Plot View
Last Updated: August 10, 2015
Tethys Platform provides two interactive plotting engines: D3 and Highcharts. The Plot view options objects have been designed to be engine independent, meaning that you can configure a D3 plot using the same syntax as a Highcharts plot. This allows you to switch which plotting engine to use via configuration. This article describes each of the plot views that are available.
Warning
Highcharts is free-of-charge for certain applications (see: Highcharts JS Licensing). If you need a guaranteed fee-free solution, D3 is recommended.
Note
D3 plotting implemented for Line Plot, Pie Plot, Bar Plot, Scatter Plot, and Timeseries Plot.
Line Plot
- class tethys_sdk.gizmos.LinePlot(series, height='500px', width='500px', engine='d3', title='', subtitle='', spline=False, x_axis_title='', x_axis_units='', y_axis_title='', y_axis_units='', **kwargs)
Used to create line plot visualizations.
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
Controller Example
pythonfrom tethys_sdk.gizmos import LinePlot line_plot_view = LinePlot( height='500px', width='500px', engine='highcharts', title='Plot Title', subtitle='Plot Subtitle', spline=True, x_axis_title='Altitude', x_axis_units='km', y_axis_title='Temperature', y_axis_units='°C', series=[ { 'name': 'Air Temp', 'color': '#0066ff', 'marker': {'enabled': False}, 'data': [ [0, 5], [10, -70], [20, -86.5], [30, -66.5], [40, -32.1], [50, -12.5], [60, -47.7], [70, -85.7], [80, -106.5] ] }, { 'name': 'Water Temp', 'color': '#ff6600', 'data': [ [0, 15], [10, -50], [20, -56.5], [30, -46.5], [40, -22.1], [50, -2.5], [60, -27.7], [70, -55.7], [80, -76.5] ] } ] ) context = { 'line_plot_view': line_plot_view, }Template Example
python{% load tethys_gizmos %} {% gizmo line_plot_view %}
Scatter Plot
- class tethys_sdk.gizmos.ScatterPlot(series=None, height='500px', width='500px', engine='d3', title='', subtitle='', x_axis_title='', x_axis_units='', y_axis_title='', y_axis_units='', **kwargs)
Use to create a scatter plot visualization.
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
Controller Example
pythonfrom tethys_sdk.gizmos import ScatterPlot male_dataset = { 'name': 'Male', 'color': '#0066ff', 'data': [ [174.0, 65.6], [175.3, 71.8], [193.5, 80.7], [186.5, 72.6], [187.2, 78.8], [181.5, 74.8], [184.0, 86.4], [184.5, 78.4], [175.0, 62.0], [184.0, 81.6], [180.0, 76.6], [177.8, 83.6], [192.0, 90.0], [176.0, 74.6], [174.0, 71.0], [184.0, 79.6], [192.7, 93.8], [171.5, 70.0], [173.0, 72.4], [176.0, 85.9], [176.0, 78.8], [180.5, 77.8], [172.7, 66.2], [176.0, 86.4], [173.5, 81.8], [178.0, 89.6], [180.3, 82.8], [180.3, 76.4], [164.5, 63.2], [173.0, 60.9], [183.5, 74.8], [175.5, 70.0], [188.0, 72.4], [189.2, 84.1], [172.8, 69.1], [170.0, 59.5], [182.0, 67.2], [170.0, 61.3], [177.8, 68.6], [184.2, 80.1], [186.7, 87.8], [171.4, 84.7], [172.7, 73.4], [175.3, 72.1], [180.3, 82.6], [182.9, 88.7], [188.0, 84.1], [177.2, 94.1], [172.1, 74.9], [167.0, 59.1], [169.5, 75.6], [174.0, 86.2], [172.7, 75.3], [182.2, 87.1], [164.1, 55.2], [163.0, 57.0], [171.5, 61.4], [184.2, 76.8], [174.0, 86.8], [174.0, 72.2], [177.0, 71.6], [186.0, 84.8], [167.0, 68.2], [171.8, 66.1] ] } female_dataset = { 'name': 'Female', 'color': '#ff6600', 'data': [ [161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0], [155.8, 