
"schema": "chartjs",
"type": "custom-tab",
"label": "Charts"
},
"content": [
{
"type": "radar",
"data": {
"labels": [
"January",
"February",
"March",
"April",
"May",
"June",
"July",
"August"
],
"datasets": [
{
"label": "D0",
"data": [14.89, 10.64, 13.39, 9.62, 11.27, 8.67, 8.72, 14.49],
"borderColor": "rgb(255, 99, 132)"
},
{
"label": "D1",
"data": [24.98, 25.5, 28.37, 21.94, 25.97, 24.06, 18.76, 25.4],
"borderColor": "rgb(255, 159, 64)",
"fill": "-1"
},
{
"label": "D2",
"data": [35.37, 39.41, 42.53, 32.59, 36.56, 35.17, 29.59, 36.15],
"borderColor": "rgb(255, 205, 86)",
"fill": 1
},
{
"label": "D3",
"data": [47.8, 53.02, 51.59, 45.38, 50.21, 43.35, 44.45, 49.77],
"borderColor": "rgb(75, 192, 192)",
"fill": false
},
{
"label": "D4",
"data": [58.59, 66.31, 59.76, 56.92, 60.97, 59.34, 57.56, 59.7],
"borderColor": "rgb(54, 162, 235)",
"fill": "-1"
},
{
"label": "D5",
"data": [74.32, 76.47, 73.24, 66.99, 74.33, 67.4, 67.22, 69.08],
"borderColor": "rgb(153, 102, 255)",
"fill": "-1"
},
{
"label": "D6",
"data": [86.67, 85.65, 82.11, 79.99, 85.62, 81.92, 79.91, 79.75],
"borderColor": "rgb(201, 203, 207)",
"fill": { "value": 85 }
}
]
}
},
{
"type": "bar",
"data": {
"labels": [
"January",
"February",
"March",
"April",
"May",
"June",
"July"
],
"datasets": [
{
"label": "Dataset 1",
"data": [
-31.57750343, 97.13820302, -11.38888889, 97.1622085,
69.70507545, -98.69855967, 24.05006859
],
"borderColor": "rgb(255, 99, 132)"
},
{
"label": "Dataset 2",
"data": [
-70.69101509, -76.74897119, 15.75274348, -57.38340192,
-78.87345679, -61.06310014, -23.70541838
],
"borderColor": "rgb(54, 162, 235)"
},
{
"label": "Dataset 3",
"data": [
-38.40534979, 60.26920439, 97.00960219, -88.67798354,
46.41632373, -28.57167353, 36.97530864
],
"borderColor": "rgb(153, 102, 255)"
},
{
"label": "Dataset 4",
"data": [
-35.95507545, 66.21742112, 5.22119342, 79.82167353,
48.04355281, 82.72633745, 60.41323731
],
"borderColor": "rgb(255, 205, 86)"
},
{
"label": "Dataset 5",
"data": [
-16.4266118, -63.84259259, -27.51371742, -60.52640604,
32.18106996, 82.70747599, -17.58916324
],
"borderColor": "rgb(75, 192, 192)"
}
]
},
"options": {
"indexAxis": "y",
"elements": { "bar": { "borderWidth": 2 } },
"responsive": true,
"plugins": {
"legend": { "position": "right" },
"title": {
"display": true,
"text": "Chart.js Horizontal Bar Chart"
}
}
}
},
{
"type": "line",
"data": {
"labels": [
"January",
"February",
"March",
"April",
"May",
"June",
"July"
],
"datasets": [
{
"label": "Dataset 1",
"data": [
-46.718107, 64.3021262, -79.20438957, 6.71296296,
-23.62482853, 76.69581619, -97.26337449
],
"borderColor": "rgb(255, 99, 132)",
"yAxisID": "y"
},
{
"label": "Dataset 2",
"data": [
86.59636488, -80.36694102, -91.98045267, 61.63237311,
-83.72599451, 89.22839506, -31.85013717
],
"borderColor": "rgb(54, 162, 235)",
"yAxisID": "y1"
}
]
},
"options": {
"responsive": true,
"interaction": { "mode": "index", "intersect": false },
"stacked": false,
"plugins": {
"title": {
"display": true,
"text": "Chart.js Line Chart - Multi Axis"
}
},
"scales": {
"y": { "type": "linear", "display": true, "position": "left" },
"y1": {
"type": "linear",
"display": true,
"position": "right",
"grid": { "drawOnChartArea": false }
}
}
}
}
]
}
]
}
{
"assets": [
{
"name": "Plotly example",
"content_type": "json",
"visual": {
"schema": "plotly",
"type": "custom-tab",
"label": "Charts"
