From 2b03d8d47d4aa787de2eaa053b090f6cf0f39f98 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 19 Nov 2019 11:09:39 +0100 Subject: [PATCH 01/34] bump copyright year in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1db4e6438..8296d958d 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ and Daniel Huppmann ([@danielhuppmann](https://github.com/danielhuppmann/)). License ------- -Copyright 2017-2018 IIASA Energy Program +Copyright 2017-2019 IIASA Energy Program The ``pyam`` package is licensed under the Apache License, Version 2.0 (the "License"); From 40f45f41d97c984e91de861b415f1587ad5549d4 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 19 Nov 2019 11:31:37 +0100 Subject: [PATCH 02/34] harmonize README with the docs --- README.md | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 8296d958d..0f67d6bae 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ -pyam: a Python toolkit for Integrated Assessment Modeling -========================================================= +pyam: analysis and visualization of integrated-assessment scenarios +=================================================================== **Documentation on [Read the Docs](https://pyam-iamc.readthedocs.io)** @@ -8,18 +8,22 @@ pyam: a Python toolkit for Integrated Assessment Modeling Overview and scope ------------------ -The ``pyam`` package provides a range of diagnostic tools and functions -for analyzing and working with IAMC-format timeseries data. +The open-source Python package ``pyam`` provides a suite of tools and functions +for analyzing and visualizing input data (i.e., assumptions/parametrization) +and results (model output) of integrated-assessment scenarios. + +Key features: + + - Simple analysis of timeseries data in the IAMC format + (more about it [here](https://pyam-iamc.readthedocs.io/en/stable/data.html)) + with an interface similar in feel and style to the widely + used [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) + - Advanced visualization and plotting functions + (see the [gallery](https://pyam-iamc.readthedocs.io/en/stable/examples/index.html)) + - Diagnostic checks for scripted validation of scenario data and results Features: - Summary of models, scenarios, variables, and regions included in a snapshot. -- Display of timeseries data as pandas.DataFrame with IAMC-specific filtering - options. -- Simple visualization and plotting functions. -- Diagnostic checks for non-reported variables or timeseries data to identify - outliers and potential reporting issues. -- Categorization of scenarios according to timeseries data or meta-identifiers - for further analysis. The package can be used with timeseries data that follows the data template convention of the [Integrated Assessment Modeling Consortium](http://www.globalchange.umd.edu/iamc/) (IAMC). From fa873fee6d1dd26fa000578a4b570434984ab84c Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 19 Nov 2019 11:32:20 +0100 Subject: [PATCH 03/34] add own (short) section on data model, cross-reference to the docs page --- README.md | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 0f67d6bae..8b5c8eba0 100644 --- a/README.md +++ b/README.md @@ -22,27 +22,30 @@ Key features: (see the [gallery](https://pyam-iamc.readthedocs.io/en/stable/examples/index.html)) - Diagnostic checks for scripted validation of scenario data and results -Features: -- Summary of models, scenarios, variables, and regions included in a snapshot. +Data model +---------- -The package can be used with timeseries data that follows the data template -convention of the [Integrated Assessment Modeling Consortium](http://www.globalchange.umd.edu/iamc/) (IAMC). -An illustrative example is shown below; -see [data.ene.iiasa.ac.at/database](http://data.ene.iiasa.ac.at/database/) -for more information. +An illustrative example of the timeseries format developed by the +[Integrated Assessment Modeling Consortium](http://www.globalchange.umd.edu/iamc/) (IAMC) +is shown below. +The row is taken from the [IAMC 1.5°C scenario explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer), +showing a scenario from the [CD-LINKS](https://www.cd-links.org) project. +[Read the docs](https://pyam-iamc.readthedocs.io/en/stable/data.html) +for more information on the IAMC format and the ``pyam`` data model. -| **model** | **scenario** | **region** | **variable** | **unit** | **2005** | **2010** | **2015** | -|--------------|--------------|------------|----------------|----------|----------|----------|----------| -| MESSAGE V.4 | AMPERE3-Base | World | Primary Energy | EJ/y | 454.5 | 479.6 | ... | -| ... | ... | ... | ... | ... | ... | ... | ... | +| **model** | **scenario** | **region** | **variable** | **unit** | **2005** | **2010** | **2015** | +|-----------|--------------|------------|----------------|----------|----------|----------|----------| +| MESSAGE | CD-LINKS 400 | World | Primary Energy | EJ/y | 454.5 | 479.6 | ... | +| ... | ... | ... | ... | ... | ... | ... | ... | Tutorial -------- -A comprehensive tutorial for the basic functions is included -in [the first tutorial](doc/source/tutorials/pyam_first_steps.ipynb) -using a partial snapshot of the IPCC AR5 scenario database. +An introduction to the basic functions is shown +in [the "first-steps" notebook](doc/source/tutorials/pyam_first_steps.ipynb). + +More tutorials are available in the folder [doc/source/tutorials](doc/source/tutorials). Documentation ------------- From 399a928b6de5cd9236e01489cab348f2447c6d0f Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 2 Dec 2019 12:46:32 +0100 Subject: [PATCH 04/34] minor docs edits --- README.md | 2 +- doc/source/index.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 8b5c8eba0..893627f26 100644 --- a/README.md +++ b/README.md @@ -98,7 +98,7 @@ conda activate pyam # may be simply `source activate pyam` or just `activate p make -B virtual-environment ``` -To check everything has installed correctly, +To check everything has installed correctly, run ``` pytest tests diff --git a/doc/source/index.rst b/doc/source/index.rst index b435097f2..2813d36b3 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -54,7 +54,7 @@ Key features: - Simple analysis of timeseries data in the IAMC format (more about it `here`_) with an interface similar in feel and style to the widely used `pandas.DataFrame`_ - - Advanced visualization and plotting function (see the `gallery`_) + - Advanced visualization and plotting functions (see the `gallery`_) - Diagnostic checks for scripted validation of scenario data and results The source code for |pyam| is available on `Github`_. From 031cadb8aec23af961b562669261a2a7bc1ebf4b Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 2 Dec 2019 12:47:37 +0100 Subject: [PATCH 05/34] flip logger message when detecting a notebook --- pyam/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyam/__init__.py b/pyam/__init__.py index da89c670f..36cc9ff10 100644 --- a/pyam/__init__.py +++ b/pyam/__init__.py @@ -25,8 +25,8 @@ logger.addHandler(stderr_info_handler) log_msg = ( - "Running in a notebook, adding stderr handler and setting " - "`{}` logging level to `logging.INFO`".format(__name__) + "Running in a notebook, setting `{}` logging level to `logging.INFO` " + "and adding stderr handler".format(__name__) ) logger.info(log_msg) From 992ce79ad415e77cfbdd8132eeddf3bdff348840 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 3 Dec 2019 14:54:17 +0100 Subject: [PATCH 06/34] rewrite header of first-steps tutorial for consistency use actual Primary-Energy-values from MESSAGE-CD-LINKS scenarios --- README.md | 2 +- doc/source/_static/iamc_template.png | Bin 56627 -> 70341 bytes doc/source/tutorials/_static/IAMC_logo.jpg | Bin 8867 -> 3036 bytes doc/source/tutorials/_static/IIASA_logo.png | Bin 31444 -> 0 bytes doc/source/tutorials/pyam_first_steps.ipynb | 29 ++++++++------------ 5 files changed, 13 insertions(+), 18 deletions(-) delete mode 100644 doc/source/tutorials/_static/IIASA_logo.png diff --git a/README.md b/README.md index 893627f26..6469b2aa1 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ for more information on the IAMC format and the ``pyam`` data model. | **model** | **scenario** | **region** | **variable** | **unit** | **2005** | **2010** | **2015** | |-----------|--------------|------------|----------------|----------|----------|----------|----------| -| MESSAGE | CD-LINKS 400 | World | Primary Energy | EJ/y | 454.5 | 479.6 | ... | +| MESSAGE | CD-LINKS 400 | World | Primary Energy | EJ/y | 462.5 | 500.7 | ... | | ... | ... | ... | ... | ... 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z^v9T|wS*tj6p(Ce^x*MhEXSbQ4J9~9<}grC;Y8YYG2;Hoyg`4;{|AO&&^I^{ZqJxhmb4{7T;e$~FkOFkcg%hBh(;j?wbxD#K0sU?MQ z(SxBv;GHJVyzB0$ygoByg7dFA2zbemhc0mz@CtwW?IO_Lvs>EQ5EhDjd*=!kB^7=FGK8O2&TofjGAlOor_5 zs`Qy$f!bffFjkYB{bm4ZB5RMyA+6$!He=7)Mh^HD?L1cN4Q%t}wiQLWAN$YC>KE_f z?<2gppGbv$WV)6ABU$0ckG}eUqZj@^pZkR1-`{{`qmPUKs5gy%A}0V+V)DQ`5yPPW E0d#etjsO4v diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index 93ae10124..d31d5f9b7 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -4,30 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# First steps with the ``pyam`` package \n", - "\n", - "## *An open-source Python package for IAM scenario analysis and visualization*\n", - "\n", - "\n", - "\n", + "# 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 and working with scenario data following the IAMC template format.\n", - "A comprehensive documentation of the package is available\n", - "at [pyam-iamc.readthedocs.io](http://pyam-iamc.readthedocs.io)\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](http://www.globalchange.umd.edu/iamc/)). The format has been used in several IPCC assessments and numerous model comparison exercises.\n", + "\n", + "\n", "\n", - "An illustrative example of the IAMC template is shown below;\n", - "see [data.ene.iiasa.ac.at/database/](http://data.ene.iiasa.ac.at/database/) for more information.\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", - "| **Model** | **Scenario** | **Region** | **Variable** | **Unit** | **2005** | **2010** | **2015** |\n", - "|---------------------|---------------|------------|----------------|----------|----------|----------|----------|\n", - "| MESSAGE V.4 | AMPERE3-Base | World | Primary Energy | EJ/y | 454.5 |\t479.6 | ... |\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", "\n", - "This notebook illustrates some basic functionality of the ``pyam`` package\n", + "This notebook illustrates the basic functionality of the **pyam** package\n", "and the ``IamDataFrame`` class:\n", "\n", "1. Importing timeseries data from `xlsx` or `csv` files.\n", From d7d62d7734d42f6f9460910297c5a483c8964dad Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 3 Dec 2019 14:56:02 +0100 Subject: [PATCH 07/34] add subsection with reference to the docs --- doc/source/tutorials/pyam_first_steps.ipynb | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index d31d5f9b7..969523dd3 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -35,6 +35,9 @@ "7. Exporting data to `xlsx` using the IAMC template.\n", "\n", "\n", + "## Read the docs\n", + "\n", + "A comprehensive documentation of the **pyam** package is available at [pyam-iamc.readthedocs.io](http://pyam-iamc.readthedocs.io)." 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zlO79LOY;iVxl-N6hC0>tlOc$|0F>#Z^;Y}{#4-vQk{eg(M#Td~`hZ^nh2pIXdP)jH zFvRXWZy0y_y;*#_WMD;3Xx^L3`;;dShdFtNS3ybPt_nZ?iG zer?vm;2?PVwSyE8Ohuk&#EQ{B9xw!kjl{QQr+vLM5X9in3xFdS#c%-(q5mdu-#|$f zdsd` zUbn;VKy9`Mg0m(u3K}5AGbzzv@jm?2ItM zt@Fbxt#)lyxBCemgs>Gw@F$0@mc#RX51EJ-03sa`)XJTq&3!sYy%!XkzEyAo16A|u zn9MgW6vC>2=(<*{Vz3H@moDr6;Bwesy?>gV-xy}ALVj?9bCt4$H5cIzQzEdGx literal 0 HcmV?d00001 diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index 969523dd3..f6beab358 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -38,54 +38,44 @@ "## Read the docs\n", "\n", "A comprehensive documentation of the **pyam** package 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 database \n", - "compiled for the IPCC's Fifth Assessment Report (AR5):\n", - "\n", - "> Krey V., O. Masera, G. Blanford, T. Bruckner, R. Cooke, K. Fisher-Vanden, H. Haberl, E. Hertwich, E. Kriegler, D. Mueller, S. Paltsev, L. Price, S. Schlömer, D. Ürge-Vorsatz, D. van Vuuren, and T. Zwickel, 2014: *Annex II: Metrics & Methodology*. \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.ene.iiasa.ac.at/iamc-1.5c-explorer). \n", + "Please read the [license](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license) when using the full scenario data for scientific analyis or other work.\n", "\n", - "> In: *Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change* [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. [Link](https://www.ipcc.ch/report/ar5/wg3/)\n", + "\n", "\n", - "The complete database is publicly available at [tntcat.iiasa.ac.at/AR5DB/](https://tntcat.iiasa.ac.at/AR5DB/).\n", + "The data snapshot used for this tutorial consists of selected data from the *Horizon 2020* [CD-LINKS](https://www.cd-links.org) project \n", + "and 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", + "Please refer to the [About](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/about) page of the *IAMC 1.5°C Scenario Explorer* for references and additional information.\n", "\n", - "\n", - "\n", + "

    \n", "\n", - "The data snapshot used for this tutorial consists of selected data from two model intercomparison projects:\n", + "**Citation of the scenario ensemble**\n", "\n", - " - Energy Modeling Forum Round 27 \n", - " ([EMF27](https://emf.stanford.edu/projects/emf-27-global-model-comparison-exercise)),\n", - " see the Special Issue in [Climatic Change 3-4, 2014](https://link.springer.com/journal/10584/123/3/page/1).\n", - " \n", - " - EU FP7 project [AMPERE](https://tntcat.iiasa.ac.at/AMPEREDB/), \n", - " see the following scientific publications:\n", - " \n", - " > - Riahi, K., et al. (2015). \"Locked into Copenhagen pledges — Implications of short-term emission targets \n", - " > for the cost and feasibility of long-term climate goals.\" \n", - " > *Technological Forecasting and Social Change* 90(Part A): 8-23. \n", - " > [DOI: 10.1016/j.techfore.2013.09.016](https://doi.org/10.1016/j.techfore.2013.09.016)\n", - " \n", - " > - Kriegler, E., et al. (2015). \"Making or breaking climate targets: The AMPERE study on \n", - " > staged accession scenarios for climate policy.\"\n", - " > *Technological Forecasting and Social Change* 90(Part A): 24-44. \n", - " > [DOI: 10.1016/j.techfore.2013.09.021](https://doi.org/10.1016/j.techfore.2013.09.021)\n", - "\n", - "
    \n", - "*The data used in this tutorial is ONLY a partial snapshot of the IPCC AR5 scenario database!* \n", - "*This tutorial is only intended for an illustration of the pyam package.*\n", - "
    " + "> D. Huppmann, E. Kriegler, V. Krey, K. Riahi, J. Rogelj, K. Calvin, F. Humpenoeder, A. Popp, S. K. Rose, J. Weyant, et al. \n", + "> *IAMC 1.5°C Scenario Explorer and Data hosted by IIASA* (release 2.0) \n", + "> Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis, 2019. \n", + "> doi: [10.5281/zenodo.3363345](https://doi.org/10.5281/zenodo.3363345) | url: [data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Import package and load data from the AR5 tutorial snapshot\n", - "\n", - "We import the snapshot timeseries data from the file ``tutorial_AR5_data.csv`` in the ``tutorial`` folder.\n", - "\n", - "As a first step, we show lists of all models, scenarios, regions, and the variables and units included in the snapshot." + "## Import package and load tutorial data" ] }, { @@ -94,12 +84,20 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import pyam\n", + "import pyam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We import the snapshot of the timeseries data from the file ``tutorial_data.csv``.\n", "\n", - "%matplotlib inline" + "
    " ] }, { @@ -108,7 +106,7 @@ "metadata": {}, "outputs": [], "source": [ - "df = pyam.IamDataFrame(data='tutorial_AR5_data.csv', encoding='utf-8')" + "df = pyam.IamDataFrame(data='tutorial_data.csv')" ] }, { diff --git a/doc/source/tutorials/tutorial_AR5_data.csv b/doc/source/tutorials/tutorial_AR5_data.csv deleted file mode 100644 index 96f32569a..000000000 --- a/doc/source/tutorials/tutorial_AR5_data.csv +++ /dev/null @@ -1,659 +0,0 @@ -model,scenario,region,variable,unit,2005,2010,2020,2030,2040,2050,2060,2070,2080,2090,2100 -AIM-Enduse 12.1,EMF27-450-Conv,ASIA,Emissions|CO2,Mt CO2/yr,10540.74,13160.18,11899.38,9545.81,7355.07,6119.50,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9126.18,11910.09,10748.33,8612.47,6776.59,5881.43,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,ASIA,Primary Energy,EJ/yr,133.56,168.75,163.43,179.86,214.20,227.27,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,LAM,Emissions|CO2,Mt CO2/yr,3285.00,3294.54,3367.62,2856.65,2207.36,1537.72,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1422.06,1648.20,1851.71,1627.47,1445.51,1224.19,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,LAM,Primary Energy,EJ/yr,27.40,31.42,34.80,40.32,46.80,47.60,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,MAF,Emissions|CO2,Mt CO2/yr,4302.21,4487.54,4238.91,3956.19,3490.81,2082.24,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2262.46,2684.96,2579.14,2610.36,2656.66,1738.95,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,MAF,Primary Energy,EJ/yr,42.68,50.12,50.13,58.29,74.20,86.57,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,OECD90,Emissions|CO2,Mt CO2/yr,12085.85,12744.33,11646.37,8272.30,4457.91,1625.18,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12000.81,12669.18,11577.18,8216.20,4423.14,1610.87,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,OECD90,Primary Energy,EJ/yr,193.12,202.29,185.63,175.80,169.68,154.18,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,REF,Emissions|CO2,Mt CO2/yr,3306.95,3604.42,3325.20,2991.24,1889.38,960.75,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3265.30,3567.61,3291.30,2963.75,1872.35,953.74,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,REF,Primary Energy,EJ/yr,48.54,52.61,51.34,54.72,52.76,51.81,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Emissions|CO2,Mt CO2/yr,34492.05,38321.78,35588.66,28531.68,20287.46,13367.27,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29048.12,33510.81,31158.83,24939.73,18061.16,12451.07,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12686.70,14578.11,11774.30,7075.03,3620.79,1619.87,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10168.56,11846.65,9437.49,4860.10,3051.71,4477.15,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Price|Carbon,US$2005/t CO2,0.00,0.00,87.11,249.53,1940.90,14260.09,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Primary Energy,EJ/yr,458.20,518.89,500.15,521.23,569.53,581.44,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Primary Energy|Coal,EJ/yr,122.98,148.41,129.99,63.71,26.81,28.26,,,,, -AIM-Enduse 12.1,EMF27-450-Conv,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,18.97,31.74,16.10,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,ASIA,Emissions|CO2,Mt CO2/yr,10540.74,13160.11,11893.80,9478.33,7367.07,5513.79,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9126.18,11910.02,10742.74,8545.00,6788.59,5275.72,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,ASIA,Primary Energy,EJ/yr,133.56,168.74,163.37,169.57,215.98,249.33,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,LAM,Emissions|CO2,Mt CO2/yr,3285.00,3286.68,3362.61,2837.11,1889.89,899.63,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1422.06,1640.34,1846.70,1607.93,1128.04,586.10,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,LAM,Primary Energy,EJ/yr,27.40,31.35,34.76,38.64,50.24,56.95,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,MAF,Emissions|CO2,Mt CO2/yr,4302.21,4487.49,4239.03,3619.25,2787.47,1671.29,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2262.46,2684.91,2579.25,2273.41,1953.32,1328.00,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,MAF,Primary Energy,EJ/yr,42.68,50.12,50.13,54.02,74.78,88.45,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,OECD90,Emissions|CO2,Mt CO2/yr,12085.85,12744.16,11659.29,8708.81,5488.86,3355.22,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12000.81,12669.01,11590.09,8652.70,5454.09,3340.91,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,OECD90,Primary Energy,EJ/yr,193.12,202.28,185.78,166.26,173.64,147.43,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,REF,Emissions|CO2,Mt CO2/yr,3306.95,3604.39,3322.95,3076.67,1977.78,1181.73,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3265.30,3567.58,3289.05,3049.19,1960.75,1174.72,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,REF,Primary Energy,EJ/yr,48.54,52.61,51.38,52.89,56.45,58.10,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Emissions|CO2,Mt CO2/yr,34492.05,38313.59,35588.85,28629.65,20458.10,13660.19,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29048.12,33502.62,31159.02,25037.70,18231.80,12743.99,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12686.70,14569.92,11774.85,5682.15,3021.37,2507.97,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10168.56,11838.83,9438.38,3554.33,1374.16,1314.03,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Price|Carbon,US$2005/t CO2,0.00,0.00,87.11,257.31,731.92,3680.57,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Primary Energy,EJ/yr,458.20,518.81,500.24,493.64,583.82,614.23,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Primary Energy|Coal,EJ/yr,122.98,148.41,130.06,45.95,24.32,21.17,,,,, -AIM-Enduse 12.1,EMF27-450-NoCCS,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,10540.74,13160.11,14124.17,14218.08,13187.66,10019.56,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9126.18,11910.02,12973.12,13284.74,12609.18,9781.49,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,133.56,168.74,187.98,208.68,237.50,252.90,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,LAM,Emissions|CO2,Mt CO2/yr,3285.00,3286.68,3445.63,3496.62,2986.08,1790.49,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1422.06,1640.34,1929.71,2267.44,2224.23,1476.96,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,LAM,Primary Energy,EJ/yr,27.40,31.35,35.91,44.04,49.72,60.09,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,MAF,Emissions|CO2,Mt CO2/yr,4302.21,4487.49,4368.48,4519.64,4294.83,2733.76,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2262.46,2684.91,2708.71,3173.80,3460.68,2390.47,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,MAF,Primary Energy,EJ/yr,42.68,50.12,51.84,62.54,78.22,94.12,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,12085.85,12744.16,12607.17,11752.01,9749.33,6501.31,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12000.81,12669.01,12537.97,11695.90,9714.55,6487.00,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,193.12,202.28,196.71,202.04,193.83,185.32,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,REF,Emissions|CO2,Mt CO2/yr,3306.95,3604.39,3826.80,3615.47,3258.31,3076.27,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3265.30,3567.58,3792.91,3587.99,3241.28,3069.26,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,REF,Primary Energy,EJ/yr,48.54,52.61,56.42,58.99,64.32,71.71,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,34492.05,38313.59,39531.61,38815.54,34676.38,25295.31,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29048.12,33502.62,35101.78,35223.59,32450.08,24379.10,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12686.70,14569.92,14628.36,12869.87,10647.96,3086.19,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10168.56,11838.83,12163.13,10602.54,8391.52,1223.97,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,0.00,0.00,5.09,56.67,136.88,247.01,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Primary Energy,EJ/yr,458.20,518.81,544.28,592.53,639.70,679.98,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,122.98,148.41,168.46,180.77,164.38,94.42,,,,, -AIM-Enduse 12.1,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,21.10,46.25,68.42,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,10540.74,13160.11,14149.89,16559.14,19658.68,23071.34,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9126.18,11910.02,12998.84,15625.80,19080.19,22833.27,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,133.56,168.74,188.32,227.21,275.41,326.92,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,LAM,Emissions|CO2,Mt CO2/yr,3285.00,3286.68,3449.84,3660.68,3850.44,3866.20,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1422.06,1640.34,1933.93,2431.49,3088.58,3552.67,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,LAM,Primary Energy,EJ/yr,27.40,31.35,35.98,44.78,55.52,64.47,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,MAF,Emissions|CO2,Mt CO2/yr,4302.21,4487.49,4371.98,4751.63,5389.48,6082.37,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2262.46,2684.91,2712.20,3405.79,4555.32,5739.09,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,MAF,Primary Energy,EJ/yr,42.68,50.12,51.85,64.44,84.12,105.05,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,12085.85,12744.16,12642.70,13332.29,13742.93,14150.35,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12000.81,12669.01,12573.50,13276.18,13708.16,14136.03,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,193.12,202.28,197.10,203.56,208.76,215.23,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,REF,Emissions|CO2,Mt CO2/yr,3306.95,3604.39,3838.82,4220.97,4866.31,5615.39,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3265.30,3567.58,3804.92,4193.48,4849.28,5608.38,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,REF,Primary Energy,EJ/yr,48.54,52.61,56.56,61.95,70.94,81.42,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,34492.05,38313.59,39612.60,43835.49,49027.80,54552.86,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29048.12,33502.62,35182.77,40243.54,46801.50,53636.66,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12686.70,14569.92,14676.46,16187.91,18347.82,20631.89,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10168.56,11838.83,12209.24,13804.27,15910.83,17935.58,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,458.20,518.81,545.24,619.43,715.12,816.88,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,122.98,148.41,168.91,203.63,239.73,274.45,,,,, -AIM-Enduse 12.1,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,ASIA,Emissions|CO2,Mt CO2/yr,10540.74,13152.56,13415.94,10147.89,7637.61,4435.80,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9126.18,11902.47,12264.89,9214.55,7059.13,4197.73,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,ASIA,Primary Energy,EJ/yr,133.56,168.68,176.70,158.38,175.97,214.79,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,LAM,Emissions|CO2,Mt CO2/yr,3285.00,3286.52,3106.39,2825.27,1784.31,899.06,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1422.06,1640.18,1590.48,1596.08,1022.46,585.53,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,LAM,Primary Energy,EJ/yr,27.40,31.35,31.20,33.42,41.91,48.99,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,MAF,Emissions|CO2,Mt CO2/yr,4302.21,4487.02,4091.19,3977.50,3659.80,3336.85,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2262.46,2684.44,2431.42,2631.66,2825.65,2993.57,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,MAF,Primary Energy,EJ/yr,42.68,50.12,47.28,52.42,63.42,74.44,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,OECD90,Emissions|CO2,Mt CO2/yr,12085.85,12750.81,10276.06,8833.95,5845.24,3473.56,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12000.81,12675.66,10206.87,8777.84,5810.47,3459.25,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,OECD90,Primary Energy,EJ/yr,193.12,202.25,166.47,152.43,146.52,140.48,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,REF,Emissions|CO2,Mt CO2/yr,3306.95,3596.74,3453.29,3468.73,3376.25,3058.68,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3265.30,3559.93,3419.39,3441.25,3359.22,3051.67,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,REF,Primary Energy,EJ/yr,48.54,52.54,51.16,51.59,55.14,59.27,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Emissions|CO2,Mt CO2/yr,34492.05,38304.41,35425.96,30395.43,23536.71,16487.83,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29048.12,33493.44,30996.14,26803.48,21310.42,15571.63,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12686.70,14560.75,12278.73,8348.83,5241.57,3370.82,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10168.56,11829.72,9951.84,6208.43,3488.80,2054.59,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Primary Energy,EJ/yr,458.20,518.64,487.22,463.48,499.48,555.22,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Primary Energy|Coal,EJ/yr,122.98,148.34,142.46,81.07,42.23,27.75,,,,, -AIM-Enduse 12.1,EMF27-G8-EERE,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,,,,, -GCAM 3.0,AMPERE3-450,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.13,38660.77,45110.97,44768.14,34990.09,19397.62,1208.73,-17387.30,-37099.22,-57844.17 -GCAM 3.0,AMPERE3-450,World,Price|Carbon,US$2005/t CO2,0.00,0.00,15.10,24.60,40.07,65.27,106.32,173.18,282.10,459.51,713.42 -GCAM 3.0,AMPERE3-450,World,Primary Energy,EJ/yr,460.41,504.35,618.51,743.09,848.71,935.92,1001.75,1091.21,1177.40,1281.61,1418.91 -GCAM 3.0,AMPERE3-450,World,Primary Energy|Coal,EJ/yr,120.76,144.95,176.44,204.42,212.84,186.02,138.23,106.98,82.44,36.55,14.89 -GCAM 3.0,AMPERE3-450,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,1.98,13.88,48.84,107.91,150.78,161.99,157.22,115.80,99.55 -GCAM 3.0,AMPERE3-450,World,Temperature|Global Mean|MAGICC6|MED,°C,0.72,0.79,1.02,1.32,1.65,1.96,2.21,2.35,2.37,2.30,2.12 -GCAM 3.0,AMPERE3-450P-CE,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.14,38603.90,46071.54,43844.74,34636.41,19108.64,1129.57,-17399.41,-37076.52,-57817.45 -GCAM 3.0,AMPERE3-450P-CE,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,2.52,33.91,65.26,106.32,173.16,282.08,459.50,713.40 -GCAM 3.0,AMPERE3-450P-CE,World,Primary Energy,EJ/yr,460.41,504.35,618.74,753.34,849.79,942.16,1005.56,1092.56,1177.72,1281.71,1418.59 -GCAM 3.0,AMPERE3-450P-CE,World,Primary Energy|Coal,EJ/yr,120.76,144.95,178.98,218.24,213.35,192.45,142.64,108.72,82.73,36.89,15.22 -GCAM 3.0,AMPERE3-450P-CE,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,2.70,14.02,49.45,110.02,154.39,163.98,158.30,117.07,100.43 -GCAM 3.0,AMPERE3-450P-CE,World,Temperature|Global Mean|MAGICC6|MED,°C,0.77,0.84,1.07,1.36,1.69,2.00,2.25,2.38,2.41,2.34,2.16 -GCAM 3.0,AMPERE3-450P-EU,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.14,39487.87,47419.61,45118.79,35081.54,19182.91,1166.07,-17384.21,-37079.51,-57832.34 -GCAM 3.0,AMPERE3-450P-EU,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,2.52,33.91,65.26,106.32,173.16,282.08,459.50,713.40 -GCAM 3.0,AMPERE3-450P-EU,World,Primary Energy,EJ/yr,460.41,504.35,624.50,769.43,857.29,943.23,1002.71,1089.48,1177.26,1282.19,1420.23 -GCAM 3.0,AMPERE3-450P-EU,World,Primary Energy|Coal,EJ/yr,120.76,144.95,189.86,241.25,224.25,191.70,136.72,102.51,80.72,35.70,14.45 -GCAM 3.0,AMPERE3-450P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,2.66,13.96,42.59,102.50,146.95,156.77,155.35,116.08,100.00 -GCAM 3.0,AMPERE3-450P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.79,0.86,1.09,1.39,1.72,2.03,2.28,2.42,2.45,2.38,2.20 -GCAM 3.0,AMPERE3-550,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.13,39660.52,47541.01,50744.18,46992.91,34172.78,17064.62,-2639.86,-21628.98,-42437.11 -GCAM 3.0,AMPERE3-550,World,Price|Carbon,US$2005/t CO2,0.00,0.00,10.30,16.78,27.33,44.52,72.51,118.11,192.39,313.39,486.56 -GCAM 3.0,AMPERE3-550,World,Primary Energy,EJ/yr,458.03,501.89,622.66,751.83,863.50,956.22,1007.66,1064.33,1146.19,1221.76,1331.16 -GCAM 3.0,AMPERE3-550,World,Primary Energy|Coal,EJ/yr,120.76,144.95,184.70,221.50,242.98,237.12,187.54,131.81,103.09,72.41,34.61 -GCAM 3.0,AMPERE3-550,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,1.52,9.76,32.75,75.68,119.13,151.33,159.48,144.44,112.71 -GCAM 3.0,AMPERE3-550,World,Temperature|Global Mean|MAGICC6|MED,°C,0.83,0.90,1.13,1.43,1.77,2.11,2.42,2.64,2.76,2.77,2.68 -GCAM 3.0,AMPERE3-Base-EUback,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.13,41826.41,52214.95,63459.29,75453.50,81730.83,86384.17,89308.29,92285.81,96090.28 -GCAM 3.0,AMPERE3-Base-EUback,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -GCAM 3.0,AMPERE3-Base-EUback,World,Primary Energy,EJ/yr,457.76,501.61,632.27,788.66,934.72,1073.33,1174.84,1261.38,1328.94,1395.49,1470.71 -GCAM 3.0,AMPERE3-Base-EUback,World,Primary Energy|Coal,EJ/yr,120.76,144.95,201.07,271.00,344.24,424.09,478.42,518.29,539.32,557.29,559.09 -GCAM 3.0,AMPERE3-Base-EUback,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,1.17,4.63,4.59,0.00,0.00,0.00,0.00,0.00,0.00 -GCAM 3.0,AMPERE3-Base-EUback,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.88,1.11,1.43,1.80,2.23,2.70,3.19,3.68,4.18,4.64 -GCAM 3.0,AMPERE3-CF450P-EU,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.13,41826.41,52214.95,49660.44,36881.63,19617.99,995.01,-17475.49,-37146.77,-57853.52 -GCAM 3.0,AMPERE3-CF450P-EU,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,32.63,65.26,106.32,173.16,282.08,459.50,713.40 -GCAM 3.0,AMPERE3-CF450P-EU,World,Primary Energy,EJ/yr,460.41,504.35,635.96,793.04,870.95,942.83,1001.18,1087.41,1176.79,1282.69,1420.85 -GCAM 3.0,AMPERE3-CF450P-EU,World,Primary Energy|Coal,EJ/yr,120.76,144.95,201.07,271.00,248.20,197.64,135.68,100.55,79.96,35.60,14.55 -GCAM 3.0,AMPERE3-CF450P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,1.17,4.63,30.50,91.62,138.88,152.58,154.43,115.22,99.12 -GCAM 3.0,AMPERE3-CF450P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.82,0.89,1.12,1.44,1.81,2.14,2.40,2.53,2.56,2.49,2.31 -GCAM 3.0,AMPERE3-RefPol,World,Emissions|CO2,Mt CO2/yr,31473.40,31678.14,39787.42,48131.28,52770.92,54537.50,54976.51,54792.56,53594.22,51287.98,49551.47 -GCAM 3.0,AMPERE3-RefPol,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,2.52,13.29,27.81,44.07,59.50,71.32,86.41,101.47 -GCAM 3.0,AMPERE3-RefPol,World,Primary Energy,EJ/yr,457.76,501.61,622.68,764.56,884.66,995.11,1080.88,1155.01,1214.59,1275.17,1325.32 -GCAM 3.0,AMPERE3-RefPol,World,Primary Energy|Coal,EJ/yr,120.76,144.95,191.80,243.44,273.15,291.68,308.12,315.06,313.88,304.55,288.94 -GCAM 3.0,AMPERE3-RefPol,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,2.31,12.49,29.51,56.97,75.64,85.91,92.81,101.06,97.59 -GCAM 3.0,AMPERE3-RefPol,World,Temperature|Global Mean|MAGICC6|MED,°C,0.80,0.87,1.11,1.41,1.74,2.09,2.44,2.78,3.10,3.41,3.72 -GCAM 3.0,EMF27-450-Conv,ASIA,Emissions|CO2,Mt CO2/yr,10579.48,12895.66,11695.80,11358.51,6779.00,5146.00,3466.85,1863.52,335.24,-877.69,-1471.87 -GCAM 3.0,EMF27-450-Conv,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9465.69,11873.82,12644.00,10853.94,7599.85,5713.16,3980.99,2458.41,1046.20,-65.79,-599.62 -GCAM 3.0,EMF27-450-Conv,ASIA,Primary Energy,EJ/yr,135.57,163.32,200.69,252.36,298.81,332.82,356.82,375.96,381.45,370.28,348.90 -GCAM 3.0,EMF27-450-Conv,LAM,Emissions|CO2,Mt CO2/yr,2553.32,2369.08,-3697.66,-3313.88,-1880.82,-1155.81,-927.70,-906.08,-1069.30,-1372.50,-2166.92 -GCAM 3.0,EMF27-450-Conv,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1475.06,1644.90,1625.92,1445.98,1113.67,784.85,457.91,143.49,-219.17,-653.15,-1544.30 -GCAM 3.0,EMF27-450-Conv,LAM,Primary Energy,EJ/yr,29.03,31.59,35.11,41.18,46.60,52.54,59.56,69.00,78.98,87.13,91.45 -GCAM 3.0,EMF27-450-Conv,MAF,Emissions|CO2,Mt CO2/yr,3417.12,3686.46,-4784.45,-3667.70,-847.70,162.93,439.69,458.65,283.85,-48.21,-348.48 -GCAM 3.0,EMF27-450-Conv,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2401.59,2805.56,3039.21,2854.74,2350.58,1875.85,1393.03,1019.21,688.85,257.41,-122.84 -GCAM 3.0,EMF27-450-Conv,MAF,Primary Energy,EJ/yr,48.27,56.05,69.38,84.47,97.47,111.44,125.78,138.61,147.25,153.87,158.31 -GCAM 3.0,EMF27-450-Conv,OECD90,Emissions|CO2,Mt CO2/yr,11886.56,12386.37,8905.57,7105.95,3795.78,2284.64,1365.37,718.45,-118.15,-724.60,-1424.06 -GCAM 3.0,EMF27-450-Conv,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12117.42,12399.31,11142.48,8756.15,5745.18,3846.77,2699.48,1730.14,791.65,-10.12,-752.44 -GCAM 3.0,EMF27-450-Conv,OECD90,Primary Energy,EJ/yr,195.21,197.67,193.52,192.79,190.17,190.11,186.72,178.63,167.51,153.66,140.84 -GCAM 3.0,EMF27-450-Conv,REF,Emissions|CO2,Mt CO2/yr,3037.14,3363.40,42.05,-360.90,-869.87,-949.20,-1054.32,-1111.13,-1183.24,-596.35,-718.26 -GCAM 3.0,EMF27-450-Conv,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3214.36,3567.49,3343.41,2776.01,1903.55,1306.06,821.52,422.91,106.47,-174.44,-404.36 -GCAM 3.0,EMF27-450-Conv,REF,Primary Energy,EJ/yr,50.73,55.96,57.31,59.99,61.14,62.31,62.79,62.33,59.64,57.09,55.44 -GCAM 3.0,EMF27-450-Conv,World,Emissions|CO2,Mt CO2/yr,31473.62,34700.97,12161.32,11121.98,6976.38,5488.54,3289.90,1023.41,-1751.60,-3619.36,-6129.59 -GCAM 3.0,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28674.11,32291.08,31795.02,26686.81,18712.83,13526.69,9352.92,5774.16,2413.99,-646.09,-3423.57 -GCAM 3.0,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,11963.94,13919.58,13711.32,9098.15,1834.65,-1758.12,-3879.84,-4969.07,-5738.85,-6365.90,-6184.62 -GCAM 3.0,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10184.51,11898.12,11992.37,8251.59,2198.30,-916.05,-2697.31,-3498.08,-4165.62,-4452.68,-3815.31 -GCAM 3.0,EMF27-450-Conv,World,Price|Carbon,US$2005/t CO2,0.00,0.00,40.19,65.46,106.63,173.68,282.91,460.84,750.66,1222.74,1898.38 -GCAM 3.0,EMF27-450-Conv,World,Primary Energy,EJ/yr,458.82,504.59,556.01,630.79,694.18,749.23,791.67,824.53,834.82,822.03,794.94 -GCAM 3.0,EMF27-450-Conv,World,Primary Energy|Coal,EJ/yr,120.76,146.60,130.35,120.71,111.69,117.59,118.03,113.35,92.42,60.22,26.71 -GCAM 3.0,EMF27-450-Conv,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.03,7.24,52.90,120.23,176.87,210.07,222.32,201.00,155.35,98.40 -GCAM 3.0,EMF27-450-Conv,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.06,1.17,1.29,1.37,1.42,1.46,1.48,1.48,1.46 -GCAM 3.0,EMF27-450-NoCCS,ASIA,Emissions|CO2,Mt CO2/yr,10579.48,12887.52,11543.89,12802.61,9395.33,6949.79,4323.91,2140.74,61.59,-1757.29,-3163.23 -GCAM 3.0,EMF27-450-NoCCS,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9465.69,11865.65,12567.74,12405.13,9784.48,7174.16,4710.03,2525.89,513.34,-1233.89,-2554.00 -GCAM 3.0,EMF27-450-NoCCS,ASIA,Primary Energy,EJ/yr,135.57,163.27,196.24,234.50,260.44,283.53,306.89,334.65,364.50,386.09,405.93 -GCAM 3.0,EMF27-450-NoCCS,LAM,Emissions|CO2,Mt CO2/yr,2553.32,2368.14,-3909.28,-3251.88,-1694.26,-767.95,-580.60,-643.46,-803.99,-1078.48,-1291.50 -GCAM 3.0,EMF27-450-NoCCS,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1475.06,1643.95,1610.45,1636.61,1312.13,1076.99,670.81,272.74,-85.49,-476.42,-758.29 -GCAM 3.0,EMF27-450-NoCCS,LAM,Primary Energy,EJ/yr,29.03,31.58,34.62,39.81,44.20,47.37,51.53,59.97,70.44,81.38,92.22 -GCAM 3.0,EMF27-450-NoCCS,MAF,Emissions|CO2,Mt CO2/yr,3417.12,3684.30,-4926.84,-3438.25,-181.13,850.19,739.71,311.15,-368.94,-1173.42,-2057.29 -GCAM 3.0,EMF27-450-NoCCS,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2401.59,2803.40,3004.11,3134.95,2989.90,2493.84,1639.87,840.86,-20.27,-908.60,-1836.32 -GCAM 3.0,EMF27-450-NoCCS,MAF,Primary Energy,EJ/yr,48.27,56.03,68.47,81.75,92.18,101.15,111.95,129.15,150.18,170.90,200.40 -GCAM 3.0,EMF27-450-NoCCS,OECD90,Emissions|CO2,Mt CO2/yr,11886.56,12383.07,8595.39,7343.12,5217.96,3850.81,2538.33,1449.84,338.39,-504.21,-1237.98 -GCAM 3.0,EMF27-450-NoCCS,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12117.42,12396.00,10914.33,9100.15,6998.40,5211.67,3674.33,2210.14,968.14,-92.06,-859.71 -GCAM 3.0,EMF27-450-NoCCS,OECD90,Primary Energy,EJ/yr,195.21,197.64,190.45,185.64,175.74,168.23,161.23,154.96,147.64,141.14,139.78 -GCAM 3.0,EMF27-450-NoCCS,REF,Emissions|CO2,Mt CO2/yr,3037.14,3362.26,-65.11,-346.35,-625.82,-539.91,-703.68,-790.95,-891.21,-319.48,-486.19 -GCAM 