53.6], [170.0, 59.0], [159.1, 47.6], [166.0, 69.8], [176.2, 66.8], [160.2, 75.2], [172.5, 55.2], [170.9, 54.2], [172.9, 62.5], [153.4, 42.0], [160.0, 50.0], [147.2, 49.8], [168.2, 49.2], [175.0, 73.2], [157.0, 47.8], [167.6, 68.8], [159.5, 50.6], [175.0, 82.5], [166.8, 57.2], [176.5, 87.8], [170.2, 72.8], [174.0, 54.5], [173.0, 59.8], [179.9, 67.3], [170.5, 67.8], [160.0, 47.0], [154.4, 46.2], [162.0, 55.0], [176.5, 83.0], [160.0, 54.4], [152.0, 45.8], [162.1, 53.6], [170.0, 73.2], [160.2, 52.1], [161.3, 67.9], [166.4, 56.6], [168.9, 62.3], [163.8, 58.5], [167.6, 54.5], [160.0, 50.2], [161.3, 60.3], [167.6, 58.3], [165.1, 56.2], [160.0, 50.2], [170.0, 72.9], [157.5, 59.8], [167.6, 61.0], [160.7, 69.1], [163.2, 55.9], [152.4, 46.5], [157.5, 54.3], [168.3, 54.8], [180.3, 60.7], [165.5, 60.0], [165.0, 62.0], [164.5, 60.3] ] } scatter_plot_view = ScatterPlot( width='500px', height='500px', engine='highcharts', title='Scatter Plot', subtitle='Scatter Plot', x_axis_title='Height', x_axis_units='cm', y_axis_title='Weight', y_axis_units='kg', series=[ male_dataset, female_dataset ] ) context = { 'scatter_plot_view': scatter_plot_view, }Template Example
python{% load tethys_gizmos %} {% gizmo scatter_plot_view %}
Polar Plot
- class tethys_sdk.gizmos.PolarPlot(series=None, height='500px', width='500px', engine='d3', title='', subtitle='', categories=None, **kwargs)
Use to create a polar plot visualization.
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
Controller Example
pythonfrom tethys_sdk.gizmos import PolarPlot web_plot = PolarPlot( height='500px', width='500px', engine='highcharts', title='Polar Chart', subtitle='Polar Chart', pane={ 'size': '80%' }, categories=['Infiltration', 'Soil Moisture', 'Precipitation', 'Evaporation', 'Roughness', 'Runoff', 'Permeability', 'Vegetation'], series=[ { 'name': 'Park City', 'data': [0.2, 0.5, 0.1, 0.8, 0.2, 0.6, 0.8, 0.3], 'pointPlacement': 'on' }, { 'name': 'Little Dell', 'data': [0.8, 0.3, 0.2, 0.5, 0.1, 0.8, 0.2, 0.6], 'pointPlacement': 'on' } ] ) context = { 'web_plot': web_plot, }Template Example
python{% load tethys_gizmos %} {% gizmo web_plot %}
Pie Plot
- class tethys_sdk.gizmos.PiePlot(series=None, height='500px', width='500px', engine='d3', title='', subtitle='', **kwargs)
Use to create a pie plot visualization.
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
Controller Example
pythonfrom tethys_sdk.gizmos import PieChart pie_plot_view = PiePlot( height='500px', width='500px', engine='highcharts', title='Pie Chart', subtitle='Pie Chart', series=[ {'name': 'Firefox', 'value': 45.0}, {'name': 'IE', 'value': 26.8}, {'name': 'Chrome', 'value': 12.8}, {'name': 'Safari', 'value': 8.5}, {'name': 'Opera', 'value': 8.5}, {'name': 'Others', 'value': 0.7} ] ) context = { 'pie_plot_view': pie_plot_view, }Template Example
python{% load tethys_gizmos %} {% gizmo pie_plot_view %}
Bar Plot
- class tethys_sdk.gizmos.BarPlot(series=None, height='500px', width='500px', engine='d3', title='', subtitle='', horizontal=False, categories=None, axis_title='', axis_units='', group_tools=True, y_min=0, **kwargs)
Bar Plot
Displays as either a bar or column chart.
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
- horizontal
If True, bars are displayed horizontally, otherwise they are displayed vertically.
