{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# First steps with the pyam package\n", "\n", "## Scope and feature overview\n", "\n", "The **pyam** package provides a range of diagnostic tools and functions\n", "for analyzing, visualizing and working with timeseries data following the format established by the *Integrated Assessment Modeling Consortium* ([IAMC](https://www.iamconsortium.org)).\n", "\n", "The format has been used in several IPCC assessments and numerous model comparison exercises.\n", "An illustrative example of this format template is shown below;\n", "[read the docs](https://pyam-iamc.readthedocs.io/en/stable/data.html) for more information.\n", "\n", "\n", "\n", "| **Model** | **Scenario** | **Region** | **Variable** | **Unit** | **2005** | **2010** | **2015** |\n", "|-----------|--------------|------------|----------------|----------|----------|----------|----------|\n", "| MESSAGE | CD-LINKS 400 | World | Primary Energy | EJ/y | 462.5 | 500.7 | ... |\n", "\n", "This notebook illustrates the basic functionality of the **pyam** package\n", "and the **IamDataFrame** class:\n", "\n", "0. Load timeseries data from a snapshot file and inspect the scenario ensemble\n", "1. Apply filters to the ensemble and display the timeseries data \n", " as [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)\n", "2. Visualize timeseries data using the plotting library based on the [matplotlib](https://matplotlib.org/) package\n", "3. Perform scenario diagnostic and validation checks\n", "4. Categorize scenarios according to timeseries data values\n", "5. Compute quantitative indicators for further scenario characterization & diagnostics\n", "6. Export data and categorization to a file\n", "\n", "\n", "## Read the docs\n", "\n", "A comprehensive documentation is available at [pyam-iamc.readthedocs.io](http://pyam-iamc.readthedocs.io)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial data\n", "\n", "The timeseries data used in this tutorial is a partial snapshot of the scenario ensemble\n", "compiled for the IPCC's *Special Report on Global Warming of 1.5°C* ([SR15](http://ipcc.ch/sr15/)).\n", "The complete scenario ensemble data is publicly available from the [IAMC 1.5°C Scenario Explorer and Data hosted by IIASA](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer). \n", "\n", "Please read the [License](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/#/license) page of the IAMC 1.5°C Scenario Explorer before using the full scenario data for scientific analysis or other work.\n", "\n", "\n", "\n", "### Scenarios in the tutorial data\n", "\n", "The data used for this tutorial consists of selected variables from these sources:\n", "\n", " - an ensemble of scenarios from the *Horizon 2020* [CD-LINKS](https://www.cd-links.org) project \n", " - the \"Faster Transition Scenario\" from the IEA's [World Energy Outlook 2017](https://www.oecd-ilibrary.org/energy/world-energy-outlook-2017_weo-2017-en),\n", " - the \"1.0\" scenario submitted by the GENeSYS-MOD team ([Löffler et al., 2017](https://doi.org/10.3390/en10101468))\n", "\n", "Please refer to the [About](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/#/about) page of the *IAMC 1.5°C Scenario Explorer* for references and additional information.\n", "\n", "