3.0,EMF27-450-NoCCS,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3214.36,3566.35,3290.17,2849.77,2141.27,1634.17,1063.04,592.99,247.72,-38.93,-271.59 -GCAM 3.0,EMF27-450-NoCCS,REF,Primary Energy,EJ/yr,50.73,55.95,56.43,57.59,55.83,54.45,53.27,54.28,54.50,55.40,59.28 -GCAM 3.0,EMF27-450-NoCCS,World,Emissions|CO2,Mt CO2/yr,31473.62,34685.29,11238.06,13109.25,12112.07,10342.93,6317.67,2467.32,-1664.16,-4832.87,-8236.19 -GCAM 3.0,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28674.11,32275.35,31386.80,29126.62,23226.18,17590.84,11758.08,6442.62,1623.44,-2749.90,-6279.90 -GCAM 3.0,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,11963.94,13900.88,13392.88,10743.78,5826.34,2174.45,-1243.63,-3798.41,-5912.97,-7883.72,-9630.72 -GCAM 3.0,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10184.51,11879.50,11691.33,9534.34,5465.51,3069.37,1386.22,892.57,654.31,400.35,218.61 -GCAM 3.0,EMF27-450-NoCCS,World,Price|Carbon,US$2005/t CO2,0.00,0.00,45.30,73.80,120.21,195.80,318.94,519.52,846.25,1378.45,2140.14 -GCAM 3.0,EMF27-450-NoCCS,World,Primary Energy,EJ/yr,458.82,504.45,546.22,599.28,628.39,654.74,684.87,733.00,787.27,834.91,897.61 -GCAM 3.0,EMF27-450-NoCCS,World,Primary Energy|Coal,EJ/yr,120.76,146.48,120.21,86.45,38.15,16.54,9.28,6.82,6.08,4.77,0.01 -GCAM 3.0,EMF27-450-NoCCS,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.03,0.06,0.09,0.11,0.13,0.13,0.10,0.09,0.05,0.00 -GCAM 3.0,EMF27-450-NoCCS,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.07,1.17,1.30,1.40,1.47,1.52,1.53,1.52,1.49 -GCAM 3.0,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,10579.48,12887.52,14115.35,17022.20,13967.14,9888.24,6897.94,5364.43,4181.83,3304.53,2651.28 -GCAM 3.0,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9465.69,11865.65,14476.24,16389.26,14387.01,10322.91,7408.29,5996.96,4883.82,4051.53,3428.49 -GCAM 3.0,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,135.57,163.27,210.19,266.39,313.43,346.42,369.30,390.36,413.16,424.25,419.14 -GCAM 3.0,EMF27-550-LimBio,LAM,Emissions|CO2,Mt CO2/yr,2553.32,2368.14,-2317.65,-2030.61,-1062.81,-630.26,-596.68,-536.67,-430.27,-384.31,-415.81 -GCAM 3.0,EMF27-550-LimBio,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1475.06,1643.95,1784.07,1895.64,1704.41,1407.18,1043.31,763.76,565.92,388.55,201.05 -GCAM 3.0,EMF27-550-LimBio,LAM,Primary Energy,EJ/yr,29.03,31.58,36.04,42.20,48.08,53.98,60.41,71.03,83.96,96.67,107.09 -GCAM 3.0,EMF27-550-LimBio,MAF,Emissions|CO2,Mt CO2/yr,3417.12,3684.30,-3698.31,-2400.19,218.89,1117.70,1210.17,1294.55,1413.30,1560.36,1614.95 -GCAM 3.0,EMF27-550-LimBio,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2401.59,2803.40,3327.90,3707.01,3440.87,2977.12,2341.77,2004.29,1911.85,1905.99,1863.85 -GCAM 3.0,EMF27-550-LimBio,MAF,Primary Energy,EJ/yr,48.27,56.03,71.07,87.09,101.48,115.31,129.69,146.42,160.74,171.11,183.14 -GCAM 3.0,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,11886.56,12383.07,10348.10,10015.95,7673.18,4855.63,3314.12,2517.07,1995.87,1803.63,1664.01 -GCAM 3.0,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12117.42,12396.00,12096.87,11440.53,9239.03,6227.14,4577.64,3570.54,2928.20,2516.95,2288.52 -GCAM 3.0,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,195.21,197.64,199.79,203.64,200.78,196.24,193.24,191.36,187.41,182.16,185.30 -GCAM 3.0,EMF27-550-LimBio,REF,Emissions|CO2,Mt CO2/yr,3037.14,3362.26,722.38,669.65,389.65,-61.06,-412.51,-541.34,-555.02,161.99,315.26 -GCAM 3.0,EMF27-550-LimBio,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3214.36,3566.35,3650.49,3595.26,3014.33,2134.77,1479.05,1049.18,779.63,678.47,685.16 -GCAM 3.0,EMF27-550-LimBio,REF,Primary Energy,EJ/yr,50.73,55.95,59.79,63.84,65.65,65.95,65.09,65.97,66.08,65.62,70.16 -GCAM 3.0,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,31473.62,34685.29,19169.88,23277.01,21186.04,15170.25,10413.05,8098.03,6605.72,6446.21,5829.69 -GCAM 3.0,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28674.11,32275.35,35335.58,37027.69,31785.65,23069.12,16850.07,13384.73,11069.42,9541.48,8467.08 -GCAM 3.0,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,11963.94,13900.88,15276.28,15787.23,11362.52,3908.63,-830.44,-3101.46,-4276.87,-4950.79,-5575.23 -GCAM 3.0,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10184.51,11879.50,13178.67,13850.31,10187.61,3692.27,-362.46,-1943.61,-2747.55,-3178.70,-3119.35 -GCAM 3.0,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,0.00,0.00,20.68,33.69,54.87,89.38,145.59,206.61,277.65,348.70,419.74 -GCAM 3.0,EMF27-550-LimBio,World,Primary Energy,EJ/yr,458.82,504.45,576.89,663.16,729.41,777.90,817.73,865.14,911.34,939.80,964.83 -GCAM 3.0,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,120.76,146.48,152.96,164.35,150.79,126.73,116.50,114.85,115.93,110.20,112.60 -GCAM 3.0,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.03,2.14,18.30,59.09,116.61,156.88,176.26,188.95,183.44,179.97 -GCAM 3.0,EMF27-550-LimBio,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.88,1.06,1.20,1.39,1.55,1.67,1.74,1.80,1.84,1.89 -GCAM 3.0,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,10579.48,12887.52,18632.25,24145.73,28659.06,32966.15,36148.01,38312.50,39483.38,39586.94,38153.80 -GCAM 3.0,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9465.69,11865.65,17225.08,22668.66,27726.77,32101.17,35709.04,38191.08,39579.64,39783.93,38475.88 -GCAM 3.0,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,135.57,163.27,230.04,303.67,375.35,443.14,502.88,551.48,588.02,607.14,610.39 -GCAM 3.0,EMF27-Base-FullTech,LAM,Emissions|CO2,Mt CO2/yr,2553.32,2368.14,2768.74,3156.74,3346.44,3740.06,3931.27,4388.97,5047.87,5700.86,6138.32 -GCAM 3.0,EMF27-Base-FullTech,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1475.06,1643.95,1991.69,2376.85,2800.32,3277.30,3734.04,4354.02,5091.13,5798.76,6318.59 -GCAM 3.0,EMF27-Base-FullTech,LAM,Primary Energy,EJ/yr,29.03,31.58,37.74,45.32,53.14,62.14,71.38,83.96,99.32,114.73,127.16 -GCAM 3.0,EMF27-Base-FullTech,MAF,Emissions|CO2,Mt CO2/yr,3417.12,3684.30,4663.55,5564.81,6539.66,7810.02,8609.39,9748.36,11015.22,12119.42,13155.77 -GCAM 3.0,EMF27-Base-FullTech,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2401.59,2803.40,3760.11,4710.85,5783.85,7107.99,8314.61,9690.33,11080.73,12271.07,13407.34 -GCAM 3.0,EMF27-Base-FullTech,MAF,Primary Energy,EJ/yr,48.27,56.03,74.25,93.50,112.98,135.64,159.39,186.43,213.14,235.64,257.62 -GCAM 3.0,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,11886.56,12383.07,13174.50,13813.53,14049.70,14492.54,14758.92,14788.33,14765.86,14857.80,15033.27 -GCAM 3.0,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12117.42,12396.00,13009.45,13445.96,13856.23,14268.99,14583.64,14745.46,14820.50,14931.13,15140.38 -GCAM 3.0,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,195.21,197.64,207.63,217.85,225.18,231.90,235.42,236.62,236.06,235.17,236.49 -GCAM 3.0,EMF27-Base-FullTech,REF,Emissions|CO2,Mt CO2/yr,3037.14,3362.26,4079.43,4552.55,4713.51,4978.87,4934.01,4886.82,4935.51,4941.59,5108.01 -GCAM 3.0,EMF27-Base-FullTech,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3214.36,3566.35,4043.50,4377.68,4647.79,4875.71,4854.17,4837.98,4869.16,4933.64,5124.61 -GCAM 3.0,EMF27-Base-FullTech,REF,Primary Energy,EJ/yr,50.73,55.95,63.44,69.68,74.87,79.63,80.62,81.41,82.38,84.12,87.93 -GCAM 3.0,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,31473.62,34685.29,43318.48,51233.36,57308.36,63987.64,68381.59,72124.98,75247.84,77206.61,77589.17 -GCAM 3.0,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28674.11,32275.35,40029.84,47579.99,54814.96,61631.16,67195.51,71818.87,75441.15,77718.53,78466.79 -GCAM 3.0,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,11963.94,13900.88,17440.71,21676.38,26038.88,30539.69,33650.36,35996.28,37865.02,38911.09,38689.60 -GCAM 3.0,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10184.51,11879.50,14769.66,18164.19,21284.54,24075.09,25735.44,26670.92,27601.09,28454.49,29074.67 -GCAM 3.0,EMF27-Base-FullTech,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -GCAM 3.0,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,458.82,504.45,613.10,730.03,841.52,952.45,1049.69,1139.91,1218.93,1276.80,1319.59 -GCAM 3.0,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,120.76,146.48,192.62,243.82,296.82,352.59,400.30,437.52,463.42,475.25,464.06 -GCAM 3.0,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.03,0.05,0.09,0.12,0.16,0.20,0.25,0.27,0.29,0.29 -GCAM 3.0,EMF27-Base-FullTech,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.87,1.11,1.41,1.77,2.15,2.55,2.96,3.36,3.76,4.16 -IMAGE 2.4,AMPERE3-450,World,Emissions|CO2,Mt CO2/yr,34111.29,35344.27,36059.37,32234.66,19608.88,15150.28,6668.57,-677.70,-3156.89,-6273.64,-7112.29 -IMAGE 2.4,AMPERE3-450,World,Price|Carbon,US$2005/t CO2,0.00,0.66,47.13,66.73,76.80,109.31,130.10,162.86,188.72,223.19,211.20 -IMAGE 2.4,AMPERE3-450,World,Primary Energy,EJ/yr,441.25,473.91,544.13,577.42,638.17,685.99,751.05,763.70,778.29,821.22,863.04 -IMAGE 2.4,AMPERE3-450,World,Primary Energy|Coal,EJ/yr,111.62,138.69,148.60,121.24,102.62,101.41,111.41,138.40,181.03,224.03,264.77 -IMAGE 2.4,AMPERE3-450,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.84,18.09,64.24,118.63,167.20,212.15,270.53,309.41,347.80 -IMAGE 2.4,AMPERE3-450,World,Temperature|Global Mean|MAGICC6|MED,°C,0.87,0.92,1.14,1.39,1.59,1.72,1.80,1.83,1.84,1.82,1.78 -IMAGE 2.4,AMPERE3-450P-CE,World,Emissions|CO2,Mt CO2/yr,34111.14,35343.93,38844.85,40453.35,31255.38,21628.42,13883.06,5169.80,-2754.38,-6409.44,-8174.45 -IMAGE 2.4,AMPERE3-450P-CE,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -IMAGE 2.4,AMPERE3-450P-CE,World,Primary Energy,EJ/yr,441.25,473.91,580.13,654.46,676.13,695.56,750.32,766.26,792.33,837.14,879.08 -IMAGE 2.4,AMPERE3-450P-CE,World,Primary Energy|Coal,EJ/yr,111.62,138.69,161.72,154.18,125.14,105.32,120.83,151.58,192.67,249.50,294.40 -IMAGE 2.4,AMPERE3-450P-CE,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.57,4.51,22.20,67.16,145.10,210.40,267.38,333.57,374.04 -IMAGE 2.4,AMPERE3-450P-CE,World,Temperature|Global Mean|MAGICC6|MED,°C,0.79,0.84,1.06,1.33,1.59,1.78,1.91,1.98,2.01,1.99,1.95 -IMAGE 2.4,AMPERE3-450P-EU,World,Emissions|CO2,Mt CO2/yr,34111.14,35343.93,40612.22,46400.38,37347.97,25392.08,17060.16,7214.90,-2789.63,-6821.32,-7928.68 -IMAGE 2.4,AMPERE3-450P-EU,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,108.28,128.76,161.29,186.78,221.30,209.21 -IMAGE 2.4,AMPERE3-450P-EU,World,Primary Energy,EJ/yr,441.25,473.91,598.26,696.60,692.53,699.38,747.23,763.69,791.18,836.82,879.48 -IMAGE 2.4,AMPERE3-450P-EU,World,Primary Energy|Coal,EJ/yr,111.62,138.69,173.83,193.98,153.90,122.29,131.70,153.42,187.40,249.47,300.00 -IMAGE 2.4,AMPERE3-450P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.56,2.07,16.30,47.17,122.11,197.41,259.28,328.35,374.52 -IMAGE 2.4,AMPERE3-450P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.80,0.85,1.07,1.35,1.65,1.87,2.02,2.11,2.15,2.13,2.09 -IMAGE 2.4,AMPERE3-550,World,Emissions|CO2,Mt CO2/yr,34111.29,35318.50,37365.86,37226.82,32352.89,30493.37,22154.07,12673.92,7180.83,909.23,-2504.16 -IMAGE 2.4,AMPERE3-550,World,Price|Carbon,US$2005/t CO2,0.00,0.72,21.68,35.25,38.77,40.03,47.03,73.74,110.20,110.20,96.36 -IMAGE 2.4,AMPERE3-550,World,Primary Energy,EJ/yr,441.25,473.79,559.87,603.66,689.49,758.18,815.96,844.50,862.31,905.22,942.64 -IMAGE 2.4,AMPERE3-550,World,Primary Energy|Coal,EJ/yr,111.62,138.64,156.04,137.59,123.93,116.23,113.90,137.33,187.18,256.33,322.07 -IMAGE 2.4,AMPERE3-550,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,9.58,29.96,65.53,115.25,171.92,237.14,317.31,383.77 -IMAGE 2.4,AMPERE3-550,World,Temperature|Global Mean|MAGICC6|MED,°C,0.76,0.82,1.03,1.29,1.54,1.75,1.94,2.07,2.16,2.18,2.19 -IMAGE 2.4,AMPERE3-RefPol,World,Emissions|CO2,Mt CO2/yr,34124.32,35746.98,40855.54,46771.34,48448.44,51487.30,48906.51,43724.96,40676.94,36602.42,32884.37 -IMAGE 2.4,AMPERE3-RefPol,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,21.93,46.41,54.97,59.43,62.01,71.13,83.86,101.85 -IMAGE 2.4,AMPERE3-RefPol,World,Primary Energy,EJ/yr,441.25,473.91,600.73,699.99,804.90,871.53,925.99,997.16,1054.42,1110.85,1166.97 -IMAGE 2.4,AMPERE3-RefPol,World,Primary Energy|Coal,EJ/yr,111.62,138.69,174.83,195.40,202.72,198.87,195.17,227.89,293.07,373.46,462.00 -IMAGE 2.4,AMPERE3-RefPol,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.22,0.99,2.42,6.92,22.26,67.78,137.19,211.06,292.60 -IMAGE 2.4,AMPERE3-RefPol,World,Temperature|Global Mean|MAGICC6|MED,°C,0.88,0.93,1.14,1.42,1.72,2.02,2.31,2.58,2.84,3.07,3.27 -IMAGE 2.4,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,9876.96,12317.63,15977.12,15978.17,13782.78,11363.91,8466.82,5300.11,4109.71,3089.31,2071.34 -IMAGE 2.4,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9005.85,11840.19,15892.96,16042.63,14006.61,10981.75,8206.71,5742.29,4715.92,3637.76,3143.81 -IMAGE 2.4,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,124.97,158.78,210.87,232.27,265.68,280.51,302.73,322.02,353.54,373.11,380.50 -IMAGE 2.4,EMF27-550-LimBio,LAM,Emissions|CO2,Mt CO2/yr,3041.94,4224.06,1007.75,2296.82,2065.26,1818.44,1455.57,847.61,470.72,361.52,114.99 -IMAGE 2.4,EMF27-550-LimBio,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1591.95,1600.61,1932.33,2104.06,2109.02,1902.11,1750.96,1430.08,1205.88,1101.28,969.70 -IMAGE 2.4,EMF27-550-LimBio,LAM,Primary Energy,EJ/yr,28.32,29.24,34.73,39.87,49.66,56.28,64.41,70.96,78.93,83.60,85.08 -IMAGE 2.4,EMF27-550-LimBio,MAF,Emissions|CO2,Mt CO2/yr,5282.32,3489.70,5358.70,5747.01,3738.94,4233.17,4109.44,3306.26,3222.54,2377.13,1544.10 -IMAGE 2.4,EMF27-550-LimBio,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2337.23,2647.89,3040.71,3307.77,3566.47,3430.65,3452.04,3160.40,2919.03,2468.67,2005.09 -IMAGE 2.4,EMF27-550-LimBio,MAF,Primary Energy,EJ/yr,41.16,46.69,53.40,60.47,74.89,90.43,115.56,138.83,168.34,193.84,212.19 -IMAGE 2.4,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,12429.84,11599.41,10663.21,8915.51,6826.21,4599.45,3255.11,1879.47,1509.92,948.43,861.04 -IMAGE 2.4,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12666.48,12033.86,11096.64,9350.51,7053.95,4597.04,3141.83,1810.07,1453.40,1045.28,900.39 -IMAGE 2.4,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,194.63,183.99,180.88,172.48,171.98,167.21,165.82,163.52,164.84,162.49,156.15 -IMAGE 2.4,EMF27-550-LimBio,REF,Emissions|CO2,Mt CO2/yr,3628.20,3963.54,4286.15,3530.15,2497.52,2180.21,2243.60,1974.13,1643.44,1420.29,1392.89 -IMAGE 2.4,EMF27-550-LimBio,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3561.09,3848.09,3944.30,3321.75,2355.16,1780.31,1488.20,1174.95,999.18,846.94,676.84 -IMAGE 2.4,EMF27-550-LimBio,REF,Primary Energy,EJ/yr,52.17,55.47,57.61,51.94,47.67,45.36,44.25,42.71,42.46,40.46,39.34 -IMAGE 2.4,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,34259.27,35594.34,37292.93,36467.66,28910.72,24195.19,19530.54,13307.58,10956.33,8196.68,5984.36 -IMAGE 2.4,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29162.60,31970.63,35906.95,34126.72,29091.21,22691.86,18039.74,13317.79,11293.41,9099.93,7695.83 -IMAGE 2.4,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12827.82,14377.10,16493.37,15246.98,10437.35,5150.42,2033.36,826.67,1958.74,1081.38,252.96 -IMAGE 2.4,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10624.05,12250.78,14339.66,13255.98,8443.08,3306.33,331.23,-530.35,956.12,350.71,-318.63 -IMAGE 2.4,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,0.00,0.73,28.41,47.08,61.69,87.31,101.67,122.25,140.04,144.68,141.76 -IMAGE 2.4,EMF27-550-LimBio,World,Primary Energy,EJ/yr,441.25,474.16,537.49,557.02,609.88,639.80,692.77,738.05,808.10,853.49,873.27 -IMAGE 2.4,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,111.62,138.99,158.85,140.09,118.53,113.34,124.43,163.33,260.68,334.20,374.28 -IMAGE 2.4,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,8.50,40.46,96.12,154.60,221.12,336.58,410.66,441.75 -IMAGE 2.4,EMF27-550-LimBio,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.86,1.08,1.34,1.57,1.74,1.88,1.98,2.05,2.11,2.15 -IMAGE 2.4,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,9603.68,12152.18,17221.64,20718.73,24306.43,27761.78,31509.72,33739.59,36602.92,40359.66,41556.90 -IMAGE 2.4,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9005.85,11839.85,17453.02,21061.92,24781.75,27776.48,31351.52,34667.57,38426.73,41130.97,42955.45 -IMAGE 2.4,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,124.97,158.79,229.81,279.92,342.70,397.36,451.31,494.96,533.25,557.89,573.83 -IMAGE 2.4,EMF27-Base-FullTech,LAM,Emissions|CO2,Mt CO2/yr,2960.36,4741.04,695.89,2672.12,3245.95,3731.67,4082.85,4273.66,4618.59,5293.36,6156.89 -IMAGE 2.4,EMF27-Base-FullTech,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1591.95,1601.84,2147.54,2750.49,3453.89,3930.25,4528.68,5226.65,5787.59,6757.41,7875.32 -IMAGE 2.4,EMF27-Base-FullTech,LAM,Primary Energy,EJ/yr,28.32,29.27,38.03,47.65,62.21,75.77,88.54,97.94,104.51,109.04,111.04 -IMAGE 2.4,EMF27-Base-FullTech,MAF,Emissions|CO2,Mt CO2/yr,5718.71,2852.81,6371.76,6327.28,5730.02,8552.68,9989.70,11876.79,15327.44,16846.21,19214.49 -IMAGE 2.4,EMF27-Base-FullTech,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2337.23,2648.16,3379.04,4355.42,5796.01,7532.51,9743.16,12191.54,14843.21,17530.38,20151.88 -IMAGE 2.4,EMF27-Base-FullTech,MAF,Primary Energy,EJ/yr,41.16,46.69,57.90,72.46,95.22,123.04,162.42,200.27,236.98,266.68,288.21 -IMAGE 2.4,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,12501.20,11574.44,11519.36,10850.46,11342.25,11636.13,11953.41,11622.07,12042.04,12345.95,12229.96 -IMAGE 2.4,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12666.48,12035.43,11927.89,11684.85,11648.74,11723.42,11819.16,11826.87,12419.99,13011.42,12954.15 -IMAGE 2.4,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,194.63,184.04,192.10,197.11,203.92,208.11,207.71,203.08,200.45,196.97,191.63 -IMAGE 2.4,EMF27-Base-FullTech,REF,Emissions|CO2,Mt CO2/yr,3607.44,3925.83,4575.89,4188.66,4046.56,4238.40,4351.49,4236.71,3978.19,3493.29,3616.53 -IMAGE 2.4,EMF27-Base-FullTech,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3561.09,3848.17,4315.91,4061.72,3919.28,3893.65,3729.54,3572.01,3415.49,3110.35,3036.98 -IMAGE 2.4,EMF27-Base-FullTech,REF,Primary Energy,EJ/yr,52.17,55.47,62.48,60.97,60.25,60.99,60.74,58.65,56.60,53.99,52.58 -IMAGE 2.4,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,34391.39,35246.31,40384.52,44757.28,48671.22,55920.66,61887.17,65748.83,72569.20,78338.48,82774.78 -IMAGE 2.4,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29162.60,31973.46,39223.40,43914.38,49599.67,54856.31,61172.06,67484.66,74893.01,81540.53,86973.77 -IMAGE 2.4,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,12827.82,14377.78,18086.41,20232.12,23041.89,26763.43,32839.41,42701.97,54858.59,64383.91,71701.07 -IMAGE 2.4,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10624.05,12250.76,15727.51,17772.13,20346.92,23915.83,30093.30,40278.02,53114.11,63229.32,70866.05 -IMAGE 2.4,EMF27-Base-FullTech,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -IMAGE 2.4,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,441.25,474.26,580.31,658.11,764.29,865.28,970.72,1054.90,1131.78,1184.57,1217.28 -IMAGE 2.4,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,111.62,138.99,177.68,193.91,214.05,239.36,289.02,374.63,505.73,627.07,730.09 -IMAGE 2.4,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -IMAGE 2.4,EMF27-Base-FullTech,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.86,1.09,1.38,1.68,2.00,2.33,2.70,3.10,3.56,4.07 -MERGE_EMF27,EMF27-450-Conv,ASIA,Emissions|CO2,Mt CO2/yr,9322.05,12318.09,13553.77,11003.99,6588.85,1892.59,-289.69,-1353.86,-1982.84,-1446.10,-1365.91 -MERGE_EMF27,EMF27-450-Conv,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9322.05,12318.09,13553.77,11003.99,6588.85,1892.59,-289.69,-1353.86,-1982.84,-1446.10,-1365.91 -MERGE_EMF27,EMF27-450-Conv,ASIA,Primary Energy,EJ/yr,109.58,143.95,165.43,178.88,210.66,241.49,258.30,277.64,292.41,295.86,304.28 -MERGE_EMF27,EMF27-450-Conv,OECD90,Emissions|CO2,Mt CO2/yr,12663.79,13066.99,8591.75,3831.06,17.79,-2604.38,-2614.76,-2522.19,-2617.50,-2728.29,-2776.12 -MERGE_EMF27,EMF27-450-Conv,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12663.79,13066.99,8591.75,3831.06,17.79,-2604.38,-2614.76,-2522.19,-2617.50,-2728.29,-2776.12 -MERGE_EMF27,EMF27-450-Conv,OECD90,Primary Energy,EJ/yr,207.86,216.96,180.78,154.76,151.43,151.00,144.27,143.12,147.15,154.22,162.80 -MERGE_EMF27,EMF27-450-Conv,World,Emissions|CO2,Mt CO2/yr,28501.22,33303.16,30506.60,22718.89,13174.43,4174.12,-628.46,-2784.94,-4736.63,-5526.81,-6008.83 -MERGE_EMF27,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28501.22,33303.16,30506.60,22718.89,13174.43,4174.12,-628.46,-2784.94,-4736.63,-5526.81,-6008.83 -MERGE_EMF27,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,9500.06,11018.96,7160.83,1537.57,-1522.26,-4407.57,-5772.41,-6569.27,-6987.55,-7239.05,-7391.15 -MERGE_EMF27,EMF27-450-Conv,World,Price|Carbon,US$2005/t CO2,,,236.33,389.26,622.35,1010.98,1630.84,2655.96,4366.76,7416.75,6013.14 -MERGE_EMF27,EMF27-450-Conv,World,Primary Energy,EJ/yr,410.59,466.52,455.07,443.22,478.58,517.84,542.97,578.67,616.68,651.48,686.46 -MERGE_EMF27,EMF27-450-Conv,World,Primary Energy|Coal,EJ/yr,122.41,145.30,121.04,67.69,51.68,17.40,7.75,3.98,2.39,1.43,0.86 -MERGE_EMF27,EMF27-450-Conv,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,4.19,20.26,38.40,59.43,50.75,30.39,18.19,10.89,6.52 -MERGE_EMF27,EMF27-450-Conv,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.13,1.35,1.52,1.60,1.64,1.66,1.66,1.66,1.66 -MERGE_EMF27,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,9322.05,12318.09,16147.04,17648.93,13566.46,9516.50,5566.86,3654.24,2251.84,4388.75,3670.81 -MERGE_EMF27,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9322.05,12318.09,16147.04,17648.93,13566.46,9516.50,5566.86,3654.24,2251.84,4388.75,3670.81 -MERGE_EMF27,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,109.58,143.95,192.57,248.79,300.92,323.61,356.60,385.74,391.72,406.06,422.84 -MERGE_EMF27,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,12663.79,13066.99,10612.61,6435.14,3437.40,592.55,-1130.18,-1193.52,-1212.78,-657.81,-644.91 -MERGE_EMF27,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12663.79,13066.99,10612.61,6435.14,3437.40,592.55,-1130.18,-1193.52,-1212.78,-657.81,-644.91 -MERGE_EMF27,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,207.86,216.96,197.04,176.35,165.83,160.03,160.55,165.18,172.02,181.48,193.25 -MERGE_EMF27,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,28501.22,33303.16,36019.89,34031.54,26705.70,17872.93,10598.82,7675.84,4542.86,6515.47,5797.86 -MERGE_EMF27,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28501.22,33303.16,36019.89,34031.54,26705.70,17872.93,10598.82,7675.84,4542.86,6515.47,5797.86 -MERGE_EMF27,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,9500.06,11018.96,10743.34,6639.25,1264.84,-2415.06,-4040.74,-4883.94,-5572.68,-5812.19,-5849.43 -MERGE_EMF27,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,,,54.90,93.11,156.25,265.67,294.32,265.80,286.09,249.24,245.88 -MERGE_EMF27,EMF27-550-LimBio,World,Primary Energy,EJ/yr,410.59,466.52,506.70,556.82,613.85,641.45,703.27,761.91,788.10,833.97,890.62 -MERGE_EMF27,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,122.41,145.30,164.83,156.93,160.30,155.52,167.60,181.42,163.79,201.88,227.82 -MERGE_EMF27,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,4.39,20.68,44.92,73.53,114.07,141.57,130.30,142.82,162.72 -MERGE_EMF27,EMF27-550-LimBio,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.13,1.37,1.56,1.71,1.82,1.89,1.96,2.01,2.07 -MERGE_EMF27,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,9322.05,12318.09,19174.76,27922.72,34129.78,38970.71,41691.88,45537.22,47291.05,50413.24,54752.47 -MERGE_EMF27,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9322.05,12318.09,19174.76,27922.72,34129.78,38970.71,41691.88,45537.22,47291.05,50413.24,54752.47 -MERGE_EMF27,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,109.58,143.95,221.26,327.33,412.48,490.07,537.10,582.21,620.08,639.66,670.48 -MERGE_EMF27,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,12663.79,13066.99,13555.99,13619.92,13552.81,14264.63,17145.37,18670.63,20636.20,22094.18,23809.53 -MERGE_EMF27,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12663.79,13066.99,13555.99,13619.92,13552.81,14264.63,17145.37,18670.63,20636.20,22094.18,23809.53 -MERGE_EMF27,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,207.86,216.96,214.58,216.07,225.05,223.08,238.65,252.39,270.88,294.83,323.04 -MERGE_EMF27,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,28501.22,33303.16,43021.90,54681.24,64214.82,73116.83,81405.99,90072.94,98476.95,108882.84,120493.31 -MERGE_EMF27,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28501.22,33303.16,43021.90,54681.24,64214.82,73116.83,81405.99,90072.94,98476.95,108882.84,120493.31 -MERGE_EMF27,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,9500.06,11018.96,15670.45,22125.02,28032.50,31766.44,34881.12,38322.17,41168.64,44154.27,47395.33 -MERGE_EMF27,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,410.59,466.52,563.32,700.78,830.91,948.52,1063.25,1180.58,1275.24,1359.75,1467.02 -MERGE_EMF27,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,122.41,145.30,226.33,331.57,463.22,605.76,745.35,870.21,980.95,1116.21,1261.61 -MERGE_EMF27,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -MERGE_EMF27,EMF27-Base-FullTech,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.12,1.40,1.70,2.03,2.38,2.77,3.21,3.68,4.18 -MERGE_EMF27,EMF27-G8-EERE,ASIA,Emissions|CO2,Mt CO2/yr,9322.05,12318.09,13821.83,12300.49,8032.56,3222.63,1071.90,410.68,181.95,152.81,134.03 -MERGE_EMF27,EMF27-G8-EERE,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9322.05,12318.09,13821.83,12300.49,8032.56,3222.63,1071.90,410.68,181.95,152.81,134.03 -MERGE_EMF27,EMF27-G8-EERE,ASIA,Primary Energy,EJ/yr,109.58,143.95,169.11,192.21,225.01,235.98,242.14,244.93,239.76,232.63,227.61 -MERGE_EMF27,EMF27-G8-EERE,OECD90,Emissions|CO2,Mt CO2/yr,12663.79,13066.99,8657.91,4740.74,987.66,351.15,164.60,117.69,93.93,79.95,71.77 -MERGE_EMF27,EMF27-G8-EERE,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12663.79,13066.99,8657.91,4740.74,987.66,351.15,164.60,117.69,93.93,79.95,71.77 -MERGE_EMF27,EMF27-G8-EERE,OECD90,Primary Energy,EJ/yr,207.86,216.96,177.44,152.87,149.64,140.01,125.28,121.37,121.03,122.93,125.90 -MERGE_EMF27,EMF27-G8-EERE,World,Emissions|CO2,Mt CO2/yr,28501.22,33303.16,31636.36,27381.84,19962.34,14278.68,10533.13,9599.27,9211.55,8061.24,8393.79 -MERGE_EMF27,EMF27-G8-EERE,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,28501.22,33303.16,31636.36,27381.84,19962.34,14278.68,10533.13,9599.27,9211.55,8061.24,8393.79 -MERGE_EMF27,EMF27-G8-EERE,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,9500.06,11018.96,7554.62,4148.89,3166.14,2366.45,1947.82,2194.08,2149.00,1613.56,1722.84 -MERGE_EMF27,EMF27-G8-EERE,World,Price|Carbon,US$2005/t CO2,,,149.79,226.00,324.19,456.26,625.61,856.41,1228.99,2028.75,3093.77 -MERGE_EMF27,EMF27-G8-EERE,World,Primary Energy,EJ/yr,410.59,466.52,459.86,469.60,508.35,519.94,531.36,563.62,568.90,550.30,563.39 -MERGE_EMF27,EMF27-G8-EERE,World,Primary Energy|Coal,EJ/yr,122.41,145.30,117.37,77.87,50.22,17.82,14.07,20.92,20.97,13.38,14.24 -MERGE_EMF27,EMF27-G8-EERE,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -MERGE_EMF27,EMF27-G8-EERE,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.12,1.32,1.49,1.60,1.69,1.77,1.83,1.89,1.95 -MESSAGE V.4,AMPERE3-450,World,Emissions|CO2,Mt CO2/yr,34474.59,36035.69,36941.46,35238.71,26747.96,15173.32,4329.48,-1304.69,-5447.10,-8728.73,-11209.52 -MESSAGE V.4,AMPERE3-450,World,Price|Carbon,US$2005/t CO2,0.00,0.00,17.72,28.86,47.02,76.58,124.75,203.20,330.99,539.14,878.21 -MESSAGE V.4,AMPERE3-450,World,Primary Energy,EJ/yr,454.61,479.75,536.59,605.85,659.16,719.34,780.21,839.07,891.14,937.42,998.53 -MESSAGE V.4,AMPERE3-450,World,Primary Energy|Coal,EJ/yr,121.12,138.09,119.94,93.11,52.07,71.91,98.09,127.76,136.08,108.49,41.21 -MESSAGE V.4,AMPERE3-450,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.71,13.13,55.00,125.17,173.42,181.50,161.73,112.07,41.54 -MESSAGE V.4,AMPERE3-450,World,Temperature|Global Mean|MAGICC6|MED,°C,0.80,0.88,1.13,1.39,1.66,1.86,1.98,2.03,2.03,2.02,1.98 -MESSAGE V.4,AMPERE3-450P-EU,World,Emissions|CO2,Mt CO2/yr,34474.49,36036.02,40821.65,46438.23,38929.54,27622.31,12469.82,2786.95,-2585.87,-6268.49,-8535.53 -MESSAGE V.4,AMPERE3-450P-EU,World,Price|Carbon,US$2005/t CO2,0.00,0.00,7.54,39.65,47.02,76.58,124.75,203.20,330.99,539.14,878.21 -MESSAGE V.4,AMPERE3-450P-EU,World,Primary Energy,EJ/yr,454.61,479.76,565.35,663.83,692.35,735.64,790.76,846.85,898.87,936.69,989.39 -MESSAGE V.4,AMPERE3-450P-EU,World,Primary Energy|Coal,EJ/yr,121.12,138.09,149.20,165.47,95.13,64.39,81.91,108.47,126.41,110.79,59.72 -MESSAGE V.4,AMPERE3-450P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.35,2.53,21.19,70.91,158.87,183.37,172.31,125.94,61.17 -MESSAGE V.4,AMPERE3-450P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.86,0.94,1.18,1.44,1.79,2.08,2.27,2.37,2.40,2.41,2.39 -MESSAGE V.4,AMPERE3-550,World,Emissions|CO2,Mt CO2/yr,34474.51,36035.96,39222.41,42988.07,40487.80,34363.56,20847.26,10424.52,2479.89,-2730.61,-6474.82 -MESSAGE V.4,AMPERE3-550,World,Price|Carbon,US$2005/t CO2,0.00,0.00,7.43,12.10,19.71,32.11,52.30,85.18,138.76,226.02,368.16 -MESSAGE V.4,AMPERE3-550,World,Primary Energy,EJ/yr,454.61,479.76,553.56,646.14,717.20,787.04,847.90,921.04,987.37,1042.25,1110.93 -MESSAGE V.4,AMPERE3-550,World,Primary Energy|Coal,EJ/yr,121.12,138.09,135.75,137.59,86.04,90.19,102.28,171.10,228.29,214.30,193.95 -MESSAGE V.4,AMPERE3-550,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.31,5.14,18.98,49.89,113.95,214.33,271.84,240.18,196.31 -MESSAGE V.4,AMPERE3-550,World,Temperature|Global Mean|MAGICC6|MED,°C,0.77,0.85,1.09,1.34,1.66,1.94,2.16,2.30,2.39,2.42,2.44 -MESSAGE V.4,AMPERE3-RefPol,World,Emissions|CO2,Mt CO2/yr,34474.46,36035.98,40886.23,47008.04,51379.09,53497.39,50990.57,46103.64,41339.83,34733.89,27562.64 -MESSAGE V.4,AMPERE3-RefPol,World,Price|Carbon,US$2005/t CO2,0.00,0.00,7.54,26.28,32.51,37.40,46.41,49.31,61.37,92.32,115.10 -MESSAGE V.4,AMPERE3-RefPol,World,Primary Energy,EJ/yr,454.61,479.76,566.09,667.65,764.77,864.73,953.34,1039.19,1145.19,1249.78,1350.61 -MESSAGE V.4,AMPERE3-RefPol,World,Primary Energy|Coal,EJ/yr,121.12,138.09,150.13,166.96,170.55,181.91,179.23,197.24,267.97,338.53,402.07 -MESSAGE V.4,AMPERE3-RefPol,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.19,1.31,6.68,19.41,47.08,105.77,198.37,287.43,358.82 -MESSAGE V.4,AMPERE3-RefPol,World,Temperature|Global Mean|MAGICC6|MED,°C,0.77,0.85,1.09,1.37,1.71,2.06,2.39,2.71,2.99,3.26,3.49 -MESSAGE V.4,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,10319.94,12814.16,13917.06,12540.54,8915.34,5294.32,4896.94,4941.09,5035.57,5359.40,5627.94 -MESSAGE V.4,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9411.51,11950.63,13257.27,12147.62,8918.85,5822.95,5910.14,6207.44,6493.34,7037.79,7524.14 -MESSAGE V.4,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,133.39,162.95,189.34,206.19,232.61,255.35,300.17,339.27,363.30,400.78,432.35 -MESSAGE V.4,EMF27-550-LimBio,LAM,Emissions|CO2,Mt CO2/yr,3375.28,3271.45,3140.61,2676.62,1857.96,596.48,-63.36,-1040.46,-1790.37,-2173.69,-2671.53 -MESSAGE V.4,EMF27-550-LimBio,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1605.01,1650.05,1612.29,1463.28,1045.98,316.60,342.47,267.80,193.81,301.08,335.71 -MESSAGE V.4,EMF27-550-LimBio,LAM,Primary Energy,EJ/yr,28.16,29.82,32.56,36.04,40.56,43.07,49.50,53.71,57.07,61.62,64.42 -MESSAGE V.4,EMF27-550-LimBio,MAF,Emissions|CO2,Mt CO2/yr,3692.33,4341.28,4444.40,4537.13,4149.50,2285.65,2300.61,2202.18,2160.41,1347.97,988.83 -MESSAGE V.4,EMF27-550-LimBio,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2533.94,3112.04,3367.00,3579.28,3413.05,1903.37,2411.40,2876.82,3224.88,2729.99,2719.49 -MESSAGE V.4,EMF27-550-LimBio,MAF,Primary Energy,EJ/yr,40.24,49.01,60.08,78.00,102.25,133.01,171.62,208.18,236.17,257.93,276.57 -MESSAGE V.4,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,13094.10,11711.25,9584.77,7540.90,4962.71,1901.88,1487.48,1256.25,858.58,503.86,5.83 -MESSAGE V.4,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,13161.17,11845.40,9762.77,7774.92,5308.02,2349.95,2025.45,1810.79,1454.42,1172.10,780.67 -MESSAGE V.4,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,194.93,180.62,167.06,160.74,157.89,156.52,162.12,166.56,166.72,169.44,166.59 -MESSAGE V.4,EMF27-550-LimBio,REF,Emissions|CO2,Mt CO2/yr,3446.06,3346.03,2917.31,2351.23,1488.08,649.32,641.03,616.77,355.39,177.02,-25.12 -MESSAGE V.4,EMF27-550-LimBio,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3441.16,3336.24,2918.96,2372.59,1549.91,788.74,851.42,867.69,649.32,537.83,404.73 -MESSAGE V.4,EMF27-550-LimBio,REF,Primary Energy,EJ/yr,52.15,50.10,48.70,48.35,48.28,51.83,57.29,59.35,56.32,55.12,53.98 -MESSAGE V.4,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,34491.02,36087.09,34675.76,30326.54,22000.71,11312.70,9846.46,8570.05,7230.63,5834.56,4500.82 -MESSAGE V.4,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,30716.12,32497.29,31589.90,28017.81,20862.92,11766.64,12124.64,12624.77,12626.79,12398.79,12339.61 -MESSAGE V.4,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,14051.51,14231.65,11769.42,9055.71,3824.70,-1440.93,-1647.94,-2049.80,-2733.94,-3214.07,-3520.71 -MESSAGE V.4,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10905.48,11286.99,9221.17,7087.66,3138.20,298.22,196.81,-65.56,-158.32,-147.02,-114.17 -MESSAGE V.4,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,0.00,0.00,29.88,45.28,70.67,170.33,136.94,132.14,138.94,173.70,174.90 -MESSAGE V.4,EMF27-550-LimBio,World,Primary Energy,EJ/yr,455.16,479.25,505.53,537.75,590.51,649.15,750.36,837.01,889.77,955.37,1004.92 -MESSAGE V.4,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,121.19,138.87,98.82,73.76,50.02,80.94,121.00,164.78,187.12,215.56,236.57 -MESSAGE V.4,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,1.72,22.73,81.06,172.62,212.06,238.96,228.77,245.73,251.68 -MESSAGE V.4,EMF27-550-LimBio,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.14,1.39,1.63,1.80,1.90,1.99,2.05,2.11,2.16 -MESSAGE V.4,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,10319.94,12814.16,17012.52,20652.42,25083.75,29736.15,33785.46,36033.04,38046.23,40877.86,43337.42 -MESSAGE V.4,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9411.51,11950.63,16284.71,20048.03,24608.94,29395.79,33583.38,35972.84,38130.76,41109.48,43718.02 -MESSAGE V.4,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,133.39,162.95,215.29,260.15,314.99,375.90,435.04,482.45,522.62,564.43,599.41 -MESSAGE V.4,EMF27-Base-FullTech,LAM,Emissions|CO2,Mt CO2/yr,3375.28,3271.45,3637.03,3806.06,4195.36,4454.18,4288.75,3845.68,4429.47,3315.68,2357.74 -MESSAGE V.4,EMF27-Base-FullTech,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1605.01,1650.05,2083.10,2508.43,3193.99,3783.92,3979.98,3925.18,4920.97,4240.56,3735.50 -MESSAGE V.4,EMF27-Base-FullTech,LAM,Primary Energy,EJ/yr,28.16,29.82,35.42,42.53,51.50,63.46,74.51,86.37,104.50,100.89,99.11 -MESSAGE V.4,EMF27-Base-FullTech,MAF,Emissions|CO2,Mt CO2/yr,3692.33,4341.28,5480.67,7077.59,9274.64,12097.35,14559.30,16692.61,16391.66,15150.07,13020.44 -MESSAGE V.4,EMF27-Base-FullTech,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2533.94,3112.04,4368.23,6025.11,8321.32,11277.14,13901.30,16222.19,16130.61,15117.12,13232.13 -MESSAGE V.4,EMF27-Base-FullTech,MAF,Primary Energy,EJ/yr,40.24,49.01,67.54,94.40,131.24,177.04,222.73,270.77,299.81,321.19,326.36 -MESSAGE V.4,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,13094.10,11711.25,12229.92,12157.84,12558.05,12739.40,13341.93,13534.67,13099.48,12689.87,11946.14 -MESSAGE V.4,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,13161.17,11845.40,12385.30,12335.19,12757.71,12961.45,13586.27,13801.03,13387.45,12998.97,12275.81 -MESSAGE V.4,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,194.93,180.62,186.46,191.47,198.30,203.98,214.95,223.31,227.78,232.45,230.85 -MESSAGE V.4,EMF27-Base-FullTech,REF,Emissions|CO2,Mt CO2/yr,3446.06,3346.03,3774.54,3973.68,4187.97,4760.98,5098.39,4895.92,4625.91,4558.07,4573.86 -MESSAGE V.4,EMF27-Base-FullTech,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3441.16,3336.24,3770.96,3979.90,4205.26,4790.45,5140.93,4952.31,4696.76,4643.92,4675.14 -MESSAGE V.4,EMF27-Base-FullTech,REF,Primary Energy,EJ/yr,52.15,50.10,56.87,59.13,62.03,70.37,77.58,79.57,80.51,83.37,82.66 -MESSAGE V.4,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,34491.02,36087.09,42809.72,48375.99,55957.77,64431.68,71728.32,75668.28,77297.82,77283.35,75904.56 -MESSAGE V.4,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,30716.12,32497.29,39567.33,45605.05,53745.23,62852.36,70846.35,75539.88,77971.63,78801.84,78305.55 -MESSAGE V.4,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,14051.51,14231.65,15868.63,17896.11,21258.79,25655.80,30596.72,35007.32,39933.50,41377.73,40898.59 -MESSAGE V.4,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10905.48,11286.99,12764.73,14425.47,16094.57,17674.31,18432.99,17491.63,16994.64,15947.65,13851.49 -MESSAGE V.4,EMF27-Base-FullTech,World,Price|Carbon,US$2005/t CO2,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -MESSAGE V.4,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,455.16,479.25,569.38,656.10,766.97,900.11,1034.47,1152.42,1245.42,1312.78,1349.08 -MESSAGE V.4,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,121.19,138.87,158.94,182.78,212.02,257.67,315.41,360.65,421.09,463.68,492.42 -MESSAGE V.4,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -MESSAGE V.4,EMF27-Base-FullTech,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.89,1.13,1.41,1.76,2.13,2.52,2.95,3.39,3.81,4.23 -REMIND 1.5,AMPERE3-450,World,Emissions|CO2,Mt CO2/yr,33841.49,37365.91,35255.18,31679.69,25439.84,16908.36,6524.72,-910.93,-5015.05,-8196.08,-10192.88 -REMIND 1.5,AMPERE3-450,World,Price|Carbon,US$2005/t CO2,,12.69,26.12,47.87,84.54,145.48,273.47,391.15,653.40,1038.34,1625.99 -REMIND 1.5,AMPERE3-450,World,Primary Energy,EJ/yr,464.82,514.12,554.26,604.59,649.43,722.82,788.62,818.59,842.94,906.67,982.56 -REMIND 1.5,AMPERE3-450,World,Primary Energy|Coal,EJ/yr,122.20,135.47,92.72,45.87,22.33,20.39,16.64,12.35,5.94,2.36,1.77 -REMIND 1.5,AMPERE3-450,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,2.80,27.04,68.28,84.26,72.84,42.01,12.74,1.82,0.21 -REMIND 1.5,AMPERE3-450,World,Temperature|Global Mean|MAGICC6|MED,°C,0.86,0.91,1.19,1.48,1.70,1.83,1.90,1.93,1.93,1.92,1.88 -REMIND 1.5,AMPERE3-450P-CE,World,Emissions|CO2,Mt CO2/yr,33841.49,37357.58,39260.04,43283.25,34864.17,20741.40,7005.88,-622.25,-4786.58,-8275.44,-10291.58 -REMIND 1.5,AMPERE3-450P-CE,World,Price|Carbon,US$2005/t CO2,,12.38,9.87,13.94,76.15,145.48,273.47,391.15,653.40,1038.34,1625.99 -REMIND 1.5,AMPERE3-450P-CE,World,Primary Energy,EJ/yr,464.82,514.02,588.53,693.80,680.17,710.30,781.54,824.48,854.76,906.80,984.16 -REMIND 1.5,AMPERE3-450P-CE,World,Primary Energy|Coal,EJ/yr,122.20,135.40,120.77,124.53,70.15,27.36,12.26,8.34,4.12,2.14,1.65 -REMIND 1.5,AMPERE3-450P-CE,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.08,1.31,13.01,48.37,53.38,44.75,24.37,2.96,0.04 -REMIND 1.5,AMPERE3-450P-CE,World,Temperature|Global Mean|MAGICC6|MED,°C,0.75,0.80,1.07,1.35,1.66,1.86,1.95,1.98,1.99,1.97,1.93 -REMIND 1.5,AMPERE3-450P-EU,World,Emissions|CO2,Mt CO2/yr,33841.49,37356.37,41565.70,47902.12,37928.16,21615.59,6958.30,-563.45,-4783.46,-8277.63,-10290.40 -REMIND 1.5,AMPERE3-450P-EU,World,Price|Carbon,US$2005/t CO2,,12.38,1.24,7.01,75.74,145.48,273.47,391.15,653.40,1038.34,1625.99 -REMIND 1.5,AMPERE3-450P-EU,World,Primary Energy,EJ/yr,464.82,514.00,611.30,733.99,698.79,709.34,777.14,826.54,856.75,906.85,982.95 -REMIND 1.5,AMPERE3-450P-EU,World,Primary Energy|Coal,EJ/yr,122.20,135.39,147.40,179.17,103.90,33.31,10.36,6.77,3.51,2.08,1.66 -REMIND 1.5,AMPERE3-450P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.06,1.23,12.31,46.68,52.07,43.78,24.11,3.09,0.04 -REMIND 1.5,AMPERE3-450P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.82,0.87,1.12,1.42,1.76,1.98,2.08,2.11,2.11,2.09,2.05 -REMIND 1.5,AMPERE3-550,World,Emissions|CO2,Mt CO2/yr,33841.49,37366.12,38324.51,39015.27,36963.66,31733.51,22831.66,13927.89,5749.59,-327.77,-3755.24 -REMIND 1.5,AMPERE3-550,World,Price|Carbon,US$2005/t CO2,,12.69,11.77,21.68,38.45,66.52,125.16,178.46,297.93,475.33,746.40 -REMIND 1.5,AMPERE3-550,World,Primary Energy,EJ/yr,464.82,514.13,586.14,668.59,730.18,811.26,860.69,892.78,931.07,951.41,1003.11 -REMIND 1.5,AMPERE3-550,World,Primary Energy|Coal,EJ/yr,122.20,135.47,121.27,103.31,102.22,117.05,105.64,81.12,41.06,10.07,3.28 -REMIND 1.5,AMPERE3-550,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.57,10.56,58.62,129.26,143.13,115.74,61.38,15.54,3.96 -REMIND 1.5,AMPERE3-550,World,Temperature|Global Mean|MAGICC6|MED,°C,0.78,0.83,1.09,1.39,1.67,1.89,2.05,2.15,2.21,2.24,2.24 -REMIND 1.5,AMPERE3-550P-EU,World,Emissions|CO2,Mt CO2/yr,33841.49,37360.58,41568.63,47908.67,46792.96,37445.25,24413.62,14572.43,6804.20,86.14,-3693.90 -REMIND 1.5,AMPERE3-550P-EU,World,Price|Carbon,US$2005/t CO2,,12.38,1.24,7.01,22.31,66.52,125.16,178.46,297.93,475.33,746.41 -REMIND 1.5,AMPERE3-550P-EU,World,Primary Energy,EJ/yr,464.82,514.07,611.34,734.11,789.70,815.79,861.79,901.57,943.47,964.16,1005.66 -REMIND 1.5,AMPERE3-550P-EU,World,Primary Energy|Coal,EJ/yr,122.20,135.40,147.46,179.17,167.45,119.80,81.28,57.25,35.71,11.34,3.12 -REMIND 1.5,AMPERE3-550P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.06,1.23,17.03,73.53,116.29,108.24,74.35,26.50,3.51 -REMIND 1.5,AMPERE3-550P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.86,0.91,1.16,1.46,1.80,2.09,2.28,2.40,2.46,2.48,2.49 -REMIND 1.5,AMPERE3-Base-EUback,World,Emissions|CO2,Mt CO2/yr,33841.49,37371.87,44020.67,50296.87,58575.08,71744.59,82786.79,87993.16,85663.03,75402.53,66716.49 -REMIND 1.5,AMPERE3-Base-EUback,World,Price|Carbon,US$2005/t CO2,,,,5.35,2.23,,,,,, -REMIND 1.5,AMPERE3-Base-EUback,World,Primary Energy,EJ/yr,464.82,514.17,625.97,743.46,867.90,1031.30,1184.54,1303.30,1379.74,1380.24,1408.73 -REMIND 1.5,AMPERE3-Base-EUback,World,Primary Energy|Coal,EJ/yr,122.20,135.48,156.54,193.09,270.92,398.88,519.80,582.10,561.05,471.99,405.20 -REMIND 1.5,AMPERE3-Base-EUback,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.04,0.08,0.24,0.43,0.65,0.93,0.96,1.44,1.85 -REMIND 1.5,AMPERE3-Base-EUback,World,Temperature|Global Mean|MAGICC6|MED,°C,0.82,0.87,1.11,1.44,1.78,2.17,2.59,3.05,3.51,3.92,4.27 -REMIND 1.5,AMPERE3-CF450P-EU,World,Emissions|CO2,Mt CO2/yr,33841.49,37365.98,44028.80,50295.90,39726.70,22501.37,6855.42,-602.89,-4769.69,-8268.81,-10300.93 -REMIND 1.5,AMPERE3-CF450P-EU,World,Price|Carbon,US$2005/t CO2,,,,5.35,74.94,145.48,273.47,391.15,653.40,1038.34,1625.99 -REMIND 1.5,AMPERE3-CF450P-EU,World,Primary Energy,EJ/yr,464.82,514.09,626.11,743.50,703.19,709.19,773.38,825.07,857.20,907.47,983.14 -REMIND 1.5,AMPERE3-CF450P-EU,World,Primary Energy|Coal,EJ/yr,122.20,135.44,156.52,192.99,111.82,32.91,5.44,3.72,2.64,2.09,1.68 -REMIND 1.5,AMPERE3-CF450P-EU,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.04,0.08,8.36,41.88,48.30,41.80,23.94,3.23,0.04 -REMIND 1.5,AMPERE3-CF450P-EU,World,Temperature|Global Mean|MAGICC6|MED,°C,0.78,0.83,1.07,1.40,1.76,1.98,2.08,2.11,2.11,2.09,2.06 -REMIND 1.5,AMPERE3-RefPol,World,Emissions|CO2,Mt CO2/yr,33841.49,37372.53,41615.44,48455.22,55203.05,61590.57,64595.04,64737.59,62246.20,56447.99,51261.41 -REMIND 1.5,AMPERE3-RefPol,World,Price|Carbon,US$2005/t CO2,,12.38,1.24,4.92,6.56,11.75,21.18,24.18,28.81,19.03,26.37 -REMIND 1.5,AMPERE3-RefPol,World,Primary Energy,EJ/yr,464.82,514.22,611.87,737.76,851.37,978.08,1096.23,1199.82,1286.00,1323.83,1374.77 -REMIND 1.5,AMPERE3-RefPol,World,Primary Energy|Coal,EJ/yr,122.20,135.48,147.79,181.89,245.30,331.62,407.35,450.93,447.99,404.75,364.78 -REMIND 1.5,AMPERE3-RefPol,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.07,1.32,9.76,34.32,70.58,102.41,120.44,119.58,107.80 -REMIND 1.5,AMPERE3-RefPol,World,Temperature|Global Mean|MAGICC6|MED,°C,0.83,0.88,1.13,1.43,1.76,2.11,2.45,2.81,3.14,3.44,3.72 -REMIND 1.5,EMF27-450-Conv,ASIA,Emissions|CO2,Mt CO2/yr,10193.98,13196.41,10698.16,5726.12,3248.37,2254.52,1151.38,428.28,-172.88,-192.36,-185.38 -REMIND 1.5,EMF27-450-Conv,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9624.29,12668.17,10252.83,5749.65,3278.17,2282.32,1176.94,456.22,-137.33,-159.95,-166.06 -REMIND 1.5,EMF27-450-Conv,ASIA,Primary Energy,EJ/yr,135.83,171.18,162.77,186.67,189.28,181.23,169.35,157.96,142.22,130.99,119.78 -REMIND 1.5,EMF27-450-Conv,LAM,Emissions|CO2,Mt CO2/yr,2926.60,3500.82,3910.94,545.65,-87.93,-365.84,-406.25,-401.84,-471.69,-509.69,-613.32 -REMIND 1.5,EMF27-450-Conv,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1483.23,1694.47,1378.64,567.73,-57.38,-331.49,-387.35,-369.87,-439.63,-479.42,-584.07 -REMIND 1.5,EMF27-450-Conv,LAM,Primary Energy,EJ/yr,28.84,32.62,30.53,37.66,41.33,39.12,37.32,37.13,37.28,38.86,40.63 -REMIND 1.5,EMF27-450-Conv,MAF,Emissions|CO2,Mt CO2/yr,4035.32,4337.48,3458.33,1759.88,1302.03,1033.18,686.43,391.02,33.33,219.92,732.54 -REMIND 1.5,EMF27-450-Conv,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2360.58,2863.92,2387.12,1771.46,1313.09,1047.76,705.41,406.95,49.18,235.09,748.27 -REMIND 1.5,EMF27-450-Conv,MAF,Primary Energy,EJ/yr,49.52,58.17,52.28,57.71,72.03,80.49,84.52,92.41,101.07,108.11,111.02 -REMIND 1.5,EMF27-450-Conv,OECD90,Emissions|CO2,Mt CO2/yr,15111.39,15254.15,9970.42,5306.13,2034.69,413.79,-354.20,-754.70,-994.23,-980.48,-1146.75 -REMIND 1.5,EMF27-450-Conv,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,14547.59,14860.98,9918.51,5364.89,2094.12,467.83,-298.54,-697.94,-936.70,-923.39,-1088.95 -REMIND 1.5,EMF27-450-Conv,OECD90,Primary Energy,EJ/yr,225.73,232.19,178.42,179.09,168.81,145.92,131.67,118.11,105.56,101.00,95.83 -REMIND 1.5,EMF27-450-Conv,World,Emissions|CO2,Mt CO2/yr,33837.41,37977.31,29314.49,13503.92,6281.74,3040.79,787.74,-526.27,-1744.61,-1641.29,-1413.97 -REMIND 1.5,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29556.92,33624.77,24817.87,13667.44,6459.94,3214.38,950.20,-347.35,-1553.49,-1455.87,-1240.89 -REMIND 1.5,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,13765.03,15624.90,9392.07,596.45,-4325.54,-5071.51,-5477.69,-6187.86,-6523.09,-6585.18,-6610.96 -REMIND 1.5,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10245.45,11912.71,7051.88,1690.93,-78.92,94.94,365.18,929.81,1389.32,1264.50,1066.11 -REMIND 1.5,EMF27-450-Conv,World,Price|Carbon,US$2005/t CO2,,9.58,166.31,285.12,494.08,851.66,1632.61,2432.27,4353.64,4999.31,6108.24 -REMIND 1.5,EMF27-450-Conv,World,Primary Energy,EJ/yr,464.82,519.64,440.81,476.55,488.95,463.05,436.52,417.37,397.14,390.95,379.75 -REMIND 1.5,EMF27-450-Conv,World,Primary Energy|Coal,EJ/yr,122.20,145.67,83.97,21.68,1.42,0.85,0.65,0.52,1.15,2.48,3.81 -REMIND 1.5,EMF27-450-Conv,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,13.60,65.60,79.50,83.77,80.04,70.66,64.87,63.66,67.20 -REMIND 1.5,EMF27-450-Conv,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.87,1.15,1.37,1.50,1.57,1.60,1.59,1.59,1.57,1.56 -REMIND 1.5,EMF27-450-NoCCS,ASIA,Emissions|CO2,Mt CO2/yr,10193.98,13395.43,9638.80,3121.82,941.63,921.34,886.11,885.64,859.25,855.69,843.03 -REMIND 1.5,EMF27-450-NoCCS,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9624.29,12668.17,8596.40,3130.48,952.92,954.23,933.58,932.78,890.24,880.88,864.42 -REMIND 1.5,EMF27-450-NoCCS,ASIA,Primary Energy,EJ/yr,135.83,171.18,139.30,149.00,190.92,222.15,238.71,243.94,253.19,259.84,273.96 -REMIND 1.5,EMF27-450-NoCCS,LAM,Emissions|CO2,Mt CO2/yr,2926.60,3503.09,3597.06,404.91,121.08,110.73,132.03,92.25,96.53,98.33,99.79 -REMIND 1.5,EMF27-450-NoCCS,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1483.23,1694.47,1057.94,405.87,143.66,153.97,154.96,137.93,134.06,130.29,126.41 -REMIND 1.5,EMF27-450-NoCCS,LAM,Primary Energy,EJ/yr,28.84,32.62,26.36,30.80,37.50,41.80,45.44,48.49,54.55,61.34,68.87 -REMIND 1.5,EMF27-450-NoCCS,MAF,Emissions|CO2,Mt CO2/yr,4035.32,4381.13,3063.76,830.36,275.64,316.02,313.37,331.92,365.71,492.20,741.66 -REMIND 1.5,EMF27-450-NoCCS,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2360.58,2863.92,1861.60,838.07,283.59,313.89,330.47,349.49,379.72,501.64,752.67 -REMIND 1.5,EMF27-450-NoCCS,MAF,Primary Energy,EJ/yr,49.52,58.17,44.92,44.10,63.29,87.70,117.36,153.45,197.13,247.15,307.81 -REMIND 1.5,EMF27-450-NoCCS,OECD90,Emissions|CO2,Mt CO2/yr,15111.39,15254.16,8082.87,2864.75,369.53,328.11,299.06,266.24,255.25,245.07,226.35 -REMIND 1.5,EMF27-450-NoCCS,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,14547.59,14860.98,8030.94,2923.99,434.34,388.71,353.82,321.99,311.66,301.08,286.49 -REMIND 1.5,EMF27-450-NoCCS,OECD90,Primary Energy,EJ/yr,225.73,232.19,152.39,141.28,151.62,158.69,157.51,152.32,156.35,160.00,166.47 -REMIND 1.5,EMF27-450-NoCCS,World,Emissions|CO2,Mt CO2/yr,33837.41,38224.94,25524.60,7358.64,1691.05,1663.77,1616.52,1555.47,1553.00,1665.20,1883.11 -REMIND 1.5,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29556.92,33624.77,20285.08,7486.40,1857.16,1846.71,1805.53,1770.55,1742.22,1838.79,2053.77 -REMIND 1.5,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,13765.03,15624.90,9171.87,2423.55,-0.07,53.56,-99.86,-450.59,-700.89,-755.50,-995.45 -REMIND 1.5,EMF27-450-NoCCS,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10245.45,11912.71,6701.26,1800.94,53.13,16.35,6.58,2.54,2.79,3.15,5.42 -REMIND 1.5,EMF27-450-NoCCS,World,Price|Carbon,US$2005/t CO2,,9.58,519.32,854.56,1534.52,2803.70,5650.92,8683.09,16493.11,3647.56,23725.73 -REMIND 1.5,EMF27-450-NoCCS,World,Primary Energy,EJ/yr,464.82,519.64,377.80,377.72,458.30,525.50,573.12,611.42,674.70,741.91,831.52 -REMIND 1.5,EMF27-450-NoCCS,World,Primary Energy|Coal,EJ/yr,122.20,145.67,83.83,21.43,0.52,0.21,0.06,0.09,0.15,0.16,0.14 -REMIND 1.5,EMF27-450-NoCCS,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -REMIND 1.5,EMF27-450-NoCCS,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.87,1.15,1.36,1.47,1.50,1.51,1.51,1.52,1.54,1.54 -REMIND 1.5,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,10193.98,13239.55,14218.37,11920.79,8135.32,5963.84,4486.53,3100.11,2246.06,1843.16,1570.02 -REMIND 1.5,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9624.29,12668.17,13643.62,11954.04,8162.41,5987.99,4513.08,3130.89,2272.65,1868.24,1589.43 -REMIND 1.5,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,135.83,171.18,204.11,231.31,252.30,273.61,273.07,271.03,277.28,286.47,289.83 -REMIND 1.5,EMF27-550-LimBio,LAM,Emissions|CO2,Mt CO2/yr,2926.60,3478.79,4413.41,1831.96,1357.42,934.84,712.03,523.57,418.39,359.64,107.38 -REMIND 1.5,EMF27-550-LimBio,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1483.23,1694.47,1947.21,1860.26,1379.91,961.58,742.73,543.99,453.68,371.75,132.07 -REMIND 1.5,EMF27-550-LimBio,LAM,Primary Energy,EJ/yr,28.84,32.62,38.38,41.99,45.76,51.74,54.53,59.64,68.76,78.43,84.86 -REMIND 1.5,EMF27-550-LimBio,MAF,Emissions|CO2,Mt CO2/yr,4035.32,4381.03,4504.49,3368.89,3582.70,3883.52,3663.91,3349.07,3064.56,2919.43,3153.50 -REMIND 1.5,EMF27-550-LimBio,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2360.58,2863.92,3302.63,3379.05,3589.65,3893.88,3675.41,3359.78,3075.16,2929.83,3163.76 -REMIND 1.5,EMF27-550-LimBio,MAF,Primary Energy,EJ/yr,49.52,58.17,66.29,73.96,92.18,114.62,131.30,153.61,191.88,241.54,294.07 -REMIND 1.5,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,15111.39,15241.56,13016.52,10555.13,7238.06,4454.98,2745.80,1531.01,766.12,275.60,-82.62 -REMIND 1.5,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,14547.59,14860.98,13002.38,10613.03,7296.10,4513.71,2802.71,1580.53,825.44,332.94,-26.82 -REMIND 1.5,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,225.73,232.19,215.66,204.62,196.31,192.36,178.65,170.49,173.26,176.47,176.13 -REMIND 1.5,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,33837.41,37970.11,37657.41,28699.50,20936.83,15389.20,11536.73,8368.71,6360.33,5299.36,4644.20 -REMIND 1.5,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29556.92,33624.77,33182.40,28864.11,21097.85,15556.56,11706.11,8524.28,6534.93,5438.31,4789.00 -REMIND 1.5,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,13765.03,15624.90,14383.60,9949.80,3331.14,-695.21,-1581.95,-3014.44,-4484.33,-5086.02,-5491.50 -REMIND 1.5,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10245.45,11912.71,11165.91,7661.60,3781.91,2481.51,1794.92,1052.69,452.30,294.97,222.36 -REMIND 1.5,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,,9.58,36.07,65.38,118.50,206.64,366.21,493.86,708.52,880.10,1282.49 -REMIND 1.5,EMF27-550-LimBio,World,Primary Energy,EJ/yr,464.82,519.64,547.58,572.70,606.22,650.99,654.96,672.68,728.85,800.52,862.14 -REMIND 1.5,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,122.20,145.67,105.57,44.80,13.06,14.03,26.60,41.00,52.35,57.59,61.06 -REMIND 1.5,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,4.97,41.99,91.01,110.62,115.94,104.84,97.85,96.00,90.01 -REMIND 1.5,EMF27-550-LimBio,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.87,1.16,1.45,1.68,1.81,1.88,1.94,1.97,2.00,2.01 -REMIND 1.5,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,10193.98,13478.78,20256.01,24006.74,28404.78,33016.66,35977.35,36397.92,34529.79,28622.83,23400.42 -REMIND 1.5,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,9624.29,12668.17,18963.57,23851.49,28388.64,33027.52,35996.03,36416.91,33978.26,27869.99,22441.84 -REMIND 1.5,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,135.83,171.18,252.53,321.99,382.22,444.47,492.91,526.59,547.09,531.61,521.51 -REMIND 1.5,EMF27-Base-FullTech,LAM,Emissions|CO2,Mt CO2/yr,2926.60,3508.40,5067.35,5464.43,4402.98,5424.51,5869.57,5988.95,6096.94,5152.73,4074.45 -REMIND 1.5,EMF27-Base-FullTech,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1483.23,1694.47,2512.31,3230.36,3824.19,4499.22,4921.07,5113.10,4931.64,4201.68,3448.14 -REMIND 1.5,EMF27-Base-FullTech,LAM,Primary Energy,EJ/yr,28.84,32.62,43.91,52.52,58.60,67.24,74.19,81.55,88.24,92.46,99.67 -REMIND 1.5,EMF27-Base-FullTech,MAF,Emissions|CO2,Mt CO2/yr,4035.32,4381.09,5364.84,5862.75,8659.61,12865.82,17680.13,22674.41,27242.60,29150.29,29981.48 -REMIND 1.5,EMF27-Base-FullTech,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,2360.58,2863.92,4162.81,5825.71,8658.60,12871.31,17686.59,22681.07,27249.17,29156.88,29988.54 -REMIND 1.5,EMF27-Base-FullTech,MAF,Primary Energy,EJ/yr,49.52,58.17,78.02,99.22,133.41,184.76,243.74,311.42,384.72,434.63,481.80 -REMIND 1.5,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,15111.39,15234.63,15486.57,16326.59,16610.24,16943.56,16515.90,15922.43,14587.22,11864.62,9683.61 -REMIND 1.5,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,14547.59,14860.98,15493.22,16327.23,16533.88,16784.66,16567.34,15978.22,14639.15,11919.27,9753.72 -REMIND 1.5,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,225.73,232.19,239.50,248.43,249.55,254.06,256.24,258.30,259.36,245.13,236.85 -REMIND 1.5,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,33837.41,38293.08,48134.42,53343.82,59836.10,70077.89,77941.21,82914.15,84109.23,75995.09,68004.38 -REMIND 1.5,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,29556.92,33624.77,42690.49,50865.41,59177.44,69041.15,77103.39,82154.70,82487.12,74391.24,66547.93 -REMIND 1.5,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,13765.03,15624.90,19879.59,24808.67,30403.31,37686.30,44599.07,48221.13,48169.87,42729.91,35078.86 -REMIND 1.5,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10245.45,11912.71,15970.42,20578.04,24231.99,26435.62,26743.55,25504.71,23336.08,19950.06,17229.15 -REMIND 1.5,EMF27-Base-FullTech,World,Price|Carbon,US$2005/t CO2,,9.58,,,,,,,,, -REMIND 1.5,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,464.82,519.64,640.55,749.95,853.16,979.35,1095.43,1206.29,1306.33,1328.79,1363.91 -REMIND 1.5,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,122.20,145.67,196.93,239.86,302.90,389.32,481.27,534.46,531.06,461.18,374.48 -REMIND 1.5,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00 -REMIND 1.5,EMF27-Base-FullTech,World,Temperature|Global Mean|MAGICC6|MED,°C,0.81,0.87,1.13,1.45,1.81,2.20,2.65,3.14,3.62,4.08,4.44 -WITCH_EMF27,EMF27-450-Conv,ASIA,Emissions|CO2,Mt CO2/yr,9895.45,13210.18,13914.12,12004.49,10538.51,8767.49,7410.94,6299.16,3794.59,2865.46,2437.77 -WITCH_EMF27,EMF27-450-Conv,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,8629.01,12114.61,13995.34,12400.26,11036.76,9533.21,8176.67,7064.88,4560.32,3631.19,3203.49 -WITCH_EMF27,EMF27-450-Conv,ASIA,Primary Energy,EJ/yr,123.50,163.27,191.65,198.46,201.86,195.81,183.80,170.77,160.74,154.07,147.65 -WITCH_EMF27,EMF27-450-Conv,LAM,Emissions|CO2,Mt CO2/yr,4660.57,4644.17,1851.46,1537.68,1421.58,658.62,-161.02,-1398.20,-1659.55,-1631.41,-1586.79 -WITCH_EMF27,EMF27-450-Conv,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1299.42,1476.50,1454.72,1213.55,881.67,452.92,-366.72,-1603.90,-1865.25,-1837.11,-1792.49 -WITCH_EMF27,EMF27-450-Conv,LAM,Primary Energy,EJ/yr,27.65,30.70,30.22,30.79,29.87,30.54,39.27,60.04,67.19,67.45,68.75 -WITCH_EMF27,EMF27-450-Conv,MAF,Emissions|CO2,Mt CO2/yr,2508.31,2673.95,2224.44,1932.65,1907.50,1703.00,1588.40,1493.36,1413.90,1303.02,1118.21 -WITCH_EMF27,EMF27-450-Conv,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1832.71,2227.75,2170.26,1904.47,1869.65,1728.82,1614.22,1519.18,1439.72,1328.84,1144.03 -WITCH_EMF27,EMF27-450-Conv,MAF,Primary Energy,EJ/yr,39.92,45.58,43.91,46.37,49.20,50.57,51.44,52.21,52.39,51.27,46.73 -WITCH_EMF27,EMF27-450-Conv,OECD90,Emissions|CO2,Mt CO2/yr,12644.40,12597.55,9780.38,6560.69,4755.20,3257.79,2240.72,1399.83,795.67,359.17,224.28 -WITCH_EMF27,EMF27-450-Conv,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12503.93,12460.64,9752.18,6561.61,4747.55,3314.89,2297.82,1456.92,852.77,416.27,281.38 -WITCH_EMF27,EMF27-450-Conv,OECD90,Primary Energy,EJ/yr,197.26,198.49,169.04,154.92,144.40,131.47,117.00,104.92,98.04,93.91,94.31 -WITCH_EMF27,EMF27-450-Conv,REF,Emissions|CO2,Mt CO2/yr,3870.58,4035.17,2381.75,2055.81,1733.11,1347.51,1064.51,815.36,611.13,444.37,383.41 -WITCH_EMF27,EMF27-450-Conv,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3706.21,3932.25,2367.30,2069.19,1760.54,1425.66,1142.66,893.50,689.27,522.52,461.56 -WITCH_EMF27,EMF27-450-Conv,REF,Primary Energy,EJ/yr,53.59,57.32,41.75,43.62,42.42,39.46,35.56,32.14,29.68,28.12,27.45 -WITCH_EMF27,EMF27-450-Conv,World,Emissions|CO2,Mt CO2/yr,33579.32,37161.03,30152.15,24091.32,20355.91,15734.40,12143.56,8609.50,4955.74,3340.62,2576.88 -WITCH_EMF27,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,27971.27,32211.76,29739.80,24149.07,20296.16,16455.50,12864.65,9330.59,5676.83,4061.71,3297.97 -WITCH_EMF27,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,10291.72,11568.26,8804.20,3670.67,2021.17,1268.04,873.48,-406.52,-927.68,-1089.56,-1124.96 -WITCH_EMF27,EMF27-450-Conv,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10273.07,11436.19,8574.50,3329.22,1603.28,838.56,415.80,-818.51,-1286.38,-1371.73,-1372.39 -WITCH_EMF27,EMF27-450-Conv,World,Price|Carbon,US$2005/t CO2,,,65.47,144.69,290.26,546.15,983.66,1674.44,2574.33,3910.42,5676.58 -WITCH_EMF27,EMF27-450-Conv,World,Primary Energy,EJ/yr,441.92,495.35,476.57,474.15,467.75,447.85,427.06,420.07,408.05,394.82,384.87 -WITCH_EMF27,EMF27-450-Conv,World,Primary Energy|Coal,EJ/yr,121.71,131.26,106.92,85.24,71.54,66.24,66.45,60.63,56.87,54.01,51.41 -WITCH_EMF27,EMF27-450-Conv,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.07,0.05,8.52,65.76,74.30,83.76,93.06,91.90,111.72,116.47,112.81 -WITCH_EMF27,EMF27-550-LimBio,ASIA,Emissions|CO2,Mt CO2/yr,9895.98,13341.76,17280.06,18745.41,16414.52,12419.72,10012.01,9373.38,8937.92,9270.47,9214.63 -WITCH_EMF27,EMF27-550-LimBio,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,8629.54,12246.19,17214.62,19086.19,16894.44,13167.11,10777.74,10139.10,9703.64,10036.19,9980.36 -WITCH_EMF27,EMF27-550-LimBio,ASIA,Primary Energy,EJ/yr,123.51,164.47,229.56,254.17,246.74,238.63,239.20,241.33,244.27,247.62,250.18 -WITCH_EMF27,EMF27-550-LimBio,LAM,Emissions|CO2,Mt CO2/yr,4660.84,4612.38,2729.37,2515.92,2286.12,1309.50,1048.52,677.15,470.62,-66.27,-210.35 -WITCH_EMF27,EMF27-550-LimBio,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1299.68,1444.70,1727.63,1916.79,1672.87,1067.14,842.82,471.45,264.92,-271.97,-416.05 -WITCH_EMF27,EMF27-550-LimBio,LAM,Primary Energy,EJ/yr,27.65,30.23,34.60,36.74,38.51,40.29,40.60,45.43,47.70,56.58,57.45 -WITCH_EMF27,EMF27-550-LimBio,MAF,Emissions|CO2,Mt CO2/yr,2508.81,2621.97,2773.63,2885.03,2775.24,2552.54,2579.22,2758.68,2928.88,3067.60,3139.20 -WITCH_EMF27,EMF27-550-LimBio,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1833.21,2175.78,2664.44,2820.18,2737.40,2578.36,2605.03,2784.50,2954.70,3093.42,3165.01 -WITCH_EMF27,EMF27-550-LimBio,MAF,Primary Energy,EJ/yr,39.93,44.68,50.78,54.81,59.43,61.94,66.71,72.38,77.03,81.48,86.29 -WITCH_EMF27,EMF27-550-LimBio,OECD90,Emissions|CO2,Mt CO2/yr,12645.78,12542.87,11852.60,10275.13,7908.88,5310.74,4461.18,4405.41,4236.36,4016.45,3860.45 -WITCH_EMF27,EMF27-550-LimBio,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12505.30,12405.96,11806.06,10276.06,7901.23,5349.51,4518.28,4462.51,4293.46,4073.55,3917.55 -WITCH_EMF27,EMF27-550-LimBio,OECD90,Primary Energy,EJ/yr,197.28,197.59,193.27,178.42,168.97,157.27,151.52,148.52,147.42,147.99,149.60 -WITCH_EMF27,EMF27-550-LimBio,REF,Emissions|CO2,Mt CO2/yr,3871.04,4062.72,3764.23,3380.51,2617.25,1957.66,1686.23,1658.31,1628.48,1550.83,1510.95 -WITCH_EMF27,EMF27-550-LimBio,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3706.66,3959.80,3731.44,3393.89,2644.67,2035.81,1764.37,1736.46,1706.63,1628.98,1589.09 -WITCH_EMF27,EMF27-550-LimBio,REF,Primary Energy,EJ/yr,53.60,57.29,54.48,53.15,51.94,49.00,47.80,47.82,48.21,48.73,49.40 -WITCH_EMF27,EMF27-550-LimBio,World,Emissions|CO2,Mt CO2/yr,33582.45,37181.70,38399.88,37802.01,32002.02,23550.17,19787.16,18872.93,18202.25,17839.07,17514.87 -WITCH_EMF27,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,27974.40,32232.43,37144.20,37493.10,31850.61,24197.92,20508.25,19594.02,18923.34,18560.16,18235.96 -WITCH_EMF27,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,10291.72,11919.30,14022.37,12885.87,7885.12,3746.57,3215.51,3577.53,4139.50,4014.43,3976.99 -WITCH_EMF27,EMF27-550-LimBio,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10273.07,11813.97,13729.89,12333.23,7020.13,2763.69,1950.23,1465.64,1410.86,1010.91,950.64 -WITCH_EMF27,EMF27-550-LimBio,World,Price|Carbon,US$2005/t CO2,,,17.12,45.42,91.51,213.97,333.37,461.89,637.35,860.77,1152.36 -WITCH_EMF27,EMF27-550-LimBio,World,Primary Energy,EJ/yr,441.97,494.25,562.69,577.30,565.59,547.13,545.83,555.49,564.63,582.40,592.92 -WITCH_EMF27,EMF27-550-LimBio,World,Primary Energy|Coal,EJ/yr,121.71,135.79,152.12,138.41,113.10,91.18,85.62,84.23,80.71,77.56,74.13 -WITCH_EMF27,EMF27-550-LimBio,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.07,0.05,0.02,6.48,59.47,99.49,125.42,141.31,152.83,153.24,156.00 -WITCH_EMF27,EMF27-Base-FullTech,ASIA,Emissions|CO2,Mt CO2/yr,9893.46,13378.34,20016.55,26248.47,30889.38,34562.46,37566.05,40325.64,42647.52,44874.72,46657.52 -WITCH_EMF27,EMF27-Base-FullTech,ASIA,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,8627.02,12282.77,19566.11,26277.58,31185.97,35126.53,38130.11,40889.70,43211.58,45438.78,47221.58 -WITCH_EMF27,EMF27-Base-FullTech,ASIA,Primary Energy,EJ/yr,123.48,165.07,256.66,334.32,389.71,434.38,473.18,512.81,550.32,587.81,622.03 -WITCH_EMF27,EMF27-Base-FullTech,LAM,Emissions|CO2,Mt CO2/yr,4659.58,4623.98,4524.39,4644.99,4937.36,5250.67,5698.25,6117.40,6522.66,6945.51,7358.61 -WITCH_EMF27,EMF27-Base-FullTech,LAM,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1298.42,1456.30,1909.32,2414.19,3040.78,3688.30,4135.88,4555.04,4960.30,5383.14,5796.24 -WITCH_EMF27,EMF27-Base-FullTech,LAM,Primary Energy,EJ/yr,27.63,30.41,37.45,44.31,50.61,57.08,62.78,68.77,74.79,81.24,87.61 -WITCH_EMF27,EMF27-Base-FullTech,MAF,Emissions|CO2,Mt CO2/yr,2506.45,2642.28,3291.19,4063.34,5028.41,6038.17,7017.40,8032.94,8851.50,9680.49,10373.40 -WITCH_EMF27,EMF27-Base-FullTech,MAF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,1830.86,2196.09,2980.33,3815.16,4843.89,5917.32,6896.55,7912.09,8730.66,9559.64,10252.55 -WITCH_EMF27,EMF27-Base-FullTech,MAF,Primary Energy,EJ/yr,39.89,45.07,56.21,67.01,78.06,89.45,100.27,113.21,125.57,138.46,152.25 -WITCH_EMF27,EMF27-Base-FullTech,OECD90,Emissions|CO2,Mt CO2/yr,12639.28,12598.84,13097.95,13835.62,14969.12,15784.59,16540.18,17249.21,17924.86,18566.23,19180.64 -WITCH_EMF27,EMF27-Base-FullTech,OECD90,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,12498.80,12461.93,12996.42,13744.88,14869.79,15676.69,16432.28,17141.31,17816.96,18458.33,19072.74 -WITCH_EMF27,EMF27-Base-FullTech,OECD90,Primary Energy,EJ/yr,197.19,198.33,207.18,214.30,220.98,228.47,236.73,248.62,261.66,276.76,291.23 -WITCH_EMF27,EMF27-Base-FullTech,REF,Emissions|CO2,Mt CO2/yr,3868.87,4077.28,4636.23,5039.14,5412.35,5886.80,6279.44,6439.80,6722.19,7040.23,7284.21 -WITCH_EMF27,EMF27-Base-FullTech,REF,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,3704.49,3974.36,4585.11,5015.84,5421.44,5928.28,6320.92,6481.28,6763.67,7081.71,7325.69 -WITCH_EMF27,EMF27-Base-FullTech,REF,Primary Energy,EJ/yr,53.57,57.47,66.23,72.03,76.86,81.20,85.49,91.45,98.70,106.60,113.73 -WITCH_EMF27,EMF27-Base-FullTech,World,Emissions|CO2,Mt CO2/yr,33567.64,37320.72,45566.30,53831.56,61236.62,67522.70,73101.32,78164.98,82668.74,87107.17,90854.38 -WITCH_EMF27,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry,Mt CO2/yr,27959.60,32371.45,42037.29,51267.65,59361.87,66337.12,71915.75,76979.41,81483.17,85921.60,89668.81 -WITCH_EMF27,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply,Mt CO2/yr,10291.70,11952.38,17199.90,22861.08,28342.18,34236.72,40068.17,44665.83,48710.83,52310.80,55378.38 -WITCH_EMF27,EMF27-Base-FullTech,World,Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity,Mt CO2/yr,10273.05,11842.28,16831.09,21875.17,25746.31,29193.90,32596.59,35923.66,39209.42,42397.07,45297.83 -WITCH_EMF27,EMF27-Base-FullTech,World,Primary Energy,EJ/yr,441.76,496.35,623.75,731.97,816.22,890.57,958.45,1034.85,1111.04,1190.87,1266.85 -WITCH_EMF27,EMF27-Base-FullTech,World,Primary Energy|Coal,EJ/yr,121.71,135.95,182.54,230.98,265.24,294.28,322.18,348.38,374.01,398.06,418.77 -WITCH_EMF27,EMF27-Base-FullTech,World,Primary Energy|Fossil|w/ CCS,EJ/yr,0.07,0.05,0.02,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 \ No newline at end of file diff --git a/doc/source/tutorials/tutorial_data.csv b/doc/source/tutorials/tutorial_data.csv new file mode 100644 index 000000000..a078825ae --- /dev/null +++ b/doc/source/tutorials/tutorial_data.csv @@ -0,0 +1,997 @@ +Model,Scenario,Region,Variable,Unit,2010,2020,2030,2040,2050,2060,2070,2080,2090,2100 +AIM/CGE 2.1,CD-LINKS_INDCi,R5ASIA,Emissions|CO2,Mt CO2/yr,11231.088,14359.2801,14873.5967,15238.9081,15180.1854,15513.176,16003.206,16343.3124,17097.8681,17722.1245 +AIM/CGE 2.1,CD-LINKS_INDCi,R5ASIA,Primary Energy,EJ/yr,145.7409,191.0565,216.2135,234.2793,245.9771,258.3201,268.7644,275.0764,283.1479,288.6838 +AIM/CGE 2.1,CD-LINKS_INDCi,R5ASIA,Primary Energy|Biomass,EJ/yr,23.6647,24.0751,25.9262,27.3646,29.6938,29.8102,30.1178,30.0109,29.6166,29.5846 +AIM/CGE 2.1,CD-LINKS_INDCi,R5ASIA,Primary Energy|Fossil,EJ/yr,116.1932,155.0735,168.2376,179.0562,185.2168,195.6202,203.4916,207.3614,214.7828,217.7714 +AIM/CGE 2.1,CD-LINKS_INDCi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.5139,9.2641,17.0767,22.0967,25.3211,26.6589,27.949,29.3259,29.3942,30.6799 +AIM/CGE 2.1,CD-LINKS_INDCi,R5LAM,Emissions|CO2,Mt CO2/yr,1726.7268,1879.6481,1595.849,1811.5221,1917.1625,2028.0104,2169.4228,2376.7236,2574.0901,2726.3699 +AIM/CGE 2.1,CD-LINKS_INDCi,R5LAM,Primary Energy,EJ/yr,28.0027,32.0987,36.1859,40.5731,44.9717,49.0909,53.5396,57.9241,62.0934,65.7424 +AIM/CGE 2.1,CD-LINKS_INDCi,R5LAM,Primary Energy|Biomass,EJ/yr,4.42,5.6088,7.3968,8.47,10.2275,11.7108,13.5447,15.0261,16.6165,18.0013 +AIM/CGE 2.1,CD-LINKS_INDCi,R5LAM,Primary Energy|Fossil,EJ/yr,20.8407,23.0387,24.3075,27.0192,29.068,30.9499,33.0596,35.298,37.5039,39.222 +AIM/CGE 2.1,CD-LINKS_INDCi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.6276,3.2336,4.095,4.7247,5.3829,6.1443,6.6332,7.263999999999999,7.6012,8.1042 +AIM/CGE 2.1,CD-LINKS_INDCi,R5MAF,Emissions|CO2,Mt CO2/yr,3515.5008,4340.093,4739.7751,5549.9787,6366.1266,7529.0404,8686.5914,10016.1261,11481.2018,12905.8992 +AIM/CGE 2.1,CD-LINKS_INDCi,R5MAF,Primary Energy,EJ/yr,50.5362,63.9347,81.2055,97.7259,118.1993,142.1247,166.0257,191.3211,217.831,241.6213 +AIM/CGE 2.1,CD-LINKS_INDCi,R5MAF,Primary Energy|Biomass,EJ/yr,10.7167,11.6645,12.9009,14.9367,18.587,23.0344,28.5694,34.4229,40.2591,43.64899999999999 +AIM/CGE 2.1,CD-LINKS_INDCi,R5MAF,Primary Energy|Fossil,EJ/yr,39.2533,50.8944,66.1157,79.7224,95.53299999999999,113.3785,129.5259,145.9138,163.8021,181.4282 +AIM/CGE 2.1,CD-LINKS_INDCi,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.4941,0.8867,1.3187,1.9288,2.7267,3.9466,5.5859,7.88,9.7078,11.3129 +AIM/CGE 2.1,CD-LINKS_INDCi,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13520.2437,14200.0035,9761.8867,10265.5285,10405.5503,10639.7436,11062.4859,11385.6402,11617.8399,11391.433 +AIM/CGE 2.1,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy,EJ/yr,210.7124,235.0973,220.7491,248.0198,263.6788,277.3443,289.2712,299.758,308.8487,314.0952 +AIM/CGE 2.1,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.5975,14.2154,25.8661,30.1291,37.7897,44.1726,51.7987,58.2061,63.8483,69.7509 +AIM/CGE 2.1,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,186.3378,205.4428,168.5643,184.2108,184.1208,188.418,190.2207,190.26,189.7515,183.7378 +AIM/CGE 2.1,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,5.7088,6.8816,15.7708,22.9763,31.306,33.6214,34.845,37.2786,39.6482,42.8136 +AIM/CGE 2.1,CD-LINKS_INDCi,R5REF,Emissions|CO2,Mt CO2/yr,2899.4549,3235.3277,3962.3917,4240.4285,4119.9651,4120.2142,4262.124,4389.9802,4512.6969,4609.8211 +AIM/CGE 2.1,CD-LINKS_INDCi,R5REF,Primary Energy,EJ/yr,43.5337,51.9472,63.2371,70.3571,73.2197,75.936,79.26,81.798,84.3205,86.2892 +AIM/CGE 2.1,CD-LINKS_INDCi,R5REF,Primary Energy|Biomass,EJ/yr,0.4748,0.5424,0.7421,1.6496,3.1326,4.3723,5.0821,5.5245,6.0094,6.3177 +AIM/CGE 2.1,CD-LINKS_INDCi,R5REF,Primary Energy|Fossil,EJ/yr,41.0368,48.4316,58.3756,62.5113,60.8776,60.82899999999999,62.1668,63.2224,64.2865,64.9776 +AIM/CGE 2.1,CD-LINKS_INDCi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.0598,1.5378,2.0989,3.6175,6.0661,6.9029,7.3402,7.8075,8.3656,8.9316 +AIM/CGE 2.1,CD-LINKS_INDCi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.8922892370000001,1.15743982,1.431834066,1.702271022,1.983333039,2.221275762,2.463413183,2.719927454,3.000583314,3.284530669 +AIM/CGE 2.1,CD-LINKS_INDCi,World,Emissions|CO2,Mt CO2/yr,33954.0254,39274.5709,36068.4425,38447.2877,39519.9596,41523.0761,44033.1912,46515.0323,49433.6268,51588.3074 +AIM/CGE 2.1,CD-LINKS_INDCi,World,Primary Energy,EJ/yr,492.671,590.9495,632.7404,708.8229,766.413,825.305,881.3968,932.4239,984.7002,1025.9565 +AIM/CGE 2.1,CD-LINKS_INDCi,World,Primary Energy|Biomass,EJ/yr,49.8737,56.1061,72.8321,82.55,99.4309,113.1002,129.113,143.1905,156.3499,167.3033 +AIM/CGE 2.1,CD-LINKS_INDCi,World,Primary Energy|Fossil,EJ/yr,417.8071,499.6961,500.75,550.3875,575.1827,611.6849,643.0008,668.6021,698.5855,716.6617 +AIM/CGE 2.1,CD-LINKS_INDCi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.4043,21.8039,40.3603,55.3441,70.8027,77.2739,82.3531,89.556,94.7169,101.8421 +AIM/CGE 2.1,CD-LINKS_NPi,R5ASIA,Emissions|CO2,Mt CO2/yr,11116.609,13801.7203,14728.5209,15565.8154,15906.4875,16433.5155,17305.6503,17520.4537,17699.6084,17835.4469 +AIM/CGE 2.1,CD-LINKS_NPi,R5ASIA,Primary Energy,EJ/yr,145.4432,186.1766,212.2721,234.0775,248.7292,262.0285,275.4139,281.2838,284.8196,286.1799 +AIM/CGE 2.1,CD-LINKS_NPi,R5ASIA,Primary Energy|Biomass,EJ/yr,23.6516,23.9701,25.2414,26.101,27.5023,26.9858,27.0246,26.8837,26.3612,26.6428 +AIM/CGE 2.1,CD-LINKS_NPi,R5ASIA,Primary Energy|Fossil,EJ/yr,115.8453,150.4224,168.056,184.2335,194.6512,204.936,215.6441,218.7878,221.4293,218.9481 +AIM/CGE 2.1,CD-LINKS_NPi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.5255,9.0793,15.1297,19.7186,22.436,25.3705,27.2613,29.3583,30.2423,32.834 +AIM/CGE 2.1,CD-LINKS_NPi,R5LAM,Emissions|CO2,Mt CO2/yr,1763.1316,1970.7359,2150.8582,2378.8748,2579.0634,2829.1623,3065.9345,3325.434,3571.2204,3776.9759 +AIM/CGE 2.1,CD-LINKS_NPi,R5LAM,Primary Energy,EJ/yr,28.1311,33.2069,38.8979,44.2656,49.6193,54.607,59.3256,64.0922,68.5452,73.1659 +AIM/CGE 2.1,CD-LINKS_NPi,R5LAM,Primary Energy|Biomass,EJ/yr,4.4401,5.7634,7.3604,7.8613,8.4259,8.845,9.9479,10.9918,12.1258,13.7564 +AIM/CGE 2.1,CD-LINKS_NPi,R5LAM,Primary Energy|Fossil,EJ/yr,20.9316,24.0353,27.5567,31.7477,35.6177,39.5347,42.9815,46.5356,49.7027,52.3961 +AIM/CGE 2.1,CD-LINKS_NPi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.6297,3.1588,3.7293,4.3804,5.2639,5.8615,5.9964,6.1307,6.2474,6.4992 +AIM/CGE 2.1,CD-LINKS_NPi,R5MAF,Emissions|CO2,Mt CO2/yr,3547.6143,4347.1195,5024.8604,5827.2791,6729.8144,8086.3468,9545.5418,11384.5879,13365.6316,15365.9162 +AIM/CGE 2.1,CD-LINKS_NPi,R5MAF,Primary Energy,EJ/yr,50.1391,63.9126,79.7001,97.6433,119.0168,144.4043,169.9097,198.5263,229.2634,259.7954 +AIM/CGE 2.1,CD-LINKS_NPi,R5MAF,Primary Energy|Biomass,EJ/yr,10.6818,11.5588,12.5079,14.08,16.9077,19.6914,22.9598,26.9837,31.1785,36.1503 +AIM/CGE 2.1,CD-LINKS_NPi,R5MAF,Primary Energy|Fossil,EJ/yr,38.8853,51.0612,65.2947,80.9947,98.4309,119.3065,139.4421,162.7398,187.731,210.9039 +AIM/CGE 2.1,CD-LINKS_NPi,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5199,0.922,1.3157,1.7938,2.6813,4.099,5.801,6.5442,7.444,8.9852 +AIM/CGE 2.1,CD-LINKS_NPi,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13564.514,14524.2143,15271.4324,15937.8944,16146.0348,16740.7917,17446.8473,18116.9795,18771.7763,18823.2772 +AIM/CGE 2.1,CD-LINKS_NPi,R5OECD90+EU,Primary Energy,EJ/yr,211.1914,234.5017,257.0928,278.2176,293.1991,307.7758,320.8371,333.2719,344.5714,350.1644 +AIM/CGE 2.1,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.5564,14.9375,21.1928,27.1002,30.9054,31.5527,34.4377,37.291,39.432,42.8861 +AIM/CGE 2.1,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,187.0883,205.124,220.2442,232.748,239.1434,249.192,255.8755,262.0737,268.8384,267.01 +AIM/CGE 2.1,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,5.7162,6.6943,7.8528,9.607000000000001,12.5442,14.1208,14.9904,15.8595,16.5273,17.8138 +AIM/CGE 2.1,CD-LINKS_NPi,R5REF,Emissions|CO2,Mt CO2/yr,2875.3635,3425.6015,4011.2648,4572.527,4790.331,4909.4716,5099.6671,5212.7168,5323.0789,5337.3147 +AIM/CGE 2.1,CD-LINKS_NPi,R5REF,Primary Energy,EJ/yr,43.3225,53.3426,63.4033,73.0596,78.2673,81.7445,84.9836,87.073,89.4989,90.6837 +AIM/CGE 2.1,CD-LINKS_NPi,R5REF,Primary Energy|Biomass,EJ/yr,0.4739,0.5347,0.6589,1.0223,1.8642,2.4396,2.8258,3.1599,3.3619,3.5095 +AIM/CGE 2.1,CD-LINKS_NPi,R5REF,Primary Energy|Fossil,EJ/yr,40.8086,50.0247,59.084,67.363,70.6068,72.3009,73.9341,74.3989,75.134,74.5442 +AIM/CGE 2.1,CD-LINKS_NPi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.0723,1.3397,1.6289,2.0814,2.6357,3.1512,3.5271,3.7892,4.0242,4.1229 +AIM/CGE 2.1,CD-LINKS_NPi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892288986,1.155254827,1.432102212,1.75200038,2.103037987,2.402728561,2.709993978,3.019749571,3.355318496,3.707443456 +AIM/CGE 2.1,CD-LINKS_NPi,World,Emissions|CO2,Mt CO2/yr,33926.8721,39431.7795,42826.6846,46122.3991,48146.9873,51125.3109,54720.8902,57939.9019,61219.9601,63723.0788 +AIM/CGE 2.1,CD-LINKS_NPi,World,Primary Energy,EJ/yr,492.3542,589.2984,673.2037,751.7432,815.3478,878.7824,940.4012,995.7705,1049.6332,1094.1579 +AIM/CGE 2.1,CD-LINKS_NPi,World,Primary Energy|Biomass,EJ/yr,49.80399999999999,56.7645,66.9614,76.1649,85.6054,89.5146,97.1957,105.3102,112.4593,122.9451 +AIM/CGE 2.1,CD-LINKS_NPi,World,Primary Energy|Fossil,EJ/yr,417.6858,498.8255,562.0735,621.567,664.966,713.4923,757.8088,796.059,835.7699,857.971 +AIM/CGE 2.1,CD-LINKS_NPi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.4637,21.1941,29.6564,37.5811,45.5613,52.6029,57.5762,61.682,64.4853,70.255 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5ASIA,Emissions|CO2,Mt CO2/yr,11118.1233,13764.5834,6494.6576,3415.5028,2887.6295,1455.3372,1006.5339,1228.7585,1373.7638,1134.7492 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy,EJ/yr,145.4503,185.86,152.1268,159.4528,182.0646,189.0307,194.7717,202.1489,206.5128,207.318 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Biomass,EJ/yr,23.6527,23.9713,26.9722,41.1462,45.0972,48.2828,50.0587,49.8298,49.8724,49.2865 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Fossil,EJ/yr,115.8853,149.9262,84.0836,60.3736,85.5386,82.7672,81.8358,90.2452,94.0154,93.0699 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.5364,9.3026,35.4128,53.145,48.3274,54.5889,58.8541,57.6911,57.8781,59.7385 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5LAM,Emissions|CO2,Mt CO2/yr,1752.9286,1955.1299,1094.3093,-272.6097,-994.7088,-1462.932,-1310.8609,-1122.66,-1030.9203,-954.3041 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy,EJ/yr,28.0436,33.0089,34.5201,40.8103,47.3082,51.0517,54.6111,58.2692,61.9124,65.1842 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Biomass,EJ/yr,4.4182,5.7304,8.7592,17.2967,19.0026,21.5097,23.0894,23.7319,24.9677,26.0349 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Fossil,EJ/yr,20.8018,23.7205,20.2419,15.3523,20.2168,19.6857,20.3774,22.8969,24.6254,25.638 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.71,3.3386,5.1304,7.839,7.8939,9.6736,10.9419,11.4285,12.0943,13.2635 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5MAF,Emissions|CO2,Mt CO2/yr,3545.0698,4292.3875,2710.7798,1453.2263,355.8987,-448.7245,-228.2507,-99.3777,-120.9632,-461.0813 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy,EJ/yr,50.1185,63.2988,65.8793,76.9375,102.549,121.0347,140.4563,161.668,180.9822,197.8438 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Biomass,EJ/yr,10.682,11.5634,12.8525,18.7787,24.5703,33.0014,39.928,45.2067,50.3343,55.4693 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Fossil,EJ/yr,38.8582,50.3051,49.7367,47.1629,64.001,66.6009,73.4678,86.1673,95.2347,100.3287 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5048,0.9109,2.3756,9.8484,12.7813,19.9921,25.2414,28.129,32.7979,38.8211 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13558.3238,14241.3641,10181.779,5548.1678,3870.8359,968.9566,13.9215,149.9788,222.9673,-394.6023 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy,EJ/yr,211.1641,231.8437,222.3359,225.9648,252.8324,264.6834,276.5952,289.5177,299.7478,307.6541 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.5494,15.039,26.7014,43.5175,48.5685,62.7999,72.658,77.6652,82.6913,88.1489 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,186.9954,201.5313,169.8419,137.8014,155.038,144.457,142.0039,148.7726,151.2009,149.633 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,5.6867,6.8545,15.4105,34.1072,40.3499,47.4495,50.9093,51.6624,53.7698,56.8458 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5REF,Emissions|CO2,Mt CO2/yr,2875.2714,3231.5664,2418.9373,1073.4334,459.0926,-49.1885,-85.0832,-13.9317,51.9301,17.6091 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5REF,Primary Energy,EJ/yr,43.3222,52.1359,48.4208,52.2247,64.7442,67.6801,70.1538,72.9618,75.3795,77.0172 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Biomass,EJ/yr,0.4739,0.5377,0.937,5.3946,6.1987,8.9837,9.8137,10.2596,10.7959,11.0221 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Fossil,EJ/yr,40.8083,48.6568,40.1401,32.6607,43.4063,41.3462,41.7333,43.5812,44.2908,43.8277 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.0723,1.4981,5.3123,11.5767,11.9787,13.4977,13.9106,13.3962,13.3144,13.6607 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892288882,1.155622196,1.447042219,1.569221896,1.617374666,1.614467766,1.58294457,1.561630952,1.558710098,1.560135221 