},
"content": [
{
"data": [
{
"x": [
"2017-01-17",
"2017-01-18",
"2017-01-19",
"2017-01-20",
"2017-01-23",
"2017-01-24",
"2017-01-25",
"2017-01-26",
"2017-01-27",
"2017-01-30",
"2017-01-31",
"2017-02-01",
"2017-02-02",
"2017-02-03",
"2017-02-06",
"2017-02-07",
"2017-02-08",
"2017-02-09",
"2017-02-10"
],
"close": [
120, 119.989998, 119.779999, 120, 120.080002, 119.970001,
121.879997, 121.940002, 121.949997, 121.629997, 121.349998,
128.75, 128.529999, 129.080002, 130.289993, 131.529999,
132.039993, 132.419998, 132.119995
],
"decreasing": { "line": { "color": "#7F7F7F" } },
"high": [
120.239998, 120.5, 120.089996, 120.449997, 120.809998,
120.099998, 122.099998, 122.440002, 122.349998, 121.629997,
121.389999, 130.490005, 129.389999, 129.190002, 130.5,
132.089996, 132.220001, 132.449997, 132.940002
],
"increasing": { "line": { "color": "#17BECF" } },
"line": { "color": "rgba(31,119,180,1)" },
"low": [
118.220001, 119.709999, 119.370003, 119.730003, 119.769997,
119.5, 120.279999, 121.599998, 121.599998, 120.660004,
120.620003, 127.010002, 127.779999, 128.160004, 128.899994,
130.449997, 131.220001, 131.119995, 132.050003
],
"open": [
118.339996, 120, 119.400002, 120.449997, 120, 119.550003,
120.419998, 121.669998, 122.139999, 120.93, 121.150002,
127.029999, 127.980003, 128.309998, 129.130005, 130.539993,
131.350006, 131.649994, 132.460007
],
"type": "candlestick",
"xaxis": "x",
"yaxis": "y"
}
],
"layout": {
"dragmode": "zoom",
"margin": {
"r": 10,
"t": 25,
"b": 40,
"l": 60
},
"showlegend": false,
"xaxis": {
"autorange": true,
"rangeslider": {
"range": ["2017-01-17 12:00", "2017-02-10 12:00"]
},
"title": {
"text": "Date"
},
"type": "date"
},
"yaxis": {
"autorange": true,
"type": "linear"
},
"annotations": [
{
"x": "2017-01-31",
"y": 0.9,
"xref": "x",
"yref": "paper",
"text": "largest movement",
"font": { "color": "magenta" },
"showarrow": true,
"xanchor": "right",
"ax": -20,
"ay": 0
}
],
"shapes": [
{
"type": "rect",
"xref": "x",
"yref": "paper",
"x0": "2017-01-31",
"y0": 0,
"x1": "2017-02-01",
"y1": 1,
"fillcolor": "#d3d3d3",
"opacity": 0.2,
"line": {
"width": 0
}
}
]
}
},
{
"data": [
{
"type": "funnel",
"name": "Montreal",
"y": [
"Website visit",
"Downloads",
"Potential customers",
"Requested price"
],
"x": [120, 60, 30, 20],
"textinfo": "value+percent initial"
},
{
"type": "funnel",
"name": "Toronto",
"y": [
"Website visit",
"Downloads",
"Potential customers",
"Requested price",
"invoice sent"
],
"x": [100, 60, 40, 30, 20],
"textposition": "inside",
"textinfo": "value+percent previous"
},
{
"type": "funnel",
"name": "Vancouver",
"y": [
"Website visit",
"Downloads",
"Potential customers",
"Requested price",
"invoice sent",
"closed deals"
],
"x": [90, 70, 50, 30, 10, 5],
"textposition": "outside",
"textinfo": "value+percent total"
}
],
"layout": {
"funnelmode": "stack",
"showlegend": "true"
}
},
{
"data": [
{
"type": "scatter",
"mode": "lines",
"x": [
"2015-02-17",
"2015-02-18",
"2015-02-19",
"2015-02-20",
"2015-02-23",
"2015-02-24",
"2015-02-25",
"2015-02-26",
"2015-02-27",
"2015-03-02",
"2015-03-03",
"2015-03-04",
"2015-03-05",
"2015-03-06",
"2015-03-09",
"2015-03-10",
"2015-03-11",
"2015-03-12",
"2015-03-13",
"2015-03-16",
"2015-03-17",
"2015-03-18",
"2015-03-19",
"2015-03-20",
"2015-03-23",
"2015-03-24",
"2015-03-25",
"2015-03-26",
"2015-03-27",
"2015-03-30",
"2015-03-31",