- Type:
bool
Controller Example
pythonfrom tethys_sdk.gizmos import BarPlot bar_plot_view = BarPlot( height='500px', width='500px', engine='highcharts', title='Bar Chart', subtitle='Bar Chart', vertical=True, categories=[ 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ], axis_units='millions', axis_title='Population', series=[{ 'name': "Year 1800", 'data': [100, 31, 635, 203, 275, 487, 872, 671, 736, 568, 487, 432] }, { 'name': "Year 1900", 'data': [133, 200, 947, 408, 682, 328, 917, 171, 482, 140, 176, 237] }, { 'name': "Year 2000", 'data': [764, 628, 300, 134, 678, 200, 781, 571, 773, 192, 836, 172] }, { 'name': "Year 2008", 'data': [973, 914, 500, 400, 349, 108, 372, 726, 638, 927, 621, 364] } ] ) context = { 'bar_plot_view': bar_plot_view, }Template Example
python{% load tethys_gizmos %} {% gizmo bar_plot_view %}
Time Series
- class tethys_sdk.gizmos.TimeSeries(series=None, height='500px', width='500px', engine='d3', title='', subtitle='', y_axis_title='', y_axis_units='', y_min=0, **kwargs)
Use to create a timeseries plot visualization
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
Controller Example
pythonfrom tethys_sdk.gizmos import TimeSeries timeseries_plot = TimeSeries( height='500px', width='500px', engine='highcharts', title='Irregular Timeseries Plot', y_axis_title='Snow depth', y_axis_units='m', series=[{ 'name': 'Winter 2007-2008', 'data': [ [datetime(2008, 12, 2), 0.8], [datetime(2008, 12, 9), 0.6], [datetime(2008, 12, 16), 0.6], [datetime(2008, 12, 28), 0.67], [datetime(2009, 1, 1), 0.81], [datetime(2009, 1, 8), 0.78], [datetime(2009, 1, 12), 0.98], [datetime(2009, 1, 27), 1.84], [datetime(2009, 2, 10), 1.80], [datetime(2009, 2, 18), 1.80], [datetime(2009, 2, 24), 1.92], [datetime(2009, 3, 4), 2.49], [datetime(2009, 3, 11), 2.79], [datetime(2009, 3, 15), 2.73], [datetime(2009, 3, 25), 2.61], [datetime(2009, 4, 2), 2.76], [datetime(2009, 4, 6), 2.82], [datetime(2009, 4, 13), 2.8], [datetime(2009, 5, 3), 2.1], [datetime(2009, 5, 26), 1.1], [datetime(2009, 6, 9), 0.25], [datetime(2009, 6, 12), 0] ] }] ) context = { 'timeseries_plot': timeseries_plot, }Template Example
python{% load tethys_gizmos %} {% gizmo timeseries_plot %}
Area Range
- class tethys_sdk.gizmos.AreaRange(series=None, height='500px', width='500px', engine='d3', title='', subtitle='', y_axis_title='', y_axis_units='', **kwargs)
Use to create a area range plot visualization.
- engine
The plot engine to be used for rendering, either 'd3' or 'highcharts'. Defaults to 'd3'.
- Type:
str
Controller Example
pythonfrom tethys_sdk.gizmos import AreaRange averages = [ [datetime(2009, 7, 1), 21.5], [datetime(2009, 7, 2), 22.1], [datetime(2009, 7, 3), 23], [datetime(2009, 7, 4), 23.8], [datetime(2009, 7, 5), 21.4], [datetime(2009, 7, 6), 21.3], [datetime(2009, 7, 7), 18.3], [datetime(2009, 7, 8), 15.4], [datetime(2009, 7, 9), 16.4], [datetime(2009, 7, 10), 17.7], [datetime(2009, 7, 11), 17.5], [datetime(2009, 7, 12), 17.6], [datetime(2009, 7, 13), 17.7], [datetime(2009, 7, 14), 16.8], [datetime(2009, 7, 15), 17.7], [datetime(2009, 7, 16), 16.3], [datetime(2009, 7, 17), 17.8], [datetime(2009, 7, 18), 18.1], [datetime(2009, 7, 19), 17.2], [datetime(2009, 7, 20), 14.4], [datetime(2009, 7, 21), 13.7], [datetime(2009, 7, 22), 15.7], [datetime(2009, 7, 23), 14.6], [datetime(2009, 7, 24), 15.3], [datetime(2009, 7, 25), 15.3], [datetime(2009, 7, 26), 15.8], [datetime(2009, 7, 27), 15.2], [datetime(2009, 7, 28), 14.8], [datetime(2009, 7, 29), 14.4], [datetime(2009, 7, 30), 15], [datetime(2009, 7, 31), 13.6] ] ranges = [ [datetime(2009, 7, 1), 14.3, 