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,World,Emissions|CO2,Mt CO2/yr,33909.3468,38682.1867,23423.4814,11684.0263,7141.7513,1032.6013,-25.3164,773.4874,1162.8789,20.1297 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,World,Primary Energy,EJ/yr,492.225,582.1141,530.248,561.5937,656.9442,700.9352,744.1080000000001,792.7008,833.0616,863.6652 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,World,Primary Energy|Biomass,EJ/yr,49.7764,56.8418,76.2222,126.1336,143.4373,174.5773,195.5478,206.6931,218.6614,229.9617 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,World,Primary Energy|Fossil,EJ/yr,417.4756,490.1066,371.0092,299.5547,375.6465,362.3118,366.9382,399.7986,417.8939,421.1449 +AIM/CGE 2.1,CD-LINKS_NPi2020_1000,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.5102,21.9047,63.6416,116.5163,121.3311,145.2017,159.8573,162.3073,169.8545,182.3295 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5ASIA,Emissions|CO2,Mt CO2/yr,11231.0699,14372.6638,6802.9638,5555.0081,4851.7714,3679.4033,3358.9628,3612.755,3617.7262,2987.1205 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy,EJ/yr,145.7406,191.127,156.0514,168.051,181.6019,191.098,201.4649,206.3022,209.4784,212.5944 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Biomass,EJ/yr,23.6646,24.128,27.3336,31.7774,37.7556,42.9186,47.2298,46.3451,45.8773,47.8797 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Fossil,EJ/yr,116.193,155.0913,86.6136,86.8001,92.6028,89.4035,92.63600000000001,96.1461,97.6764,96.9756 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.5139,9.264,36.3683,44.8068,47.11,54.1898,56.4646,58.0483,59.5515,60.8455 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5LAM,Emissions|CO2,Mt CO2/yr,1726.7207,1888.2127,1079.5663,277.5255,-123.9694,-327.8985,-288.623,-40.9872,146.9175,24.8103 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy,EJ/yr,28.0022,32.1358,33.3147,37.332,41.3678,46.4214,50.7057,53.7611,57.0009,61.4503 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Biomass,EJ/yr,4.4198,5.6632,8.4028,10.7043,13.5113,17.855,20.2336,20.9319,21.9391,24.6346 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Fossil,EJ/yr,20.8406,23.0217,19.6942,20.1782,20.1485,19.3428,20.5208,21.9529,23.345,24.3117 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.6275,3.2335,4.8313,6.1213,7.452000000000001,8.9667,9.6794,10.5786,11.3952,12.1582 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5MAF,Emissions|CO2,Mt CO2/yr,3515.4965,4345.1236,2836.6233,2268.9951,1717.905,1151.7375,1295.5963,1661.0102,1693.0447,1131.9792 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy,EJ/yr,50.5361,63.9218,67.2945,81.3311,97.91799999999999,119.1474,142.7426,160.5701,178.3951,198.8151 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Biomass,EJ/yr,10.7167,11.6589,13.1135,16.3984,21.491,30.4143,40.5351,44.8917,49.2562,56.5954 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Fossil,EJ/yr,39.2533,50.8871,50.9897,57.5166,63.0565,68.1796,77.3231,85.2767,92.4365,100.2011 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.4941,0.8867,2.3211,6.3351,12.1108,18.9715,22.9137,27.8891,33.5513,38.1793 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13520.1428,14317.764,10688.5861,8816.918,7346.3554,4867.6898,4192.5269,4464.9154,4397.9877,3371.7704 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy,EJ/yr,210.7108,233.6314,229.0034,244.574,257.7356,268.6588,281.9462,291.1575,299.5029,308.6106 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.5975,14.5346,25.1989,33.2883,42.6309,51.3977,60.4542,65.3072,69.8492,75.9664 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,186.3366,203.4433,178.04,174.0044,166.4836,159.5661,159.8643,159.3857,158.5391,157.3842 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,5.7085,7.0925,15.2118,26.3494,37.1384,44.1584,46.5564,49.8435,52.8878,55.5893 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5REF,Emissions|CO2,Mt CO2/yr,2899.4497,3155.2749,2494.4104,1886.213,1406.282,792.4576,666.4027,843.2166,901.8086,661.6629 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5REF,Primary Energy,EJ/yr,43.5337,51.2522,49.0867,56.9858,61.4697,65.5731,69.7695,71.6738,74.0017,76.9353 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Biomass,EJ/yr,0.4748,0.5407,0.9206,2.7939,5.2529,6.8334,7.8697,8.328,8.783,9.6014 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Fossil,EJ/yr,41.0367,47.6611,40.9484,43.4431,41.7294,41.9164,44.27399999999999,44.9231,45.6494,46.2888 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.0598,1.615,5.1973,8.1701,11.344,12.9915,12.9548,12.7289,12.6285,12.5844 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892289247,1.157686741,1.465715939,1.641865397,1.765079989,1.830190189,1.88063373,1.923433982,1.979542951,2.039751845 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,World,Emissions|CO2,Mt CO2/yr,33953.8904,39252.2177,24445.3488,19392.8449,15833.9838,10806.3148,9903.2324,11263.5854,11511.9539,8941.5908 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,World,Primary Energy,EJ/yr,492.6686,587.7207,541.9851,596.0988,648.5398,699.4328,755.6239,793.0377,828.3067,868.3893 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,World,Primary Energy|Biomass,EJ/yr,49.8736,56.5253,74.9693,94.9623,120.6418,149.419,176.3224,185.8037,195.7048,214.6775 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,World,Primary Energy|Fossil,EJ/yr,417.8053,495.7566,383.5202,389.7671,392.4675,386.9425,403.6132,417.2574,427.5742,435.1448 +AIM/CGE 2.1,CD-LINKS_NPi2020_1600,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.4039,22.0917,63.9298,91.7827,115.1553,139.2778,148.5687,159.0885,170.0146,179.3566 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5ASIA,Emissions|CO2,Mt CO2/yr,11118.1933,13764.7936,5937.3393,2348.9719,-156.2849,-747.685,-814.5061,-754.487,-797.5235,-799.7415 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy,EJ/yr,145.4506,185.8611,148.606,155.0328,157.7451,162.7879,169.9443,170.087,170.5798,167.5815 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Biomass,EJ/yr,23.6527,23.9713,27.4003,42.9518,45.8933,45.4782,46.8157,44.9294,43.3619,40.7277 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Fossil,EJ/yr,115.886,149.9278,77.5594,47.4958,28.7035,25.0796,28.6356,24.0261,22.4377,16.9405 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.536,9.3023,37.9886,59.011,77.0453,85.4329,87.081,92.4403,94.9258,98.9521 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5LAM,Emissions|CO2,Mt CO2/yr,1752.9672,1954.9693,964.5018,-1043.7494,-2163.2272,-2277.0012,-2119.3973,-1927.0496,-1771.4419,-1659.9923 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5LAM,Primary Energy,EJ/yr,28.0439,33.0094,34.0734,41.3089,44.378,46.2233,49.7499,51.1604,52.7652,53.0334 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Biomass,EJ/yr,4.4182,5.7304,9.0938,19.776,21.0346,20.7542,21.9823,21.7169,21.7112,20.8808 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Fossil,EJ/yr,20.8026,23.7216,19.3841,12.3781,9.7774,9.0828,10.5841,9.8607,10.0805,8.4743 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.7095,3.338,5.2068,8.7994,13.2444,16.0751,16.8594,19.1948,20.5265,23.1328 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5MAF,Emissions|CO2,Mt CO2/yr,3545.1323,4292.5187,2485.9288,398.4673,-1513.656,-1682.8858,-1634.0672,-1670.0792,-1474.7654,-1422.1624 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5MAF,Primary Energy,EJ/yr,50.1191,63.2999,64.5591,75.3358,90.65,103.3808,119.8037,130.447,142.3503,150.0321 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Biomass,EJ/yr,10.682,11.5634,12.9578,20.6308,28.7287,32.8546,37.8088,40.315,42.7058,42.8107 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Fossil,EJ/yr,38.8591,50.3067,48.0471,40.8091,33.2813,30.8417,37.2837,34.4552,34.2197,27.5433 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5047,0.9106,2.6399,12.7156,27.3731,38.1951,42.8617,53.2399,62.2614,75.6525 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13559.1692,14241.8791,9543.8113,4063.762,-1047.0259,-2089.0455,-2285.4127,-2454.121,-2643.5142,-2841.2523 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy,EJ/yr,211.1693,231.8482,218.0222,220.0692,229.7459,236.1552,249.2184,253.2536,259.4629,256.9875 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.5499,15.0397,27.237,48.2441,60.1424,61.033,66.9445,69.5282,72.4896,73.2736 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,187.0058,201.5412,163.8563,121.0253,90.9572,87.65799999999999,94.0037,88.4374,88.5009,78.992 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,5.6837,6.8513,16.5504,39.0298,64.9712,71.7949,70.9944,75.2635,74.3911,77.1106 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5REF,Emissions|CO2,Mt CO2/yr,2875.2391,3231.5025,2181.2445,609.4483,-423.6789,-590.979,-545.2303,-500.1365,-484.9371,-481.338 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5REF,Primary Energy,EJ/yr,43.3218,52.135,46.5631,49.3973,53.6632,55.6623,59.0989,58.4566,59.14899999999999,57.8869 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Biomass,EJ/yr,0.4739,0.5377,1.0003,6.1283,6.8826,7.7926,8.4809,8.476,8.4916,8.2469 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Fossil,EJ/yr,40.8079,48.6558,37.2896,27.3284,24.0678,23.4446,25.7626,22.624,21.167,16.4208 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.0723,1.4981,6.2418,13.348,19.5524,20.5725,20.1591,21.6319,22.512,24.7126 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892288882,1.15616597,1.445393593,1.555303613,1.555622422,1.475121073,1.403649889,1.338990337,1.291968488,1.245401842 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,World,Emissions|CO2,Mt CO2/yr,33910.33,38682.7636,21604.8704,6795.0827,-4957.6403,-7023.2802,-7004.2178,-6913.2913,-6773.2306,-6818.7646 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,World,Primary Energy,EJ/yr,492.231,582.1197,518.3762,546.7075,580.7813,609.0369,653.0266,668.5937,689.5823,690.6219 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,World,Primary Energy|Biomass,EJ/yr,49.7768,56.8425,77.6891,137.7311,162.6816,167.9127,182.0323,184.9655,188.7602,185.9396 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,World,Primary Energy|Fossil,EJ/yr,417.4880000000001,490.1194,352.6891,254.6003,191.3863,180.9341,201.481,184.5926,181.6809,153.4714 +AIM/CGE 2.1,CD-LINKS_NPi2020_400,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.5064,21.9003,68.6275,132.9037,202.1864,232.0706,237.9554,261.7701,274.6167,299.5607 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5ASIA,Emissions|CO2,Mt CO2/yr,11877.7992,15121.4013,17673.3546,19966.5676,20863.2225,20297.6769,20534.2588,21773.0207,22100.7089,22142.335 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5ASIA,Primary Energy,EJ/yr,152.5406,197.7588,237.0277,273.3489,296.2317,299.7722,307.9475,323.8546,330.1936,331.5655 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Biomass,EJ/yr,23.3728,22.8296,22.8191,24.4522,28.2541,28.6561,29.6592,29.9213,29.8934,30.2194 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Fossil,EJ/yr,124.6366,166.107,201.1931,232.9124,249.3238,249.1333,253.4693,266.5374,269.6135,266.4822 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.3703,5.456,7.9594,9.375,10.31,11.6717,12.2676,12.3274,12.5709,13.1524 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5LAM,Emissions|CO2,Mt CO2/yr,1812.3839,2127.3836,2420.5127,2643.6993,2745.9985,3049.3206,3348.1361,3637.4412,3815.9772,3948.5396 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5LAM,Primary Energy,EJ/yr,28.1766,33.6442,39.3081,45.4109,51.9324,57.3228,62.5065,67.5754,71.5311,75.5294 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Biomass,EJ/yr,3.7651,4.0146,4.4924,5.8275,8.2483,8.4775,9.4231,10.4745,11.5712,13.0987 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Fossil,EJ/yr,21.6534,26.3527,31.0827,35.1499,38.3387,42.8589,46.9022,50.7152,53.2258,55.2518 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.6275,2.9843,3.4249,4.0917,4.9647,5.5197,5.6305,5.7385,5.9699,6.2711 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5MAF,Emissions|CO2,Mt CO2/yr,3542.4259,4332.7965,5024.8889,5845.4981,6771.071999999999,8224.7289,9775.9273,11696.422,13515.451,15233.9944 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5MAF,Primary Energy,EJ/yr,50.0731,63.6651,79.4419,97.6099,119.4632,145.7999,172.7779,203.8849,233.6184,261.0742 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Biomass,EJ/yr,10.6745,11.5364,12.486,14.1115,17.0278,19.9638,23.5351,28.0588,33.5992,38.5485 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Fossil,EJ/yr,38.8284,50.8431,65.0695,80.9404,98.7563,120.3951,141.7627,167.0669,189.4937,209.474 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5184,0.9182,1.3099,1.7899,2.6892,4.1372,5.7779,6.5108,7.5659,9.1731 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13841.9961,15586.8022,16899.209,17478.1202,17368.5273,18074.9758,18957.0157,19741.5871,20071.2329,20069.2902 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy,EJ/yr,211.5568,239.7885,265.1117,291.1752,309.7139,325.846,340.9991,355.1739,364.9478,370.7847 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,7.8588,8.8728,12.7969,24.3988,34.2449,34.9811,38.7324,42.4689,45.7046,49.5246 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,190.1111,216.6756,236.9919,249.2122,253.259,264.872,273.1757,280.461,283.6587,281.6357 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,5.6617,6.5968,7.7554,9.583,12.7142,14.464,15.322,16.1444,17.0849,18.5008 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5REF,Emissions|CO2,Mt CO2/yr,2868.3163,3410.7321,3993.9819,4559.0667,4781.8147,4913.932,5113.8478,5242.8192,5316.2333,5314.0705 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5REF,Primary Energy,EJ/yr,43.2307,53.1478,63.1855,72.8903,78.2103,81.7256,85.0546,87.25,89.2554,90.292 +AIM/CGE 2.1,CD-LINKS_NoPolicy,R5REF,Primary Energy|Biomass,EJ/yr,0.4736,0.5341,0.6607,1.0388,1.9526,2.6334,3.1007,3.4154,3.5108,3.6055 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Energy|Biomass,EJ/yr,5.367792677,4.407702081,4.468680614,4.31494276,3.986439285,4.132561266000001,4.340087389,4.589039365,5.092130751,7.011939074 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5LAM,Primary Energy|Fossil,EJ/yr,23.23964745,29.61090011,35.04172253,41.92883069,47.00640578,48.82263115,51.04764766,59.01247331,69.69546866,76.66198526 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.882060589,3.831755943,5.357124549,7.689744393,9.796979078,12.67814371,16.04005875,17.83059298,19.66601542,21.55722747 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5MAF,Emissions|CO2,Mt CO2/yr,4713.274932,5568.695779,6832.228837000001,8309.725095,9774.703237,12003.79389,14209.39212,17485.23195,22021.82345,26760.23275 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5MAF,Primary Energy,EJ/yr,57.65596237,70.19002176,91.20599455,116.6526934,141.933959,174.1082747,208.7233811,253.7200054,307.7118314,372.1589065 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5MAF,Primary 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Energy|Biomass,EJ/yr,10.29497115,11.84454495,10.95857884,10.44701099,10.04584825,9.809850812,9.70359252,9.695757064,9.654920857999999,9.602099631 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,169.5099835,157.5529508,144.8035073,151.9678887,168.5718869,169.4331983,171.4699242,179.9632367,184.5302005,189.8672273 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,7.940671257000001,14.02055179,18.12740626,24.39747784,26.53388718,33.86985501,38.64186281,41.83961682,43.04263184,42.87742839 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5REF,Emissions|CO2,Mt CO2/yr,2644.923459,2723.14045,2768.397446,2901.180508,3102.008316,3219.077942,3137.821168,3153.175623,3086.183286,3576.799565 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5REF,Primary Energy,EJ/yr,38.87767264,40.65259324,41.51855878,44.33642629,49.24273503,52.1212545,53.63614251,54.55360604,55.31622591,58.59694414 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_INDCi,R5REF,Primary 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1.0,CD-LINKS_NPi,R5ASIA,Primary Energy,EJ/yr,170.2791885,202.3581183,249.4711097,283.0629175,320.144703,348.516081,383.0783256,414.7825588,444.9140859,465.1380046 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5ASIA,Primary Energy|Biomass,EJ/yr,24.35644881,22.05219388,19.6375638,15.8225589,12.0887887,8.64378083,6.461655227000001,5.501553861000001,5.160994227,6.479634458 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5ASIA,Primary Energy|Fossil,EJ/yr,140.2388692,164.1169679,203.5906552,229.212898,262.4127131,287.7953939,318.7462689,343.0042205,365.9768134,373.769545 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.675895808,13.58942366,23.32911713,33.63300271,41.93222087,50.00964674,57.7031959,66.26669192,71.90900476,79.00686732 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5LAM,Emissions|CO2,Mt CO2/yr,4169.982706000001,4211.525206,4588.688093,4930.204941,5155.565729,5057.904185,5375.697546,6488.394261,8468.268494,9541.869054 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5LAM,Primary 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Energy,EJ/yr,195.6502557,191.1667198,199.3346023,211.3219289,226.0397373,232.7992494,238.6475371,250.13129,260.3638853,267.9337434 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.29497115,11.84454495,10.53577469,9.08229709,8.41808244,8.816725446,8.385301541,8.847210611,9.176532941,9.302922988999999 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,169.5099835,157.5529508,162.7684454,171.7579232,188.6201583,189.5214897,190.8867677,195.7132438,202.9226086,207.5690648 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,7.940671257000001,14.02055179,16.94763312,22.3478211,24.34282823,31.74958742,37.1573666,42.13039616,44.3679186,44.36579113 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5REF,Emissions|CO2,Mt CO2/yr,2644.923459,2723.14045,2737.369757,2812.186417,3060.511237,3179.347264,3118.537437,3113.568755,3059.946007,3503.938101 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5REF,Primary Energy,EJ/yr,38.87767264,40.65259324,41.37951163,43.76255545,48.96014086,51.75053848,53.45124096,54.34818733,55.00083358,58.39368476 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5REF,Primary Energy|Biomass,EJ/yr,0.497385814,0.460420756,0.7909665109999999,0.7897095940000001,1.090281741,1.300004477,1.468891613,1.546405618,1.634613848,1.690235122 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5REF,Primary Energy|Fossil,EJ/yr,36.51760473,38.03678797,38.19626794,41.12154195,45.90565047,47.74165671,48.58286114,48.34600793,47.34193991,48.79895423 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.9757712759999999,1.791015525,1.947908153,2.480639087,3.421641762,4.090676605,4.639395232,5.559392914,6.790979983,7.534043134 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893095724,1.171911907,1.42127591,1.688060759,1.960682456,2.246896089,2.548800378,2.896058863,3.261694698,3.670107671 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,World,Emissions|CO2,Mt CO2/yr,38542.01816,39615.22255,44490.08064,48326.74212,53595.16479,57741.63044,63446.61697999999,71076.87071,80021.92967,86705.02672000001 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,World,Primary Energy,EJ/yr,500.739995,550.7518985,636.7892162,719.2688691000001,809.8267007999999,883.5201265000001,968.5471164999999,1068.128998,1177.947712,1284.782597 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,World,Primary Energy|Biomass,EJ/yr,54.28033904,49.54555133,47.0198816,41.635461,36.40337555,31.92018969,27.40574708,24.95768362,23.21565554,28.75904262 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,World,Primary Energy|Fossil,EJ/yr,418.9895513,453.7714691,525.2528110000001,593.2695962,675.4768587,730.4089752,792.8978047999999,867.3778434000001,954.270009,1024.746441 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,17.588137,36.58066413,53.11335606,75.37279881,93.79239134,118.83796,145.3528268,170.1885729,191.1389512,212.6835225 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_1000,R5ASIA,Emissions|CO2,Mt CO2/yr,13264.60633,14548.09498,11683.42162,8485.71649,4862.366837,1973.818053,235.5237966,-818.780951,-1864.561992,-2449.123822 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy,EJ/yr,170.2791885,202.3581183,211.7404267,225.6244015,246.9756203,270.3561941,291.838167,310.6327434,322.1283647,329.2711504 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Biomass,EJ/yr,24.35644881,22.05219388,20.25538989,16.99681508,25.6131444,35.29788539,43.84686944,49.00007068,55.47731899999999,60.15840726 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Fossil,EJ/yr,140.2388692,164.1169679,154.5400179,150.7495259,138.6337806,127.3925122,116.5185069,105.1903896,81.14217841,55.98167478 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.675895808,13.58942366,29.73273269,44.79780426,58.56273179,70.85072715,76.61958320000001,95.62315078,113.9568826,134.7375829 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Energy,EJ/yr,195.6502557,191.1667198,172.511175,163.8252225,169.5889456,175.8811863,178.6668265,183.7223969,188.5849372,194.1363424 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.29497115,11.84454495,12.02765593,12.28585679,23.22209235,38.72741165,49.93525817,57.74080935,64.09889003,69.79116289 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,169.5099835,157.5529508,128.6155989,105.0527277,86.60912227,54.38055684,24.73620596,11.55355981,6.733171482,5.245351936 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,7.940671257000001,14.02055179,20.90475989,32.13515056,42.48906819,59.56351681,79.82625689,92.06338507,104.1672752,114.7672498 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5REF,Emissions|CO2,Mt CO2/yr,2644.923459,2723.14045,1198.620867,438.5153795,-30.37569691,-423.5804569,-682.5344977999999,-793.2603781,-975.9033009999999,-1046.157604 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy,EJ/yr,38.87767264,40.65259324,30.80926573,29.77985803,28.27813289,28.74264388,28.64458944,29.32731141,30.07424342,30.67716804 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Biomass,EJ/yr,0.497385814,0.460420756,1.624316705,2.457920798,3.49910942,4.576998992,5.677864819,6.385992418,7.150854565,7.966072077000001 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Fossil,EJ/yr,36.51760473,38.03678797,23.65416319,19.59335931,13.25395818,9.617603567,4.343992231000001,2.977381049,2.555283896,2.056082749 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.9757712759999999,1.791015525,4.824924352,7.774081982999999,9.605613175,12.06859583,15.99419716,18.47853346,20.45021321,22.23677314 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893095724,1.173574411,1.446638824,1.583938051,1.619795241,1.580840498,1.508634362,1.409886014,1.307358337,1.203779173 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,World,Emissions|CO2,Mt CO2/yr,38542.01816,39615.22255,23824.22308,12039.05505,2737.839477,-7103.209038,-11119.14575,-14120.38951,-16521.49954,-17726.89656 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,World,Primary Energy,EJ/yr,500.739995,550.7518985,518.0269356000001,531.9898261,594.7364884,658.5826624,717.5550779,769.173895,823.6380254,882.5509517 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,World,Primary Energy|Biomass,EJ/yr,54.28033904,49.54555133,68.37658012,65.74836424,122.0417326,174.1722777,213.5783512,236.1963811,255.9404496,281.2453914 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,World,Primary Energy|Fossil,EJ/yr,418.9895513,453.7714691,352.2694866000001,310.1046448,252.8989764,178.0642951,97.78329746,55.54688306,40.60241734,37.29547308 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NPi2020_400,World,Primary Energy|Non-Biomass Renewables,EJ/yr,17.588137,36.58066413,77.10322898,122.1397014,161.1096382,210.0653408,278.9971124,342.4786379,411.9181998,480.2824711 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5ASIA,Emissions|CO2,Mt CO2/yr,13264.60633,15800.24663,18331.93739,20039.87518,22592.74114,24431.62542,27775.71075,30137.07969,31800.46798,31520.20741 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy,EJ/yr,170.2791885,209.398455,252.1552923,288.0595846000001,327.3890715,355.729724,390.2954011,422.4588646000001,452.6501345,472.0431915 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Biomass,EJ/yr,24.35644881,19.30182555,16.73689274,13.65732855,10.67097844,7.767581973,5.787489632000001,4.885588606000001,4.592978848,5.928121256 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Fossil,EJ/yr,140.2388692,176.4989267,211.970882,239.7323257,275.0803755,298.9292052,328.6572702,351.9195445,374.7218919,380.1651323 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,4.675895808,11.76814051,20.62618235,31.13909368,38.63526776,47.74084589,55.99274385,65.81038849,71.78190074,80.19457238 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5LAM,Emissions|CO2,Mt CO2/yr,4169.982706000001,4326.796915999999,4645.446676,4992.363047,5264.874479,5108.519992,5456.096953,6619.387526,8722.171873000001,9663.207378000001 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy,EJ/yr,31.53050978,38.38860691,45.48432879,54.49430535,63.3385052,67.02314304,73.84677751,85.02075511,100.2910292,110.880628 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Biomass,EJ/yr,5.367792677,2.854956177,2.641812846,2.218471749,2.340361777,3.050402708,3.624336876,4.119143473999999,4.540851024,4.934468173 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Fossil,EJ/yr,23.23964745,31.61518527,37.73546613,45.148754,51.88663348,51.70078485,53.46316852,61.49249819,74.36365477,82.57607666 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Energy|Fossil,EJ/yr,169.5099835,179.2278933,182.4657659,186.4501633,200.2057207,203.9228416,203.9961242,212.8791139,219.2128228,222.4195702 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,7.940671257000001,10.18483261,13.98743428,19.61071593,24.45360228,31.16727577,37.38409019,41.09681685,43.94524125,45.3118032 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5REF,Emissions|CO2,Mt CO2/yr,2644.923459,2723.197071,2835.818283,3013.323291,3171.301055,3278.840856,3139.402559,3105.412408,3092.541811,3559.168416 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5REF,Primary Energy,EJ/yr,38.87767264,40.97380089999999,42.70377085,45.87920486,50.22449147,52.71747809,53.09332085,53.82230348,55.04696864,58.28025211 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5REF,Primary Energy|Biomass,EJ/yr,0.497385814,0.460420756,0.468102483,0.460915464,0.462578796,0.467088133,0.721395645,1.092554789,1.351125723,1.51596524 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5REF,Primary Energy|Fossil,EJ/yr,36.51760473,38.37039794,40.363923,44.02526137,47.85136383,49.60721935,49.21358767,48.50536553,47.95517204,49.04497502 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.9757712759999999,1.778613222,1.969767452,2.539435097,3.5577366,4.080205362,4.624213503,5.480352357999999,6.645588977999999,7.509600552999999 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892965937,1.172247435,1.437229246,1.723332665,2.012002306,2.315831284,2.637206138,3.001911246,3.376596455,3.810955915 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,World,Emissions|CO2,Mt CO2/yr,38542.01816,42471.7362,46689.43383,50476.05394,55919.642,60177.6732,65983.1784,74060.47269,83010.45023999999,89455.10514 +MESSAGEix-GLOBIOM 1.0,CD-LINKS_NoPolicy,World,Primary Energy,EJ/yr,500.739995,571.7360413,651.5543861000001,733.4744687000001,828.4766164,904.4612329,986.679077,1089.824029,1201.254614,1306.794566 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CO2/yr,33410.28906,38084.53516,38458.46094,39698.01953,41799.37891,42771.67969,44792.00781,47305.52344,49844.07422,54239.39844 +POLES CD-LINKS,CD-LINKS_INDCi,World,Primary Energy,EJ/yr,511.5497741999999,591.4586792,656.6965332000001,725.6063232000001,801.9833984,867.1793822999999,939.9309082000001,1023.587952,1114.031616,1230.045044 +POLES CD-LINKS,CD-LINKS_INDCi,World,Primary Energy|Biomass,EJ/yr,51.75862503,60.02207184,74.43312836,87.01903534,100.4877548,115.0174866,130.096756,150.8611298,178.3393555,205.052002 +POLES CD-LINKS,CD-LINKS_INDCi,World,Primary Energy|Fossil,EJ/yr,435.166687,492.7774658,519.5649414,549.2770386,592.3634643999999,621.8179932,650.3787842,684.9743652000001,716.7467651000001,771.4134521000001 +POLES CD-LINKS,CD-LINKS_INDCi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.70900631,27.28543854,45.05956268,63.98454666,79.26991272,93.68740082,114.335701,137.0825043,167.5691376,200.2545929 +POLES CD-LINKS,CD-LINKS_NPi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893155293,1.186281844,1.458940219,1.750345386,2.06791697,2.369938106,2.67049662,2.984503104,3.314635593,3.657120412 +POLES CD-LINKS,CD-LINKS_NPi,World,Emissions|CO2,Mt CO2/yr,33410.28906,38548.38281,44502.10938,50206.66797,53557.99219,56460.47656,60029.51172,63593.07812999999,65330.61719,65952.88281 +POLES CD-LINKS,CD-LINKS_NPi,World,Primary Energy,EJ/yr,511.5497741999999,594.97229,694.7386475,798.8026732999999,874.6004027999999,943.4614257999999,1022.091248,1106.119385,1189.96814,1282.329468 +POLES CD-LINKS,CD-LINKS_NPi,World,Primary Energy|Biomass,EJ/yr,51.75862503,59.89888000000001,71.14794922,82.99362183,94.31829071,107.7866669,122.5828857,144.033844,176.54129030000001,211.5833588 +POLES CD-LINKS,CD-LINKS_NPi,World,Primary Energy|Fossil,EJ/yr,435.166687,497.6175537,569.6946411,642.2964477999999,688.1627197,724.8615722999999,766.9397583,805.3405151000001,818.2994995,821.7904662999999 +POLES CD-LINKS,CD-LINKS_NPi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.70900631,26.74145699,40.47192764,55.96110535,71.57435608,84.09262848,99.45980835,119.9466858,150.1547241,192.1508331 +POLES CD-LINKS,CD-LINKS_NPi2020_1000,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893155293,1.186799681,1.451180009,1.640802383,1.732965228,1.7354987,1.703892742,1.651978985,1.585675121,1.526570447 +POLES CD-LINKS,CD-LINKS_NPi2020_1000,World,Emissions|CO2,Mt CO2/yr,33410.28906,38551.99609,32734.76367,21075.55469,11418.37012,3820.002197,-1607.404053,-6542.607422,-9843.076172,-12973.55859 +POLES CD-LINKS,CD-LINKS_NPi2020_1000,World,Primary Energy,EJ/yr,511.5497741999999,594.9638062000001,619.3252562999999,584.2005615,584.4039307,618.8830566,662.2244262999999,716.3175659,794.8240967,883.4985352 +POLES CD-LINKS,CD-LINKS_NPi2020_1000,World,Primary Energy|Biomass,EJ/yr,51.75862503,59.88308334,77.79786682,108.4647141,148.5373993,178.810379,207.7939148,236.637558,277.6866455,316.1776733 +POLES CD-LINKS,CD-LINKS_NPi2020_1000,World,Primary Energy|Fossil,EJ/yr,435.166687,497.6171875,475.3866882,364.9449463,299.5336609,279.8308716,263.5180359,260.6799011,277.0352173,304.5376587 +POLES CD-LINKS,CD-LINKS_NPi2020_1000,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.70900631,26.74747276,47.75049591,74.65822601,96.31022644,106.6266403,127.0505524,150.2962646,171.878006,201.1369934 +POLES CD-LINKS,CD-LINKS_NPi2020_1600,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893155293,1.186710983,1.449498618,1.66129814,1.819707136,1.881898016,1.904231519,1.899273074,1.86628582,1.833542371 +POLES CD-LINKS,CD-LINKS_NPi2020_1600,World,Emissions|CO2,Mt CO2/yr,33410.28906,38551.99609,35566.13281,30633.02344,21434.00586,12403.19824,6310.85791,1346.434448,-3475.012695,-5588.541992 +POLES CD-LINKS,CD-LINKS_NPi2020_1600,World,Primary Energy,EJ/yr,511.5497741999999,594.9638062000001,645.5490722999999,663.5064697,662.5419312000001,698.9066162,742.572876,803.2327881,881.315918,975.6468506000001 +POLES CD-LINKS,CD-LINKS_NPi2020_1600,World,Primary Energy|Biomass,EJ/yr,51.75862503,59.88308334,75.19828033,94.39742279,125.0939941,167.2025299,199.3632202,232.6823578,272.0851746,309.0293579 +POLES CD-LINKS,CD-LINKS_NPi2020_1600,World,Primary Energy|Fossil,EJ/yr,435.166687,497.6171875,508.5823058999999,470.9768372,405.7408447,381.1898499,363.8564148,367.6329041,379.8633423,410.7503052 +POLES CD-LINKS,CD-LINKS_NPi2020_1600,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.70900631,26.74747276,45.33234024,69.66957092,94.61107635,103.6108704,123.0472488,142.2631989,168.7640228,199.1721039 +POLES CD-LINKS,CD-LINKS_NPi2020_400,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893155293,1.18710092,1.460284465,1.597108699,1.629901114,1.594221477,1.525009943,1.437988685,1.341138613,1.245642578 +POLES CD-LINKS,CD-LINKS_NPi2020_400,World,Emissions|CO2,Mt CO2/yr,33410.28906,38551.99609,24099.94531,12185.68457,3858.141357,-3048.635986,-9210.530273,-12924.87793,-18214.25781,-21672.30273 +POLES CD-LINKS,CD-LINKS_NPi2020_400,World,Primary Energy,EJ/yr,511.5497741999999,594.9638062000001,538.8311767999999,495.3410339,509.4347228999999,530.6717529,571.1760864,628.3713379,703.4735107000001,767.9169922 +POLES CD-LINKS,CD-LINKS_NPi2020_400,World,Primary Energy|Biomass,EJ/yr,51.75862503,59.88308334,88.23204041,121.0147781,162.0822601,184.0659943,213.4346313,249.8118896,299.8327637,330.962738 +POLES CD-LINKS,CD-LINKS_NPi2020_400,World,Primary Energy|Fossil,EJ/yr,435.166687,497.6171875,371.0761108,254.5559998,207.6720734,168.0068207,146.4059753,139.9837494,145.47612,149.9609375 +POLES CD-LINKS,CD-LINKS_NPi2020_400,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.70900631,26.74747276,53.80437088,76.17578888,89.9420929,110.323555,131.9131165,153.0986633,174.281723,198.9486542 +POLES CD-LINKS,CD-LINKS_NoPolicy,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.893156608,1.180212736,1.455759327,1.76962537,2.107326673,2.425688319,2.745210045,3.075033996,3.431680163,3.790658709 +POLES CD-LINKS,CD-LINKS_NoPolicy,World,Emissions|CO2,Mt CO2/yr,33410.28906,39914.55078,47383.4375,52321.16406,55972.65234,59632.30078,63904.79297,67613.20313,67885.28906,68130.30469 +POLES CD-LINKS,CD-LINKS_NoPolicy,World,Primary Energy,EJ/yr,511.5498047,604.223999,718.9290161,815.2231445,893.8519897,965.2816162,1045.956299,1127.242554,1200.848755,1291.537964 +POLES CD-LINKS,CD-LINKS_NoPolicy,World,Primary Energy|Biomass,EJ/yr,51.75863266,56.561409,69.14776611,82.44009399,94.82110596,107.7986755,121.1794205,143.5255432,177.1661987,213.0999451 +POLES CD-LINKS,CD-LINKS_NoPolicy,World,Primary Energy|Fossil,EJ/yr,435.1667175,510.8157348999999,596.9584351,660.0652466,707.1826782,747.7692261,793.6566772000001,827.3300171,823.6904907,823.3115234 +POLES CD-LINKS,CD-LINKS_NoPolicy,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.70900631,26.0188427,39.03312302,55.06229401,71.35621643,83.46845245,98.94379425,120.4007721,153.9985657,196.3602448 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5ASIA,Emissions|CO2,Mt CO2/yr,13319.094,17953.8288,18860.5696,20919.9611,21346.1127,20797.3295,20655.9721,20852.5609,19611.0956,18684.4507 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5ASIA,Primary Energy,EJ/yr,160.5996,234.5947,288.156,352.2832,402.4998,445.8724,484.0864,525.0028,542.3875,559.8452 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5ASIA,Primary Energy|Biomass,EJ/yr,22.3057,24.1898,24.6199,25.2827,25.1562,26.2549,28.5443,30.8629,33.1284,34.9503 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5ASIA,Primary Energy|Fossil,EJ/yr,133.8078,198.1775,237.6019,274.0638,281.0275,275.0711,272.9519,283.9179,281.9163,278.9226 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.5101,9.8455,20.5981,43.0997,80.6917,123.8049,159.0385,186.7133,206.3205,227.7407 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5LAM,Emissions|CO2,Mt CO2/yr,3841.7418,4100.6996,3293.7924,2044.4228,2348.7068,2546.613,2466.7808,2357.3265,2160.2127,1898.8835 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5LAM,Primary Energy,EJ/yr,29.8951,39.0774,42.9932,51.5912,60.7617,69.9218,78.8821,88.9604,96.07799999999999,101.8294 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5LAM,Primary Energy|Biomass,EJ/yr,4.864,6.3489,6.6343,6.1658,5.9597,6.1192,6.682,7.0931,7.8861,8.4858 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5LAM,Primary Energy|Fossil,EJ/yr,22.266,27.9422,28.3666,32.8956,36.7161,39.2791,41.0925,43.9788,44.5915,45.1225 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.657,4.6398,7.8323,12.3833,17.9729,24.4645,31.0912,37.8869,43.6004,48.2211 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5MAF,Emissions|CO2,Mt CO2/yr,3338.42,5794.1257,7223.7294,8619.6929,10859.7901,12689.7547,13635.7874,14349.2884,14149.6996,13582.5528 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5MAF,Primary Energy,EJ/yr,54.0155,79.3982,100.5798,126.3771,161.52,206.4539,248.7081,297.1574,334.8106,364.1688 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5MAF,Primary Energy|Biomass,EJ/yr,12.7719,13.6775,11.91,9.104,6.4812,7.8783,9.3921,10.9059,12.4136,13.3631 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5MAF,Primary Energy|Fossil,EJ/yr,40.462,64.2602,85.5585,109.3307,135.2775,157.8203,171.8376,188.5503,196.674,200.3554 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.7731,1.333,2.8828,7.7198,19.5635,40.6251,67.4489,97.685,125.712,150.4463 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13425.7405,11886.5214,9499.4507,8374.9951,7484.1184,6456.215,5624.210999999999,5243.8898,4656.6352,4315.5721 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy,EJ/yr,210.3054,201.8249,192.196,201.9765,212.4955,221.1029,227.5567,238.1622,240.9411,249.0803 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.8308,15.9821,17.1748,16.969,17.3673,18.6851,19.0383,19.8595,20.7418,21.5619 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,185.5435,168.3671,147.8441,141.772,132.8658,122.3279,113.5548,111.9453,107.4143,105.7079 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,6.851,11.9682,22.4746,38.5082,57.1205,74.7248,89.7767,102.0005,109.4646,119.2557 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5REF,Emissions|CO2,Mt CO2/yr,1437.8754,1692.1186,1856.6008,1959.4409,2085.8475,2021.2251,1720.9301,1374.2122,1067.1381,894.9409 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5REF,Primary Energy,EJ/yr,23.5996,27.6324,29.1332,31.4546,32.3682,31.9663,30.5155,29.2096,27.7875,27.1487 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5REF,Primary Energy|Biomass,EJ/yr,0.4032,1.1725,1.3183,1.3542,1.3788,1.3997,1.4237,1.4561,1.5035,1.5454 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5REF,Primary Energy|Fossil,EJ/yr,22.1774,25.0594,26.1622,27.9859,27.8384,25.5224,21.1713,16.925,13.8701,12.5754 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5315,0.6786,0.9688,1.5455,2.7404,4.8821,7.9138,10.8285,12.4139,13.0278 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892607261,1.187077733,1.514069822,1.811170138,2.098683572,2.34766849,2.572986694,2.772386926,2.968429018,3.156375532 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,World,Emissions|CO2,Mt CO2/yr,36463.2378,42681.5938,42239.835,43758.0831,46292.8512,47006.2941,46746.4588,47048.3046,44683.5341,42516.1259 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,World,Primary Energy,EJ/yr,478.4152,582.5275,653.0584,763.6826,869.6453,975.3173,1069.749,1178.492,1242.005,1302.072 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,World,Primary Energy|Biomass,EJ/yr,51.1756,61.3706,61.6574,58.8756,56.3432,60.3372,65.0804,70.1774,75.6733,79.9065 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,World,Primary Energy|Fossil,EJ/yr,404.2566,483.8063,525.5333,586.0479,613.7252,620.0208,620.6081,645.3174,644.4663,642.6839 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_INDCi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.3226,28.465,54.7565,103.2565,178.0891,268.5013,355.2691,435.1141,497.5113,558.6915 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5ASIA,Emissions|CO2,Mt CO2/yr,13220.7444,17822.1068,20654.7162,24211.2922,26772.5449,27780.1917,28050.7187,29186.9431,28444.9586,27678.1683 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5ASIA,Primary Energy,EJ/yr,160.5996,234.5947,299.5833,375.372,436.1302,488.9466,526.4544,570.1559,586.6579,604.8107 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5ASIA,Primary