"2015-04-01",
"2015-04-02",
"2015-04-06",
"2015-04-07",
"2015-04-08",
"2015-04-09",
"2015-04-10",
"2015-04-13",
"2015-04-14",
"2015-04-15",
"2015-04-16",
"2015-04-17",
"2015-04-20",
"2015-04-21",
"2015-04-22",
"2015-04-23",
"2015-04-24",
"2015-04-27",
"2015-04-28",
"2015-04-29",
"2015-04-30",
"2015-05-01",
"2015-05-04",
"2015-05-05",
"2015-05-06",
"2015-05-07",
"2015-05-08",
"2015-05-11",
"2015-05-12",
"2015-05-13",
"2015-05-14",
"2015-05-15",
"2015-05-18",
"2015-05-19",
"2015-05-20",
"2015-05-21",
"2015-05-22",
"2015-05-26",
"2015-05-27",
"2015-05-28",
"2015-05-29",
"2015-06-01",
"2015-06-02",
"2015-06-03",
"2015-06-04",
"2015-06-05",
"2015-06-08",
"2015-06-09",
"2015-06-10",
"2015-06-11",
"2015-06-12",
"2015-06-15",
"2015-06-16",
"2015-06-17",
"2015-06-18",
"2015-06-19",
"2015-06-22",
"2015-06-23",
"2015-06-24",
"2015-06-25",
"2015-06-26",
"2015-06-29",
"2015-06-30",
"2015-07-01",
"2015-07-02",
"2015-07-06",
"2015-07-07",
"2015-07-08",
"2015-07-09",
"2015-07-10",
"2015-07-13",
"2015-07-14",
"2015-07-15",
"2015-07-16",
"2015-07-17",
"2015-07-20",
"2015-07-21",
"2015-07-22",
"2015-07-23",
"2015-07-24",
"2015-07-27",
"2015-07-28",
"2015-07-29",
"2015-07-30",
"2015-07-31",
"2015-08-03",
"2015-08-04",
"2015-08-05",
"2015-08-06",
"2015-08-07",
"2015-08-10",
"2015-08-11",
"2015-08-12",
"2015-08-13",
"2015-08-14",
"2015-08-17",
"2015-08-18",
"2015-08-19",
"2015-08-20",
"2015-08-21",
"2015-08-24",
"2015-08-25",
"2015-08-26",
"2015-08-27",
"2015-08-28",
"2015-08-31",
"2015-09-01",
"2015-09-02",
"2015-09-03",
"2015-09-04",
"2015-09-08",
"2015-09-09",
"2015-09-10",
"2015-09-11",
"2015-09-14",
"2015-09-15",
"2015-09-16",
"2015-09-17",
"2015-09-18",
"2015-09-21",
"2015-09-22",
"2015-09-23",
"2015-09-24",
"2015-09-25",
"2015-09-28",
"2015-09-29",
"2015-09-30",
"2015-10-01",
"2015-10-02",
"2015-10-05",
"2015-10-06",
"2015-10-07",
"2015-10-08",
"2015-10-09",
"2015-10-12",
"2015-10-13",
"2015-10-14",
"2015-10-15",
"2015-10-16",
"2015-10-19",
"2015-10-20",
"2015-10-21",
"2015-10-22",
"2015-10-23",
"2015-10-26",
"2015-10-27",
"2015-10-28",
"2015-10-29",
"2015-10-30",
"2015-11-02",
"2015-11-03",
"2015-11-04",
"2015-11-05",
"2015-11-06",
"2015-11-09",
"2015-11-10",
"2015-11-11",
"2015-11-12",
"2015-11-13",
"2015-11-16",
"2015-11-17",
"2015-11-18",
"2015-11-19",
"2015-11-20",
"2015-11-23",
"2015-11-24",
"2015-11-25",
"2015-11-27",
"2015-11-30",
"2015-12-01",
"2015-12-02",
"2015-12-03",
"2015-12-04",
"2015-12-07",
"2015-12-08",
"2015-12-09",
"2015-12-10",
"2015-12-11",
"2015-12-14",
"2015-12-15",
"2015-12-16",
"2015-12-17",
"2015-12-18",
"2015-12-21",
"2015-12-22",
"2015-12-23",
"2015-12-24",
"2015-12-28",
"2015-12-29",
"2015-12-30",
"2015-12-31",
"2016-01-04",
"2016-01-05",
"2016-01-06",
"2016-01-07",
"2016-01-08",
"2016-01-11",
"2016-01-12",
"2016-01-13",
"2016-01-14",
"2016-01-15",
"2016-01-19",
"2016-01-20",
"2016-01-21",
"2016-01-22",
"2016-01-25",
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"fillColor": "#fdbf6f",
"fillOpacity": 0.5,
"radius": 48,
"weight": 0
}
}
},
{
"id": "location-8",
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-96.860071, 33.081335]
},
"properties": {
"metadata": [
{ "key": "my_value", "value": 1243 },
{ "key": "some_other_value", "value": 123 }
],
"style": {
"fillColor": "#fdbf6f",
"fillOpacity": 0.5,
"radius": 17,
"weight": 0
},
"vehicleId": "vehicle-1"
}
}
]
}
}
]
}