Quickstart

The aim of the tutorial is to get you started using the Scenario-Tool as quickly as possible. At the end you will be able to

  • Login

  • Create a new project

  • Setup a simple workflow

  • Run queries to analyse the results

  • Analyse the results with pandas

Project Setup

Getting your login token

from scenario_tool_interface import ScenarioToolInterface
sti = ScenarioToolInterface()
# Login with your username and password
sti.login("username", "password")

Setup project and run baseline

A project provides the shell for the your scenarios.

To setup the city database the project region eg. Melbourne and the case study boundary need to be defined. Further for each model the performance assessment model may be defined.

# Create a new project
project_id = sti.create_project()

# Obtain region code
region_id = sti.get_region("melbourne")

# Load geoson file
with open("../resources/test.geojson", 'r') as file:
         geojson_file = json.loads(file.read())

# Upload boundary
geojson_id = sti.upload_geojson(geojson_file, project_id)

# Set project parameters
sti.update_project(project_id, {
    "name": "my project",
    "active": True,
    'region_id': region_id,
    "case_study_area_id": geojson_id,
})

# Setup data model
sti.set_project_data_model(project_id, {"data_model_id": 1, "parameters":
                                        {"micro_climate_grid.grid_size": "20",
                                         "district.source": 1,
                                         "district.layer_name":
                                         "sa1_2011_aust",
                                         "district.epsg_from": "4283",
                                         "parcel.source": 2,
                                         "parcel.layer_name": "property_vic",
                                         "parcel.epsg_from": "4283",
                                         "landcover_geoscape.raster_file": 3
                                        }})

# Add assessment models
lst_model = sti.get_assessment_model("Land Surface Temperature")

# Set assessment models
sti.set_project_assessment_models(project_id, [{"assessment_model_id": lst_model}])


# Create and run baseline
baseline_id = sti.create_scenario(token, project_id, None)
sti.execute_scenario(baseline_id)

# Scenarios are executed asynchronous.
while True:
     # A status code smaller than 7 means the simulation is still executed.
     r = sti.check_status(baseline_id)
     status = r["status"]
     if status > 6:
        break
     time.sleep(1)

Create Scenario

After initialising the base model the model is ready to explore different scenarios. Scenarios are defined as a workflow of nodes. The nodes range from sub dividing a green field development or adding trees. To show a list of a available nodes use:

# Print a list of available nodes
sti.show_nodes()

# Nodes are defined as below
residential_node = {
    "node_type_id": sti.get_node_id("Residential"),
    "area": geojson_id,
    "parameters":
        {
            "dance4water_building.site_coverage": 0.6,
            "dance4water_number_of_trees.equation": 1,
            "dance4water_tree_spacing.equation": 22
        }
}

nodes = []
# Several nodes can are combined to a workflow be adding them to a vector. The
# nodes are executed in the order the are added
nodes.append(residential_node)

# Scenarios need a parent. In this case we use the base line scenario created before
baseline_scenario_id = sti.get_baseline(project_id)

# Crate a new scenario
scenario_id = sti.create_scenario(project_id, baseline_scenario_id, "my new scenario")

# Set workflow
sti.set_scenario_workflow(scenario_id, nodes)

# Execute scenario
sti.execute_scenario(scenario_id)

# Scenarios are executed asynchronous
while True:
    # A status code smaller than 7 means the simulation is still executed.
    r = sti.check_status(scenario_id)
    status = r["status"]
    if status > 6:
        break
    time.sleep(1)

Analysis

This section will show how the results of the before created base line and scenario can be analysed

# Before running an analysis check if the scenarios have been executed
# The scenario of interest should start return a 7 as simulation status which indicate the performance
# assessment model has been successfully executed
sti.show_scenarios(project_id)

# The results can be obtained buy running SQL queries on the result database
# Queries are executed asynchronous. We execute wait therefore until the return status has
# changed to loaded

 while True:
     r = sti.run_query(scenario_id,
                       "SELECT avg(tree_cover_fraction) as tf from micro_climate_grid")

     if r['status'] != 'loaded':
        # Break the loop when query is loaded
        break
 print(r['data'])