Energy|Biomass,EJ/yr,22.3057,24.1898,24.6924,25.2808,25.1519,26.2416,28.5179,30.8629,33.1284,35.0116 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5ASIA,Primary Energy|Fossil,EJ/yr,133.8078,198.1775,251.8058,307.107,332.6303,339.627,335.6872,345.8994,339.2022,334.6728 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.5101,9.8455,18.2165,34.9513,66.1689,106.52,142.0148,170.9431,192.0623,214.4695 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5LAM,Emissions|CO2,Mt CO2/yr,3852.3752,4150.588,3608.2485,2948.1188,3865.5765,5118.412,5829.7378,6081.8616,5727.375,5121.407 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5LAM,Primary Energy,EJ/yr,29.8951,39.0774,47.4268,58.8739,73.0771,90.7119,104.5094,115.5156,119.4568,119.8467 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5LAM,Primary Energy|Biomass,EJ/yr,4.864,6.3489,6.3371,5.2613,5.7193,6.2035,6.4879,7.0292,7.5084,7.6689 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5LAM,Primary Energy|Fossil,EJ/yr,22.266,27.9422,33.4673,42.2598,51.1759,62.39899999999999,69.4977,73.1531,70.6454,65.8962 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.657,4.6398,7.4697,11.2194,16.082,22.0616,28.5138,35.3327,41.303,46.2816 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5MAF,Emissions|CO2,Mt CO2/yr,3339.383,5906.1451,7365.0359,8832.6354,10801.4481,14044.6709,15495.7819,16708.5279,17618.4055,18368.4565 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5MAF,Primary Energy,EJ/yr,54.0155,79.3982,101.8216,127.9546,163.9237,211.2151,257.125,311.1844,354.9038,393.4169 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5MAF,Primary Energy|Biomass,EJ/yr,12.7719,13.6775,11.91,9.1028,6.4828,7.8921,9.3868,10.9059,12.4136,13.3594 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5MAF,Primary Energy|Fossil,EJ/yr,40.462,64.2602,86.9801,111.9859,140.5339,167.5483,186.3998,208.5664,221.6894,233.714 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.7731,1.333,2.707,6.6464,16.7032,35.613,61.2382,91.6059,120.7125,146.2864 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13494.1172,12317.0405,11244.0652,10714.2302,10241.0335,9596.6142,8669.5392,8012.8893,6840.6466,6344.6324 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy,EJ/yr,210.3054,201.8249,207.9397,218.06,225.5591,232.5946,236.6349,245.5527,242.5237,248.4485 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.8308,15.9821,18.6072,17.9381,17.1972,18.4223,19.0829,19.893,20.7134,21.5387 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,185.5435,168.3671,165.2965,166.2739,160.3442,150.3441,137.3549,129.5953,114.3626,108.6768 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,6.851,11.9682,19.8313,30.6209,45.3141,61.3746,77.6954,93.4675,104.9192,115.8815 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5REF,Emissions|CO2,Mt CO2/yr,1438.9057,1631.9133,1897.4922,2108.8875,2199.7575,2221.5535,2008.1141,1719.999,1424.0075,1258.4556 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5REF,Primary Energy,EJ/yr,23.5996,27.6324,28.9574,31.6026,33.319,33.5868,32.3555,31.3039,29.5538,28.9283 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5REF,Primary Energy|Biomass,EJ/yr,0.4032,1.1725,1.3183,1.3542,1.3788,1.3994,1.4237,1.4561,1.4982,1.5414 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5REF,Primary Energy|Fossil,EJ/yr,22.1774,25.0594,26.0313,28.3483,29.3793,28.21,24.5406,20.5273,16.6948,14.9907 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5315,0.6786,0.9237,1.3311,2.1501,3.8152,6.3844,9.3205,11.3608,12.3962 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892619899,1.180472085,1.526336684,1.866075693,2.232296696,2.580474447,2.922291848,3.24978827,3.540648768,3.796107962 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,World,Emissions|CO2,Mt CO2/yr,36445.8913,43082.0934,46315.7386,50722.0565,56121.0939,61381.9682,62880.2979,64825.9655,63383.9778,62281.2344 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,World,Primary Energy,EJ/yr,478.4152,582.5275,685.7288,811.8631,932.0089999999999,1057.055,1157.079,1273.712,1333.096,1395.451 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,World,Primary Energy|Biomass,EJ/yr,51.1756,61.3706,62.8651,58.9372,55.9301,60.1589,64.8993,70.14699999999999,75.2618,79.1199 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,World,Primary Energy|Fossil,EJ/yr,404.2566,483.8063,563.581,655.9749,714.0637,748.1284,753.4803,777.7415,762.5943,757.9507 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.3226,28.465,49.1482,84.7692,146.4182,229.3845,315.8466,400.6698,470.3577,535.3152 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5ASIA,Emissions|CO2,Mt CO2/yr,13220.7444,17637.0913,12246.2273,7670.4859,5255.6533,2502.5318,966.0797,224.6233,-291.8497,-623.7859 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy,EJ/yr,160.5996,234.5947,237.4344,272.518,320.5609,359.505,385.5779,413.4906,435.5334,456.4226 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Biomass,EJ/yr,22.3057,24.1898,27.3484,29.427,35.9215,47.2139,53.1817,57.8899,62.4186,64.7402 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Fossil,EJ/yr,133.8078,198.1775,173.0695,149.8328,129.4217,106.466,90.4848,81.2442,74.3651,70.2149 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.5101,9.8455,30.5143,80.7079,138.2135,187.3453,224.1655,257.5121,279.8931,297.1341 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5LAM,Emissions|CO2,Mt CO2/yr,3852.3752,3777.5196,1876.0279,-6.7009,-1924.8428,-3305.3893,-3922.8145,-3994.0655,-3981.3005,-4032.9298 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy,EJ/yr,29.8951,39.0774,41.5816,50.3653,66.1363,82.0435,94.1641,105.0306,112.6593,119.36 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Biomass,EJ/yr,4.864,6.3489,7.2778,12.1804,25.1727,39.1737,46.9956,49.5443,49.5574,48.4409 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Fossil,EJ/yr,22.266,27.9422,25.5389,22.9219,18.2623,12.1874,8.6521,8.3439,7.9079,7.8763 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.657,4.6398,8.6092,15.126,22.598,30.632,38.5054,47.142,55.1939,63.0428 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5MAF,Emissions|CO2,Mt CO2/yr,3339.383,5458.2208,4255.1211,2979.2109,1478.5722,71.61,-494.6348,-688.4802,-1188.1192,-1727.5492 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy,EJ/yr,54.0155,79.3982,80.2817,88.5165,113.4709,145.2882,178.9035,222.9034,273.4106,317.5615 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Biomass,EJ/yr,12.7719,13.6775,12.0661,11.1504,15.066,23.9722,28.8649,34.1035,44.8386,53.2044 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Fossil,EJ/yr,40.462,64.2602,63.0051,59.4472,59.3318,55.8991,53.5955,51.2867,46.1843,44.6853 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.7731,1.333,4.8529,17.4399,38.5994,65.05199999999999,96.2687,137.4636,182.3826,219.6716 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13494.1172,12227.805,8647.3376,5931.9982,3532.4241,1069.7914,-915.3447,-2245.8386,-2934.3867,-3246.1688 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy,EJ/yr,210.3054,201.8249,187.9816,192.5499,204.4871,219.9029,232.2549,245.84,255.906,266.2036 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.8308,15.9821,17.8922,20.9448,30.7149,44.8857,59.3853,71.1236,77.7313,80.9908 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,185.5435,168.3671,138.4679,112.3969,86.7392,66.6852,48.0179,34.3477,27.2416,23.0994 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,6.851,11.9682,27.1533,55.4253,83.6502,105.2231,121.8786,137.5516,148.2774,159.6188 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5REF,Emissions|CO2,Mt CO2/yr,1438.9057,1671.7516,1205.5996,566.0777,326.2087,223.6203,130.3283,20.7033,-141.8416,-109.1707 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5REF,Primary Energy,EJ/yr,23.5996,27.6324,22.2452,18.7587,18.4338,18.6534,19.2369,20.4102,21.9078,23.2007 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Biomass,EJ/yr,0.4032,1.1725,1.3183,1.3545,1.3971,1.5146,2.1202,3.253,5.124,6.6927 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Fossil,EJ/yr,22.1774,25.0594,18.3306,11.318,7.4698,5.6826,4.7206,3.7,2.5666,1.7948 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5315,0.6786,1.8578,5.4454,9.0877,11.2376,12.3615,13.4548,14.2171,14.7132 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892619899,1.180747693,1.500982072,1.684304123,1.758067489,1.771149617,1.744812595,1.707966365,1.65836978,1.598854993 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,World,Emissions|CO2,Mt CO2/yr,36445.8913,42026.68799999999,29728.5776,18793.0676,10289.4231,2024.0599,-3011.8106,-5674.8561,-7641.2143,-8996.9473 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,World,Primary Energy,EJ/yr,478.4152,582.5275,569.5246,622.7085,723.0889,825.3931,910.1372,1007.675,1099.417,1182.748 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,World,Primary Energy|Biomass,EJ/yr,51.1756,61.3706,65.9029,75.0572,108.2722,156.7601,190.5478,215.9143,239.6699,254.0692 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,World,Primary Energy|Fossil,EJ/yr,404.2566,483.8063,418.412,355.917,301.2247,246.9202,205.4708,178.9225,158.2655,147.6706 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1000,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.3226,28.465,72.9876,174.1447,292.1488,399.4899,493.1796,593.1241,679.9641,754.1804 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5ASIA,Emissions|CO2,Mt CO2/yr,13220.7444,17646.65,14156.7889,10894.9048,8000.4705,5603.191,4128.8669,3023.2097,2087.9136,1246.064 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy,EJ/yr,160.5996,234.5947,255.7134,288.4374,330.9751,368.5523,397.1296,423.9952,442.519,463.4153 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Biomass,EJ/yr,22.3057,24.1898,26.4088,27.2409,28.4182,33.6665,39.1326,46.2113,53.2673,60.3097 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Fossil,EJ/yr,133.8078,198.1775,194.9958,176.6724,158.8959,141.8302,130.886,123.0228,113.2085,102.3134 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.5101,9.8455,28.1182,72.6282,126.8802,174.1955,208.9329,238.3625,259.4005,280.1018 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5LAM,Emissions|CO2,Mt CO2/yr,3852.3752,3750.7601,2305.2108,1279.8359,-66.5102,-1948.633,-2918.8738,-3397.0463,-3564.7392,-3703.7021 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy,EJ/yr,29.8951,39.0774,43.2175,51.49100000000001,62.9452,76.6572,87.9727,98.79899999999999,106.6781,115.3262 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Biomass,EJ/yr,4.864,6.3489,6.8648,8.5389,15.2027,25.7786,34.2388,40.3067,43.0049,46.6577 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Fossil,EJ/yr,22.266,27.9422,27.8891,28.5361,26.263,21.6719,17.0971,14.0279,12.4246,11.1921 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.657,4.6398,8.3133,14.2878,21.3846,29.1635,36.6299,44.4645,51.2485,57.4764 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5MAF,Emissions|CO2,Mt CO2/yr,3339.383,5485.3977,5071.3634,4588.2706,3022.3864,1672.1734,1082.1199,886.5512,848.8685,844.2729 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy,EJ/yr,54.0155,79.3982,85.5348,96.0846,116.3937,146.1985,179.1191,219.2637,257.0298,292.3838 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Biomass,EJ/yr,12.7719,13.6775,12.2472,10.755,10.0831,14.5801,20.0544,23.8534,25.392,28.5978 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Fossil,EJ/yr,40.462,64.2602,68.6268,69.536,70.5647,71.2152,71.9284,74.0772,73.9762,72.0361 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.7731,1.333,4.3452,15.4013,35.3639,60.1186,87.0133,121.2971,157.657,191.7498 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13494.1172,12231.1575,9897.9101,7497.5523,5396.0535,3666.8539,2018.8261,739.9734,-171.3553,-1024.7582 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy,EJ/yr,210.3054,201.8249,198.951,200.984,208.4651,219.4337,229.9594,242.5356,250.0723,261.2688 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.8308,15.9821,17.4528,17.3981,19.864,26.3412,35.4037,44.3929,51.6026,61.7311 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,185.5435,168.3671,152.1003,130.7204,110.2615,93.9591,79.5042,68.259,57.5115,46.5367 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,6.851,11.9682,25.0297,49.2857,75.1791,96.2271,112.2439,127.2069,138.4716,150.691 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5REF,Emissions|CO2,Mt CO2/yr,1438.9057,1653.3816,1439.4949,853.6647,402.4653,282.001,210.8997,159.9588,99.7412,99.5846 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5REF,Primary Energy,EJ/yr,23.5996,27.6324,24.6232,21.2262,18.8659,19.3403,19.562,20.4278,21.2491,21.8892 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Biomass,EJ/yr,0.4032,1.1725,1.3183,1.357,1.3807,1.4052,1.5765,1.9053,2.4783,3.5391 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Fossil,EJ/yr,22.1774,25.0594,21.0191,14.9179,8.9597,6.9229,5.7637,5.2565,4.7233,4.0046 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5315,0.6786,1.5614,4.3303,8.0654,10.8095,12.1955,13.2647,14.0475,14.3455 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892619899,1.180486659,1.502288257,1.736212941,1.873639589,1.920775612,1.943807449,1.939650052,1.92819649,1.911311065 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,World,Emissions|CO2,Mt CO2/yr,36445.8913,42021.6469,34421.7996,26907.3445,18635.6956,11073.4659,6105.3905,2818.9686,658.5260000000001,-1327.8201 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,World,Primary Energy,EJ/yr,478.4152,582.5275,608.0397,658.2230000000001,737.6451,830.1821,913.7428,1005.021,1077.548,1154.283 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,World,Primary Energy|Biomass,EJ/yr,51.1756,61.3706,64.2919,65.2899,74.9487,101.7715,130.4061,156.6695,175.7451,200.8354 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,World,Primary Energy|Fossil,EJ/yr,404.2566,483.8063,464.6311,420.3828,374.9448,335.5993,305.1793,284.6433,261.8441,236.0827 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_1600,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.3226,28.465,67.3679,155.9332,266.8731,370.5143,457.0155,544.5957,620.825,694.3644 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5ASIA,Emissions|CO2,Mt CO2/yr,13220.7444,17618.7418,10519.922,4957.5099,1822.3785,-319.9177,-1037.8583,-1164.5026,-1326.387,-1718.9073 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy,EJ/yr,160.5996,234.5947,223.9155,256.2096,309.6696,342.2746,366.2664,392.7239,413.5575,435.4649 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Biomass,EJ/yr,22.3057,24.1898,27.373,35.7507,48.5484,56.1077,59.1681,61.6569,62.7731,63.28899999999999 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Fossil,EJ/yr,133.8078,198.1775,156.1801,116.912,92.4716,67.6387,55.7618,51.8576,49.0296,46.59399999999999 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.5101,9.8455,33.7524,91.5939,154.0257,203.6692,236.9838,263.7836,281.434,298.1117 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5LAM,Emissions|CO2,Mt CO2/yr,3852.3752,3769.5612,1074.1404,-1487.4317,-3075.2989,-3758.6264,-4314.2714,-4721.2221,-5022.8129,-5291.9625 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5LAM,Primary Energy,EJ/yr,29.8951,39.0774,39.444,50.602,69.0605,79.814,91.7546,100.7653,107.2279,118.3447 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Biomass,EJ/yr,4.864,6.3489,7.7406,16.6548,32.7973,40.7224,46.7718,45.9413,42.7142,45.0034 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Fossil,EJ/yr,22.266,27.9422,22.663,18.1473,13.0493,7.949,5.6554,6.0506,5.8545,5.3313 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.657,4.6398,8.8885,15.6703,23.1178,31.0983,39.32,48.7733,58.6593,68.01 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5MAF,Emissions|CO2,Mt CO2/yr,3339.383,5297.6902,3158.2234,1222.1223,-473.6514,-2566.6801,-3460.6495,-3763.3399,-3657.851,-3389.6015 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5MAF,Primary Energy,EJ/yr,54.0155,79.3982,72.6791,79.1482,106.38,140.2357,178.3073,225.32,268.685,307.3981 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Biomass,EJ/yr,12.7719,13.6775,12.0312,12.4819,21.5003,37.0713,49.3472,57.5189,62.3333,64.3346 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Fossil,EJ/yr,40.462,64.2602,54.5732,44.7632,38.1751,28.2862,23.3165,22.989,23.3248,24.488 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.7731,1.333,5.6705,21.3656,46.1939,74.4874,105.466,144.7779,183.026,218.5755 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13494.1172,12228.4714,7508.1214,3568.4867,272.2153,-1799.7195,-3026.4789,-3613.4761,-4060.6871,-4572.1765 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy,EJ/yr,210.3054,201.8249,178.5958,185.4678,206.7548,223.6106,232.3278,246.5663,260.4589,273.9807 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.8308,15.9821,20.5871,35.2812,55.5007,70.6323,78.5654,83.3174,87.1715,91.0655 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,185.5435,168.3671,123.2936,83.0105,55.1507,37.7973,23.9333,16.4345,13.5214,10.5145 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,6.851,11.9682,30.19,63.1878,92.2473,111.3476,125.9987,143.1976,156.512,169.5095 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5REF,Emissions|CO2,Mt CO2/yr,1438.9057,1687.3132,901.7263,374.1632,238.2976,67.0605,-132.8028,-278.5019,-475.0203,-635.7881 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy,EJ/yr,23.5996,27.6324,18.7586,16.3422,17.2798,17.9376,19.4758,21.7677,24.4973,27.6991 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Biomass,EJ/yr,0.4032,1.1725,1.3183,1.3861,1.8158,3.1572,5.1911,6.9043,9.2806,13.0519 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Fossil,EJ/yr,22.1774,25.0594,14.4391,7.8334,5.2818,3.3259,1.9096,1.1588,1.1372,0.9919 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5315,0.6786,2.2094,6.4031,9.6254,11.1707,12.305,13.6971,14.0794,13.6553 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892619899,1.180672636,1.493470538,1.634441,1.657205782,1.618022512,1.553468966,1.47747166,1.393240784,1.309975398 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,World,Emissions|CO2,Mt CO2/yr,36445.8913,41856.0778,24570.5493,10060.4881,94.5885,-7360.4691,-11298.7437,-13138.914,-14203.1937,-15378.92 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,World,Primary Energy,EJ/yr,478.4152,582.5275,533.393,587.7699,709.1447,803.8726,888.1318,987.1432,1074.427,1162.887 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,World,Primary Energy|Biomass,EJ/yr,51.1756,61.3706,69.0502,101.5547,160.1625,207.6907,239.0436,255.3388,264.2726,276.7445 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,World,Primary Energy|Fossil,EJ/yr,404.2566,483.8063,371.1489,270.6663,204.1285,144.9971,110.5765,98.4905,92.8673,87.9196 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NPi2020_400,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.3226,28.465,80.7109,198.2206,325.21,431.7732,520.0735,614.2296,693.7107,767.8617 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5ASIA,Emissions|CO2,Mt CO2/yr,13026.4846,18921.1526,23094.1627,27023.0668,29256.1541,29768.1742,29618.8397,31154.2003,31067.1205,30716.2892 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy,EJ/yr,160.5996,245.0765,317.8615,394.5501,457.9025,510.7195,551.0415,602.2139,622.8792,642.4503 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Biomass,EJ/yr,22.3057,22.5956,22.581,23.1707,24.0074,26.4545,28.5228,30.8628,33.1284,34.9876 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Fossil,EJ/yr,133.8078,211.4618,274.744,330.5731,356.1772,358.6089,353.5537,369.934,369.425,368.2287 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.5101,8.89,16.5835,33.747,65.8987,108.0946,146.0648,175.62,195.1631,216.6577 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5LAM,Emissions|CO2,Mt CO2/yr,3764.8943,4361.4235,3737.8335,3485.7951,4312.4983,5031.2639,5193.4935,4884.9038,4233.4293,3704.5951 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy,EJ/yr,29.8951,39.5995,47.7421,59.4944,74.5471,88.8763,98.7638,105.3458,106.5957,107.7905 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Biomass,EJ/yr,4.864,5.5866,5.8878,5.271,5.7666,6.2726,6.5585,7.0931,7.5084,7.7104 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Fossil,EJ/yr,22.266,29.4423,34.6648,43.2447,52.8291,60.5415,63.6586,62.9823,57.8896,53.9426 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.657,4.4242,7.0352,10.8423,15.8486,22.0119,28.5354,35.2698,41.1977,46.1375 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5MAF,Emissions|CO2,Mt CO2/yr,3344.9985,5563.3008,6933.0151,8215.305,10365.9394,12491.5233,14064.2303,15338.3351,16417.3408,17797.3956 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5MAF,Primary Energy,EJ/yr,54.0155,77.0034,96.8458,122.7984,157.6394,201.241,246.0838,299.4538,344.7914,388.9597 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Biomass,EJ/yr,12.7719,13.6769,11.9112,9.113,6.7956,7.9922,9.3815,10.9059,12.4136,13.3581 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Fossil,EJ/yr,40.462,61.8557,81.96700000000001,106.7056,133.5756,156.8575,174.9457,196.9048,212.0417,230.0552 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.7731,1.3434,2.7389,6.7528,17.0512,36.2103,61.6379,91.5237,120.2399,145.4872 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,13492.6367,13094.4185,13523.2356,15057.6717,16661.547,16900.1095,15225.7068,12783.3216,9533.0923,8204.327 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy,EJ/yr,210.3054,210.9373,228.0067,255.0215,280.4933,295.3828,293.4522,286.3069,264.5014,262.4534 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,10.8308,14.7601,17.6013,17.9305,17.5017,18.0512,18.9748,19.8835,20.7115,21.4209 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,185.5435,179.555,190.676,209.74,220.7456,217.727,198.2845,174.8513,140.5689,127.8979 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,6.851,11.1768,15.7666,24.4337,39.7921,57.2041,73.4483,88.598,100.3825,110.6205 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5REF,Emissions|CO2,Mt CO2/yr,1439.7155,1607.5718,1960.7184,2076.2583,2229.8859,2224.5497,1988.5565,1688.2157,1409.5116,1267.1879 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5REF,Primary Energy,EJ/yr,23.5996,27.0083,28.804,31.8938,33.5033,33.58,32.2158,31.0589,29.4737,28.9925 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5REF,Primary Energy|Biomass,EJ/yr,0.4032,1.1788,1.3183,1.3542,1.3788,1.3997,1.4237,1.4561,1.4982,1.5414 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5REF,Primary Energy|Fossil,EJ/yr,22.1774,24.4791,25.9759,28.7269,29.6278,28.2808,24.4754,20.3438,16.6616,15.1067 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.5315,0.6284,0.8243,1.2422,2.0845,3.7363,6.3095,9.259,11.3139,12.3443 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.892945378,1.195584303,1.55035118,1.935647993,2.360313744,2.792105206,3.233079911,3.646864509,4.042318646,4.398880807 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,World,Emissions|CO2,Mt CO2/yr,36169.0957,44930.9071,50890.5915,57830.7904,65153.2663,69147.2249,69124.3418,69234.5572,66252.8004,65476.0274 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,World,Primary Energy,EJ/yr,478.4152,599.625,719.2602,863.7583,1004.0856,1129.7995,1221.5569,1324.3794,1368.2414,1430.6463 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,World,Primary Energy|Biomass,EJ/yr,51.1756,57.7979,59.2997,56.8394,55.4501,60.1703,64.8611,70.2013,75.26,79.0184 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,World,Primary Energy|Fossil,EJ/yr,404.2566,506.7939,608.0276,718.9903,792.9552,822.0155,814.9179,825.0160000000001,796.5868,795.2312 +REMIND-MAgPIE 1.7-3.0,CD-LINKS_NoPolicy,World,Primary Energy|Non-Biomass Renewables,EJ/yr,14.3226,26.4628,42.9485,77.0179,140.675,227.2572,315.9958,400.2706,468.2972,531.2475 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5ASIA,Emissions|CO2,Mt CO2/yr,12144.61527,15037.64715,16762.45643,19153.3193,20514.6739,21902.56845,23117.95693,23755.72455,24081.62931,23426.83686 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5ASIA,Primary Energy,EJ/yr,157.1972832,201.1841293,235.0170197,270.0368133,303.618196,338.2315234,367.9340572,390.1390101,410.4034939,426.5755789 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5ASIA,Primary Energy|Biomass,EJ/yr,21.80551402,21.21676487,19.30352037,13.94051015,11.6105663,8.385930328999999,3.750538928,2.745387852,6.436354187,19.81173701 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5ASIA,Primary Energy|Fossil,EJ/yr,130.0183662,163.6166677,188.2981901,220.9762616,246.2510899,274.8021096000001,298.444634,308.3479853,311.0301597,299.7508766 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.762940987,10.54248298,16.74870107,26.39356785,38.35146802,47.78978951,58.16722482,70.57693789,82.77295883,94.39896102 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5LAM,Emissions|CO2,Mt CO2/yr,2707.832349,2853.385497,2635.095058,3064.20657,3485.43685,3957.574466,4536.862226,5149.245303,5477.482365999999,5086.201443999999 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5LAM,Primary Energy,EJ/yr,32.85971975,39.95729979999999,38.24324442,52.93771818,59.47711454,65.77405776,71.08161731,75.80480175,80.55773295,85.33755247 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5LAM,Primary Energy|Biomass,EJ/yr,3.690533477,3.985105077,9.019138537,3.727746323,3.2246848,2.448746895,2.68783844,2.053199986,3.350807809,18.87374749 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5LAM,Primary Energy|Fossil,EJ/yr,26.15677891,31.09803162,21.91317493,41.95612928,48.9303178,55.25505027,59.23698104,62.82800893,64.53458894,51.12115448 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.680578675,4.394850335,6.828372992,6.648466104,6.645895242000001,7.296157681,8.223035775,9.747294931,11.15753382,13.39113609 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5MAF,Emissions|CO2,Mt CO2/yr,4119.074481000001,4749.200071,5531.182413,6812.662194,8249.556471,9831.715846,11629.67327,13613.2383,14875.08329,14692.99669 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5MAF,Primary Energy,EJ/yr,60.44684508,79.93444146,97.20920267,120.0740426,144.6414921,169.7697975,195.9321936,221.0583673,244.1483944,266.1073211 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5MAF,Primary Energy|Biomass,EJ/yr,12.2928501,15.09741628,19.27321962,20.60858724,21.26742761,17.89510403,11.83861029,2.920723421,2.833440245,16.3474912 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5MAF,Primary Energy|Fossil,EJ/yr,47.71902866,63.74665473,75.65938461,96.74622513,119.1077988,146.0624035,175.2140713,205.2592063,222.4011906,222.8205793 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.434804516,1.089940808,2.27489727,2.718126833,4.265461941,5.811273121,8.87718197,12.87385907,18.90723828,26.93022941 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,8626.748762000001,8392.51757,6778.406590000001,7356.311366,7751.325811,8125.027499,8723.548498999999,9313.091689,9498.030354999999,9137.317504999999 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy,EJ/yr,136.2325806,138.8465883,124.4461373,138.7017917,146.4130229,154.3730284,162.1024107,167.0533309,172.6068262,179.1473979 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,2.525359638,3.441037604,3.127997646,2.296435142,2.384702877,2.893449337,2.961176356,3.018945515,4.004930179,11.90354691 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,117.7122032,111.4359699,91.30059525,103.3650439,107.6980051,111.5960539,113.2335907,109.3349056,103.7223164,93.78039335 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,3.209607881,6.07625912,10.20431346,15.99742806,20.89282562,25.12498398,30.35114061,37.04845428,44.13296709,48.82263796 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5REF,Emissions|CO2,Mt CO2/yr,2556.028086,2965.823728,3650.646667,4214.647598,4789.859978,5306.520263,5905.578649,6534.681886,6850.622012999999,6881.480264 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5REF,Primary Energy,EJ/yr,54.46244273,64.09764656,78.24385276,87.2395208,95.60346259999999,102.8693141,110.4576816,116.1888722,121.3012893,126.263331 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5REF,Primary Energy|Biomass,EJ/yr,0.060239215,0.156719158,0.183455856,0.170082323,0.172712478,0.165843961,0.165745769,0.16563099,0.208460661,0.779219955 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5REF,Primary Energy|Fossil,EJ/yr,50.46850414,55.11665008,67.12217308,75.30597907,82.87590227,89.25642978,94.74616941,97.4454589,98.44833054,98.0973 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.12062363,1.730426975,2.172547802,2.35166881,2.801085104,3.200216742,3.819918766,4.757246404,5.934413993,6.988414458999999 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.891542068,1.125980903,1.365200069,1.594403717,1.859055452,2.106198508,2.35897737,2.632319558,2.929886768,3.223265742 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,World,Emissions|CO2,Mt CO2/yr,35674.1208,38751.41118,38970.5278,44853.88343,49282.25282,53804.89151,58699.34854,63405.64797999999,66094.40774,63844.49209 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,World,Primary Energy,EJ/yr,515.5231132,593.3593909,641.1141877,750.0235365,838.1716313,926.5697244999999,1009.698544,1078.682478,1140.280359,1202.759422 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,World,Primary Energy|Biomass,EJ/yr,44.94703909,49.38096625,56.60295597,44.66823811,42.66946546,35.40273375,25.28988479,14.78354568,20.93584679,76.81259001 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,World,Primary Energy|Fossil,EJ/yr,429.3604359,473.6480665,487.7987978,594.4746862000001,666.0942212,743.1365166,809.3622644999999,853.7867674,869.3535856000001,831.1297622000001 +WITCH-GLOBIOM 4.4,CD-LINKS_INDCi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.77053131,30.36088914,48.86020293,66.0973382,88.27461799,107.6444359,131.5484599,160.7190425,191.1341175,223.7376797 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5ASIA,Emissions|CO2,Mt CO2/yr,12144.61527,16565.4709,23118.45826,27420.11049,30198.76864,32033.7011,33664.79051,34406.36524,34461.21406000001,33120.67606000001 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5ASIA,Primary Energy,EJ/yr,157.1972832,215.918249,283.7876396,333.4302534,371.5182268,400.7257281,424.9233682,443.7523718,458.5137433,471.0830644 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5ASIA,Primary Energy|Biomass,EJ/yr,21.80551402,21.78764833,17.06510172,14.08220989,11.7067201,8.199869555,3.520008602,2.008995621,3.303715042,16.64480177 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5ASIA,Primary Energy|Fossil,EJ/yr,130.0183662,177.9959608,247.2890158,294.5542185,327.5473235,351.4781701000001,371.5025388,381.2025934,380.3498773,365.1932565 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.762940987,10.27773927,13.49036215,18.36854984,26.08549257,34.94468782,43.20291094,52.79086899,65.53814485,77.68088844 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5LAM,Emissions|CO2,Mt CO2/yr,2707.832349,2909.68088,3575.691595,4072.27467,4525.712125,5053.107136,5703.296272,6372.480867,6762.658593000001,6379.827292 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5LAM,Primary Energy,EJ/yr,32.85971975,40.45840687,47.59723877,54.34123888,60.53594254,66.31033892,71.73771998,76.51465006,80.75508939,84.82369627 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5LAM,Primary Energy|Biomass,EJ/yr,3.690533477,4.049539879,3.54555822,2.98281505,2.188011572,1.249851205,2.68783844,2.887232043,2.837110078,11.65823092 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5LAM,Primary Energy|Fossil,EJ/yr,26.15677891,31.92581492,38.71790764,45.25849094,51.42062569,57.27936035,60.15333514,62.96262954,65.45791317,57.82493402 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.680578675,3.992605448,4.707330071,5.423408333999999,6.188926832999999,6.94577624,7.899634702,9.420068527,10.85554527,13.26555831 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5MAF,Emissions|CO2,Mt CO2/yr,4119.074481000001,4753.089204999999,5820.109536,7141.339428,8541.482887,10089.75748,11906.69203,13840.46322,15096.63399,14813.94562 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5MAF,Primary Energy,EJ/yr,60.44684508,79.92660099,100.8196145,123.7192777,147.2547336,172.0339185,197.6223557,222.3281559,245.1040787,266.7276237 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5MAF,Primary Energy|Biomass,EJ/yr,12.2928501,15.13556578,17.70159759,19.46454977,19.56951302,16.99099246,11.31033525,2.737411957,2.271330805,17.16259509 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5MAF,Primary Energy|Fossil,EJ/yr,47.71902866,63.80172739,81.56469329,101.9941248,123.9296428,149.8434563,178.3119873,207.972505,225.9566926,225.2367752 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.434804516,0.9888816009999999,1.552620553,2.25959227,3.754118736,5.197398379,7.996815757,11.61326707,16.86902926,24.31851679 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,8626.748762000001,8711.56449,9595.174786,10266.95049,10850.21775,11378.22975,12269.49957,13188.19057,13538.11961,13082.97426 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5OECD90+EU,Primary Energy,EJ/yr,136.2325806,140.8173833,153.6056939,162.1941434,169.8962674,176.4206488,182.9226741,188.4467951,192.4892324,196.6868713 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,2.525359638,3.441037604,2.294655345,2.137888082,2.150017733,2.21392426,2.840947563,2.860093102,3.162006744,7.282978607 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,117.7122032,114.6059321,126.1242407,132.9588965,138.1750873,141.7203567,142.6567842,140.2960013,134.2099166,124.351424 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,3.209607881,5.637149872,8.014549149,11.41990389,14.89646591,18.34816229,22.46330985,28.48520319,35.47056571,41.8812385 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5REF,Emissions|CO2,Mt CO2/yr,2556.028086,2942.395626,3509.014081,4129.812327000001,4634.761957,5103.726212,5614.697628,6138.935183,6377.815878,6324.540585 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5REF,Primary Energy,EJ/yr,54.46244273,63.64571974,75.67545972,86.25382595,93.82389661,100.6129183,107.1653218,112.4770134,116.5097076,120.7376932 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5REF,Primary Energy|Biomass,EJ/yr,0.060239215,0.156719158,0.165210053,0.170083548,0.172716459,0.165530392,0.165457514,0.165369166,0.246039061,1.120174777 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5REF,Primary Energy|Fossil,EJ/yr,50.46850414,54.71235349,64.56149914,73.81529249,80.39821887,86.22693153,90.57841481,92.59956347,92.12208468,90.41542265 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.12062363,1.730445709,1.97306179,2.269285521,2.732080567,3.126062796,3.739316426,4.767053627,6.102464961,7.093877716000001 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.891532861,1.124489613,1.387996123,1.707568716,2.083885355,2.437546411,2.788226593,3.160973488,3.5303189,3.901737196 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,World,Emissions|CO2,Mt CO2/yr,35674.1208,40656.52654,50895.54633,58608.10905,64620.45677999999,69738.37601,75368.93649,80425.06009,83021.41446,79704.64131 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,World,Primary Energy,EJ/yr,515.5231132,610.1908576000001,742.8016594,849.5596212000002,940.0019348,1019.906936,1094.857166,1158.624932,1215.318116,1266.199768 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,World,Primary Energy|Biomass,EJ/yr,44.94703909,50.24939976,44.50971795,42.73355018,39.0800101,31.28563265,22.95052249,13.15558926,14.64729234,58.83956975 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,World,Primary Energy|Fossil,EJ/yr,429.3604359,491.7685164,617.0933422,713.9834989,793.0214396000001,863.5111691999999,922.9623784,965.2491086000001,981.2741819,941.6689233 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.77053131,29.01095552,38.56063782,50.74064577,67.40105264,85.10670117,105.399199,130.7482622,160.8766651,194.896667 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5ASIA,Emissions|CO2,Mt CO2/yr,12144.61527,16565.4709,8599.054665000001,6395.302256,4863.680489,3456.792118,2192.265989,840.8516175000001,-371.4316018,-1238.069074 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy,EJ/yr,157.1972832,215.918249,162.7574432,163.8477691,184.8390628,206.6398446,220.4427661,225.950897,237.4751861,249.9606222 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Biomass,EJ/yr,21.80551402,21.78764833,15.28339545,18.94567364,30.30453947,42.44529781,46.75143252,49.51110747,52.43898991,53.86900151 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Fossil,EJ/yr,130.0183662,177.9959608,115.1618364,102.0105584,93.37410514,85.38471452,75.31689313,60.26612053,50.13136264,41.29434032 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.762940987,10.27773927,16.6522881,30.7099286,52.05995115,71.44189458,90.57322199,106.9998263,123.8842306,140.7849667 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5LAM,Emissions|CO2,Mt CO2/yr,2707.832349,2909.68088,1129.482725,848.1371158,220.3320275,-798.9579583999999,-966.1435449999999,-1147.558543,-1255.725922,-1376.021341 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy,EJ/yr,32.85971975,40.45840687,33.76174974,33.60172069,36.17718563,42.7767176,50.65805015,57.89886921,63.33004538,69.67052055 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Biomass,EJ/yr,3.690533477,4.049539879,3.04166078,2.738973787,6.719157402,20.74783281,25.10046843,28.28210338,29.56499315,31.58976409 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Fossil,EJ/yr,26.15677891,31.92581492,24.68495747,23.21578869,18.01734688,6.387047197,5.829703474,5.406993663,4.953135103999999,4.517120067 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.680578675,3.992605448,5.071726423,6.733920931,10.45165214,14.43767369,18.31336834,22.44167955,26.56432731,30.70572874 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5MAF,Emissions|CO2,Mt CO2/yr,4119.074481000001,4753.089204999999,3195.763279,2901.486224,2436.1562,1639.730893,1625.924998,778.8831856,-500.3483721,-1213.658872 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy,EJ/yr,60.44684508,79.92660099,69.27667161,72.31965574,79.32859338,96.2757941,118.5825837,123.5604828,133.6245684,164.0174524 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Biomass,EJ/yr,12.2928501,15.13556578,14.92371111,17.10461275,20.14896543,32.96340004,46.66958388,44.14296177,44.2242295,45.49586118 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Fossil,EJ/yr,47.71902866,63.80172739,52.68795368,52.13790286,51.39012243,45.82316943,49.70830322,46.57231586,46.69960451,66.2397584 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.434804516,0.9888816009999999,1.663608996,3.075028406,7.78632698,17.48491356,22.1958106,32.82760222,42.67833508,52.25930368 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,8626.748762000001,8711.56449,4067.637227,2526.005908,1250.91346,19.58758144,-881.1468143999999,-1688.514484,-2440.470608,-2878.551262 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy,EJ/yr,136.2325806,140.8173833,113.101675,111.7264303,115.2402653,125.3589907,141.1386932,148.7792705,155.3533137,161.8331901 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,2.525359638,3.441037604,5.010961639,14.03318614,22.69188272,34.02868698,49.22551137,51.92715829999999,52.17913681,52.45569151 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,117.7122032,114.6059321,70.43822728,59.69434684,48.15588559,40.91413867,35.73414682,31.97352596,28.44939396,25.18760945 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,3.209607881,5.637149872,9.523153608,17.23962275,28.90621063,36.61400685,41.84055179,48.24401906,54.91992054,60.1253188 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5REF,Emissions|CO2,Mt CO2/yr,2556.028086,2942.395626,1192.532687,774.5033332,518.9555114,228.3574868,-50.91971738,-296.8995082,-572.5905757,-732.9207395 