Create a project

In this tutorial you will learn how to create and explore a stride project.

This tutorial uses the global-test dataset which is a small test dataset. For real projects, use the global dataset instead (omit the --dataset option).

Discover available data

Before creating a project, you can explore what countries and years are available in the dataset.

List available countries:

$ stride datasets list-countries --dataset global-test
Countries available in the 'global-test' dataset (2 total):

  country_1
  country_2

List available model years:

$ stride datasets list-model-years --dataset global-test

List available weather years:

$ stride datasets list-weather-years --dataset global-test

Create the project

  1. Create a project configuration file using the stride projects init command.

    $ stride projects init --country country_1 -o my_project.json5

    This creates a JSON5 configuration file with default settings. You can edit this file to customize the project ID, description, model years, scenarios, and model parameters (such as heating/cooling thresholds and shoulder month smoothing).

  2. Create the project from the configuration file.

    $ stride projects create my_project.json5 --dataset global-test

Upon successful completion there will be a directory called country_1_project in the current directory. You will use this path for subsequent commands.

Explore project data

There are several commands for exploring project data from the command line. For example:

  1. List the scenarios in the project. The default template includes a baseline scenario and an EV projection scenario.

    $ stride scenarios list country_1_project

    Scenarios in project with project_id=country_1_project:
  baseline
  ev_projection

  2. List the data tables that are available in every scenario of each project.

    $ stride data-tables list

    energy_intensity gdp hdi load_shapes population

  3. Display a portion of a data table in the console.

    $ stride data-tables show country_1_project gdp --scenario baseline

    ┌────────────────┬───────────┬────────────┐
│     value      │ geography │ model_year │
│     double     │  varchar  │   int64    │
├────────────────┼───────────┼────────────┤
│ 500000000000.0 │ country_1 │       2025 │
│ 500000000000.0 │ country_1 │       2030 │
│ 500000000000.0 │ country_1 │       2035 │
│ 500000000000.0 │ country_1 │       2040 │
│ 500000000000.0 │ country_1 │       2045 │
│ 500000000000.0 │ country_1 │       2050 │
├────────────────┴───────────┴────────────┤
│ 6 rows                        3 columns │
└─────────────────────────────────────────┘

  4. List calculated tables.

    $ stride calculated-tables list country_1_project

    Calculated tables for all scenarios:
  electricity_per_vehicle_km_country
  energy_intensity_com_ind_tra
  energy_intensity_com_ind_tra_gdp
  energy_intensity_com_ind_tra_gdp_applied_regression
  energy_intensity_com_ind_tra_pivoted
  energy_intensity_parsed
  energy_intensity_res
  energy_intensity_res_hdi
  energy_intensity_res_hdi_population
  energy_intensity_res_hdi_population_applied_regression
  energy_intensity_res_pivoted
  energy_projection
  energy_projection_com_ind_tra_load_shapes
  energy_projection_res_load_shapes
  ev_annual_energy_tj
  ev_energy_by_type
  ev_stock_share_country
  ev_stock_split
  ev_stock_total
  gdp_country
  hdi_country
  km_per_vehicle_year_applied
  km_per_vehicle_year_parsed
  km_per_vehicle_year_pivoted
  load_shapes_expanded
  phev_share_country
  population_country
  temperature_multipliers
  vehicle_per_capita_parsed
  vehicle_per_capita_pivoted
  vehicle_per_capita_population
  vehicle_stock_total
  weather_bait_daily
  weather_degree_days
  weather_degree_days_grouped

Override tables by scenario:
  Scenario: baseline
    None

  Scenario: ev_projection
    None

  5. Display a portion of a calculated table in the console.

    $ stride calculated-tables show -s ev_projection country_1_project energy_projection

    ┌─────────────────────┬────────────┬───────────┬────────────┬─────────┬────────────────────┬───────────────┐
│      timestamp      │ model_year │ geography │   sector   │ metric  │       value        │   scenario    │
│      timestamp      │   int64    │  varchar  │  varchar   │ varchar │       double       │    varchar    │
├─────────────────────┼────────────┼───────────┼────────────┼─────────┼────────────────────┼───────────────┤
│ 2018-01-31 17:00:00 │       2025 │ country_1 │ Industrial │ other   │  67.02282323645481 │ ev_projection │
│ 2018-03-30 01:00:00 │       2025 │ country_1 │ Industrial │ other   │  61.76006113474705 │ ev_projection │
│ 2018-03-30 07:00:00 │       2025 │ country_1 │ Industrial │ other   │  65.62973915070864 │ ev_projection │
│ 2018-03-30 21:00:00 │       2025 │ country_1 │ Industrial │ other   │ 63.849687263366306 │ ev_projection │
│ 2018-04-30 07:00:00 │       2025 │ country_1 │ Industrial │ other   │  63.46271946177015 │ ev_projection │
│ 2018-04-30 14:00:00 │       2025 │ country_1 │ Industrial │ other   │   62.6113902982586 │ ev_projection │
│ 2018-04-30 22:00:00 │       2025 │ country_1 │ Industrial │ other   │  60.83133841091626 │ ev_projection │
│ 2018-05-31 10:00:00 │       2025 │ country_1 │ Industrial │ other   │  64.39144218560092 │ ev_projection │
│ 2018-05-31 15:00:00 │       2025 │ country_1 │ Industrial │ other   │  64.62362286655862 │ ev_projection │
│ 2018-06-29 07:00:00 │       2025 │ country_1 │ Industrial │ other   │  65.55234559038941 │ ev_projection │
│ 2018-06-29 08:00:00 │       2025 │ country_1 │ Industrial │ other   │   66.0167069523048 │ ev_projection │
│ 2018-06-29 22:00:00 │       2025 │ country_1 │ Industrial │ other   │  62.53399673793936 │ ev_projection │
│ 2018-07-31 14:00:00 │       2025 │ country_1 │ Industrial │ other   │  61.83745469506628 │ ev_projection │
│ 2018-09-30 01:00:00 │       2025 │ country_1 │ Industrial │ other   │  61.45048689347012 │ ev_projection │
│ 2018-09-30 02:00:00 │       2025 │ country_1 │ Industrial │ other   │ 61.373093333150884 │ ev_projection │
│ 2018-09-30 03:00:00 │       2025 │ country_1 │ Industrial │ other   │  61.45048689347012 │ ev_projection │
│ 2018-09-30 04:00:00 │       2025 │ country_1 │ Industrial │ other   │  61.83745469506628 │ ev_projection │
│ 2018-09-30 06:00:00 │       2025 │ country_1 │ Industrial │ other   │  64.15926150464324 │ ev_projection │
│ 2018-09-30 11:00:00 │       2025 │ country_1 │ Industrial │ other   │  65.01059066815479 │ ev_projection │
│ 2018-09-30 12:00:00 │       2025 │ country_1 │ Industrial │ other   │   64.5462293062394 │ ev_projection │
├─────────────────────┴────────────┴───────────┴────────────┴─────────┴────────────────────┴───────────────┤
│ 20 rows                                                                                        7 columns │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────┘

  1. Export the final dataset to file

    The energy_projection table is the final table of results and can be exported for all scenarios to energy_projection.csv with the command:

    $ stride projects export-energy-projection country_1_project

    To programmatically access specific views of the data, see the Process results with Python.

Visually inspect project data

The project data can be visually inspected by running the command:

$ stride view country_1_project

And then opening the displayed address in a web browser:

Dash is running on http://127.0.0.1:8050/

  * Serving Flask app 'STRIDE'
  * Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
  * Running on http://127.0.0.1:8050
Press CTRL+C to quit

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