27.7], [datetime(2009, 7, 2), 14.5, 27.8], [datetime(2009, 7, 3), 15.5, 29.6], [datetime(2009, 7, 4), 16.7, 30.7], [datetime(2009, 7, 5), 16.5, 25.0], [datetime(2009, 7, 6), 17.8, 25.7], [datetime(2009, 7, 7), 13.5, 24.8], [datetime(2009, 7, 8), 10.5, 21.4], [datetime(2009, 7, 9), 9.2, 23.8], [datetime(2009, 7, 10), 11.6, 21.8], [datetime(2009, 7, 11), 10.7, 23.7], [datetime(2009, 7, 12), 11.0, 23.3], [datetime(2009, 7, 13), 11.6, 23.7], [datetime(2009, 7, 14), 11.8, 20.7], [datetime(2009, 7, 15), 12.6, 22.4], [datetime(2009, 7, 16), 13.6, 19.6], [datetime(2009, 7, 17), 11.4, 22.6], [datetime(2009, 7, 18), 13.2, 25.0], [datetime(2009, 7, 19), 14.2, 21.6], [datetime(2009, 7, 20), 13.1, 17.1], [datetime(2009, 7, 21), 12.2, 15.5], [datetime(2009, 7, 22), 12.0, 20.8], [datetime(2009, 7, 23), 12.0, 17.1], [datetime(2009, 7, 24), 12.7, 18.3], [datetime(2009, 7, 25), 12.4, 19.4], [datetime(2009, 7, 26), 12.6, 19.9], [datetime(2009, 7, 27), 11.9, 20.2], [datetime(2009, 7, 28), 11.0, 19.3], [datetime(2009, 7, 29), 10.8, 17.8], [datetime(2009, 7, 30), 11.8, 18.5], [datetime(2009, 7, 31), 10.8, 16.1] ] area_range_plot_object = AreaRange( title='July Temperatures', y_axis_title='Temperature', y_axis_units='*C', width='500px', height='500px', series=[{ 'name': 'Temperature', 'data': averages, 'zIndex': 1, 'marker': { 'lineWidth': 2, } }, { 'name': 'Range', 'data': ranges, 'type': 'arearange', 'lineWidth': 0, 'linkedTo': ':previous', 'fillOpacity': 0.3, 'zIndex': 0 }] ) context = { 'area_range_plot_object': area_range_plot_object, }Template Example
python{% load tethys_gizmos %} {% gizmo area_range_plot_object %}
JavaScript API
For advanced features, the JavaScript API can be used to interact with the HighCharts object that is generated by the Plot View JavaScript library.
TETHYS_PLOT_VIEW.initHighChartsPlot(jquery_element)
This method initializes a chart generated from an AJAX request. An example is demonstrated in the Dam Break javascript tutorial.
Note
In order to use this, you will either need to use a PlotView gizmo on
the main page or register the dependencies in the main html template page
using the import_gizmo_dependency tag with the plot_view name
in the import_gizmos block.
For example:
{% block import_gizmos %}
{% import_gizmo_dependency plot_view %}
{% endblock %}
Four elements are required:
1) A controller for the AJAX call with a plot view gizmo.
@login_required()
def hydrograph_ajax(request):
"""
Controller for the hydrograph ajax request.
"""
# add a plot view gizmo
flood_plot = TimeSeries(
...
)
context = {'flood_plot': flood_plot}
return render(request, 'dam_break/hydrograph_ajax.html', context)
2) A template for with the tethys gizmo (e.g. hydrograph_ajax.html)
{% load tethys_gizmos %}
{% gizmo flood_plot %}
3) A url map to the controller in app.py
...
UrlMap(name='hydrograph_ajax',
url='dam-break/map/hydrograph',
controller='dam_break.controllers.hydrograph_ajax'),
...
4) The AJAX call in the javascript
$(function() { //wait for page to load
$.ajax({
url: 'hydrograph',
method: 'GET',
data: {
'peak_flow': 500, //example data to pass to the controller
},
success: function(data) {
// add plot to page
$("#plot_view_div").html(data);
//Initialize Plot
TETHYS_PLOT_VIEW.initHighChartsPlot($('.highcharts-plot'));
}
});
});
Highcharts JavaScript API
The Highcharts plots can be modified via JavaScript by using jQuery to select the Highcharts div and calling the highcharts() method on it. This will return the JavaScript object that represents the plot, which can be modified using the Highcharts API.
var plot = $('#my-plot').highcharts();