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5REF,Primary Energy,EJ/yr,54.46244273,63.64571974,48.54852065,46.50580232,49.88450461,56.04886947,58.71229433,60.2934399,61.68365592,63.91664336 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Biomass,EJ/yr,0.060239215,0.156719158,1.045565675,1.156281113,3.040747565,6.185530461,7.291715591,8.021859931,9.001983635,9.638815337999999 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Fossil,EJ/yr,50.46850414,54.71235349,30.2439179,27.52089587,27.98711146,28.39654983,26.98724405,21.92366854,16.8604919,13.6699466 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.12062363,1.730445709,2.364686027,3.72793775,6.302129249,8.376503734,10.3172546,13.57070209,16.04847987,17.37632466 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.891532861,1.12649778,1.362256945,1.447929459,1.506935743,1.526884006,1.520008798,1.509956473,1.479784603,1.434692443 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,World,Emissions|CO2,Mt CO2/yr,35674.1208,40656.52654,20765.69975,15379.19882,10330.785,4908.629446,1820.006029,-1909.802031,-5802.118874,-8298.142767 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,World,Primary Energy,EJ/yr,515.5231132,610.1908576000001,489.2508496,491.4777819,530.280932,595.097008,661.9811675,692.3823902,733.7183195,801.7799052 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,World,Primary Energy|Biomass,EJ/yr,44.94703909,50.24939976,44.42313668,62.69898011,96.08566113,153.560948,196.0469548,205.9517358,212.6342954,219.5822054 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,World,Primary Energy|Fossil,EJ/yr,429.3604359,491.7685164,329.6907101,298.2767077,265.9629881,227.9040739,209.8853734,177.0471932,157.1472332,163.7334476 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1000,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.77053131,29.01095552,44.83717403,74.43222133,123.4399583,171.8096852,211.4412804,257.1838091,301.909777,342.7366046 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5ASIA,Emissions|CO2,Mt CO2/yr,12144.61527,16565.4709,13289.68823,8735.68226,7568.029638,6193.388786,4920.204753,3709.935989,2208.401925,1054.44155 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy,EJ/yr,157.1972832,215.918249,198.9704896,187.8911243,208.7655001,235.1042988,255.5728826,259.4909557,261.2870643,265.0029621 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Biomass,EJ/yr,21.80551402,21.78764833,15.21379158,14.10809489,19.54824896,33.75187723,43.32200632,47.52611619,50.49505360000001,53.71603976 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Fossil,EJ/yr,130.0183662,177.9959608,155.7902319,132.5714994,130.3765518,128.5566526,123.7617901,105.4357564,82.26333047,61.4347667 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.762940987,10.27773927,15.87167074,29.56635191,50.10669903,65.50933867,80.85898345,97.43365658,117.6676849,136.3297681 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5LAM,Emissions|CO2,Mt CO2/yr,2707.832349,2909.68088,1473.599611,1274.559469,891.7976772,515.1454892,-72.8275407,-899.2472464,-1091.830256,-1132.220978 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy,EJ/yr,32.85971975,40.45840687,38.87122566,39.40325976,41.57030378,45.30682356,48.87660289999999,57.16426147,64.04492642,70.98725142 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Biomass,EJ/yr,3.690533477,4.049539879,3.227045272,2.795854926,2.556269876,4.888007948,11.11720077,27.01787215,30.38552145,32.94467388 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Fossil,EJ/yr,26.15677891,31.92581492,29.82289431,29.48660243,29.40065786,26.96273354,19.16754201,7.545581599,6.728709038,6.128186272000001 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.680578675,3.992605448,5.032307401000001,6.30061516,8.699250111,12.28289641,17.11104637,20.80676228,24.67878266,29.06405251 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5MAF,Emissions|CO2,Mt CO2/yr,4119.074481000001,4753.089204999999,3869.068292,3926.976462,3755.884452,3709.318383,3666.35336,2434.884579,1754.871193,818.4068133 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy,EJ/yr,60.44684508,79.92660099,82.02667220000001,89.26726468,98.11177338,112.7738183,129.3237335,145.45696,165.9543985,193.3254519 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Biomass,EJ/yr,12.2928501,15.13556578,16.25928989,16.8037863,16.56448919,19.13102598,23.0395104,40.65095309,44.2192951,45.49528697 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Fossil,EJ/yr,47.71902866,63.80172739,64.11527061,69.45971813,74.24573336,78.27109804,87.5585177,78.51846714,84.06218415,100.3331181 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.434804516,0.9888816009999999,1.651913525,3.002679681,7.299182369,15.3682318,18.71916805,26.27587741,37.65648808,47.48101346 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,8626.748762000001,8711.56449,5563.763754,3739.791021,2668.895998,1574.998262,947.6501314999999,-79.5997983,-1283.122247,-2008.464768 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy,EJ/yr,136.2325806,140.8173833,125.4237068,121.4505931,126.4954928,131.071368,135.5234561,149.2200188,162.7180615,167.6765302 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,2.525359638,3.441037604,4.166012154,10.38664342,17.88357828,24.86496522,28.02464543,43.25448843,53.40941674,53.68387561 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,117.7122032,114.6059321,86.97140335,74.61087738,67.25442704,58.45359938,51.03371745,42.80526143,38.47970094,34.22061227 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,3.209607881,5.637149872,9.357996857,16.24265957,26.10888163,33.62824514,41.39627922,46.22554949,51.2097773,55.99839929 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5REF,Emissions|CO2,Mt CO2/yr,2556.028086,2942.395626,1799.738686,1278.790874,951.8732403999999,716.0182168,619.1405287,367.4739838,0.5883465999999999,-278.6634315 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5REF,Primary Energy,EJ/yr,54.46244273,63.64571974,54.59141642,54.67093042,57.6370764,64.47503713,70.45351083,71.82644451,71.26072669,72.27860352 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Biomass,EJ/yr,0.060239215,0.156719158,0.9285855590000001,1.53561704,3.040747565,5.947120965,7.443599976000001,8.125917015,9.012832807999999,9.655445827000001 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Fossil,EJ/yr,50.46850414,54.71235349,38.94073487,36.34052892,35.92337865,37.90903966,40.31166138,35.28526002,28.50221194,23.25066466 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.12062363,1.730445709,2.274523878,3.38611862,5.693002811,7.279770357999999,8.393712159,11.71177649,13.62216637,15.48086452 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.891532861,1.125803089,1.366072062,1.50094513,1.597712237,1.658384189,1.69735843,1.72174363,1.728187956,1.717742757 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,World,Emissions|CO2,Mt CO2/yr,35674.1208,40656.52654,29171.76972,21745.47999,18078.09929,14107.42283,10973.66258,5829.84889,1374.398081,-1964.924847 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,World,Primary Energy,EJ/yr,515.5231132,610.1908576000001,568.1057579,562.4217653,606.2773667,664.8367511,718.1918546,764.9513507,812.3164221,860.8327828 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,World,Primary Energy|Biomass,EJ/yr,44.94703909,50.24939976,44.75604774,51.61504957,68.6118461,102.1200872,128.7478845,187.3518764,212.4603698,222.6414391 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,World,Primary Energy|Fossil,EJ/yr,429.3604359,491.7685164,418.8813891,385.4591303999999,378.9060221,364.9466762,350.8639916,292.500066,258.0761118,237.7986704 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_1600,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.77053131,29.01095552,43.59222362,70.82055603,114.0952778,155.4213602,193.2999126,232.8227692,279.8990135,324.8870869 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5ASIA,Emissions|CO2,Mt CO2/yr,12144.61527,16565.4709,6590.404786,3607.501953,1943.706455,515.6910996,-391.650052,-1458.209074,-2451.018245,-3091.639939 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy,EJ/yr,157.1972832,215.918249,151.5776335,137.3080115,156.4159679,173.7903355,193.3803567,210.2257843,225.1781514,242.7464716 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Biomass,EJ/yr,21.80551402,21.78764833,28.15179129,28.97960721,41.7863917,47.4025426,48.72260838,49.38950404,51.01004265,52.08626689 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Fossil,EJ/yr,130.0183662,177.9959608,87.00166153,61.64215915,46.92121883,38.21549499,43.16734371,42.05579336,36.74727979,35.5011664 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.762940987,10.27773927,17.43352858,31.97269998,57.75893173,79.53009585,93.91332488,110.3182344,126.8144512,141.7355014 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5LAM,Emissions|CO2,Mt CO2/yr,2707.832349,2909.68088,702.3546708,-128.9157318,-974.4207502999999,-1695.136368,-2775.455386,-4846.372098,-5513.21781,-2876.75914 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5LAM,Primary Energy,EJ/yr,32.85971975,40.45840687,31.75941592,28.31659531,41.05536711,52.76223002,69.07976711,95.09533940000001,105.8578609,77.90635661 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Biomass,EJ/yr,3.690533477,4.049539879,6.224514939,11.74035701,25.04091757,33.63986975,46.88634796,70.87459029,79.58377816,44.9993974 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Fossil,EJ/yr,26.15677891,31.92581492,18.63242245,8.070898311,4.301309917,4.228618675,3.915167644,3.528894972,3.185616245,2.923776907 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.680578675,3.992605448,5.197958988,7.258294567999999,10.80099424,13.92209469,17.09879056,19.22366327,21.22644341,27.29049033 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5MAF,Emissions|CO2,Mt CO2/yr,4119.074481000001,4753.089204999999,2843.294183,1942.086961,1141.380428,602.9680248999999,335.9014811,-352.2958812,-1566.602079,-2545.867781 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5MAF,Primary Energy,EJ/yr,60.44684508,79.92660099,64.76235111,59.54762214,64.25272136,85.66929945,93.01073558,102.9535933,114.9674749,137.7552407 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Biomass,EJ/yr,12.2928501,15.13556578,16.10575381,20.35247019,28.11418088,39.30751332,42.34264611,43.40388508,44.22471384,45.49586118 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Fossil,EJ/yr,47.71902866,63.80172739,46.98270931,36.08484433,28.25147148,26.64151568,24.31922752,24.44662487,26.77112154,37.28137157 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.434804516,0.9888816009999999,1.667985114,3.095942664,7.884121348,19.71344918,26.3356686,35.07941665,43.94964726,54.93379141 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,8626.748762000001,8711.56449,2899.458385,1058.541292,-208.7795663,-1425.091761,-2082.099821,-2683.419332,-3163.921388,-3478.000458 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy,EJ/yr,136.2325806,140.8173833,110.1992502,99.90515808,110.6344859,122.8838655,133.3024499,141.5269386,149.1116977,158.4445683 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,2.525359638,3.441037604,11.2982336,18.49312027,35.0498865,45.78620566,49.46339416,50.23848933,50.57047876,52.19767767 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,117.7122032,114.6059321,54.39293716,37.33611023,27.4642722,24.63662226,23.96384009,21.35679904,19.20793133,17.39693846 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,3.209607881,5.637149872,9.622124461,18.55275891,30.33249865,38.5939409,46.48997606,53.84996919,59.38641043,64.69445141 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5REF,Emissions|CO2,Mt CO2/yr,2556.028086,2942.395626,730.5717337,146.6306935,-180.1134318,-440.0181246,-631.7518474999999,-774.1645967000001,-953.4577224,-1046.629947 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5REF,Primary Energy,EJ/yr,54.46244273,63.64571974,46.096733,39.42898652,37.8519282,42.53603038,46.32344534,50.27226282,53.0766692,55.82735768 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Biomass,EJ/yr,0.060239215,0.156719158,1.802466719,4.197157913,5.415431194,6.406055929,7.526199141,8.312626519,8.993143069,9.638815337999999 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Fossil,EJ/yr,50.46850414,54.71235349,21.76886369,16.0686144,13.77012662,16.0963016,12.97446851,10.63712158,8.721991915,7.256923924 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.12062363,1.730445709,2.393275508,3.778790769,6.969135584,9.250558574,12.80563616,16.14540638,17.6841651,18.23558428 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.891532861,1.126767756,1.352326276,1.411051926,1.425284107,1.407084339,1.362410001,1.298038018,1.219375774,1.143734134 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,World,Emissions|CO2,Mt CO2/yr,35674.1208,40656.52654,15430.84104,7508.62485,1743.386378,-2903.374517,-6157.606671,-10988.89363,-14723.04929,-14222.97141 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,World,Primary Energy,EJ/yr,515.5231132,610.1908576000001,459.2702803999999,419.7286414,468.7415344,539.6948052,602.2479172999999,673.7250947000001,728.1500413,758.458933 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,World,Primary Energy|Biomass,EJ/yr,44.94703909,50.24939976,73.25493467,96.41153778,153.4476604,193.6727236,216.7213191,245.5604107,257.9806319,227.162166 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,World,Primary Energy|Fossil,EJ/yr,429.3604359,491.7685164,252.7009553,179.3601716,135.5512981,118.7532447,116.7102871,109.7949933,101.8713774,107.2048535 +WITCH-GLOBIOM 4.4,CD-LINKS_NPi2020_400,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.77053131,29.01095552,46.83999892,79.20547884,133.3517663,187.1931127,227.0383568,269.5737117,308.9175808,351.006826 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5ASIA,Emissions|CO2,Mt CO2/yr,12144.61527,17877.70745,24188.15296,28559.78766,31334.22362,33084.32993,34648.34166000001,35344.59873999999,35290.08187,33816.93137 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5ASIA,Primary Energy,EJ/yr,157.1972832,222.9501178,291.9481843999999,341.6356203,379.9536969,408.3918189,431.8183597,450.100656,464.3102922,475.2608813 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Biomass,EJ/yr,21.80551402,21.60234009,17.24603123,13.4508024,11.73715778,8.213248923,3.528129112,2.013425575,3.452306747,16.78100216 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Fossil,EJ/yr,130.0183662,189.341199,256.7671451,304.7337977,337.5026053,360.7962313,380.0064624,389.198611,387.6450093,370.7654692 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,3.762940987,7.872217728,11.71341531,17.10817229,24.59199151,33.35810088,41.64651802,51.2077654,63.95885498,76.23549622 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5LAM,Emissions|CO2,Mt CO2/yr,2707.832349,2902.14795,3620.17358,4129.44462,4614.954291,5160.586176,5819.37551,6509.836653,6903.710048000001,6469.989947999999 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5LAM,Primary Energy,EJ/yr,32.85971975,40.09107054,47.56323731,54.23083158,60.44775645,66.24602658,71.53673891,76.3603428,80.70715089,84.61338113 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Biomass,EJ/yr,3.690533477,3.893359742,3.541275636,2.975757078,2.184181523,1.432506893,2.68783844,2.89111579,2.895113149,12.13678551 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Fossil,EJ/yr,26.15677891,31.78408122,38.71898329,45.16580001,51.3402571,57.03331992,59.93581191,62.79074592,65.3529205,57.10508701 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,2.680578675,3.919120702,4.676597323,5.411881909,6.18290317,6.942337601,7.909426692,9.427358842,10.84912575,13.28678079 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5MAF,Emissions|CO2,Mt CO2/yr,4119.074481000001,4689.064562,5872.079074,7180.142586,8573.572781,10132.47049,11944.11907,13860.63209,15102.52543,14810.54103 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5MAF,Primary Energy,EJ/yr,60.44684508,78.50823267,100.7476151,123.4368393,146.9814868,171.7368044,197.2187549,221.8344408,245.137798,266.1175948 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Biomass,EJ/yr,12.2928501,14.94345112,17.6678136,19.43920581,19.53222376,16.95951872,11.35325239,2.747541886,2.29580829,17.15900877 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Fossil,EJ/yr,47.71902866,62.57346465,81.55065179,101.7568036,123.6947802,149.5936489,177.8453826,207.4632204,225.9889624,224.5536113 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,0.434804516,0.9908335620000001,1.528197118,2.239588124,3.752849821,5.181354658,8.016819788,11.61859974,16.84598706,24.39518875 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,8626.748762000001,8906.457772,9676.439848,10340.90418,10888.19788,11419.45638,12284.33131,13134.64943,13458.10379,12973.87938 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy,EJ/yr,136.2325806,143.0433375,154.9301603,163.6097474,170.6254479,176.9116735,182.8719862,188.0107377,192.4637533,196.1700079 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,2.525359638,2.561071491,2.294655347,2.145067646,2.149973625,2.212509075,2.841052005,2.861757753,3.175899559,7.372702471 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,117.7122032,117.8319241,127.5788236,134.5031226,138.9961599,142.407043,142.5949557,139.5580506,133.8913177,123.4508711 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,3.209607881,5.598931026,7.81361459,11.23697587,14.76735497,18.14219777,22.41196675,28.70219698,35.65730062,42.07140676 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5REF,Emissions|CO2,Mt CO2/yr,2556.028086,2921.304358,3522.59342,4125.757551,4630.155831,5094.758836999999,5614.090891,6184.968059000001,6442.483494,6321.121925 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5REF,Primary Energy,EJ/yr,54.46244273,63.08364346,75.81159841,86.1410402,93.69953726,100.4152616,106.8509556,111.9398868,116.2480849,119.8696669 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5REF,Primary Energy|Biomass,EJ/yr,0.060239215,0.156719158,0.165210053,0.170083559,0.172716628,0.165215984,0.165215985,0.165215985,0.256212558,1.201073999 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5REF,Primary Energy|Fossil,EJ/yr,50.46850414,54.29237294,64.75752822,73.69960214,80.28038249,86.02215739,90.23050616,92.01989887,91.69929065,89.41337432 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,1.12062363,1.596836581,1.912053032,2.287381392,2.72077392,3.113270666,3.731368241,4.758795362,6.145564436,7.0872215 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,World,AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED,°C,0.891653824,1.125916796,1.411609813,1.757287068,2.15754813,2.542648564,2.9228829,3.312922841,3.706575016,4.108135853 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,World,Emissions|CO2,Mt CO2/yr,35674.1208,42656.04102,52444.21934,60199.06862000001,66161.48381,71218.41287,76719.27197,81678.20906000001,84177.35881,80467.95668999999 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,World,Primary Energy,EJ/yr,515.5231132,623.4734990000001,755.0736657000001,861.0068139,950.9990677999999,1029.708573,1102.434732,1165.691466,1221.04626,1268.358433 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,World,Primary Energy|Biomass,EJ/yr,44.94703909,46.94052467,44.65021435,41.50756074,39.06614997,31.46032982,23.01251771,13.18717766,14.90931874,59.67367609 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,World,Primary Energy|Fossil,EJ/yr,429.3604359,512.8354776000001,631.6142543999999,728.7524,806.2965815,875.7150244,932.5470997000001,974.2061596000001,988.2314649,944.2225607999999 +WITCH-GLOBIOM 4.4,CD-LINKS_NoPolicy,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.77053131,25.10335321,35.97768936,48.84624041,65.27831718,82.69641242,103.4214578,128.852889,159.2508644,193.5792232 From 19bac08278a02516abb8f802633a48b70f11ffa7 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 3 Dec 2019 21:42:33 +0100 Subject: [PATCH 09/34] add GENESYS-MOD data to tutorial snapshot --- doc/source/tutorials/pyam_first_steps.ipynb | 9 ++++--- doc/source/tutorials/tutorial_data.csv | 30 +++++++++++++++++++++ 2 files changed, 36 insertions(+), 3 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index f6beab358..215049a62 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -49,12 +49,15 @@ "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.ene.iiasa.ac.at/iamc-1.5c-explorer). \n", - "Please read the [license](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license) when using the full scenario data for scientific analyis or other work.\n", + "*Please read the [License](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license) page of the IAMC 1.5°C before using the full scenario data for scientific analyis or other work.*\n", "\n", "\n", "\n", - "The data snapshot used for this tutorial consists of selected data from the *Horizon 2020* [CD-LINKS](https://www.cd-links.org) project \n", - "and 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 data snapshot used for this tutorial consists of selected data from the following sources:\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.ene.iiasa.ac.at/iamc-1.5c-explorer/#/about) page of the *IAMC 1.5°C Scenario Explorer* for references and additional information.\n", "\n", "
    \n", diff --git a/doc/source/tutorials/tutorial_data.csv b/doc/source/tutorials/tutorial_data.csv index a078825ae..34886a954 100644 --- a/doc/source/tutorials/tutorial_data.csv +++ b/doc/source/tutorials/tutorial_data.csv @@ -185,6 +185,36 @@ AIM/CGE 2.1,CD-LINKS_NoPolicy,World,Primary Energy,EJ/yr,499.6943,606.1502,705.8 AIM/CGE 2.1,CD-LINKS_NoPolicy,World,Primary Energy|Biomass,EJ/yr,46.1448,47.7874,53.2548,69.8286,89.7278,94.7119,104.4506,114.339,124.2792,134.9967 AIM/CGE 2.1,CD-LINKS_NoPolicy,World,Primary Energy|Fossil,EJ/yr,430.0702,527.9702,615.0358,689.8695,736.6658,777.6283,819.0666,870.7303,903.7796,921.1514 AIM/CGE 2.1,CD-LINKS_NoPolicy,World,Primary Energy|Non-Biomass Renewables,EJ/yr,13.2463,17.2845,22.065,26.9093,33.3037,38.9125,42.4811,44.456,47.1731,51.2092 +GENeSYS-MOD 1.0,1.0,R5ASIA,Emissions|CO2,Mt CO2/yr,,72195.0,41226.0,28275.0,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5ASIA,Primary Energy,EJ/yr,,214.869,197.146,190.303,168.584,,,,, +GENeSYS-MOD 1.0,1.0,R5ASIA,Primary Energy|Biomass,EJ/yr,,22.74,54.784,68.469,56.278,,,,, +GENeSYS-MOD 1.0,1.0,R5ASIA,Primary Energy|Fossil,EJ/yr,,182.314,105.481,68.362,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5ASIA,Primary Energy|Non-Biomass Renewables,EJ/yr,,9.815,36.881,53.472,112.306,,,,, +GENeSYS-MOD 1.0,1.0,R5LAM,Emissions|CO2,Mt CO2/yr,,5424.0,2756.0,1497.0,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5LAM,Primary Energy,EJ/yr,,22.763,20.915,22.732,18.874,,,,, +GENeSYS-MOD 1.0,1.0,R5LAM,Primary Energy|Biomass,EJ/yr,,3.311,7.45,11.633,8.171,,,,, +GENeSYS-MOD 1.0,1.0,R5LAM,Primary Energy|Fossil,EJ/yr,,16.176,7.749,4.257,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5LAM,Primary Energy|Non-Biomass Renewables,EJ/yr,,3.276,5.716,6.842000000000001,10.703,,,,, +GENeSYS-MOD 1.0,1.0,R5MAF,Emissions|CO2,Mt CO2/yr,,19199.0,11298.0,6141.0,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5MAF,Primary Energy,EJ/yr,,70.992,59.68,58.084,54.885,,,,, +GENeSYS-MOD 1.0,1.0,R5MAF,Primary Energy|Biomass,EJ/yr,,8.562999999999999,16.953,22.161,18.053,,,,, +GENeSYS-MOD 1.0,1.0,R5MAF,Primary Energy|Fossil,EJ/yr,,60.369,36.005,20.345,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5MAF,Primary Energy|Non-Biomass Renewables,EJ/yr,,2.06,6.722,15.578,36.832,,,,, +GENeSYS-MOD 1.0,1.0,R5OECD90+EU,Emissions|CO2,Mt CO2/yr,,39630.0,18495.0,7852.0,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5OECD90+EU,Primary Energy,EJ/yr,,131.583,99.71,83.95200000000001,81.154,,,,, +GENeSYS-MOD 1.0,1.0,R5OECD90+EU,Primary Energy|Biomass,EJ/yr,,8.085,24.012,23.14,20.176,,,,, +GENeSYS-MOD 1.0,1.0,R5OECD90+EU,Primary Energy|Fossil,EJ/yr,,116.086,50.832,20.65,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5OECD90+EU,Primary Energy|Non-Biomass Renewables,EJ/yr,,7.412000000000001,24.866,40.162,60.978,,,,, +GENeSYS-MOD 1.0,1.0,R5REF,Emissions|CO2,Mt CO2/yr,,20801.0,10316.0,4875.0,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5REF,Primary Energy,EJ/yr,,62.08600000000001,44.317,30.49,26.08,,,,, +GENeSYS-MOD 1.0,1.0,R5REF,Primary Energy|Biomass,EJ/yr,,0.851,9.918,10.745,7.188,,,,, +GENeSYS-MOD 1.0,1.0,R5REF,Primary Energy|Fossil,EJ/yr,,59.688,29.397,13.838,0.0,,,,, +GENeSYS-MOD 1.0,1.0,R5REF,Primary Energy|Non-Biomass Renewables,EJ/yr,,1.547,5.002,5.907,18.892,,,,, +GENeSYS-MOD 1.0,1.0,World,Emissions|CO2,Mt CO2/yr,,31449.0,16818.0,9727.0,0.0,,,,, +GENeSYS-MOD 1.0,1.0,World,Primary Energy,EJ/yr,,502.291,421.768,385.561,349.573,,,,, +GENeSYS-MOD 1.0,1.0,World,Primary Energy|Biomass,EJ/yr,,43.552,113.118,136.15,109.864,,,,, +GENeSYS-MOD 1.0,1.0,World,Primary Energy|Fossil,EJ/yr,,434.631,229.465,127.451,0.0,,,,, +GENeSYS-MOD 1.0,1.0,World,Primary Energy|Non-Biomass Renewables,EJ/yr,,24.108,79.185,121.96,239.709,,,,, IEA World Energy Model 2017,Faster Transition Scenario,R5ASIA,Primary Energy,EJ/yr,184.4367981,231.5846713,241.1119318,249.4599266,252.7764888,,,,, IEA World Energy Model 2017,Faster Transition Scenario,R5ASIA,Primary Energy|Biomass,EJ/yr,23.75385262,26.62507308,32.63508426,40.27100663,44.8885595,,,,, IEA World Energy Model 2017,Faster Transition Scenario,R5ASIA,Primary Energy|Fossil,EJ/yr,150.8821836,182.638806,152.9159892,121.8465293,104.0178552,,,,, From 4619651214d49794854f22f3103aa7e605557cd2 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Wed, 4 Dec 2019 14:35:01 +0100 Subject: [PATCH 10/34] minor edits of first section --- doc/source/tutorials/pyam_first_steps.ipynb | 28 ++++++++++++--------- 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index 215049a62..b7c977781 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -20,12 +20,11 @@ "| **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", "\n", "This notebook illustrates the basic functionality of the **pyam** package\n", "and the ``IamDataFrame`` class:\n", "\n", - "1. Importing timeseries data from `xlsx` or `csv` files.\n", + "1. Load timeseries data from a snapshot file and inspect the scenario ensemble.\n", "2. Listing models, scenarios and variables included in the data.\n", "3. Display of timeseries data \n", " as [pd.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).\n", @@ -49,10 +48,12 @@ "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.ene.iiasa.ac.at/iamc-1.5c-explorer). \n", - "*Please read the [License](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license) page of the IAMC 1.5°C before using the full scenario data for scientific analyis or other work.*\n", + "*Please read the [License](https://data.ene.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 analyis or other work.*\n", "\n", "\n", "\n", + "### Scenarios in the tutorial data\n", + "\n", "The data snapshot used for this tutorial consists of selected data from the following sources:\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", @@ -66,7 +67,7 @@ " This tutorial is only intended as an illustration of the pyam package.\n", "
    \n", "\n", - "**Citation of the scenario ensemble**\n", + "### Citation of the scenario ensemble\n", "\n", "> D. Huppmann, E. Kriegler, V. Krey, K. Riahi, J. Rogelj, K. Calvin, F. Humpenoeder, A. Popp, S. K. Rose, J. Weyant, et al. \n", "> *IAMC 1.5°C Scenario Explorer and Data hosted by IIASA* (release 2.0) \n", @@ -74,26 +75,22 @@ "> doi: [10.5281/zenodo.3363345](https://doi.org/10.5281/zenodo.3363345) | url: [data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import package and load tutorial data" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import pyam" + "import pyam\n", + "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "## Load timeseries data from a snapshot file and inspect the scenario ensemble\n", + "\n", "We import the snapshot of the timeseries data from the file ``tutorial_data.csv``.\n", "\n", "
    \n", @@ -112,6 +109,13 @@ "df = pyam.IamDataFrame(data='tutorial_data.csv')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a first step, we show lists of all models, scenarios, regions, and the variables (including units) in the snapshot." + ] + }, { "cell_type": "code", "execution_count": null, From 4be42c71f3bc36fa47eb58731ae424b224274afe Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Wed, 4 Dec 2019 14:44:09 +0100 Subject: [PATCH 11/34] rewrite filtering and plotting-gallery sections --- doc/source/tutorials/pyam_first_steps.ipynb | 157 +++++++++++++------- 1 file changed, 102 insertions(+), 55 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index b7c977781..cfb49b277 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -24,11 +24,11 @@ "This notebook illustrates the basic functionality of the **pyam** package\n", "and the ``IamDataFrame`` class:\n", "\n", - "1. Load timeseries data from a snapshot file and inspect the scenario ensemble.\n", - "2. Listing models, scenarios and variables included in the data.\n", - "3. Display of timeseries data \n", - " as [pd.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).\n", - "4. Visualization tools for timeseries data using the [matplotlib](https://matplotlib.org/) package.\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", "5. Evaluating the model data and executing a range of diagnostic checks for identifying outliers.\n", "6. Categorization of scenarios according to timeseries data values or checks on required variables.\n", "7. Exporting data to `xlsx` using the IAMC template.\n", @@ -156,21 +156,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tutorial on data filtering\n", - "A selection of the timeseries data of an ``IamDataFrame`` can be obtained by applying the ``filter()`` funtion,\n", - "which takes a dictionary of filter criteria as argument.\n", - "The function `filter()` returns a filtered clone of the ``IamDataFrame``.\n", + "## Apply filters to the ensemble and display the timeseries data\n", + "\n", + "A selection of the timeseries data of an ``IamDataFrame`` can be obtained by applying the [filter()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.filter) function,\n", + "which takes keyword-arguments of criteria.\n", + "The function returns a down-selected clone of the ``IamDataFrame``.\n", "\n", "### Filtering by model names, scenarios and regions\n", "\n", "The feature for filtering by **model, scenario or region** \n", - "are implemented using exact string matching, where ``*`` can be used as a wildcard:\n", + "are implemented using exact string matching, where ``*`` can be used as a wildcard.\n", "\n", - "> Applying the keyword argument filter ``model='MESSAGE'`` to the ``IamDataFrame``\n", - " will return an empty array.\n", + "First, we want to display the list of all scenarios submitted by the **MESSAGE** modeling team.\n", "\n", - "> Filtering for ``model='MESSAGE*'`` will return all scenarios from the *\"MESSAGE\"* family,\n", - "> identified by the model name \"MESSAGE\" and a version identifier." + "> Applying the filter argument ``model='MESSAGE'`` will return an empty array \n", + "> (because the **MESSAGE** model in the tutorial data is actually called **MESSAGEix-GLOBIOM 1.0**)" ] }, { @@ -179,7 +179,14 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(model='MESSAGE').models()" + "df.filter(model='MESSAGE').scenarios()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Filtering for ``model='MESSAGE*'`` will return all scenarios provided by the **MESSAGEix-GLOBIOM 1.0** model" ] }, { @@ -188,14 +195,16 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(model='MESSAGE*')[['model', 'scenario']].drop_duplicates()" + "df.filter(model='MESSAGE*').scenarios()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Using keyword `keep=False` allows you to discard everything that is found by the filter rather than keeping it" + "### Inverting the selection\n", + "\n", + "Using the keyword `keep=False` allows you to select the inverse of the filter arguments." ] }, { @@ -204,7 +213,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(region=\"World\", keep=False).regions()" + "df.filter(region='World').regions()" ] }, { @@ -213,39 +222,31 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(region=\"World\", keep=True).regions()" + "df.filter(region='World', keep=False).regions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Filtering by variables and hierarchy levels\n", + "### Filtering by variables and levels\n", "\n", "Filtering for **variable** strings works in an identical way as above,\n", "with ``*`` available as a wildcard.\n", "\n", - "> Filtering for _Primary Energy_ will return only exactly those data.\n", - "\n", - "> Filtering for _Primary Energy|*_ will return all sub-categories of \n", - "> primary-energy level (and only the sub-categories).\n", - "\n", - "In additon, IAM variables can be filtered by the **level**,\n", - "i.e., the \"depth\" of the variable in a hierarchical reading of the string separated by `'|'`.\n", - "That is, the variable _Primary Energy_ has level 0, while _Primary Energy|Coal_ has level 1.\n", - "Filtering by both **variables** and **level** will search for the hierarchical depth \n", - "_following the variable string_, so filter arguments ``'variable': 'Primary Energy|*'`` and ``'level': 0``\n", - "will return all variables immediately below ``'Primary Energy'``.\n", - "Filtering by **level** only will return all variables at that depth.\n", + "> Filtering for ``Primary Energy`` will return only exactly those data\n", "\n", - "To illustrate the functionality of the filters, we first show all sub-categories of the ``'Emissions'`` variable. \n", - "Then, we reduce variables to only one hierarchical levels below ``'Emissions|'``; the list returned by the function call will include only ``'Emissions|CO2|Fossil Fuels and Industry'``, because the level argument (by default) only shows variables at exactly the hierarchical levels below ``'Emissions|...'``.\n", + "> Filtering for ``Primary Energy|*`` will return all sub-categories of \n", + "> primary energy (and only the sub-categories)\n", "\n", - "The third example illustrates another use case of the level argument - filtering by `'1-'` instead of `1` will return all variables *up to* the specified depth.\n", + "In additon, variables can be filtered by their **level**,\n", + "i.e., the \"depth\" of the variable in a hierarchical reading of the string separated by `|` (*pipe*, not L or i).\n", + "That is, the variable ``Primary Energy`` has level 0, while ``Primary Energy|Fossil`` has level 1.\n", "\n", - "The last cell shows how to filter only by hierarchical level, without providing a variable string.\n", - "The function returns all variables that are at the top hierarchical level (i.e., ``'Primary Energy'``) and those at the first sub-category level.\n", - "Keep in mind that there are no variables ``'Emissions'`` or ``'Price'`` (no top level)." + "Filtering by both **variables** and **level** will search for the hierarchical depth \n", + "_following the variable string_ so filter arguments ``variable='Primary Energy*'`` and ``level=1``\n", + "will return all variables immediately below ``Primary Energy``.\n", + "Filtering by **level** only will return all variables at that depth." ] }, { @@ -254,7 +255,14 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable='Emissions|*').variables()" + "df.filter(variable='Primary Energy*', level=1).variables()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell illustrates another use case of the **level** filter argument - filtering by `1-` (as string) instead of `1` (as integer) will return all timeseries data for variables *up to* the specified depth." ] }, { @@ -263,7 +271,15 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable='Emissions|*', level=1).variables()" + "df.filter(variable='Primary Energy*', level='1-').variables()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last cell shows how to filter only by **level** without providing a **variable** argument.\n", + "The example returns all variables that are at the second hierarchical level (i.e., not ``Primary Energy``)." ] }, { @@ -272,7 +288,29 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable='Emissions|*', level='1-').variables()" + "df.filter(level=1).variables()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filtering by year\n", + "\n", + "Filtering for **years** can be done by one integer value, a list of integers, or the Python class [range](https://docs.python.org/3/library/stdtypes.html#ranges).\n", + "\n", + "**Note**: the last year of a range is not included, so ``range(2010, 2015)``\n", + "is interpreted as ``[2010, 2011, 2012, 2013, 2014]``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Displaying timeseries data\n", + "\n", + "As a next step, we want to view a selection of the timeseries data.\n", + "The [timeseries()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.timeseries) function returns the data as a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) in the standard IAMC format, i.e., _wide format_ where years are shown as columns." ] }, { @@ -281,27 +319,38 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(level='1-').variables()" + "display_df = df.filter(model='MESSAGE*', variable='Primary Energy', region='World')\n", + "display_df.timeseries()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Filtering by year\n", + "### Parallels to the *pandas* data analysis toolkit\n", "\n", - "Filtering for **years** can be done by integer number, a list of integers, or the Python class ``range``. \n", - "Note that the last year of a range is not included, so ``range(2010,2015)``\n", - "is interpreted as ``[2010, 2011, 2012, 2013, 2014]``." + "When developing **pyam**, we followed the syntax of the Python package **pandas** ([read the docs](https://pandas.pydata.org)) closely where possible. In many cases, you can use of similar functions directly on the ``IamDataFrame``.\n", + "\n", + "In the next cell, we illustrate this parallel behaviour. The function [pyam.IamDataFrame.head()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.head) is similar to [pandas.DataFrame.head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html): \n", + "it returns the first n rows of the ``data`` table in *long format* (i.e., columns are in year/value format)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Getting help\n", + "### Getting help\n", "\n", - "When in doubt, you can look at the help for any function by appending it with a ``?``." + "When in doubt, you can look at the help for any function by appending a ``?``." ] }, { @@ -317,10 +366,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Displaying timeseries data\n", + "## Visualize timeseries data using the plotting library\n", "\n", - "As a next step, we want to view a selection of the data in the tutorial snapshot using the IAMC standard.\n", - "The `timeseries()` function returns the data in the standard IAMC format as a `pd.DataFrame`." + "This section provides an illustrative example of the plotting features of the **pyam** package. Please look at the [plotting gallery](https://pyam-iamc.readthedocs.io/en/stable/examples/index.html) for more examples.\n", + "\n", + "In the next cell, we show a simple line plot of global CO2 emissions. The colours are assigned randomly by default, and **pyam** deactivates the legend if there are too many lines." ] }, { @@ -329,10 +379,7 @@ "metadata": {}, "outputs": [], "source": [ - "(df\n", - " .filter(scenario='AMPERE3-450', variable='Primary Energy|Coal', region='World')\n", - " .timeseries()\n", - ")" + "df.filter(variable='Emissions|CO2', region='World').line_plot()" ] }, { From 4bec414114f670c60a896b6e634bff01b64d20bd Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Wed, 4 Dec 2019 14:48:43 +0100 Subject: [PATCH 12/34] rewrite validation section --- doc/source/tutorials/pyam_first_steps.ipynb | 101 +++++++++++++------- 1 file changed, 65 insertions(+), 36 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index cfb49b277..df138767c 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -29,7 +29,7 @@ " 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", - "5. Evaluating the model data and executing a range of diagnostic checks for identifying outliers.\n", + "4. Categorize scenarios according to timeseries data values.\n", "6. Categorization of scenarios according to timeseries data values or checks on required variables.\n", "7. Exporting data to `xlsx` using the IAMC template.\n", "\n", @@ -386,13 +386,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For displaying data in a different format, the class ``IamDataFrame`` has a wrapper of the ``pd.DataFrame.pivot_table()`` function. It allows to flexibly specify the columns and rows.\n", - "The function automatically aggregates by summation or counting (specified by the parameter `aggfunc`) \n", - "over all timeseries data identifiers ('model', 'scenario', 'variable', 'region', 'unit', 'year')\n", - "which are not used as `index` or `columns`.\n", + "## Perform scenario diagnostic and validation checks\n", "\n", - "In the example below, the filter of the timeseries data is set for all subcategories of 'Primary Energy', \n", - "which are then summed up in the displayed table." + "When analyzing scenario results, it is often useful to check whether certain timeseries data exist or the values are within a specific range. For example, it may make sense to ensure that reported data for historical periods are close to established reference data or that near-term developments are reasonable.\n", + "\n", + "The following section provides three illustrations of the diagnostic tools:\n", + "0. Verify that a timeseries `Primary Energy` exists in each scenario (in at least one year and in the last year of the horizon).\n", + "1. Validate whether scenarios deviate by more than 10% from the `Primary Energy` reference data reported in the *IEA Energy Statistics* in 2010.\n", + "2. Use the `exclude_on_fail` option of the validation function to create a sub-selection of the scenario ensemble.\n", + "\n", + "For simplicity, the example in this section operates on a down-selected data ensemble that only contains global values." ] }, { @@ -401,18 +404,25 @@ "metadata": {}, "outputs": [], "source": [ - "(df\n", - " .filter(variable='Primary Energy', region='World')\n", - " .pivot_table(index=['year'], columns=['scenario'], values='value', aggfunc='sum')\n", - ")" + "df_world = df.filter(region='World')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you are familiar with the `python` package `pandas`, you can use many of usual functions directly on the ``IamDataFrame``. The function ``head()``, for example, will show the first `n` rows of the data in long form \n", - "(columns are in year/value format)." + "### Check for required variables\n", + "\n", + "We first use the [require_variable()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.require_variable) function to assert that the scenarios contain data for the expected timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_world.require_variable(variable='Primary Energy')" ] }, { @@ -421,16 +431,20 @@ "metadata": {}, "outputs": [], "source": [ - "df.head()" + "df_world.require_variable(variable='Primary Energy', year=2100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualization of timeseries\n", + "The two cells above show that all scenarios report primary-energy data, but not all scenarios provide this timeseries until the end of the century.\n", + "\n", + "### Validate numerical values in the timeseries data\n", + "\n", + "The [validate()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.validate) function performs checks on specific values of timeseries data. The `criteria` argument specifies a valid range by an upper and lower bound (`up`, `lo`) for a variable and a subset of years to which the validation is applied - all scenarios with a value in at least one year outside that range are considered to *not satisfy* the validation. The function returns a list of data points not satisfying the criteria.\n", "\n", - "This section provides one illustrative example of the plotting features of the ``pyam`` package. Please see the [plotting gallery](https://pyam-iamc.readthedocs.io/en/latest/examples/index.html) for more examples on plotting with `pyam`." + "According to the [IEA Energy Statistics](https://www.iea.org/statistics/), *Total Primary Energy Supply* was ~540 EJ in 2010. In the next cell, we show all data points that deviate (downwards) by more than 10% from this reference value." ] }, { @@ -439,30 +453,20 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable='Emissions|CO2', region='World').line_plot(legend=False)" + "df_world.validate(criteria={'Primary Energy': {'lo': 540 * 0.9, 'year': 2010}})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Validation and diagnostic assessment of timeseries data\n", + "### Use the `exclude_on_fail` feature to create a sub-selection of the scenario ensemble\n", "\n", - "When analyzing scenario results, it is often useful to check whether certain timeseries exist or the values are within a specific range. For example, it may make sense to ensure that reported data for historical periods are close to established reference data or that near-term developments are reasonable.\n", + "Per default, the functions above only report how many scenarios or which data points do not satisfy the validation criteria above. However, they also have an option to `exclude_on_fail`, which marks all scenarios failing the validation. This feature can be particularly helpful when a user wants to perform a number of validation steps and then efficiently remove all scenarios violating any of the criteria as part of a scripted workflow.\n", "\n", - "The following section provides three illustrations:\n", - "1. Check whether a timeseries `'Primary Energy'` exists in each scenario (in at least one year).\n", - "2. Check for every scenario whether the value for `'Primary Energy'` at the global level exceeds 515 EJ/y \n", - " in the reference year 2010\n", - " (the value must satisfy an upper bound of 515 EJ/y in this notation).\n", - "3. Check for every scenario from the `AMPERE` project\n", - " whether the value for `'Primary Energy|Coal'` exceeds 400 EJ/y in mid-century.\n", + "Any scenario (by a particular model) failing the validation criteria is then marked as `exclude=True`. This \"exclusion flag\" is implemented in the `meta` table of the `IamDataFrame`, which can be used categorization and quantitative indicators (more below). \n", "\n", - "The `validate()` function performs the checks on models and scenarios.\n", - "\n", - "The ``criteria`` argument can specify a valid range by an upper and lower bound (``up``, ``lo``) for a variable and a subset of years to which the validation is applied - all scenarios with a value in at least one year outside that range are considered to *not satisfy* the validation.\n", - "\n", - "By setting the argument ``exclude=True``, all scenarios failing the validation will be categorized as ``exclude`` in the metadata. This allows to remove these scenarios from subsequent analysis or figures." + "We illustrate a simple validation workflow using the CO2 emissions. The next cell shows the trajectories of CO2 emissions across all scenarios." ] }, { @@ -471,7 +475,14 @@ "metadata": {}, "outputs": [], "source": [ - "df.require_variable(variable='Primary Energy')" + "df_world.filter(variable='Emissions|CO2').line_plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next two cells perform validation to exclude all scenarios that have unplausibly low emissions in 2020 (i.e., unrealistic near-term behaviour) as well as those that do not reduce emissions over the century (i.e., exceed a value of 45000 MT CO2 in any year)." ] }, { @@ -480,7 +491,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.validate(criteria={'Primary Energy': {'up': 515, 'year': 2010}})" + "df_world.validate(criteria={'Emissions|CO2': {'lo': 38000, 'year': 2020}}, exclude_on_fail=True)" ] }, { @@ -489,9 +500,27 @@ "metadata": {}, "outputs": [], "source": [ - "pyam.validate(df.filter(region='World', scenario='AMPERE*'), \n", - " criteria={'Primary Energy|Coal': {'up': 400, 'year': 2050}}\n", - ")" + "df_world.validate(criteria={'Emissions|CO2': {'up': 45000}}, exclude_on_fail=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can select all scenarios that have *not* been marked to be excluded by adding `exclude=False` to the [filter()]() statement.\n", + "\n", + "To highlight the difference between the full scenario set and the reduced scenario set based on the validation exclusions, the next cell puts the two plots side by side with a shared y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(8, 4), sharey=True)\n", + "df_world.filter(variable='Emissions|CO2').line_plot(ax=ax[0])\n", + "df_world.filter(exclude=False, variable='Emissions|CO2').line_plot(ax=ax[1])" ] }, { From e8103a17f9b2dd0db6beb89148fd9983d833f669 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Wed, 4 Dec 2019 15:32:51 +0100 Subject: [PATCH 13/34] rewrite categorization section --- doc/source/tutorials/pyam_first_steps.ipynb | 110 ++++++++++---------- 1 file changed, 55 insertions(+), 55 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index df138767c..c8e1eb388 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -30,7 +30,7 @@ "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", - "6. Categorization of scenarios according to timeseries data values or checks on required variables.\n", + "5. Export data and categorization using the IAMC template.\n", "7. Exporting data to `xlsx` using the IAMC template.\n", "\n", "\n", @@ -527,17 +527,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Categorization of scenarios by timeseries characteristics\n", + "## Categorize scenarios according to timeseries data values\n", "\n", - "It is often useful to apply categorization to classes of scenarios according to specific characteristics of the timeseries data. In the following example, we use the temperature change assessment by MAGICC 6 to group scenarios by the median global warming by the end of the century (year 2100).\n", + "It is often useful to apply categorization to classes of scenarios according to specific characteristics of the timeseries data. In the following example, we use the median global mean temperature assessment (computed using MAGICC 6 in the AR5 configuration) to categorize scenarios by their warming by the end of the century (year 2100).\n", "\n", - "We proceed in the following steps:\n", + "### Cleaning up a scenario ensemble for simpler processing\n", "\n", - "0. Plot the timeseries data of the variable that we want to use. \n", - " This provides some insights on useful thresholds for the categorization.\n", - "0. Use the function ``categorize()`` to apply a categorization (and colour code for later use) \n", - " to all scenarios that satisfy a number of specific criteria.\n", - "0. Use the categorization of scenarios for analysis of other timeseries data." + "When displaying the list of variables in the scenario ensemble earlier, you probably noticed that the variable for the temperature assessment had a rather unwieldy name: `AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED`.\n", + "\n", + "To simplify further processing, we use the [rename()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.rename) function to change the variable of this timeseries data to `Temperature`. By adding the argument `inplace=True`, the renaming is performed directly on the `IamDataFrame` rather than returning a copy with the change." ] }, { @@ -546,21 +544,15 @@ "metadata": {}, "outputs": [], "source": [ - "v = 'Temperature|Global Mean|MAGICC6|MED'\n", - "df.filter(region='World', variable=v).line_plot(legend=False)" + "df.rename(variable={'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED': 'Temperature'},\n", + " inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now use the categorization feature of the ``pyam`` package to group scenarios by temperature outcome by the end of the century.\n", - "\n", - "The first cell sets the ``'Temperature'`` categorization to the default `\"uncategorized\"`.\n", - "This is not necessary per se (setting a meta column via the categorization will mark all non-assigned rows as `\"uncategorized\"` (if the value is a string) or `np.nan`.\n", - "Still, having this cell may be helpful in this tutorial if you are going back and forth between cells to reset the assignment.\n", - "\n", - "The function `categorize()` takes `color` and similar arguments, which can then be used by the plotting library." + "In the next cell, we display the list of variables again to verify that the renaming was successful." ] }, { @@ -569,20 +561,14 @@ "metadata": {}, "outputs": [], "source": [ - "df.set_meta(meta='uncategorized', name='Temperature')" + "df.variables(include_units=True)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "df.categorize(\n", - " 'Temperature', 'Below 1.6C',\n", - " criteria={v: {'up': 1.6, 'year': 2100}},\n", - " color='cornflowerblue'\n", - ")" + "Now, we display the timeseries data of the warming outcome as a line plot. This helps to decide where to set the thresholds for the categories." ] }, { @@ -591,11 +577,22 @@ "metadata": {}, "outputs": [], "source": [ - "df.categorize(\n", - " 'Temperature', 'Below 2.0C',\n", - " criteria={'Temperature|Global Mean|MAGICC6|MED': {'up': 2.0, 'lo': 1.6, 'year': 2100}},\n", - " color='forestgreen'\n", - ")" + "df.filter(variable='Temperature').line_plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Categorization assignment\n", + "\n", + "We now use the categorization feature to group scenarios by their temperature outcome by the end of the century.\n", + "\n", + "The first cell sets the `Temperature` categorization to the default `uncategorized`.\n", + "This is not necessary per se (setting a meta column via the categorization will mark all non-assigned rows as `uncategorized` (if the value is a string) or `np.nan`.\n", + "Still, having this cell may be helpful in this tutorial if you are going back and forth between cells to reset the assignment.\n", + "\n", + "The function [categorize()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.categorize) takes `color` and similar arguments, which can then be used by the plotting library." ] }, { @@ -604,11 +601,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.categorize(\n", - " 'Temperature', 'Below 2.5C',\n", - " criteria={v: {'up': 2.5, 'lo': 2.0, 'year': 2100}},\n", - " color='gold'\n", - ")" + "df.set_meta(meta='uncategorized', name='warming category')" ] }, { @@ -618,9 +611,9 @@ "outputs": [], "source": [ "df.categorize(\n", - " 'Temperature', 'Below 3.5C',\n", - " criteria={v: {'up': 3.5, 'lo': 2.5, 'year': 2100}},\n", - " color='firebrick'\n", + " 'warming category', 'below 1.6C',\n", + " criteria={'Temperature': {'up': 1.6, 'year': 2100}},\n", + " color='xkcd:baby blue'\n", ")" ] }, @@ -631,17 +624,23 @@ "outputs": [], "source": [ "df.categorize(\n", - " 'Temperature', 'Above 3.5C',\n", - " criteria={v: {'lo': 3.5, 'year': 2100}},\n", - " color='magenta'\n", + " 'warming category', 'below 2.0C',\n", + " criteria={'Temperature': {'up': 2.0, 'lo': 1.6, 'year': 2100}},\n", + " color='xkcd:green'\n", ")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "Two models included in the snapshot have not been assessed by MAGICC6 regarding their long-term climate and warming impact. Therefore, the timeseries ``'Temperature|Global Mean|MAGICC6|MED'`` does not exist, and they have not been categorized." + "df.categorize(\n", + " 'warming category', 'below 3.5C',\n", + " criteria={'Temperature': {'up': 3.5, 'lo': 2.5, 'year': 2100}},\n", + " color='xkcd:goldenrod'\n", + ")" ] }, { @@ -650,13 +649,19 @@ "metadata": {}, "outputs": [], "source": [ - "df.require_variable(variable=v, exclude_on_fail=False)" + "df.categorize(\n", + " 'warming category', 'above 3.5C',\n", + " criteria={'Temperature': {'lo': 3.5, 'year': 2100}},\n", + " color='xkcd:crimson'\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "### Apply categories to timeseries analysis\n", + "\n", "Now, we again display the median global temperature increase for all scenarios, but we use the colouring by category to illustrate the common charateristics across scenarios." ] }, @@ -666,16 +671,16 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable=v).line_plot(color='Temperature', legend=True)" + "df.filter(variable='Temperature').line_plot(color='warming category')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As a last step, we display the aggregate CO2 emissions by category. This allows to highlight alternative pathways within the same category. \n", + "As a last step, we display the aggregate CO2 emissions, but apply the color scheme of the categorization by temperature. This allows to highlight alternative pathways within the same category.\n", "\n", - "In this step, we also export this figure as a png using the option ``savefig``. The figure will be saved in the tutorials folder." + "Note that the emissions plot also includes two `uncategorized` scenarios. The `GENeSYS-MOD` scenario did not provide timeseries data until the end of the century, and the `World Energy Model` did not submit sufficient detail of non-CO2 emission trajectories to perform the climate assessment with MAGICC6." ] }, { @@ -684,12 +689,7 @@ "metadata": {}, "outputs": [], "source": [ - "fig, ax = plt.subplots()\n", - "(df\n", - " .filter(variable='Emissions|CO2', region='World')\n", - " .line_plot(ax=ax, color='Temperature', legend=True)\n", - ")\n", - "fig.savefig('co2_emissions.png')" + "df.filter(variable='Emissions|CO2', region='World').line_plot(color='warming category')" ] }, { From 2411c7a4245f70e9f25afde72135994ba7794ded Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Wed, 4 Dec 2019 16:38:01 +0100 Subject: [PATCH 14/34] rewrite section on quantitative indicators, one more round of clean-uos --- doc/source/tutorials/pyam_first_steps.ipynb | 89 ++++++++++++--------- 1 file changed, 49 insertions(+), 40 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index c8e1eb388..84e8431a5 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -24,14 +24,14 @@ "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", + "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. Export data and categorization using the IAMC template.\n", - "7. Exporting data to `xlsx` using the IAMC template.\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", @@ -81,6 +81,7 @@ "metadata": {}, "outputs": [], "source": [ + "import numpy as np\n", "import pyam\n", "import matplotlib.pyplot as plt" ] @@ -589,8 +590,8 @@ "We now use the categorization feature to group scenarios by their temperature outcome by the end of the century.\n", "\n", "The first cell sets the `Temperature` categorization to the default `uncategorized`.\n", - "This is not necessary per se (setting a meta column via the categorization will mark all non-assigned rows as `uncategorized` (if the value is a string) or `np.nan`.\n", - "Still, having this cell may be helpful in this tutorial if you are going back and forth between cells to reset the assignment.\n", + "This is not necessary per se (setting a meta column via the categorization will mark all non-assigned rows as `uncategorized` (if the value is a string) or [np.nan](https://numpy.org/devdocs/reference/constants.html#numpy.NAN).\n", + "However, having this cell may be helpful in this tutorial notebook if you are going back and forth between cells to reset the assignment.\n", "\n", "The function [categorize()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.categorize) takes `color` and similar arguments, which can then be used by the plotting library." ] @@ -601,7 +602,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.set_meta(meta='uncategorized', name='warming category')" + "df.set_meta(meta='uncategorized', name='warming-category')" ] }, { @@ -611,7 +612,7 @@ "outputs": [], "source": [ "df.categorize(\n", - " 'warming category', 'below 1.6C',\n", + " 'warming-category', 'below 1.6C',\n", " criteria={'Temperature': {'up': 1.6, 'year': 2100}},\n", " color='xkcd:baby blue'\n", ")" @@ -624,8 +625,8 @@ "outputs": [], "source": [ "df.categorize(\n", - " 'warming category', 'below 2.0C',\n", - " criteria={'Temperature': {'up': 2.0, 'lo': 1.6, 'year': 2100}},\n", + " 'warming-category', 'below 2.5C',\n", + " criteria={'Temperature': {'up': 2.5, 'lo': 1.6, 'year': 2100}},\n", " color='xkcd:green'\n", ")" ] @@ -637,7 +638,7 @@ "outputs": [], "source": [ "df.categorize(\n", - " 'warming category', 'below 3.5C',\n", + " 'warming-category', 'below 3.5C',\n", " criteria={'Temperature': {'up': 3.5, 'lo': 2.5, 'year': 2100}},\n", " color='xkcd:goldenrod'\n", ")" @@ -650,7 +651,7 @@ "outputs": [], "source": [ "df.categorize(\n", - " 'warming category', 'above 3.5C',\n", + " 'warming-category', 'above 3.5C',\n", " criteria={'Temperature': {'lo': 3.5, 'year': 2100}},\n", " color='xkcd:crimson'\n", ")" @@ -671,7 +672,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable='Temperature').line_plot(color='warming category')" + "df.filter(variable='Temperature').line_plot(color='warming-category')" ] }, { @@ -680,7 +681,7 @@ "source": [ "As a last step, we display the aggregate CO2 emissions, but apply the color scheme of the categorization by temperature. This allows to highlight alternative pathways within the same category.\n", "\n", - "Note that the emissions plot also includes two `uncategorized` scenarios. The `GENeSYS-MOD` scenario did not provide timeseries data until the end of the century, and the `World Energy Model` did not submit sufficient detail of non-CO2 emission trajectories to perform the climate assessment with MAGICC6." + "Note that the emissions plot also includes one `uncategorized` scenario. The `GENeSYS-MOD` scenario did not provide timeseries data until the end of the century and hence could not be assessed for its warming outcome with MAGICC6 in the SR15 process." ] }, { @@ -689,19 +690,20 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(variable='Emissions|CO2', region='World').line_plot(color='warming category')" + "df.filter(variable='Emissions|CO2', region='World').line_plot(color='warming-category')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Using metadata indicators for further scenario characterization & diagnostics\n", + "## Compute quantitative indicators for further scenario characterization & diagnostics\n", "\n", - "In the example above, we classified scenarios by their end-of-century warming. However, for some research questions, it may be of interest to know the maximum temperature over the course of the century (a.k.a. peak warming).\n", + "In the previous section, we classified scenarios in distinct groups by their end-of-century warming outcome. In other use cases, however, it may be of interest to easily derive quantitative indicators and use those for more detailed scenario assessment.\n", "\n", - "In this section, we illustrate two ways to add quantitative metadata indicators.\n", - "First, we add indicators derived directly from the data in the `IamDataFrame`." + "In this section, we illustrate two ways to add quantitative indicators. These are stored in the `meta` table of the `IamDataFrame`, which was already mentioned in relation to the `exclude_on_fail` feature described above.\n", + "\n", + "First, we add two indicators derived directly from timeseries data: the warming at the end of the century (`end-of-century-temperature`) and the peak temperature over the entire model horizon (`peak-temperature`). For the end-of-century indicator, we can pass the year of relevant as a filter argument to the [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) function." ] }, { @@ -710,16 +712,15 @@ "metadata": {}, "outputs": [], "source": [ - "eoc = 'End-of-century-temperature'\n", - "df.set_meta_from_data(name='End-of-century-temperature', variable=v, year=2100)" + "eoc = 'end-of-century-temperature'\n", + "df.set_meta_from_data(name=eoc, variable='Temperature', year=2100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If the filter arguments passed to `set_meta_from_data()` do not yield a unique value,\n", - "we can pass a `method` to aggregate." + "If the filter arguments passed to [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) do not yield a unique value (in this case without a specific year), we can pass a `method` to aggregate or select a specific value (e.g., the maximum using the **numpy** package)." ] }, { @@ -728,18 +729,18 @@ "metadata": {}, "outputs": [], "source": [ - "peak = 'Peak-temperature'\n", - "df.set_meta_from_data(name='Peak-temperature', variable=v, method=np.max)" + "peak = 'peak-temperature'\n", + "df.set_meta_from_data(name=peak, variable='Temperature', method=np.max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The second method to define metadata indicators can take any `pandas.Series` with an index including `model` and `scenario`.\n", + "The second method to define quantitative indicators is the function [set_meta()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta). It can take any [pandas.Series](https://pandas.pydata.org/pandas-docs/stable/reference/series.html) with an index including `model` and `scenario`.\n", "\n", "In the example, we can now easily derive the \"overshoot\", i.e., the reduction in global temperature after the peak,\n", - "by computing the difference between the two metadata indicators." + "by computing the difference between the two quantitative indicators." ] }, { @@ -748,7 +749,8 @@ "metadata": {}, "outputs": [], "source": [ - "overshoot = df.meta[peak] - df.meta[eoc]" + "overshoot = df.meta[peak] - df.meta[eoc]\n", + "overshoot.head()" ] }, { @@ -757,16 +759,14 @@ "metadata": {}, "outputs": [], "source": [ - "df.set_meta(name='Overshoot', meta=overshoot)" + "df.set_meta(name='overshoot', meta=overshoot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As a last step of the illustration, we show the first rows of the `meta` dataframe\n", - "downselected to the scenarios in the Temperature-category `Below 2.0C`.\n", - "This table now includes the three new metadata indicators." + "As a last step of this illustrative example, we show the first rows (i.e., the 'head') of the `meta` table for the scenarios in the `IamDataFrame`. This table now includes the `exclude` column, the categ three new metadata indicators." ] }, { @@ -775,19 +775,19 @@ "metadata": {}, "outputs": [], "source": [ - "df.filter(Temperature='Below 2.0C').meta.head()" + "df.meta.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exporting timeseries data for further analysis\n", + "## Export data and categorization to a file using the IAMC template\n", "\n", - "The `IamDataFrame` can be exported to `xlsx` and `csv` in the IAMC (wide) format. When writing to `xlsx`, both the timeseries data and the `meta` dataframe of categorization and quantitative indicators will be written to the file,\n", - "to two sheets named `data` and `meta` respectively.\n", + "The `IamDataFrame` can be exported [to_excel()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.to_excel) and [to_csv()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.to_csv) in the IAMC (wide) format.\n", + "When writing to `xlsx`, both the timeseries data and the `meta` table of categorization and quantitative indicators will be written to the file, to two sheets named `data` and `meta` respectively.\n", "\n", - "This feature is based on [pd.DataFrame.to_excel()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html) and [pd.DataFrame.to_csv()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html). It can use any keyword arguments of that function." + "As discussed before, these **pyam** functions closely follow the similar **pandas** functions [pd.DataFrame.to_excel()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html) and [pd.DataFrame.to_csv()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html). It can use any keyword arguments of those functions." ] }, { @@ -798,6 +798,15 @@ "source": [ "df.to_excel('tutorial_export.xlsx')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Questions?\n", + "\n", + "Take a look at the next tutorials - then join our [mailing list](https://groups.io/g/pyam)!" + ] } ], "metadata": { From ae369d07d7f1ad00c10fb90707377ce05bc6df70 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Thu, 5 Dec 2019 14:56:41 +0100 Subject: [PATCH 15/34] update the legends tutorial --- doc/source/tutorials/legends.ipynb | 283 +++-------------------------- 1 file changed, 27 insertions(+), 256 deletions(-) diff --git a/doc/source/tutorials/legends.ipynb b/doc/source/tutorials/legends.ipynb index 89b13ff8b..ee80199d2 100644 --- a/doc/source/tutorials/legends.ipynb +++ b/doc/source/tutorials/legends.ipynb @@ -6,146 +6,21 @@ "source": [ "# Customizing Legends\n", "\n", - "This is a short tutorial showing a few examples of how different arguments to the `legend` keyword in various plotting methods affects where the legend is located.\n", + "This is a short tutorial showing how different arguments to the `legend` keyword in the **pyam** plotting library affects where the legend is located.\n", "\n", - "First, let's get an example dataset." + "We use the scenario ensemble from the **first-steps tutorial** ([link](https://github.com/IAMconsortium/pyam/blob/master/doc/source/tutorials/pyam_first_steps.ipynb))." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Reading `tutorial_AR5_data.csv`\n" - ] - }, - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " model scenario region variable unit year \\\n", - "90 AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 2005 \n", - "91 AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 2010 \n", - "92 AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 2020 \n", - "93 AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 2030 \n", - "94 AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 2040 \n", - "\n", - " value \n", - "90 34492.05 \n", - "91 38321.78 \n", - "92 35588.66 \n", - "93 28531.68 \n", - "94 20287.46 " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pyam\n", "\n", "df = (\n", - " pyam.IamDataFrame(data='tutorial_AR5_data.csv', encoding='utf-8')\n", + " pyam.IamDataFrame(data='tutorial_data.csv')\n", " .filter(variable='Emissions|CO2', region='World')\n", ")\n", "\n", @@ -161,37 +36,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:>=13 labels, not applying legend\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.line_plot()" ] @@ -200,35 +47,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By using the `color` argument, we restrict the number of legend entries, and it will show up with `matplotib` defaults." + "By using the `color` argument, we tell the **pyam** plotting library to apply colours by model family.\n", + "This reduces the number of legend entries (from 38 model-scenario combinations to 8 model families), and the legend will be shown by default with **matplotib** standard settings." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.line_plot(color='model')" ] @@ -237,35 +64,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can use standard `matplotlib` legend arguments by passing a dictionary of keyword arguments." + "You can use standard **matplotlib** [legend](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html) arguments by passing a dictionary of keyword arguments." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pWrUK36uvQSyGc/58Cu65B/cHrsZkt490E08aQ3QMDAwMzhLinZ30/O1vdK1aRaymFlNWFrm330b2LbdgHzdupJt3WjBEx8DAwGAEUfE4gXfeoefvf8f3yquoWAzHvHkUfPGLuK+55py2agbDEB0DAwODYUYpRXjHDnpWP4f3xRdJdHRgysoie/lycm65GfuECSPdxDOGIToGBgYGw0S0vh7vc8/Rs/o5oocPIzYbriVLyLp+Ga5LLz0rx9WcbgzRMTAwMDiDxLu68L38Mj2rnyO0eTMAzgsuIPeTd+G55hrMHs8It3B4MUTHwMDA4DSTjETwr1lLz+rV2lw1sRi28eMo+NrXyLruWqxFRSPdxBHDEB0DAwOD04BKJglWVeF97jm8/3iZpM+HuSCf3DvuIOv6ZdinTEFERrqZI44hOgYGBganQOTAAXqeXU3P888Tb2pCnE48V1+N5/plZC5ciJjNI93EswpDdAwMDAxOkFhrK94XXqTnudVEdu0Gs5nMSy5m1Ne+hvvKKzA5nSPdxLOWYREdEfkDcB3QqpSarpflAk8AFUA1cItSqks0+/NnwIeAIPAJpdRm/Zg7gXv10/6XUupPevk84DHAAbwIfFkppY52jTN8uwYGBu9DkoEAvldfpefZ1QTWr4dkkowZMyj87nfxfOiDWPLzR7qJ5wTDZek8BvwC+HNa2beB15RSD4nIt/XP3wI+CEzQlwuBXwMX6gLyPWA+oIBNIrJaF5FfA58B3kUTnaXAS0Ncw8DAwOCYpAZurn4O32uvoUIhrCUl5N/9OTzXLTsnc5+NNMMiOkqpdSJSMaD4BuByfftPwBo0QbgB+LNSSgHrRSRbRMbodV9RSnUCiMgrwFIRWQN4lFLr9fI/Azeiic7RrmFgYGAwKNrAzZ30rF7db+Bm1g3Xk3X99TjmzDECAk6BkezTKVRKNenbzUChvl0M1KXVq9fLhiqvH6R8qGv0Q0Q+C3wWoOwsmmHPwMBg+DAGbg4PZ0Uggd7/okbqGkqp3wG/A5g/f/4ZbYeBgcHZgYrFCG3din/dOvxr1hLZvx84vwduDgcjKTotIjJGKdWku89a9fIGoDStXole1kCfq6y3fI1eXjJI/aGuYWBgcB4S7+jAv+5N/OvWEnjrbZI+H1gsOOfPZ9RNN+FZes15PXBzOBhJ0VkN3Ak8pK+fTSv/dxFZiRZI0KOLxsvAf4tIjl7vA8B3lFKdIuIVkYVogQQfB35+jGsYGBicB6hkkvDOnfjXrsO/di3hHTtAKcwF+bg/cDWuyy4j8+KLMbtcI93UkSHshe4a6KrWlrFLYPT0M3rJ4QqZXoFmpeSLSD1aFNpDwCoR+RRQA9yiV38RLVz6AFrI9F0Aurj8ANio1/t+b1AB8AX6QqZf0heGuIaBgcH7lITXS+DttzWhefNNEh0dIIJj1iwKvnQPrssuwz55MmIyjXRTzzyJGPTUa4KSLi5d+naos3/9D/7wjIuOaEFiBr3Mnz9fVVVVjXQzDAwMjhOlFJH9+wnofTPBLVsgkcCUlYVr0SJcl19G5qJFWHJyjn2ycw2lINipC8nhI4Wlpx5Uoq++yQLZZZBdDjkV+pK27Tj570hENiml5h+r3lkRSGBgYGBwIiSDQQLr38W/bi3+deuIN2pBqvYpU8j79KdxXXYZjpkzEMv74BEXC0F3bZ91MtBqifr7188s0ASk5AKYcXN/YfEUg2lk0/K8D34RAwOD84FobW2qbya4YQMqGkWcTjIvvgjX5z+Pa/FirIWDjoo4u4mFwNcM3kZdXKr7C4uvqX99i6NPRCoW9bdassvAfnb3TxmiY2BgcFaSjEYJVVWlhCZaXQ2ArbKSnNtuw3X5ZTjmzcN0to6fScQh0KqJRq+o+Jr1z2ll4e4BB4pmkeSUw7grdDFJExbXKDiHB6caomNgYHDWEGtpwb9Wc5kF3/kXyWAQsdlwLlhAzh134LpsMbaRHsCtFIS6NOHwpgmIr7G/uARaQSX7HytmcBWCezTkVEL5xdq2u0hbZ5dDdilY7Gf2FhKKRE+EeGeYRFeYeGeYeFeYzPmjyRiffUavbYiOgYHBiKGSScLbt+Nbswb/G2uI7NkDgGXMGDzXL9NCmi+8cPiyNkcDA6ySNOskXWASkSOPdeSCewx4xmgRYL1C0lvmHqP1twxDn4pSiqQ/RrwrTKJTF5VegemKkOgOQ7oemsCcZccxOfeMt80QHQMDg2ElGQgQ+Ne/8L3xBv6160i0t4PJhGPuHEZ9/T/IXLwY+4QJpze/WSwM/uY099Zg62aIeI881prZJxqlFx4pJO7R4BoN1ozT197jIBmOp1kqkT6LRS9Tsf5WlsllxZKbga3UjWVWAZacDMy5GVhyMzBn2RDz8ISQG6JjYGBwxok1NqasmeC776KiUUxuN5mLFsGCeQTGFNLm7aE7w4GjoxVnLIzDk4XD7cHh9mA62kRo8Sj4W44hJk2D9JsAZlufgIyaog2MTBeSXkvF7h6RPhQVTxLvjvRZKr1Wi75OBuP96ovdjCU3A0u+g4yJOVhy7H2ikpOByXZ2TCZniI6BgcFpJ+U2e+MNzW22dy8xkxCqKCN69eX4c7PpDgVor68h+vd9xzxfRoYVh92Mw6ZwmGI4TSEcyocj6cVpieEwa4vTEsNhVVg9ozTByBunRXi5C9PERBcWR86Id8gnownibSHi7UFtnWapJLxRbRKXXsyiWSc5dmwz8lNikhIVp+WcyH5tiI6BgcFpIRkI4H/nHbyvv0HrO2/RHQ7ic2YQKCzAd+EM/OGgVrH+IPauTApKSpg2dzr5bkWBtYfcRAPx7mZCPR0EfX5CCQuhuJVgwkqod4lk0pO00xzPIBSxk1SDT5xmsdlxeDQryelx4vCYcLhDOD2dODxxHO6elCXl9GSRkek6YxkKVFKR8EY0UWkLEWsLprYTPWl9QwJmjw1zTgb2cdkpQbHkam4ws9uGmM5+UTkWhugYGBicNL79+6l98TmaN1XR0dyI127Bl2EnOdoDeBCTidzRBRTneyjwCAU2L/nJBtz+LUjgH9CJtpjtkDcee34JmZWzdWtkgHUyoBNeKUU0FCTo7SHk7SHk8+rbXkI+LyFvj/bZ56WrqYGQz0s0FBr0PkRMOLOyqJg1l8mLLqds+kxMJ9jhn4wkiLeHiLcFibVpa82KCfXrXxG7GUuBA/vYLCwFDiwFDqwFTix5GYj17HCBnUmMNDgDMNLgGBgcSSIep6upgbbqQzRt2kjL7p10dnUQTnvxtpvN5OdlUzjGSUFGkAIayQvtwxJL60+xeyB/IhRM0pb8SVAwUQsVHoaorng0qgmSz4u/uxN/RweB7i6C3Z1429uo27WDWDhEZnYOky5ezJRFl1M4dnzKbZWyWlp1cWkP6VZLkERPtO9CAuacDKwFDiz5DiyjnFjyNXExua3nhBvsRDHS4BgYGJwUge4u2mqraa85TFttNW3Vh+ioryOZ1HJ4SVLhikQZZTUzKkcoKghQZDuIE7/WRRIFrAVQMBnyP6KLy0Tts3v0cfejJOIxYuEIsWiYWDhCPBohFg4Ti0aIRcLEe7d790XCffvDYb2sdwkTj4TTtiMk4vEjrmlzOhkzYRIqlqT69Y20r93HqNwKRheMx23ORvXE+1stGWYsBU7sY7N1q8WpCU2eA7GeBwlFTwJDdAwMznOioSB7//UW+9a/RWv1IYI9fZZJBuD2BakIRfDEoox2+RhT2EPW+BBmu4KsMs1SKbi0z4LJnwjOocd7JOIxuhobaK+rob2ulva6Gjob64mGginBSCYSQ55jIGIyYbVnYLXbsdozsNjtqe0Ml+vIfTY7dnGSEcvAGrVj8kO8NYgpaMJhyoQx2nmVSuJv7aE+tptkpsIzYQxFF0zHXTkKk+vctVq6w91Ue6s53HOYw97DVPdUs3zSci4uvviMXtcQHQOD8xClFA27d7LjjX+yd/1bxKNRshwWxsQCOLt8ODqjuEMR3M4oruIorpm5OGdMQUZP1l1ikyB/Atgyh7xOMpmgp7WF9roaOmprdJGpoaupISUqYjKRW1RCfmkZGS43Vpsda0YGFps9TSjsWDIytH0pQekTEWuGHZN58OgtFUsQaw9rfSyt/ftbBlot1oJszPkOYrYIbT211NRs59C+KmLxCCazGYvXRvRQCP4plM2YxdRLlzBhwUXYHMM0ePUEiSVj1PvqOdxzmGpvNdU91Smh6Y70vVxYTVbK3GX4Yr4z3iajT2cARp+OwfsZX+Nhdr30BDve3UR3TwgrCUojPYxp8uPpiSECzgoPrnmTcF2+GPvMRZA7FixD5zdTSuHv7EiJSkfvur6OeLQvQiurcDT5peXkl5aTp69zxhRjsVpP6b5SI/B7O/F7xaU9RKIr3Bd6LGDOtve5wUbp6wLnUa2WWDhM3e7tVG/dTPV7m+hqatROZTKjkglMFgvj5l7AtMuvomLWXMyWU7uXk6Er3NVPWHq36331xFWfGzEvI4/KrEoqsiqo8FRQmVVJpaeSIlcR5lPsUzvePh1DdAYwmOjEYjHq6+sJh8Mj1CqDM0VGRgYlJSVYT/Ghd1aSTEDbHuLV6zm44S127KynpsuGQsiPBChq8TO6J4A9MwPXwvlkXn0trssux5yVNeRpg94eOupraa+tTrnHOupqiAQDqTqZObkpceldcktKsWU4TumWVCJJvCOcFiGmd+i3hlDhvoerWE39+1hGObXt/FOPEOtubqL6vc0cfm8Ttdu3Eo/2BRBYbDbGzr2A2UuXUTJp6mkNw44lYtT56lKusJTIeKvpifSk6llNVso95Zq4eCqoyKqg0lNJeVY5HpvntLVnIIbonCSDic7hw4dxu93k5eWds/5bgyNRStHR0YHP56OysnKkm3Pq+FqgoQrqq6B+I60H97C92cNu7ygiWMiIxSnp9FLc5Sdv4kRcly/BtXgxGdOmDfpwjIaCdNTXpayXXgsm0N2VqmPPzCS/tKKfuOSVluFwn9rDLRmMaZFhrQNCkDvCkOx7Zpk8tpSlkgo9HuXA7LEPy5iWeCxG497dHNqykf3vvoO3rSW1z2rPoGzGbC5YdhPFk6cd1/mUUnSGO4+wWHqtlkTahGz5jvw+YekVl6xKijJP3Wo5GQzROUkGE53du3czefJkQ3Dehyil2LNnD1OmTBnpppwYsTA0b0sJDA1V0F1LMG5hR3MRO/1FdCorpqSi0BugLKoon7cAz2WXkbnokiNm0fR3dlC3a3s/6yX9AWqx28kvKUu5xHqXzJzcU/q/SARixJoDxJsDxFqCqYGTSX+sr5JZsOQ50iwWXVwKHJgyzq5uaX9nBweq3mXn2ldpPXww1W9lsdkpmjSFOddcx9h5F2AymYkn4xzsPsiujl3s6tjF7s7dHOo5hC/a169iM9ko85SlxKUyq5LKrErKPeW4be6Rus1BMUTnJDma6JxzDyWD4+as/32V0qYirt+kCUz9RmjeDkntwRyzFbO/vZKdzSbq40mSInhCESrtLiZfdCl5V15JxvTpSFr+slgkTP3undRs20z1e1voqK8FwGQ2k1tU0l9cyirIKhh1Sq4iFUtootIcJNYcINYSINYcJOnrc02Jw4K1V1TSxMWck4GYT07YkklFNBQnGo4TCyeIhOKpz9FQom87nCAWjnOsx+ExW5FWQakk3rZ9tFe/TaDrMMlENFUpnuGmx5NP0JVLwmLDIiayTFbcFvBYIMcCBTbINyts5hhWcwybRLCaIlhNYW0tUawSQkhAMq65U5MJbXrqpF6W2u4tj0MyOWBfXJuCIRmHpQ/BvDtP6rs+68fpiMgk4Im0orHAfUA28BmgTS//rlLqRf2Y7wCfAhLAl5RSL+vlS4GfAWbgEaXUQ3p5JbASyAM2AR9TSqWN4DIwOAsJ90DD5v5WTLBD22d1osbMJlxyG02HkuzeV091LELYJlgTCcZl5THt0iWUX38jlty+sGWVTNJy6ADV27ZQu30LDXt2kYjHMVutFE+exrTLrqRsxmzyS8tOqSNcJRXxjhDxFl1cmjVxiXeE+jrzLSashU4yJmRjHZ2pL05MblvfIEyliEUShEMJoi1BXSQ0cUhth3ThCMf7iUfvvkg4QTxy7LBrEbA5LFjt5qH4Up7pAAAgAElEQVRdciptQyW1h7ZKau4+lUCpvvJ4MkFMJYmiiJsvIpZ/ESrajjm4A4k2YAl7yQt7yWs9hJgysdvKsdlKwFJKXGXSgoWWo7ekHxZTFKspirVXnMxxbdsSx2qOa2tLEqsloS9JrNYEVqvCZlFYbQqrFazWJPbcyWdcFEZMdJRSe4HZACJiBhqAvwF3AT9RSv04vb6ITAWWA9OAIuBVEZmo7/4lcDVQD2wUkdVKqV3A/6efa6WI/AZNsH59xm/uDPH3v/+dm266KeXuA6iurua6665jx44drFmzhiVLlvD73/+eT3/60wBs3bqVOXPm8KMf/Yivf/3rR5zTbDYzY8aM1Ofly5fz7W9/+7jbVFFRQVVVFfn5g+fAOlWWLl3K+vXrWbRoEc8//3yq/I477qCqqgqr1cqCBQv47W9/O2gwwNGOP6tQCmrehvdWaiLTtpfUEy5/EkxcSiJnOoFGM91bDrJ/bRW11l10urRO+aJRhUy//Com33Qz1oy+9Pq+jnZqtm3RhWYrIZ+Wtr+grILZS5dRMXMOxVOmYbWd+IRhvdFi6cISaw4Qbw32hSELWPIcWAqdOGYVYB3txDo6E8my4++K0NMewtsWoue9dnpeDeHrCBMJxlLCwnE4YawZZmwZFmwOC7YMM3aHBVdOBnaHGavDgi3Dgt1hwarvs2VYsDr6tm2WGJZwM+Jr0ubOCXVCqFubpC3cPfh22lw6MeCgzcoum41ddhu7bDb22m1EdfHMRJhiymSqLZepzjFMdZVR5p5PW3eCzRv3cmD3AWKRAOHwLsLhXZitVkqnzaRi1jyKJ8/CmTWKWCTRt4QHbsdTn6Np+8KRBL6wvs+n1U0mj/2FLs4qZsYZ7t48WxyiVwIHlVI1Q/iHbwBWKqUiwGEROQAs0PcdUEodAhCRlcANIrIbuAK4Xa/zJ+B+zmHRWbFiBYsWLWLFihU88MADg9aZPn06q1atSonOihUrmDVr1lHP6XA42Lp16xlp7+ngG9/4BsFgkN/+9rf9yu+44w7++te/AnD77bfzyCOP8PnPf/64jz8rCHth2xOw8RFo2wP2LCi7EKZ/BFU8j4jfjf/drfhWraVp36PUZ2fSlOMinuPE7fJw0ZKrmL50GZ78AgCi4RCHNm9MCU1nQx0Amdk5VM6eR/msuZTPmE1mds5QrTqCZCShu8MCxJv7LJj01PomlxXr6EwyLxyDdbQTcjIIIHi7I3jbQ/S0hOjZ0Ym3LYS/K9zPjWWxmfDkO/DkOygoc6WJiAWbw6xt937OSPs8lGWilDY3jrcRvA3aBGzeRqhv0Cdk08tDXYMfb3ODI1tbMrIhfwKxjCwOWkzsIsquhJ9dkQ72hlqI6iHJmRYnU/KmsjxvKlPzpjItbxplnjJMcqRbcjTwoau0cUx1O7azY+2rHNjwDvFolOqtm6jeugkAd14+lXPmUzFrLmXTZ2F3Dj0uaigS8SSxcIJomlANFLIx44eOXDwdnC2isxxYkfb530Xk40AV8B9KqS6gGFifVqdeLwOoG1B+IZpLrVupVJB6ev1+iMhngc8ClI30VLhHwe/389Zbb/HGG2+wbNmyo4pOeXk5Xq+XlpYWRo0axT/+8Q8+9KEPnfD1KioquPPOO3nuueeIxWI8+eSTTJ48mY6ODm677TYaGhq46KKL6O0TTLe4AH784x/j9/u5//77efjhh/nNb36DxWJh6tSprFy5kkAgwD333MOOHTuIxWLcf//93HDDDUe048orr2TNmjVHlKff04IFC6ivrx/0Po52/IjSslMTmveegFgAiubCDb8iUX41gQ1b8P9zHYE3v0+gs4OGHBcNYwrwjRuDxWpl4sJFzLjiGoqnTEOpJK2HD7H7zTeo2baFhr27SSbiWKw2SqZOZ8aSqymfNZf80vLj6uxXiSTx9lA/yyXWEiTR2TdUQGwmrIWZZEzLQ2XZCVvN+JLQ44vS06YLyxsNhAOxfud2uK148h2MGZ+Fp2A0WQWayGQVOHB6bCcWjJBMau7G5gZ9hs9GXUQGLLHAkcdmFoCnCLJKtQnZPGPAU6yVuceAMw8ysogJHOw+yM72namO/n1d64kmNe+8y+piSt4Ubqu4iqm6yBxNYIbCZDJTPnM25TNnk4jHqNu1g0ObNrB/wzv4OzvwdbSz/bWX2fbqPxCTiaIJk6mYNZfyWXMoHDv+hJKSmi0mzC4TGa6RHR4w4qIjIjbgeuA7etGvgR+gGdc/AP4X+OSZbINS6nfA70ALJBiq7gPP7WRX4yCzC54CU4s8fG/Z0CGVzz77LEuXLmXixInk5eWxadMm5s2bN2jdj370ozz55JPMmTOHuXPnYrcf3X0SCoWYPXt26vN3vvMdbr31VgDy8/PZvHkzv/rVr/jxj3/MI488wgMPPMCiRYu47777eOGFF3j00UePeX8PPfQQhw8fxm63092tjYJ+8MEHueKKK/jDH/5Ad3c3CxYs4KqrriIz88Te5GKxGH/5y1/42c9+dkLHDTvxKOxeDRsfhdp3wJIB0z9CrOwGfDta8f3sVYJVD5GMx2kvzKOxopimIg9KKcZMnMTCy69m0kWXEgn6qdm2lS3/fIHaHe8R7nWZVYxl3rU3UD5jDsWTp2KxDT2YMxlNEGsKEGv0E23wa9vNAUjof/4msOQ7kHwHyVI3AZPQE03S4Y3i7Qjj3dNNPH00v4A7LwNPvoOxcwvI0gXFU+AgK9+BzXGcj5pETJuUrZ+F0isuTX1lyf6ihpj12TyLoHAqTLi6T0hSojIaLEf+LyilOOw9zNbWrew8sFMXmH1HCMztU25PCUypu/SEBeZYmC1WKmbOoWLmHJZ84rN01NVwcPNGDla9S9P+PahkkqYDe2nYu4u3V/2VDJeL8hlzUiLkzj0zLu7TzYiLDvBBYLNSqgWgdw0gIr8Heh3xDUBp2nElehlHKe8AskXEols76fXPOVasWMGXv/xlQOt3WbFixVFF55ZbbuHWW29lz5493HbbbbzzzjtHPe9Q7rUPf/jDAMybN49nnnkGgHXr1qW2r732WnJyju2qmTlzJnfccQc33ngjN954IwD//Oc/Wb16NT/+sdZ1Fw6Hqa2tPeEosi984QssXryYSy+99ISOGza662DTY7D5TxBoQ2VXEJ74JfxNbnyP/YvInq8AEBs/joYrF3HY20koGMCZYWX+NR9m4sJFBLo7qdm2larn/0ZXo2bRZebkMm7uBZTPnEPZ9FlDusySwRjRRl1gGv3EGv3E2/o69sVpQeVkECh24UtCRzBOa1cE334vSvW9YPW6wbIKHJROze0nLO68DMzHM91xIq4JR3cNdNX0X3fXajN9DuzMsTj6LJKyi/oLSW/5gGkPhiKWjLGnYw+bWzezuWUzW1q30BXR3Gxuq3tYBOZYiAj5ZRXkl1Vw4Y03E/T2cHhLFQc3vcvhrZuJR8JEgkEObFzP3n+9CUBeSRkVs+ZSMWvuSffVDQdng+jcRpprTUTGKKWa9I83ATv07dXA4yLy/6MFEkwANqAFKU7QI9Ua0Fx1tyullIi8AXwULYLtTuDZU23ssSySM0FnZyevv/4627dvR0RIJBKICD/60Y8GrT969GisViuvvPIKP/vZz1KiU1dXx7JlywC4++67ufvuu4e8bq+FZDabiQ+SkTcdi8VCMtn35pueveGFF15g3bp1PPfcczz44INs374dpRRPP/00kyZNOvYXcBQeeOAB2trazr7+mmQSDq/RrJq9L6LiioDtEvy+K/Ct2U+8+SkwmXDMnYPpc59hb7CbA9u3Im2NVM6ZT8mUaURDYep2bmPTC38nmUhgsdkpnTqdWVctpXzmHPJKyo5wSSmlSPqimsA09AlMoquv49ucZccyJpNEiZuuaJK61iC11T7ijdo8M71usNETsplY4DhxN5hSEGhLE5Tq/sLSU6+F5vYiJsgq0aY2GHclZPWKSXGf5XKKM3wGY0G2tm1lS+sWNrdsZlvbNsIJ7e+zzF3G4pLFzCucx+xRsyn3lA+7wBwPTk8W0y67kmmXXUk8FqN+13YObtrAwU3v4mvXAn19HW1sfmk1m174O2arjZIp01IiNNjfy0gxoqIjIploUWefSyv+oYjMRnvdqe7dp5TaKSKrgF1AHPiiUtrwXBH5d+BltJDpPyildurn+hawUkT+C9gCHNsXdBby1FNP8bGPfazfw/Wyyy7jzTffPGof1Pe//31aW1sxp43NKC0tPeWggcWLF/P4449z77338tJLL9HVpb0hFhYW0traSkdHBy6Xi+eff56lS5eSTCapq6tjyZIlLFq0iJUrV+L3+7nmmmv4+c9/zs9//nNEhC1btjBnzpzjbscjjzzCyy+/zGuvvYbpDM34eMKEumDr47DxURLNh/B3FODzXUBgTwfJ4CHE0UTmJRfjuuce2vOz2bDmFerWv441w0Hl3PmgJ+E8WPUuAKMqxzH/upsonzmHoklT++UnU0oLTdaEJaC5yBr9/QZVWvId2ErdWBaMJmgx09wdob7aS9OWdmJ6KHFuUSZTLi6ieFI2RROycbiGdssBWgDEYJZK7zoW7F8/s0ATleL5MP0j2nZOubbOKgHz6e1jaA+1pwRmc+tm9nbuJaESmMTEpJxJfHTiR5kzag5zRs2hwFlwWq89HFis1pSYXHHX52ivq+GQLkBN+/cCYDKbaD64j5ptW1j7l0dx5eVTMXMO5TPnUD5j9ilnjDil9o/YlQGlVACtwz+97GND1H8QeHCQ8heBFwcpP0RfhNs5y4oVK/jWt77Vr+wjH/nIoOW9XHzx8aUnH9ins3TpUh566KGj1v/e977HbbfdxrRp07j44otTome1WrnvvvtYsGABxcXFqZDuRCLBv/3bv9HT04NSii996UtkZ2fzn//5n3zlK19h5syZJJNJKisrBw1pvvTSS9mzZw9+v5+SkhIeffRRrrnmGu6++27Ky8u56KKLAM0VeN9991FVVcVvfvMbHnnkkSGPP600boWNjxD71zP4asHXPopgQzEkkpjzgrg/9EHcV1yJ/YL57Ktaz2vP/42O+loy3B4Kx46ns7GBQ5s2kJmdw7gLFlIxcw5lM2bj9GiRRCqhiLcFCTR29glMkx8V1segmEQb9zIpF2tRJpYxmXgTisbDPhr2ddG4oZWYXjdntJNJC0dTPDGHognZOD2DiEw8orkFu6sHEZbqIyO+bG5NRHLHwrgluqhU6MJSdsxM1KeCUopaX21KYLa0bqHGWwNAhjmDmQUz+fSMTzN31FxmjZpFpvXMtWUkEBEKyiooKKvgwptuIdjTzaEtVRzatIHq9zYDYLJYUMkke95ex443XgERRo+boAnXzLmMmTAJk3n40uYYGQkGYGQkOP84qd83FkbteIbwC7/Gv+UwvkYHkS7tHc42dizuK6/AdcUVOGbNIhIM8t6rL7H5pdUEu7twuN0kEgmiwSA2h5OJCy9hyqLLKZk6HUkIseZAyjUWbQwQawpAXHNditWEdUwm1iIX1qJMbEUuLAVOOlqCNOzromFfN437u4mGNBdWdqGT4onZFE/KoXhiTp/IJGKagLTvh479+vqgJizeRvr1q5ismnj0Wifp65zKU3Z/nQjxZJy9nXtTArO5ZTMdYW3gbLY9mzmj5jB31FzmFs5lSu4UrKfZijqXiMdi1O/cxsHNGzi4aUPKDefKyUVMJvydHSilsDmclE2fRcWsuYydd8FJByQYaXBOEkN0zj9O5PdVrfsJPPFj/K+9jq8W4kELCDhmz8J91dW4rrgCu5481NvWyqYXn2Xbq/8gHo1gsdmJRyOYLRbGzl3AlEsuo6R4GsmmMNE6H9E6H7HWAPSOrcwwYytyaQJT7MJWlIkl3wkCHY1+GvZ2a5bM/m4i+piZrAKHJjCTsiken02m1XuksHTs1wQnvW/FmQ954yG38khhcY8ZlqmkByMYC7K9fXuq0/+9tvcIxbX+p2JXMfMK56WEpjKr8qzptzjbUErRXlvNwU0bOLRpA00H94FSZLjcOLOytSm8vT1c+akvMPsDJz7EAs6BNDgGBucKie4u/E/+Bv9Lz+Lf30UyZkKsNjLnzcC97GZcS5b0SznTcvgg659eyYGq9aRGQYpQNmEGUyYtosBZRrI5TPQlPx2R7dpuhwVbqRv3lFysRS5sxS7MOXZEBJVUdDYFOLirk4Z9h2jc150aB+PJz2DszFyKi8IUZzXgimyC9gOwdT+8egDSUt5jtkPeOBg1BaZcr03CljcB8sdr1spZQGe4M2XBbGndwu6O3cRVHEGYlDuJG8ffyNxRc5kzag6FmYUj3dxzBhGhoLySgvJKFn74VgLdXRzaspFDmzZQs20rsUgYs9WampL8TGKIjoHBIMQaG/H943n8z60isLcBkmDOULgvmIj7w3eSeeW1mBx9c8Mopdi/4R3efuKvdDbUYcJMrn005YXTKS2cijPmRvXEYAuETK1Yx2TinDMKW5lb6+zPd/TLO9bVFKRhbUPKZRbWAwTcHqgo8lLsqqHYvAm3fwtU10F1msfCXaQJyYyP9heWrNIRs1jSCcfDNPgbqPfVU++vp95XT52vjmpvdao/xmayMaNgBndNv4u5hXOZVTDrrMuqfC6TmZ3DjCUfYMaSDxCPRqnTo+HGjJt47INPEUN0DAzQHvSR3bvxvfYavpdfIHJAf/i5Y+RdUIDr+ltxLLsbsWX0Oy4SCvL2ir9w+O2NuFUO5faJzCu5ghzbKERpUXXmmA1bmQdbqRtbmRtrkQuTrf/D39sRonZnJw2722jY10UooImIyx6g3Lmf4pwNFJur8FjawAeEMzUhKVkAs+/QXGO9i911zPuNRcL4uzrxd3ZoS1cnwZ5uLRW/Uih9AaUZayqpJ7QEhUIle/fpZSqpHdf7XcbDBKIBQrEgwXhQW8dChGMhIomIloxZ10mLmMk2O1hocXJN1qWMLiilpHAs7ow8MiUbZyQb5Q0T99iPOejV4MSx2GxUzp5H5ezBx/2d9usNy1UMDM5ClFKoUIiE18vBq68mVq+NHXbkRxg1F1xXfwD7tV/W3FFpJEJRatZspmHdVmwBG2X2ciYWTNV2msFW6sFW5sGuWzHmrCMH6SUTSZr3d1D97l5qdnvp7NbELNPUQaltB8We7RTbd+HJsyP543Vr5Sp9PUHrZxmk/yKZSBDobCfQ2Ymvq4NAZyf+rg78qXUH/q4OIoEjU8RYrDZMFjMiJhAQBEQ0C6x3DakpDhIqQUIlSZAgoRLEVYJEMk5MxTVhAhBNWywmMxlmK25TJlZzNlazDavFhs1sxWK2ptyI4WYfjXs2Uht7e9DfzOZw4szKwunJ7rd2DPjszMrG4XKf1pk7DU4PhugYnFekhKbHS8Lbg4rFSPr92JI15F3gxz2rDMtln4WZt4DdraXqbw4QqfXi3dVI8GAH9mgGNhEqmYbf3kViFHguriSjIhvraCdylJH5obY2at/eSvWOTuqa3EQSGZiIM8a2j0tGHaJ8vIXsimIkfx7kL9ciw6wZqXZHAgFNOA434+/a1c9K8Xd2EOjqINDdrVkdaYjJRGZOLq6cXHLGFFM6bSaunFxcuXm4cvK0dW4eNkd/F19HuCPl+up1g/UuraHWftdwWByUuEsodY2jxF2iLS5tXewqxmY+fgtFm9YgTLC7m6C3m2BPzxHrkLeb7uYmGvftIeT1HnHPACImHB4PzqxsnJ6sfmvHgM/OrCys9gwjEGEYMETnHKGlpYWvfvWrrF+/npycHGw2G9/85je56aabANiwYQNf//rXaWlpwel0Mm/ePB5++GGcTicAN954I83Nzaxf35cz9f777+eBBx5g//79jB8/HoCf/vSnfPWrX2Xjxo3Mn98/EOVTn/oUVVVVKKWYOHEijz32GC7Xka6c//mf/+HRRx/FbDbz8MMPn/5xMSeIUopkMETS20PC60XFtP4Rk02wZCawOJOUfelqmPMxErlzCdX5ia7pIFpXTaTWC1HtgRZJhOiKNNERaUTlm5lxy4eYPOso6XeUQnUeon3LZmq2tVJdl0lLqAQw4zBZqczZQ8VYoXRuJbbxy4lZs2mtOUxrZzv+wx34Nr1DoOv5lGXi7+wkHo0ccZkMlzslIAXlFantzJw83LqYODyeoyaGTKok1d5q3ml6jd2duzWB8dXT4G9IRYn1UugspMRdwkVFF2kC4y5NiUtuxqnNIJqOiGDLcGAb7SB79Jhj1k8mE4T9foI9RwpTIK2s+cA+gt5uoqHQoOex2jPILy1n1NjxjB47nsKx48krKRvWMSznA4bonAMopbjxxhu58847efzxxwGoqalh9erVgCZIN998MytXrkwNlnzqqafw+Xw4nU66u7vZtGkTLpeLQ4cOMXbs2NS5Z8yYwcqVK7n33nsBePLJJ5k2bfBUPz/5yU/weLSRzF/72tf4xS9+ccTcO7t27WLlypXs3LmTxsZGrrrqKvbt29cvM8JwoAlNkKTXS6LHi4rHQASz3YTJlsBsTSJ2JyqjENVjojP2FSJP9JDo3KAdLwq/6qHFe4iOSCPt4UaC+Jh+2VVcdOMnyRo1IHIqEYOmbUQPvUv9tgZqau3U+KcQSOYBeYxytXLB9GYq5paSP+cD9HT30LhvD+s27aVx5X/TXluNSksjZLHayMzNxZWTR+HYCYyb12uZ9Fkombm5J5RfSylFY6CRne072dGxI5VB2R/zA9pgyl4r5aKii1KWSq+1Yjefnbm8TCazZrF4svpnYTwKsWiEkLenvwXV042/q4O26sPsWvc67/3zBUD7HQoqKikcO57CyvEUjptAXnGpIUSngCE65wCvv/46NputX6608vJy7rnnHgB++ctfcuedd6YEB7RM070888wzLFu2jMLCQlauXMl3v/vd1L4bb7yRZ599lnvvvZeDBw+SlZU16GRoQEpwlFKEQqFB32yfffZZli9fjt1up7KykvHjx7Nhw4Z+bTtTpISmR7do4nFNaDKsmBxgtkTBZCNpHU1CMlExQfUkSQbjhHb6iGbFaMo4yMGaKjpCjWARErEYDreHeR+9kVlXf4iMXssu1AV1G6FuPT379lBdbaUmNJOG6HSSTMZqjlFWFqV8tofRs8biba+laf9e3n5zDY1/+G0qO7TN4WD0+ElceNMtjB43gayCQjJz88jIdJ2y5dAeak8JzI72Hezq2EVnuBMAi8nCpJxJXDv2WqblTWN6/nQqsyqxmN7/jwSrzY41fxSe/FGD7lfJJF3NjbQc3E/L4QO0HDrIzrWvs/VlXYhsdk2IKjVraPTY8eQaQnTcvP//wk43L31bm5/+dDJ6Bnzw6Klndu7cydy5c4+6f8eOHdx559HnNV+xYgX33XcfhYWFfOQjH+knOh6Ph9LSUnbs2MGzzz7Lrbfeyh//+Mejnuuuu+7ixRdfZOrUqfzv//7vEfsbGhpYuHBh6nNJSQkNDWcuubdSimQgQMLrJZkSGhNmpx2Ty4RJwoCVpKmAOA5UQrTJzkWRNCdJmGNEVIiVu/8bALszE5VMklBxckeVMn/ZTUy55HIs/gY4+DzUridRU0VTA1RH5lETmU93YhEA2dkJpi/II6/cRjzaRMvBGja89jIdj9Wm+hxyi0sZP/9CxkyYTNGESeSWlJ7QnChHwxv1srN9Jzs7drKjXROZlqCWsN0kJsZmjWVxyWKm501nWv40JuZMPKF+lvMJMZnILSoht6iEKZcuATQh6mxqoPXQAZoPHaDl0AF2rn2NrS9rqZt6hWj02Am6VTTutP227zcM0TkH+eIXv8hbb72FzWZj48aNQ9ZtaWlh//79LFq0CBHBarWyY8cOpk+fnqqzfPlyVq5cmUqgOZTo/PGPfySRSHDPPffwxBNPcNddd522+zpeVDJJMhgk0dND0utDJeJgMmHOdGKygag4SllRZBNX+oM1CUlTkrhEiMSCxBLaXCkmsxklioKKsXQ1NRAJBiiZMo0LLplNZWYHUvcXePjfCfREqI3MpSa+kNrIvcQSNkxmGF1pZ0yBH5EWOhsOsu31vYQDmrvK7sxkzIRJTFhwMUUTJjF6wiQyMo8dznwsgrEgezr3aOLSoVkwveNbQMucPLdwbkpgpuROwWl1nvJ1z2fEZCKvuJS84tKUECWTCboaG3Vr6AAth/az441X2PKP5wCw2O2MKh9L4bjxKasot7jkvBciQ3ROlCEskjPFtGnTePrpp1Off/nLX9Le3p7q6J82bRqbNm0adObNVatW0dXVRWVvahavlxUrVvDgg315U6+77jq+8Y1vMH/+/JQLbSjMZjPLly/nhz/84RGiU1xcTF1d30Su9fX1FBcPOmHrCaGSyf4WTSKBmEyYXG5Mdhskk6iEhWSibxxNUhLEVIhIPEQiGUOJwmqzY3bYMCsb8ViMeDRCMh7H39bEpHI3s/MijA78GfXWb2mNj6OGK6iJPUirNw+lFBlOLznFLQjN+NprOFT1/9g77wA7ynKN/75vyqnb+252s5tkNx0ISQhFEEKVjgUVCyr32r3eq16791oQQQGvoqIIVoqIdCkBqaGkkgRIIZuyu9mSzfbdU6d994+ZPbubhBBCQMA8YZiZb8qZkzOZZ972vDt81QEhKJ1UR+PRx1HVOJ3qxpkUV9e85pRd27XZPLCZF3tf9K2YvhfZOrgVL7CcKqIVzCmdw3lTz2N26Wxml8ymIPT6txw+BD+WVDKplpJJtczanYi2NftEtH0LLzz6EGuy44iofioVU6bmrKKi6pp/KSI6RDpvASxevJhvfvObXHvttXzmM58BIJUak4///Oc/z1FHHcVZZ53FokWLAD+Oc9xxx3HLLbfw4IMP5mIq27dv55RTTplAOtFolCuuuIKmppevRlZKsXXrVqZNm4ZSinvuuSenJD0e5557LhdddBFf+tKX6OzspLm5maOOOjCh7xzRDA3hjYwERKMh8wr9Ik0PlKvh2X7sw8XG9gh3GYQAACAASURBVJLYbhbHsxGaxAiHCQXdSF3bxkqnsLN+FpihC+KmS0y3+EztQ9jEacucwyO8n9aRKlIJC8/tIprXQiSynER/G0ODKYY6/ayxqsbpzDzuBKoaZ1A5rYlQ9LVZE67nsm1oW85Ftr53PS8NvIQddMksChUxu3Q2i2sXM6d0DnNK51AaeWt0i/xXwQQiOmExMEpEHXRv28LObc10b9vqE9EDPhEZoTDlDVOoaJhGWf0UyurqKZlU97YthD1EOm8BCCG46667+K//+i9+/OMfU1ZWRiwW44orrgDIJQh85StfYdeuXUgpOeGEE5gxYwatra0TYiwNDQ0UFBSwfPnyCZ/xgQ98YJ/XoJTi4osvZnh4GKUUhx9+ONdeey0A99xzD6tWreL73/8+s2fP5sILL2TWrFnous4vf/nLV5W5pjwPL5EILJoRlOcijAgyr9TXDnODOhIbXGVjeylsz8JRFpphYkbCRENFCClwLItsMkEyKISUAkLSwTQdQtJF6Ca2zMfRi7hDv5OdO3px7E4EG5DiUbJJX5V3OCkprZvMrOPfSVXjDKoaZ1BUVf2aA/22Z7OhbwMrd65k1c5VrNm1hpTjv0zEjBizSmbx4ZkfZnapH+ivjr32zzyENx4+EdVRMqluAhH1d7TnrKHurVt4/tElOMELkRCSoqpqSic3+K0LJtdTVtdAXmnZW/4eOKQyvRsOqUy/8cglAwwM4o0Mg9DBjCJDUWAsk85Vjk8wnoWLhR4KYYQjGOEImqFjpxJYyQTZrIXn+fe1IV1C0iWkK/RwBEfGsb0Ili2xslmUZ9HSvo2nf3U1yvP/wUfy8qlqmkF1QDCVU6dhRl57TMT2bDb0rmdl57Os6lrOc30vknb9z6zTS2jwSjmpdBLzomXU63lI5fpK0N7o3NnLuruX7Xs5Rnn73j56HiH8bp6BGkFuPTcWLO+xn3yZfcePsY/9gskIgxH1JzMKRgyMyD6Wg7kR9Zc18w1rs3Cw4XkuQ9076Wlroae1hd627fS0tTDUvTO3jxmJUja5ntK6MTIqrZ18UO7P14pDKtOH8KaHchzcgQGcwWFQGphRRP4kX34Fn2QcL4WtLJTKoJsaRjyfSKwcXQqc1DDZVILESD92oNIvhSKkuZghHTMSQekxbC9EMuthJdIoz0Kpft9UCiClYu7ik6iZPovqppkUVFS+/Nuk50J22O+emRnyp+y45XHjTmaQDdkeVtqDrCTNGk2Rkv55p1kW52WyzE96zE6bhJ0BbJUhLDcSkUMIkQAx7oVQSJD62LT7utReZn3cuBHZ9zGj31kpn6RG54yujxsbVQDYY2wv++b2e7l9x43Zab/zqJ0CKwXe2O+0XxDaOMKK+OQ0fnmvpBVsN+O+2nasBKLBZMbfMBKTUqOoqoaiqhqaFh2XG7fSKXp3tNLT2kJPm09GG5c+xrr0mIu9oKKSsrp6SoOGbqV1DRRWVr4pY0WHLJ3dcMjSeX2hlMJNJLAHRlAOSD2MlL414yk35yoTpNGEhRkyMeKFSD2EZ6XIppJYWZusK/GU/zAwNEXI1DGjMbRwHrajk01lsTJpPMdCYYHf2RwhBHoo5Fe8hyMYoRAvbdrATK0NBrZDenCfRII1stfv5SlJ0stjg17M86EKmrUi2lUc6UYJ2TGK7Tzys/lErDwMO47wIkgMhNr7Q0FIiMQNInlmbormmUTyjbHlPJNInkE030Q333wPl4MC1x4joPFktNfl5J6k9UrLQRbjy0IL+eQznoiipbuNlY7bVnzQ22/vDUopRnp76GnbPkZGrdsZ6OrMpefrZojS2jrfKppcnyOl16tV9VumiZsQogVfN9cFHKXUAiFEMXArUA+0ABcqpQaE//r5M+BMIAV8TCn1XHCei4FvB6e9VCn1x2B8PvAHIILf0vqLah9f+hDpHDwopXAdB8fK4qSzqLSNVBq6DCEQKMDxLDxhAyk0lcKQLrpUCM1ACYnjeGRtyHoatuc/WKUAM2QQisYwYgU4NmSTmYBksihlM9oJTUiJEQoTioQxDA1DeggnC04mmLJsbO1m5pIL/WtGYoUqyWiVZPRyMrKMrCgiQyEZL5+MGyPjRsjaIdKWwUhakckA9ss7DVwUjiYQYY1wTCe/IERpcYS8/BDhuEEoqhOOG+ghiZVySY9YpEdsUiMW6WGL9IhFasQmPWLl2k7vDiOkEckbI6lovplbjwbkFMn3l0MxAynfmi6ogw7XmUha6QFI9kKqD1LBPNkXrI8bywy9/DnDBROJaG/kFCv1CSpaCqG8g2ZN2VaW/vYdOYuop7WFntbtpINiZMCXTKqrH4sX1dVTVD0JTX9tjq+DQjpCCA1Yr5TaM03pICEgnQVKqd5xYz8G+pVSlwshvg4UKaW+JoQ4E/gCPuksAn6mlFoUkNQqYAG+qO1qYH5AVCuA/wCW45POz5VSD7zc9RwinQOD57o4lhVMWWwrCzYYwsCQITTh39CeclHSRYRA9wbQnERAPwACT/kEY7kaWU8fs2aC2IypuXgiTNYLY7kSz3MDkvHPIaWGYeiETIGpKTRshJOZ8EarELgyiiOiuITY3LKDbQ/2MzQkyWTGPEJ7gxYWWHqWhBxmWPaR1kfI6klSAtJeDMsrojSvjoaqCqZOymdWfSFNNXkk3SF2pXbtdepOdbMrtYtha5g8I4+SSAnF4WJKIiWUhEsmrBfKYuJeAWErjkpLn5CGfZLyycoiNRwsJ+ygBcFECAHhwIryyWnMYiqqiFFcHSO/LHKImPYF14ZU/27k1BuM9Y6RVHIcWb2cVSWNMSIKF0Ao35+H8ycuT9g2upwPenifpKWUIjU0SE/r9pxF1NPWQl/7DjzX90tLTaekZhJHv/eDE1x7rwYHJaajlHKFEC8JIeqUUm0HdCUHhvOAE4PlPwKPA18Lxv8UWCrLhBCFQoiqYN+HlVL9AEKIh4EzhBCPA/lKqWXB+J+A84GXJZ1D2DeUUrhBfYtjWdjB3LVtpNAwZAhThgjJAoQufIl71wLdRcszMZxBRHYoJ6Jpe5KMa2B5Gq7SGH1GSk1ixqKEwiFQEittkbYskpaNUg7gB+A1KTE0j5DMYkoXLYiDKBs8W8dWBg4RXApwlYmrdFw18bZ3bIXWt54GbSeRyBAh0yEcNxHxKM2mYLVMsI5uthutuFoGADdbhmE3Mik6g5klRzKrJER+PIUnB+lJr6U71c2m1C5+v3oXPU/14IxvDY2vElASLqE8Wk5tXi3zK+ZTFC5iODtMX6aPvnQfWwa3sDy9nGFrmL0hokcoCZdQHCmmpKSEkkljJFUTLqE4VEyeKiTq5CHSRkBMdmA5jVpRNrtahkmPWFjjrCjNkBRVRimu8kmouDpOcVWM/JIw4hAZ+S60vAp/2h8oBVZiTyLKkVWfT1iZIRhqh13rfbdudnjfb0HgJ0+8HFGFChDhAmLhfGLhAuqn5cOchRA+BVePMTCQomdnDz3tO+ht3Y4RCu/7sw4C9seeKgLWBxZDrgmHUurcg3QNCnhICKGA3yilrgMqlFJdwfadwOgvWwPsGHdsezC2r/H2vYxPgBDik8AnAerq6l7r93nbwLdestiWhZPN5qyYUetYIDDNCFE9H13XEV6Qzuy5kE2gpIeWH0GTKURmEG/EI+1qZD0Tx5O4amLhpO89VQghENLAythkkulcPAYEmpSYGoRkBlM6gMTFxBV5pJWB6+n+pHQUYw9HgYcmbHQyhKWFJmx/wqJP6+X84v/BlgZP509jiR5mkzbM1rBFOnjAljguTY5DzFJ40mDI6GdXaDltPEPbADAw9j2iepTyaDkV0QoWVCygPFq+x1QaKd1vnTPbtenL9NGf6acv3ZcjpfHzHSM7WNezjoHMwGgnmwkwpemTU7iEklgJJSV7WlNFsoRYspjhnVn6OxP0dyXpbB5k84ru3Hl0U/pEVBWjqNqfl9TEiQettQ/hZSCE70YL5UFR/f4fN0pWo7HFXKxxGDKDE5Naxm9LdI8t23v2TgLQgNJgmmnEfKISs4DXt5nb/tz133ldrwDeoZTqEEKUAw8LITaN36iUUgEhvW4IiO468N1rr+dnHSji8TiJRIKWlhYaGhr41re+xaWXXgpAb28vVVVVfOpTn+IXv/hF7pgjjjiCGTNm8Je//GXCua6++mquu+46DMNASsnJJ5/MZZf9EOU4zJg1m1gshgwI4OiFC7n0f76D1DR0M0Q0r5Bf/fo6/nDjn9ClTmlJKddd+Uvqqivxsklws8i8KFrYxrESpBLDXHjJ52lpa+fxB+4buwgBmq6jmyGE1P2+NdkMrmsH1tSYjL8uIaK56JpvDTmESXsFJBwDj4kBdA0bTboYuo2ugdQFwhAoXcOTBrYyGXFMMk4G29VwlU6XZvLh2qk4mo0nkkDSfxUa9xDt0zUGdJ1SLUw5OnWOw4JMmor0EOWOQ7njUOG6lHuCeH4NFArQokEG1CTIq4fCyb4L5VU+nA3NoDJWSWWs8hX3dT2XgezABFLqz/RPIKieVA+b+jbRn+nHURMtMF3qTCmYQmNFI00zmmgqaqIhPBM5GGGgK0V/Z5K+zgRtG/vZtGwsldcIazky8i2jGMVVcWKF5iEyei0YT1YHKjTh2pAd2Uu25e4kNgT51Qf18veG/SGdw4AblVIDr7jnAUAp1RHMdwkh7gSOArqFEFVKqa7AfTbaMaqDieLlk4KxDsbccaPjjwfjk/ay/1saDQ0N3HfffTnS2Vs7go0bN+K6LkuXLiWZTBILqvJ//etf89BDD7Fs2TLy8/IYHujn6quvZsdLm8iLx/E8jzv/cguVVZXoZgjdMNGEAbZCZRxU1mPe9MP51P1PEg1p/Pr6X/GN736ZP11zDa4JtvJwskm8jABC3LdkCdFoDIRfea0ZJqDhWDaunSab2vtb2BgEjqcY8TSwBQgPsBHSAZFGCVBSoaTCkx4eCg+Fi4enPDxbgYO/H37LAvDXg9OjhIdrKMJanIpICdMcRV3vNsrTw1TEqymf/V7K536QkrzqPa0T1/bdIYOtMNAazFv85Zfuh6DANAcjBkWTfQLKzev95aJ6P4X3NUCTGqWR0v1SKvCUx3B2OEdKO5M72Tq4lc0Dm1m1cxX3bRt7SSgMFdJU1ERTfRNN85pYWNTIJKOOVI9Hf2fSn7oStLzQy8ZnunLHhaL6BKuouDpGSXWcSJ5xiIzeKGhGkLRQ/M++EmD/SKcCWCmEeA74HbBkX9lfrwZCiBgglVIjwfJpwPeBe4CLgcuD+d3BIfcAnxdC/AU/kWAoIKYlwGVCiKJgv9OAbyil+oUQw0KIo/ETCT4KXHMwrv2fiWg0ysyZM1m1ahULFizg1ltv5cILL6SzszO3zy233MJHPvIRNm7cyN13381FF10EwA9/+EMeuv8+VDpFT38vSim+8OlPEY7FCcfiaLpOcUU18VghXsZFJV08lfVr+AwNYbicsGA2VraXEVdjzpFzufHeuxiWgAMgEYAmIW0rfvvHG7n68qv4xKc/gZ21sLOZl/lW0q+xUA6jSQEIiRLCJwqlEEohgjdzv/WMAgRCgfDPMPF8u43sgaAFczJr8oVVCwjF4tQffiSNi46lfFINYv0d8Owv4R+XwTPXwcJ/g4WXQHycJL5mQHGDP+0N2QQMtu1GSgExtSz1XSe569Fg1nlwzOdh0uvfr14KSWG4kMJwIVOYssf2oewQmwc20zzQnJvf3nx7rrmbQDA5fzKNRY00zWyi6dgmFhQ1UaRKGexK09+VpK8zSX9ngq3P7WJDcsyqCseMwBoaZxlVx4jE357SL4cwhlckHaXUt4UQ38F/kH8c+IUQ4q/ADUqpra/x8yuAO4M3Hh24WSn1oBBiJfBXIcQlQCtwYbD//fiZa1vwU6Y/HlxjvxDiB8Co5PL3R5MKgM8yljL9AK8xieCKFVewqX/TK+/4KjCjeAZfO+prr+qYUWXoiooKNE2jurp6AunceuutPPzww2zatImf//znXHDuOfR0dTEyPExRNIJjZYkWFBCO5aGHQqisi5d1wVWcdOJJOemaj1z0Yb7w6U+TSfRjpSw86Wd/Yfq1CDfe9jcWv/N4dAlSC6G0GK5t43oZLv3R9/jkxz6CIS3fN81oQFSA0BHCBBFCiIl1DR5ZlEoiPBvh+R4GT/ewDQ9bKjRbYtoS6fmElDE9XMNFCoWpFIYCXYGuBLrQkEIDoaGERCHH5kHenG4YVE6ZwsjAICvuuo3ld95KQXkFjYuOo3Hx9VRp3YgV18ITl8NTV8PcC+GYz0LF3pvdTUAoDhWz/Gl3KOUHjwdbfBJqXw1r/gzr74DaRXDM52DG2X4R5z8BBaECFlYuZGHlwtyYpzzaR9rZPLA5N23q38TDrQ/n9okZMaYVTvMtoyObaDq5iVMKj0DLhMZZRf5884qdExIYInmGbxmNuuqC5UOW0dsH+xXJDOIqO/GD+g5+csHfhBAPK6W+eqAfrpTaBhy+l/E+4OS9XQfwuZc51+/wLbHdx1cBc/Y84q2NM844g+985ztUVFTw/ve/f8K2VatWUVpaSnlJMbE5s3hu9SpaXtqEGQqBEBTXTMLQQzxw3/1849vfZGhwiD9ecwPHLFwEAh64617y8yNksyk8z2NgMAgkS18rQBN+avJf77yPdS9u4s6bb8XxBHhZCGIxL27YQGtbG9//9jfZ0d4JCIQsCEjGf3h4wsWVWZAJhLD8dRVGemEMtwApNJSyUCqFdCxMW2IKQHfQTAtDKTxXR1oaytLQhEdEdwlrDrpUQewkeKAp8FPj9jTSI94QZw1eDrpJ6uRj2BI7ieaWQZ67/x5W3XsH8eISpi08iabTPkZN38PI52+BtTfClBPh6M/BtFPgQNSkhfBrOGIlUDMf5rwHTvoGrLkJlv0K/vpRKKyDRZ+BeR/2s5H+yZBCUpdfR11+HadMPiU3nrJTbBncMoGMHmx5kNs235bbpzpWTVNRk28ZTWliXlETtXmHkR12g1hRkoGdARktn0hG4ZhBUdVYNt0oKUXzD8WM3mp4RdIRQnwR3y3VC1wP/LdSyhZCSKAZOGDSeSvi1VokrxdM02T+/PlcddVVbNiwgXvuuQelFJlkkj/ccD0bN2ygcfoMBJBIJHl82Qo+dcmnyIvHaXl+G/XVdZwy/52csuQZzrv4PYxYQwxmd+F6LsMjPRiG7/8VgBQaQobwiKKUhasyPPbk41z9y//jzptvwjTHp3T6VszqtRtY9+IGjnrnKTiuQ29fD+dfdD5/vf1mPGlhaTa2mEgBEtBUGk1lsHUQSkd4JpoXwnDzEcoGL4Oys7i2jhIQ1j1iYQdPQdrRSNgGCdvAkC4RzSGsOchXzEMRvmvLtYl2PMFhPMFheojM6SexLX4Gzdt6efHRh1i7xCKSX8C0I75EY8EgdV13oN38PihphKM/A4d/0JdYeS0I5cHRn4aj/t2PCT37S1jyDXj8R3DkR2HRp3wiepMhakQ5rOwwDis7LDemlKI71T2BiJoHmlnasRQ3yEg0pcnUwqk+GVU3UtVYxbRoOeWRRqJ2PiM7Ld8q2plkoDPJltW7yC4dc9OFojpFlbGcq26UmGKFh7Lp3qzYH0unGHi3Uqp1/KBSyhNCnP36XNYh7A++/OUvc8IJJxALh0gND5EZGaa/s507776bZ554nIYp09AxePQfj3LZT37EJed9lP/+zJf43Fe/yA3X/JJIzMTxbJKpESw7nZPPkCKC0Erwo+wpPJVFuSl8jya8sH4DX/32d7j59zdQWlIGwkTIMBACAZ5w+NDHP86F//ZBHGmxvaOVz33os1x/728ZIYnpgulohFwd4fqp1lJ5CDyUDCGMMHooQiikEzI1hFQk3QTJlIWdBcPJw3AFysuQsi1StkRKXwanMB7DsbJkEiMMWxYjjq9GEI6E/HPtTURTG4T8Ghjp8rW+pA6ORXj7g8ziQWYZUayzTmd73kk0b+1m04oVvJBJE4oextSpp9I4vJ7J934F49EfwPyP+4TxWrOApAYzz/GnjtXw7K9g2bW+BTTzXN/1VntgLSPeKAghcll3J0w6ITduuRbbhrb5RNTvk9FTHU9x99a7Jx6PoDhc7KefV1ZQPqWcskg5FVSQlyhBH4rj9eskdllsW9PDhqfG3MtmWMtZQ2PzKHnF4UNk9E/Gy5KOEGI18BR+DKR7b/sopTa+Ttd1CK+AbDpFbXkZZSeewEBXJ3Y2i6YbrN+8nUk1tUytmIYa9HDJcuy8o9jQvJHmjk1c+P5z6B3pZvF578I0TWLRGAsXLGLu3BNAiwDw7g+9D03z3UUzp0/nmit/EnyqbxH84IorSaYyfPILX0YJRXV1Nb/+02+wtQHOX3wutz9+O6YLYVcStjSKUgaaJygZDu9W6KYAB4SGQAOho9wsuGnczAApYZAWocAlpxPW8olIgdI9HN3G8ULghdBcD+XZZBJDZBJDCKljhGLEiwvxXItMYoRMKo3UNMLxPCLxIvTQuDfhXTb81wu+DMqWf8DGe2Hzg36aqZBgpzBfupPp3Mn0cAHOuWfTWngyzS+1sXXVcjYkIxjmiTSUQeP9NzJl6S8wDzvfj/tUz3vtP3bNfHjvDXDq92DFdbD6D7DhLpi0MIj7nAPaW0e719RMZhTPYEbxDJg6Nj6YGWRnaucElYbR5Y5kB2t61jCU3VN+JlIZoXxKOdVaLVVWPUXpSsyRYoaHHPqfH8F+ZszSNUJarui1aFyKd17xoaLXNwovK4MjhNCBdwBnACcBfcAS4AGl1OY37ArfYLxZZXCUUtiZDJnkCJlkAs9xEVIQjuQRNmNIT6Kyri/ci69pZntZbC+Lqxy/4BIB6EFtixsUXQZqv3uF9IP8MgLCxBMujrSwtCy2lkEql7CjoXsSzRPg+f4ytUcFtU9WQuhIzZ90w0Q3DXTTQDMkmiYRUqCUwkqnySZTWOkUThAjElJD08NILQzC9JX6XcWE+1d5KJUFlUGpQHJE6EgZRmo6Sll4ThqlFJpuEIrlEcnLo3nrFmbN2i3Q71iw/UnYeA9sus+vHkdM/LuKluLOuoAdRSfTvGErW1YuIzU0iCYF9fEBGmPdTJ3VSPj4z8L0dx28hIBsAtbe7Fs9A9uhoM53ux35Eb/A722MjJOhJ9WzBylNkBZK75qgABGyoxSnq5jkTKUyW0dhuoLISAEyFcrtIw1BYUWE6ilFVE4toGpqAXklh6yiV4ODLvgphKjGJ6AzgGnAMqXUZ1/TVb4J8WYiHaUUdjZLNuETjes4CCGJRPIw9QjCDspWGFVozmJ5WRzPYrQYf+zn3b+4hhAhEBE8qXCkhUsWiYPmgeaBUCJ4uO9+Pj/lWUgdTdORejCZGugCNA8vqJ1xlTthProMEDfi5Jv5hHT/geA6jq8snUqRTadQnocQAiMSIRSNYYajSE3Hcz0ydpZ0NkPWyaJshe4qpOuMtTEQhv/9EAE5+cTU2tnN6pufp7BiLhX1JZTX51PRkE9JTRxNl74rbsdy2Ph32HA3DLezB/Kq8Oa8j87SU2l+YRPNy59mpL8PKRS10UGaKmDaqRcSPe4TftzmYMBzfYvs2V9C69Ng5vnEs+hTr67q/W0GT3kMZAboSffs1WoaXU4nLYrSFRSlKilKV1KSqqYiMRljtOV51CE6CSqmFNA0YxL1UyvRjbepmvdBwOuqMh0kERyjlHr6QC7uzYx/NukopYKYRMInmkDTLBKKo2MglRYoNCscz55gzbwyRhtv6Qh0350lQOGihAvKQ+AivHF9VCYgqKURAiUlriZ8ctJcHM3BFQ5KeXuVYZlwFcr310sh0ZC5uacUGS+DEmDoJvlmPvlmPmHdf+NUysPKZLBSSbKpFI7lk4ZuGJjRWEBCYYSUZN0sI9YII9YImWyasKURdjSEG0j4CBOECULRsmMrT/3iCoQ0CeUdjRJHIISOpktKa+NUBCRUXp9PQWkY0f2C74LbcDf07sXoL5yMOvwidpafQvOadTQ/9QiDA8MIFDWxJI1zp9N4/ufIazhsz2MPFJ1r/LjP+jv8327G2UHcZ9FbtqnZ643draadqZ20D7XT0zGM1akR6S2hYqSe/KxfaOtIm2RhL1SkyavTqJlaQkNlLbV5tZSES/7lraLXTDqBwvS/4VfxPzieYIQQ31ZKXXqwLvbNhH8G6ViOS2fPICKbQvdsdOWhSwNdhjGlmVNodgNrxg6smZd/uIvcJITMrQE5N5sIOj7m/uT+wYjceT38Cn6FQgmfTBSMN59y7rxx/xvbrtSEMfGKdDQRnlAoCQiBpmkYeghDN5GaREoNBbijoqNZXxNOCIEZjRKKxghFo2i6geM5OQJKZ5IYtiBsacjASmzt3MWzv30M5e3Cs7cRLShnzskXIbXJdLcM09M2ghMIlIZiOhWT8ylvyPfJKL+XSNv9foyla92eX6KkEXXkxfRWnMzmx++jeflT9A37Vl1VsU7jMSfSePoHKKx4ZYmb/cJwpx/3WfV7X5urZj4c/Vm/6PQN6PPydoLlWrQn2tna0Upbcy+DrVncnSEiA4XIoA/SQLib7rzt9Bd2oFdZlFblU1dQR11eHbV5tdTl11EWeeu3mN4fHAzSuR6IAiuAjwBPKKW+FGx7Til15EG83jcN3gjS8TyPjoE0VmKYiJtCKg9N6IS0CLow0KSR6zfjKQdXOX5LANRuJLE7gcjd1t+cUOOWFPhJcsGag8JSLlnXwVE2CBuJg1AKqXz33qtR4hNSBhpvJrphgpRYyiLtZchYGTRH0dXSzTO/+AloOmakCTvVifIGKayaw8mf+CR1s+vp70rRvX2IXS3DdLeM0N+ZyHFrXkmYioZ8KioU5WoNZV1/weh6homWooDymbDw3+mPzaT5vt/RvH4r3Wk/xbqsopimE85gzslnEC86CHIlVjKI+1wL/VshfxIs+iQceTFECl/7+f+F4VgundsHaN7UTseWARI7XFTGT7yxbdyTwQAAIABJREFUjDQ749vpytvKzvh2euJtGCGdSXmTqMsLyCi/NrdcEatAigOo8XoT4mCQzvNKqcOCZR34Fb4g6Qfx4zkHIS3nzYfXi3S6hzP0j6SJWCOEPAtNSCJaHEOG9vumC1TDUGrU5hhbnrgOarc/qAlrE9b9/wLFAOUBAqSGJ3U8TUPJYLsIzizGPmF0DBEsjtHe2P8VubEJ29WEvZBKElImxrhumq5UuJrAEoIUGRIkcEUKgUJ6EjwT6ZoYmBjST8eUwkN4LrgOnuuivH1Lw7d2dLL0lz/Gk6B5vuswFK3ASvQAioLK41l0wXuZfvQkzLBvddpZl562Ybq3j9DdMsyulmFG+n2JHyEFJZUhygv6qMg+Q8Xw/RRpbcjRAJyQUHkYLPgEQ729ND96J83dgs50PpqmMeP4xSw463xK6+r3677YJzwPmpf4cZ+Wpb7227wP+7VAxXtK3xzCq4dSisHuFF1bh9i5bYiuLYMMdvtSQUiFW5xiqKiLHbGX2KCvZsjoyx1rStMnpHzfMpoUn8SkvEnUxGuojlcT0SP/pG/16nEwSGfT7s3bhBD/A5wOlCulGg/Klb7JcLBIZyhl0TXkFzLmOyMYQESLYcgQQmh7WCGecnGUhxQaEhGoPPtBUUdZQfzGxsUN2GdfWWeQc7GJQJVMjLncGEcJ4D/8ZaBf5o3GeLBRoy0FhB4kGOwpWTMeoySkROCOEwqFhye8nHvOE66fUCACaU4xuuwfA2CgE1YhDFcn5JmElYk+gYg8HM0jIywSJMnIDB4gVAjPieB5YQjaJkghCGkQVjaGmwU7MyrcFmTQmWzfsYPktk20dm2hfdOLaANZ9rQTDfTwNGrnLOLwk+fRcEQDcjcVguRQll2tI4E15BNRNuUEn6Uoj3ZR4aykwthEudFMXPYhpAbVR0LD8Qy88CirXxph/VA1jgf1R8xnwVkXUDf38IPjnula58d9Xrzdr0+acZYf96k75lDc5yAjk7B9Ato2xM6tQ3S3DOPa/n0XLTIIT/KwSofoK2ynVXuJtmQb7SPtZNyJ2oQl4RJq8mqoiU+cJsUnURmvxJBvHpfpwSCdG/HVpR/cbfzfgGuVUm+eb3sQcaCkMzyUITGc9bXC3CEkds6S2Z1kFH5asa1cskpnYuWK/0cKMFAYeGP6YQQZbcrCUS6OEnhoQd+YgISUt5flUQvGJ4FXglQKoTxkYLl4QuAGNQxCge4pNCXQlEQJDaSGkqPzcdpmQqCU8DXO9tclZipsLU1CDOMIP+vM0AxCmJiegeZKdEebQEQKcKRDRlhkRJastFC6xNBiSBXFcSRZx8Ny/aw7QzmYnkXIs9A927d0fvcb4o1zOek976GoJp9bb7makaXrCVkCy1CYjpwQyxIyREF5LbWzmqicNoWyugZKaydjhMeaYClPMdSTpjsgoe7tw/S2j+A5/nmicoByo5kKo5kKYzPl5nZCDUeQbt/Auu44a0amkEo7lE1uYMHZFzD92OPR9IPwz264C1b+Flb9zm/PXD3Pl/OZff6huM/rBNfx6N2R8Ilo6yBdW4dIDfmJMEZIo6Ihn8opBcQnCzIlg+y0OulIdPjTSAftiXZ2JnfmsjzBlySqiFaMkVGeT0aj62XRsjfUdfe6Zq+9nXGgpLNr2zbCWgzzZS0Zn2TSSGxhY2kZ0rrf6Az88L2mIJ5RxLMSM4v/kJMSpQti02fxvvMu4Iaf/RpdGigXJs9vZOG8Bdz2uxv586038u0f/YDqigr/bAKu/9m1RKJxFp18HNOmTsOybY44bB4/+8kv0HUNpTxWP7eK71/+PXbu7CIej1FeVs63v/pVZkxv5Mr/+yk3/fWvlBSPxRhuv+lGCvLHa4D5brlly5bx0U9/gfpJNbnhH335yyw+5hiic+fygbPO4neXX4ESAtv1mHriCSw47Ahu++3vuenO2/n2FT+kqrIKhWDWjDn84urf8P0ffYcljzyAbmrU1Nfwg5//gILCApbcsYTrr7ned9MpWL9+A08seYT5s+ZNICJb2GSkha05SEMjEokR0mJYrofleD4R2TY7mjdxyw03UZtsxVQOfWXTmXzyeZx2zGyefeA6Ov7xDMrx6CuyKBsOIzwwwpXYWQWqb1xKtqCosorSunrKJjdQVtdA2eR68ssqcpaKa3v0diR8a2jbIN1bdjEYSNMKXBrDSzky715KJpfgtK1mY3YaqxNN9PUOES8qZt67zuWwU84gHIvv3w29L1gpWHeLX+/Tt8VXZVj8HTjs/QemJXcI+w2lFCN9mTGX3NYh+jv8OKHUBBX1+VQ3FVLT6NcNGSENx3P8QtkRn4zaE+05UupMdLIrvWvCZ5jSpDpePYGURq2k6ng1haHCg5rgcFBIJ2is9jlgVE53PfArpdReFQreDjhQ0knvGEQTYy4gT3mkZYahUBINi6hrE3NdQqgJhOS5YCd1PGffP37ZUUcxpa6OR/5yEzIe56Enn+ayK6+ipqqaO//wV2667VZWP/8c/3fplXjCQ4Y09GiYtp3tnHPuObz44ou4rsupp57KJZdcwoc+9CG6u7tZtGgRN910E8ccfQyep3jyiaX07OrhXaefzaWX/YBIOMJn//2zQSGpX1CqcPGb1IxZTc8sW861N9zAn66/Dk8qXKnwpMITirlN86mvn8w9f7+NgkiMxx55gh9c9hNqqiq56/d/4M+33srq55/np9/6Fsq28aSOa8R4eNkKjjn2JHRd5weX/y9Cg29971sk5Qhp15fk2bJxC//x0f9g9YbVRPUowhM4WQvPdhG2Qne1PYgoK208XaGF/ASD1q2tNE2fwaaWXTzyt79hPf8EhmuxPTKZwekncuwR08jbcC/dK1ZgGR7DUZuyoRAyL0bdjPPpbY2STXZjhgaIxIex0t0M93bnLCMzEqG0roGyUTKaXE9p7WTMiJ9EkE35LaNbl65m/VoHR4WZHFrFkQX3U106hOrbRkvsOFaNNNG2eQtGOMLck07lyDPPo6B8P9sl7wueB1sehieu8CV3ahfBmT+Bqj20eA/hdUQ27dC1ZZDO5kE6Ng/S0zaC8hRSCsrr86huKqKmsZDKqQW52OKE490snYnOHBFNIKZExx5qDjEjliOlUQvp2OpjmVJ4YLG+/SWdfcngHAfcjN8W4E/B8HxguRDiQ2/HGp39wc7LLiO7cc/WBq7t+JlVmofw7NwDedTxlQgm8FsDKFegvOCh1FBPycc//oqfffrxx/Pwo09wwWmnce9dd/HuM8/gmeeeY8jqJWMP4boZbCeJlCHIaLiZDHZ3EmW7ZDv6kBosmDOXHVu24A4P8/Orr+ajF13EMQsXIpSHpmssPuXE3OdF80zi8QhldcW4tkc645BK2zi2h/D8drdCeaCyIAxGkwY0V6C5krGYk+DkE97JY/c9xllnnsGtt9/FWee+ixUrV9GuJegzbJKGorcySqGeRzjr4aVSnLVwDl66C1cPc9Rhc7lryYOIhEmcEgrNMjA9fnPndZz57jPpTvrvQZrUiBkxYrEYMT2OqZl+4Wg6hZXJIhxFxAuhZzXI+lfoDme59Sc/p7dohI6mfpzDq8h/IUXtC+00rP0jL22Ls7q8luIjj+Pozm2U7ewiEwXbGaRl5U24tfkcvvhjpFsW0vHSAEJA07FxqqZ66EY/fe2t9LRuZ+NTj7Pu4ftzf7+FFaNWUT1ldQ3MveAwFlxUxIu3/J11a5u4c9cCKgc2cmTRQ9Q7a2nQl9F9zkdY3VfJ2ofuY82Df6fx6ONYcPb5VE2bvv838e6QEppOh2mnwrqb4eH/hd+8ExZ83Ld83iTNv97uCEV06ueWUj/XrwuyMg5dW4fo3DxIx+YB1j7UxnMPtiKkoHxyHjVNhVQ3FlE1zSehkBaioaCBhoK993ZKWIkxIgpIqSPRQftIO8u7lpN20vzvMf97wKSzv9iXYNNVwPlKqTXjxu4Junv+Br+J2iEE0FQQAHT3vl0pfKLZi0KAEIpwoc3u7lelQHkiSCgTfOCDH+CHV13NWWedxfrmLXzsve9lxarVhG0HD8Xt9/2dZ1at9JudAQ//7U6ybirIVjNIJ7KsWLWGK//nhzg9CV5cs5YPnXMO2c1jBY7CNBGmiQyF8FIpfnrdddz45z/7rqOiIh577LHcvlnbZSBhkc5opPUYy1at5uRz3g14CBTX/+Ia6if7isjnn3MBV13zcxaf+i42bmrmogvfz6rlq4mldcJZyX133sdzz65CAZ+45GIu/thHySutIywkXjLJX26/mfeedhqx5E4cPYLjRXAtk3tuv4c/3/AXaqjHMxzSIkHCTjCcHQbAkIZPQkaMeLQAQ/PrdpKZBJl0GmV7uMJjXnIGRUP50AK7wgOszX+JFUc/T3JnKzNaXC7YkqCrJMNjUwfRqyULXyqiIGUwFLWIdg6y7taf0Tw1TebYWqYPHoe7vZa2DSGI6kRm11J2fgO1ZXmYSQ+ne5BMVy/DHV30tbWyZdWynFVUVFXD1AWLOOVT8xh8djXrXijh/l1fpFhvY17hwzQ238iZ8SKO//TXea5V8PwjS9j87FJqZsxmwdkXMHX+UYgDdY1J6We2zTgbHr/cr/dZf6dPPPM/9k/r6/OvCjOsM3l2CZNnlwA+Ce3cNkpCg6z9xw6eW9KGkIKy2jg1TUVUNxVSNa2QUGTPR3vcjDO9eDrTi/d8QVFKMZAdwJSvfxO9fZFO/m6EA4BSaq0Q4iDpeLz1UPnNb+59Q+cef1W+6yyh47l7us6k7mHG3d2IRkCkyJeu91xE70sI1wLdtxrmzyihrXMHdzy1lLMuOB+zrg4tHqegaTrxklLe9+53c+l3vo3j2AghCGs65mAf21u3s+DURbS2d3D64pOZOWsmSBOhhZDRUmR+NbgWx1/wLoZHRjj5uGO56utfx00k+PxFF/GfH/uYX/djmlhtbTli0kMhKuImFIZpLY9zwvHv4Pe33M5wxiFru0jPJe35wdJZ06fS3t7GPXfdziknngRE/Gw4rQwlY5x7zgVc9oMfAllwLZzBJAODSRDw899chwyZvP8zn8aQGqFMBi+Z4tlnHyMWMpldPxk74aCEwCCP8lABWkRiaxlSKsmIPcJgdhDwxSbjRpyYEaOkqAyBoLOnm5tOW8rGrS8we7CBY1JHsLh3Iad5R4MpESdF6MxuZ93aB6hasRO7opTnGyvI9PUyv7MHzfVIxmDGlijJ9m5WzvwdXfMUVf2NNHUvpG7lbNIrJc/mv8DG8mVsL1mHKx0ogWhFlAKZR2UmTsVgBK3bYvX9d7Pq3juI5Bcw5fAjmNy3ivZd03mk9xKWy3M4wnqIWf/4Ju+cfARH/++lvLihk9X3383dV15KUVUN8886j1nvPBnDDHFAiBTCuy73Wyk88FW470u+yOiZV0LdoXfNfxbMsE7drBLqZvkkZGddn4SafUto3aM7WPNwG0JAaW1gCTUVUT2tgFB03wkiQviK3m8E9kU6QghRpJQa2G2wmFfsA/wviEBN2MtmsXe042XSE7cLgYzFMCdNQuj7oQis6VA+yxd0zAS+WDPOuaccx1f++ys8ft8d9KV9s0pIidR19HCYkrrJ2JkMqaFB0skEw55DfX09z61Zw+DICO847jjuf2oJp77znUybVs/K51dwxqmnYBhRnvr7k9xx313c/48HENEyZCiGiMbQyyvAc1FZCy+bRY2M7JbJJbE6OlCZDEXpIYpDIWTcxNWjDGX9up/+cCnvPO1dfO/yy7n95hsZ7B8AlcVjGISHQCJVFEEUJcHWbDyZ4q9/vYklD/+D2/78R4Z3+S40qWkY4TC3Pf4o7//AheQV6nipIey0gyNNHDeCnTUAnYgsoDBSgohARqRIOkkGs4P0Z/zofVgPY3s25047l28s+gZrd61lScsSrmr9E1MHazguPY9j2o6gKlNOVdHF2LUOLX0vUrnxBUR1Ps7p76L5xY0Ut63ERiDcECes0elqzbB89gYSi/OYVDqV6PZKQitnULOlCdmu0KensKf3kMjrZTg7zIg1QmtpF0uKV6NPgxlD5cwdLGTjymfxMha6uY2ymI5tNbC0792s0s5lbuYhDmt7D/OPej/zrvgJm9etZ9Xf7+If1/+Kp2+9kcNPO4t5p59FtOAAi0ErZsHF9/rSOg99B353Ghz2AV/tOu8gKSgcwgHDCGnUziymdqZPFrbl0r1tiI7mQTo3D/L84+2s/ccOEFA6KbCEGgupbiwkHPvnZSnu6+n3U+AhIcRXgOeCsfnAFcG2QxgHq60Nd3h44qAQyHgcs6Zm/4hmdwjhF/AlgqwUa4RPXHwxhcXlzJ1cxOPL1vhqyOMC+kIIzEgEMxLBsW16h4ZRyqOvYwdGOMz3v/tdrr7mGt530Yf48je/wdGLFnHqGadwxJzZKNtjMNWPEgqhGyB08AyUFfYTCbQQWmEhMhpCaKBsG2VlUVkLNA08D6enZ8JXiGsaAsU0leA/P3IRNcVFNB5xDE8sfRJXaHieg3DT4KVxvH6EEUaTIQzH4LFHVvLba//A3/56L7IwSlKm0RSE0bEyGW6/8w7uuuVm+oYG0E0ToyiGoWmEHQeVSWFbCluGSbshSAoEBgVGEWWxcryQR8pLkrSTJK0kX3z4i+hCpy6/jqmFUzl/5rtxPZfnhrdzXdcdlCULeEd6Pouto5kWPpzG6iOwVIbOlVuoiYUpvPASnntuOektz5ORIUoHdM5dGmZzezM/mPZNKopqeN9FF7JYncyOFcNsXduDWhejof4wZh1XReOxFZhhnf5MP093PM3SjqX8veNpElOGqRqIMm+4FrdDoUbWA+uRRh7Ppo9gtXk5cx9ZxeHrzmDGGV9k+qVX0vHSRlb+/Q6W3X4LK+/5G7NOWMz8s86npKb2wO7BOe+BpjNg6VXwzDW+6vaJX/eFRQ+lWL9pYJgak2YUM2mGT0KO5dK9fTggoQFefKKDdY/4JFRSE6cmyI6rbiwkHH/jfsdXyl47G78z6Gz8IMQG4CdKqXvfmMt743Gg2WuZl15C2fZrJ5qXQTweJ9H8tE8wRhTyq3n8wbu58prf8Pebfs0f7nqM//7296ipqckd86tf/Yrq6mrOPvtslj/9FKmhQRzL4pRzz+OnV17FKaefzoqVK/na175GR0cHZaWlFOTn8Z+f/QwL5s/n/679Db//wx8oLS7JhaBuu/4W6msn+yvCQxgaMmLwxLNPccH73k1DQ0MQjFJ84z//kwtOP53ipiZ6Vz+HcuzctT25ciU/++Mfue36G/j9HXey+sXn+eF3v4sM6hCUEByz+BRsy6aoqBiUYP68Bfz4sp9iaRmeXP44P7nsCu5/4GFMPKRj4VhZPHfM+jNCIXRNR3M9PEvhOBJHC/s1RYAmPMyQpGVnCwPFg6zpWcPWwa1sH9pO20gbXq6pnaQ4XIwmNAYyA5i2xjHpeZzpnMjUnkoMR8dTHkOqF2oMNrWvoK1tLY4ZQ7OSpHWDVY0O2+pb0KXB6fWn897JF6JvKWHj0130dybRQxqNC8o58rTJFFb4WW2O5/Bi74s82f4kT3U8xca+jRSPGMzoL2Nqdwytzwq+axGaMYWG+DDHzuik9L1fh+p59HXs4Ln77mbDk4/i2BZTjlzIgrMvYNKsuQeeKtu3FR78OjQ/BKXT4V1XwNSTDuxch/CGwrFddrUM0xHEhHZuG8oVrJbUxKhuKmLG0ZWUTz6wtuhv+jodIUQtflZcBf4j7Tql1M+EEN8F/h0YfWX+plLq/uCYbwCX4Ifr/0MptSQYPwP4GX5C1fVKqcuD8QbgL0AJsBr4iMo1Wtk7DpR0PNuPoxxMotkDru2rGrsWCM1vk+zZvsijkwY94nesDOXttcJcKUU2lSQ1NIiVTiOkJJKXT7SgwNclG90nmWCkvw/XtjEjEfJKSjFCYZRSeMkUXiKNl3XwdWNMv6o+gNAFIqQjTA1hSoQucw845booy0JZgZvOslBZy7eW3DGyyRgmWU2iABkUs3pCQ+ghDD2E6+iIQHHAkTZZ4ZJFQ+gGMR3CuOiejWtlcaxsrg2DpuvouoFU4NkCT5m4WoiWHVt5/o/tVBUkmfmOSdSfeRS2pmgdbmXb4Da2DW1j6+BWtg1to2W4ZUKvFqkEM9NTOLf3GOYMT6NYKwfA0iw6U1toG9hAH71Y6SG6oyUsnSpJ1LwAMktNZBofnXMRxxknsG1ZP5tXduM6ipnHVrHwrHriRWOFpgC7UrtyVtCznc+ihtNM3hVjTkeM6GAQABZRCsJ5HDE7n8P/7asYRVWkhodYu+Q+1v4/e28eZ0dV5v+/T61377073Z1OujsbCSQkISwJhAABEnAD5YvL+APF76iDvhjHEUcHnUEdHEf5ugwqzCiMigiuuEQBx4QlCSEkhJBA9k46Se/d93b33Ws9vz/q9paNEOMMYj79qledOnXq1Klb1fWp5znP8sQqCpk0tS3TWPSWtzPzwotRT/V53f14QD6DB4JMpivuel2m0T6D48NzfHoPpket43r2D3PZe2Yx66L6U+rvdEQk+AqwT0r5H0fUfwhokVJ+6pRGNtZPPVAvpdxSMkx4AbgOuBHISinvPqL9HOBh4AKgAfgDMLO0ew9wFdABbALeLaXcIYT4CfALKeUjQoj7gJeklPeeaFz/26kNXhXSh8H2sXmesimBSWthMEi37NlgxALyMaLH7caxgnmfYjaLlBIzGiVaVj7qOyKlTz6dJjeYwvc8QrE4scoqNH1MDJeeh5/L4WXzyIKDlAKhmQjVHCM9QYmAVISuBPl1VHHUl7Z0XfyihZ/L4eey+IUCSIlfIiFbDYwpxEgEbEVH0YJ5G+GHRtM92IpPEQVLSFRVIWoohBUf3XfBtXGsIp7jjI5N03TaD3Wy4zu/J6vOwtfriBX7aE0McNblrVRduRR1nDOs4zt0ZDrYPxSkW36+53n2DO4hbadBwryuSVzTuYDpyizqIs3owsCTLn3FQ/RY7XRm99I7ZQaryh1yZRtRQz2ohLmgegUfOus95DarvPJMJ0IIzrmskfNWTCUcP9qiyPEdtvZtZW3nWtZ2rOVgfxstPeWce7CRaKYI0kGgMKmxgblvup5piy5ED4fZ+cyTbF71KIPdncSralh47VuZe8UKzEjktT2HEIQU2nAPPPP/gu2lH4clt4EeOvFxZ/C6hOcGETtONWfQ6SCdF4BF8ogGpVw626SU55zSyI43ECF+BXwTuJhjk86nAaSU/1rafgK4s7T7TinlivHtgC8RSEuTpJSuEGLx+HbHw+uedEaQ6YVMKSd8uBLKS/r6XBKyPUFsrVA5xOtP+BLwXJdCeph8erhELjHiVTWjX8C+55EbGiQ/HFh/Pbv5BT73xX+d0EdLSwuPPvoovm0HpJHN4uctEGpAQHoIMT5GlCIQhopiKKNkJNSJtinS8/ALhaCvXC4gIcBVFIqajqsqiNGQPqIUmFRFkWFUAqstXwFLQEFKbCFRFUHE0IjqAlO6KJ6NY1nsbWtj3Te/DEAolEBhMo42C03UUZd6mdbyFFOWzSW+/AqMyY0cCwWnwGPtj/Hb/b9la+9W6npUFuyrYrbVSnW8hcnh6ZSrgdXRsD1Al3OAQ62SNZNctg9vxQ/tQAif+vBUVlRfTeOe+aRectEMlXOvbGL+lVOOaQY7gu5sN2sPP8Xal+5nSzrJOe0Lmd5VgVLsBJkBoGHmbKaffxGtCy9gqLeLzb95lI6dL2OEI8xdvoKF17yVRHXNcc9xXAwdht9/JkjvUNEMK/41yJR6Jp7bXxROB+m8fDxiEUK8IqU8+1j7TgVCiGbgGeAc4OPA+4A0sBn4eynloBDimwTRrX9YOuZ+4LFSFyullP+3VP//EfgQ3VlqP71U30SQavuoaxJCfBD4IMCUKVPOO3jw4IT9r0vSAbAykNofSD9aCKqmBxO7vhcYH+T6gn2RqoB8TjDp6/s++eEhcoMphBDEqqoJxxNj4Vtch2wqRSGTRlEVouWVRBJlx/UJkVIii0X8bBYvl8PP5RGKBpqJMCMBGclxLyVNQdEV0JRARacGqrkRqWhEqhpdisUg5pqqUtQ0PEWMSkFSgK8oCBFClRFEKfGcrwmK0icr/SBAqBBEdJX+jv2ksh6V2U66X97Coe0v4doWiqIj1CaE3krciTK5ewtTY0kqrriY+BVXEDr77GNev+3ZPNf9HE8ceJxXNq9j5m6DusEQoUgFjdHpTFZbqQk1oQoVSxbZGt7FxqpdvBQ9hB3NkbKS+NKnzmriyr53E+9oxIionLeymbmXTUY3TvwlahcG2fzL97Oubxtt+Uto6jyfWGYY192DcAOrvYqGRqYvuoiK+kYObnuRPRsDX+8p55zLrCVLmXH+EkKx1xhqZ//TgYl1/67A0XTll6B6+mvr4wz+bHE6SGcT8B4p5d4j6mcAD59M5yc50BjwNHCXlPIXQog6YIBgnucLBCq4W/6UpDMefzaSzghcG5J7S/M8ClROA7P0svCcQOrJJQEBsRqI1YJy/C9m17ZJD/RhFwrooRCJ6lp0c8zfw7EssqkBrHweVdOIVVYRisVfdWJa+kGUAT+bDZZiEYSC0EwUMwyaHkQ1kMd4oY4kLFUFQlNBV0GAtAv4+Rx+NhfMCwmBrapYqoqvMBpuyFeCAJ0qYZBG4HOkKXgqFJC07d/L//11N0LAnPoEFzTFmSP6iPTsoXP7C2QGgulFodaias3UZIs0d7xEtZkhfvnlxK64nOjixSjm0X4xtmezoWsDa9b9nOK63dQO6NiaT0gNU69OoSE6nUmhFiJa4PqWVfN06wP06gP06El6jAHyrqB+YD7lQ9OwQ0VqLoG3vmkZsfDx1adBZwPwyHs41L2FNf5F9A+/jXCmhqK/gxwvEc3lEBLMRJypZ88H6dPTtpd0fx+KqtF87gJmLbmUaeddePLqN88JnEqf+hI4hSCK9aW3jz2TZ/CGxekgnWuAe4B/IZhvAVg+91ttAAAgAElEQVQEfBr42Mjk/h85SB1YBTwhpfzqMfY3A6uklOecUa+dAL4fTOiWvPCJNwTkMkIErhVEFi4OBm/veB1Eao4b1FFKSTGbIZMcQPo+kbJyohWVE0L5W/k82dQAjmWhmSbxyuqJLyYpA4nLs4Lzu9aEsvQ8PEfgOwq+K5B+MFZFEyimgqIpQdRqdJA6Ei0oH2XlLxHCA+EHceFcF9+xkY6NLz1sVWCrQcTrUagaqhLC93WEMGjvbGPfJkl/XOF5q8DmziEsN1DdzaiJsrjKobV4GNn2MqlDewnyB0Uw9UbqhoaZdvAlIqokdsnFxC6/gthly9Aqj3a0czyH36//Cdt//Rv0w1kcxUeRAkUKKoxaGhIzmDZzEbFIFfZgAWXYQxnnWOwjyUsXy1MZokhb2T4G6vuoaqijsXEKUxpbaC5rIaIfQRD9e+DhdyGTbbS7c1ibv4FMYQGOSHMo/N94zl4aBg00B4Sm0TBjFrphMnD4INlUElXXaZm/iFlLljJt4QUTImkfF5leWP052PpQ8Dxe/YXA9PqMyu0Ni9MV8PMc4HYCtRfAy8DdUsrtp2GAAvg+kJJSfmxcfb2UsrtU/jvgQinlu4QQZxPEghsxJFgNzCD4oN0DLAc6CQwJ3iOlfEUI8VPg5+MMCbZJKb99onH9WZIOBC/5bG9gTABgJgL9+vjQJXY+mAeyMqDokKgP5oOO8yLwPJdsMkkhk0bVdRLVtROIRUpJMTNMdjCF53qYhkosBDpOQC7yiJhAqgGqCVppUU3QDKRqBoE+Mxm8dBo/HwTzVEwTJR5DjUUC1ZvvBhZwng+uRHoyICtfQUoFKTWO8luWPtJ3wQ+yr7p42MLHl/5oNtZD3b1s+q91+KIFPVRF64Ia1Gnl7PJtNrYP8kJ7ipwdXMuMMsFirZ/a7r3YPbuQXhFQCBs11KfTTD6wm5jjEl6wgPjyK4hdfgVm69GxsA7vfoUnHv4Phnfux1Mkih+oBRUpUMuinHfDDSy5/HooeLipIm6qSHEgQ7K7j2xHmmjaJII6QcJ0hEOvnmIwlMGOS9QKg2hNOdUNk2ia3ELZ0E7ET26CQpIBZypbctezr7gUqQiSjfvYpjxK+WCe5r4Y4ULwO1ZPacYIRxjs7qCQTqOZJtMWXsCsJUtpmb8IzXiVsCmHn4fffSLI5TP1ksDEetJpnQ4+g9cJ/hxMpi8B1gLbYXRG+B8JMpPOJ1CvtQMfGkdCdwC3AC6BtPVYqf5a4OsEJtMPSCnvKtW3EphMVwIvAu+VUlonGtfrlXSEEPzVX/0VP/zhDwFwXZf6+nouvPBCVq1axfe+9z1uv/12Ghvqgxc+8KN7v8JZF17Jx27/FGvWrAlC44RC/OTBB2ipMnjgwR/xte88jNB0fCm46667eNvb3jah/w/ccguf+6fPkBkYwHVddA2+8vWv8fNf/ZZoOAxIbnjzlfzdR/6GnGtQP2M2c86aiaoEiePe9X9u4FOf+hRoBgiF73//+zz++OM8/PDDo9c2MDDA7Nmz6ejowDRNfMfBH07jpYfx83l+9vjj3HT77Tz/h9Wcd/EShGkeV50nfR/peOC4SNdDjpCTJ4MMdUcknfClz+5De8k+/AJZd4iMkyLr+mRkCCMUp3VaGfOuPJfemmlsbM+w8UCKTe0phgsOQvoskYPMyx1CH9yL9IKMkCEjTn2xSM2Bg1TmCoSmNhO74griy68gPH8+Qh37EOjdv48Nv3iYtk0bkaXsqz4SBUGmzEe5fBYXXLSCC+ovmBCmRPqSfZt62PLrPYhhHyWWYSjWRkQI6p0aJjlVxL2J6recWiAdzmObGZTCDmKyk7iTpys/l5eLS3B8FXOyx6HabWzK/4rqfp+W/jhlw8F449XVhGIJ0n29WPkcRjjMtEUXMWvxUprPXXD8XD++B1t+AKs/D8UhOP+v4fJ/PJM2+w2G1z3pvF7xeiWdWCzG9OnT2bBhA+FwmMcee4xPf/rTTJ48eZR0Nm/ezDe/+U1wi4ETn2fz8C8f5+f/vYGf/PyXKIpCR0cH0WiUXDbLsmWXsuWJRyiLGmQt6C8qtEydDK7FY48/wb/cfQ89ff3sW/8rQJBzDT79xW/QPzDAvXd/gfKKBNmCw//71n9y5z/fiS80EuVltG17CSBQy5VXoIx7yabTaVpbWzl06BCRktR033338fzzz/PAAw8cdd1DySRvfctbyGezfPOf/olzzzoLYZqoiQRqWdkJCehISCnBl0jXxy/auPkCvu2y7/BByn7SiRKuCIwOSsi5w6TtFBknSc7LoujD1DYWqTtnBvmmC9iYr2Nj+yDPH0gxnLGZn88xv3CYSOEAvnsY8NBUnToUKg91UTuUIRJPEFu2jNgVlxO7+GKUaEAM/Yfa2fjoT9j97FrGEpMHFNldVWDzWUPUtkzjovqLuKjhIhbWLiSkhfA9n13P9bBp1QGygxbhqT6HznqBJ+3fUczlqXOqmKfNoZUmqoplRLIG8XyIGrsCU06UUgoU8aWO55e8o0KSrJGm3+9CWC4hS6AXfXzfBU2gmDrFfAbHKSIMleqpU5k0axbVLc0ohhb4aOklXy1NINwMYuM9iG0/QISjcOU/Iha890zunjcIzpDOKeL1TDq33XYbCxcu5IYbbuCmm27i7LPPZu3atUeTDgRfl4MH+Oq/38uBw53c87W7gyRdpRf0li1buOWWW3hh8yZUazhQy41zerzpb+/kzSuXc+/3HuGuf76DJZdcQt72aGqZwQvPriekqRjhMInq2gkqllgsxvDgINnBJIVMBkVViZZXTLB0e8c73sGNN97IO9/5TgAuu+wy7rjjDq666qqjrvujH/0ob3nLW/jCF77APV/7GnNbWwMVXC4HgDAM1LIy1EQCEQqdkqf9K6+8zPa9v6TjmdU0dCm05uuooh7KGiDRiB6tRRtn8m15edJOkqLXj9AHMauAxhraamazNl3O3t2DTE4WOSvXieIcwHUPIPwgsUWlEaK6N0lNf4oyD6KLLyJ++RXELr8cva6WwZ4uNv3657z85B+Q/kT15FCzyVMtBxkKWxiKwYK6BSyuX8xFDRcxMz6LHeu6eeGxdgoZh+Zzq6haKnnR28C6jnVsH9iORFIZquTihouZXz2fOqrJ9AyQ3vkcbhrwqxGYxJwEFVYNCacMVSqoCmimxBM2nmdjejqG1NHQTkMSMBdhaAhDD8hJFaCW1qXt8XWjFo0jbUbKWqmNIsbKpX2oYtQikgl1E88nVAFKsAghAgOW0jZClMqMlc9gAk4b6QghLj4yd86x6t4oeDXSWfuTPQwczh7r0FNGdVOMpTfOPGGbWCzGs88+y+c//3l++MMfctFFF/H1r3+du+++e6J6bVwYnA3PPkvy4A4uufqtlCfiLL/0Yt77gQ+zYNEFeJ7Htddey86dO1m+fDlvv+5tvGXlclB1io5P67TptLW18eCDD7J9+3buuecetm3bxs0338yWLVsoZNJkUwNIXxItryBaXoFQFFRVZe7cuUAgWdz24Q/zpquvRNX1wNItGuPnP/85Dz30EI8++ihdXV0sWrSIw4cPo6oTLdc2bdrEV77yFX7yk59wySWX8M1vfpP58+cHfbsuXjodEFA2B8iAgEYkoNdAQOPvr+d7dGW7ONC7k84tGxnY8hLh/V00D6pUqWeTr5+PjE8ipmvE1RCmOmax5vo2WTeJqwwiIxZD8Qg7inUM9oUpKwzgOPspevsxnB4EYKg6k3JFqrv7qM4WiM2eQ3TJYiIXXIDf0syWNU+w9fe/HXNmJQjvU3PRuXTPN9kw9AL7hvYBUG6Wc2H9hVxQdRHV+2bS9vQQdtFl5vl1XPCWFrx4cTSawbNdzzJsDaMIhXNrzmVp41KW1ixk5qpP0NX7EttNk22mycu1s0n3VdDSu4DmwbnovoEdzVFs6aWzfgdbss9S3gczByppGSjHKEgUoRONJXAtF0VCJFZO06y5NM6YQ3lNPXgS6fhI14fO7ch9a4PIFNXnIOsWIhUzaOP6gUrU88fUo0fV+UHbkbL7P/wBrTCOoEaIiVchLoIPP3X8cePaKCL4LhTjysoRZXHEMSPlkfMd2e9IXyPnHj+u8ccIgTE1jl5zCo7CnF7S2SKlXPhqdW8UvJ5JJ5vNsmjRIj7ykY+wd+9err766gmkM0HSGQdrqJc1q37KmvXPc/8jv+KnDz/E8pVvRkrJpk2bWL16Nffffz/vfe97ufPOO/nZz37Go48+ykMPPUQymWT+/Pm0t7fzyiuvcPPNN/Pii0Eah/u/+12+/vWvk0om+d0vf8HsefOprKkhm534+1j5HJlkEte20E0TNRJl5lmz2bdvHw888ABtbW3cc889E47xfZ/LLruMhx56iKampqNIZzyOSUC6gVqWCCSgcPiEBHSykmzPQA/P/PfvOPT880S6BWExCy8xm6gGFU4vZTKLGQoTNWuIamVj1yI98v4wWb/IkK2Q8RR63CRd9m4i1gEM30YgqPQFjd191KXS6EIhfM45qAsX0qb5bNu2GadYHO1T1XXOf+sNtF59GZuTW3iu+zme63puNGVxa2gGS/uvI7KrEXzBnIsbWHRtC7EKE8/32D6wfTSawc7UTgBqw7UsL5/NNVt/ybn5DArglE1hzw338tJgB20v9uPviVKZnIKCQm/sIH0Ne+it30uHe5DooE9rX4KZqUr0ZDCvaITDOJaF9H1ildXMWnwJs5YsZdK0mcE9sTLw9JeDlNl6FFZ+Eeb/1SlZuQXqU8aRkT+BrMYT1rHITLp+yepynCrWD7aRI+VSvWRcudRmpP1IHxP2j/RHaf9I23HHlbbH2o8rSyb2PVpf6nOkLzlxDK8V5ddNJ/a/GAZnMbAE+BgTo0ongOullG/IXLavZ/VaNpvl85//PN/4xjd46qmnSCaTJ0U6QOAzkWzj7m/fz8GObu7593sgWj36z71582be//73s337dt7xjnewbt06wuEwAH19ffzqV7/i4osvpqmpifb2duLxsZRKZ8+Zww++8580Tqpj+rz5pNPpCfM4MGaGnU0l8VyXj33q01y1YiXf+e53+epXv8qSJUsmtE+lUsycOZNYyUGxp6eHyspKfvvb37JgwYLj/k7SdfEyGbzh4UAFJyVC18ckoGMQ0KneX8fz2fJKNxt/tRWvw8IQ5SB9KgZ3Ut+7iZhIY1dOwqlswAxPIqHXENcrUMbNG+XdAkN+hm5nmLzVhWf3UPRzVMR0GpKDJF7ZheK6uIZO1+zp7FF9bG9MDWqEI1zyrpuYd+VKFFVl//B+nut+jg1dG9jUswnyGgs7r2ZO7xKEIqi/wOSq6xaSKBszMhgoDLCucx1PHX6KtR1rsX2bOmGwYmiAldk859g2YsltcNXnQQi6+/rZ8MwOul/MQTKEj8fh8l3sqdnE4cqd+KpLKActfTHmDNUR7i6CBFXT8T0XKSWJmjrOWrKUWUsupWZqC2JgL6z6GBxcDzOvgbd8IzDtP4M/CkcR1XhCPEa9EtFQjpEK+2RwOkhnGXAZ8GHgvnG7MsBvjnQafaPg9U46HR0d/OIXv+C2227jqaeeelXS2bJlC5MmTaKhoQHfsXnfe29k3qwW3nPdNfQM2yxcdg0oKt/97nf55S9/yY9+9COmT5/O4cOHMUvOjv/1X//F2rVreeCBB/jkJz9Jb28v//Ef/0EoFMLzPGbPns3jjz9OTVmCSVOmcuCV7cSragjFYke94H3fp5Ae4teP/pJ/+fKXyebytLXtGw04ejycSNI5HkYIyE+n8bLZCQSkJMpQIgEBna77m+zK8tyThziwuQ9R8PGki5/bT13fs8zq2oLpeuRCIXLVDWRrJ6PGm0joNST0ShJ6JdoRWRsLbpa8nwHDI6RYmH0HKbbtoEvJsbNcUNTG/ncjiTIuf/+HmLV46ehv7vgO2/u3s6F7Ay/ue5noS1OZ3r8IV7UZnLWP1qXlLJ56ITMrZo4ek7WzPHn4SZ5of4L1netxpUuj47Iyl2OlrTDr5scRtWeNXXNnlt0be9i5sYvisIvUPPom7WNzYg0did1IITEchaa+CGcNVlPTLcDxEUJBlqJ4l9fVc9Yly5h14SVUda5CrP58EDfwzV+Fs6//o+/LGfzP4HSq16ZKKQ+esNEbCK930hmPI0nnyDmdb3/726TTae644w4sK1B3XHD++Xz7S3fQe3A37/+7O+nqSxKKxKmpreW+++5j3bp1PPbYYzzyyCOj/aRSKWbNmkVHRweKovDZz36Wn/3sZ8TjccLhMG9605u4/fbbMQwDVVWZc9ZZSBm8WK659lq+/JWvHHU9tmXR0NjIu294B5/55O1EyiuIlpXz2X/6Jy6++GKuvfbaCe1PhXTGQ3peQEDDw2MEpOmoZQn29PYyZ968U0/zfOS5fEnnnkF2behh34t9eLZP0RQc9IewBvcwdbCN6UMdTBvuJOzaZEMRDtbPJl3TgqFHiQhJRBVEtRgRrYyoVkZES6CKidKj7ebIOEPkvDQ5d5icO4wvisxdupiW669Bi0/UzWfsDOu2b2L375PoB6soaFlebPwD3VN3cP7k81jcsJhLGi+hOlwNwLA1zJpDa3h863fYmDuEJwTNtsOKUAMr33Qf0ytnTrjmrr1D7H6+h7YX+rCLHkZcIKen2Vaxlhfc9WSdLIoHk1IhWvsTTO2NoBcmvn+iFZW0nDWdqZlnmGJtJTL/rUHG0sj/TFbLMzh1nA5J59cnOlBK+dZTHNvrGq9X0jntyCeDQI3IIHxO+dTT5jchpSSfHiabSoKUxCoriZRVBB4y0g8s66QHvofr2GTTGYoFG0URGIaGYWjoho6m6wihBOMTSmkSdNya8fUnPwcwSkDpNF4mw96eHsJ3fo74VVcRv+pKIosWIfTjx6l7LbCLLvu39rNrQw+dewZBglEfZn9UsjafRfZ1BQQ01Elruotm0yRlTqe35nwsI4JqdRMf3ITtH8CLRDCMSqJ6OREtQVQvJ6olxpHSRLWI6+URuoteGyPc2oBeE0WtCKFVmKQyNmt/s4fe3TnccJGtU/6bLeVrkIpkbvVcLmu6jGVNy5hRPgMhBKlMN3/48fU84SbZFDKRQjA9PImVs25gZctKpiamjp3X9mjfnmT3xh4OvZzE9yVVjVEaF8TZU72Z1cnH2Z3aje3ZVA0bTOkL09wdpSwX/OYjpuIAoXCeugqLKcvezrTlN1EZqz4NFnNn8KfA6SCdfuAwQTqBjRzhVSelfPo0jPN1h78Y0gGwc0HA0BFT6Wh1kJPnqJf8uG0EpdnOowhkYtnF8zwyBZ+iI9AUn4RuYSjeMYfi+Ap5V8f2VbxSrhxFSAzFw1A8dMVDE/4JuEWcJDlNLEsp2LFrP2X3fInstnak7aLEIsQuWkD8smVEl12GWlkfpA//I5FJFdm9sYfdz/Uw1JtH0xWa5lXhNkVYl8nw3IFB7r95EdOrI9jdPex/7gBbt/SSSiUAFc3qoLJvHWZuN90Jg2zYBCmJF21ino0STuCE6wjpFURHJaSyUWJSj4i5Jw3wwypDeZ/BjIsTFqTrB9kU38CTcg15tUhDtIFlTcu4bPJlLJq0COPwZvofeju/D6s8EY3wYikkzuzK2axoXsHKlpU0xsak7ULWZt/mPvY830PP/jQIaJxZwcwL6ig09fKrw4/y5OEnGbKGiOVVmgaiNPSFqB8w0XwFWfJZEgg8RdJXYTFUr2A1RTAnVVERrqTcLKcyVElFqIKKUAWVoaCuIlRBuVmOdoJYg2dw+nA6SEclyFHzbmAe8FuCQJ+vnM6Bvt7wF0U6EAQKTR0AJ38aOy2RU0kCKXoaGUvFk4KIAbGwEhgaCDUI06No49Y6rgSnWMQuFLGtIp4bEJWiKBimjqFr6IaKpgrECPnhjxHh6CKPLnNEG2DnwT5mP3EjvivI9ZhkOkNkO008W0UokkidRXyqT3xaBK2yDEIJCJUFizmuPKG+bGK9FhqVxqSU9B5Is/u5HvZu7sXKu0TLDKafV0e0wsQIqeghFcPU0EMqUkLXnkF2v9hFustG4tMbe4Wct57K4X5ihQieL1ClpD6TI54ucLgiRjYaGIIgJSHHpco3EKFq1EglYb18lJiiWhlRvewoSckRLkN6loN6B51GP4OhLFV1tcyYehYL239Lxa4f0asF5PNE4yy2FwPLuXnV81jRvIKrm69mUnTSaH9DfXn2PN/Lno09DPcXUHWFlnOrmXlBHblJfazteoYdqR3sSu2iL9ND7aBJY3+YKf0xyjKBalEKiShFJ3dNQbJO0lGZZX/FELnw0R80AkHCTFBhTiSj8SRVYY6RVVWoCv1MCu5Twml1DhVCmATk8xXgc1LK45hI/fnjL450IHj5Dh+GfBD2fkwaUI+WHEbIZIRYxjoprSTHIgDfl2QdlbyjoQpJXLcwFfc4kosI4rSVYrS5QsdxwXY87KKF5waSmaKqGKEwRjiMHgqjGcZrV71Iyc6dO5g9pTYgXisNxWFkbpD8Sy+T3bidzAt7cQayICA8OUp8eojYVIkZzo22H+9Ye0yoxhEkFaw9vYL2wRZ2dU7hUFcZvnxt45dCYqlZsuxCKbYRzvUhfBfViFAebyKf6aJgZ0qNoTLvc1aPBdLCUTwszadgqOQNDS9WiYhUoxnlRPQEUS0xOqcU1RLoysQo2o60sWQKze8lSi92KM2O2XN5xn6Rjc5WBowMC+oWsLJlJVdNvWp0rkhKSW97mj3P9bB3cx/FnEMopjPjvFqa51XTMKOcjJ9m9+BudiV3sTO1kwMdu/D299HQH6KhP4zhBZllUQTCD569eF0dVWdNJzytATmlnGFyDFqDDBZLS6mcKqYYsoZG05EfiXKznOpw9TGXmnBNUI5UE9dfPbr6XxJOV8BPE3gTAeE0A78miG3WeZrG+brDXyTpjMD3xqmk/jRwikWGB/pwLQszEiFRWRHEaaOkqnPtUjTq4lh5wstB4ComjtSxfQXbkXhesH+EhPRwGOM1kNCr3V8pJdaePWRWryb7h9UUd+wAwJg2jXgpplrorGkIOxsQUHF4jIyKQ6V1+hj7xtW7BXyp4MgQjgxjywhOtBk72owTacIx67H1WhytElspw7ZhsCfPYE+O3LAdcL7qY4s8vnUAUWhDOu3B70pZaZ0ZvSZFn40WuRpFUVGki+oWUT0L1bNQ3CLCz+LLPJYKtgau4qLJIjFcIoogrIWPIKYEpjrRcMH3XWxrCMdK4RSTSHsQTeYJKT6q6qBoLtIw6dcn08EUup1afFQUfKpDGeoiGSbFMpSFHRRVxRU+yWI/vYfX0zE8QJIyHCeC5pVSrY9TxQGUVVbQPKWVKVOmU1ffiKobQWoMRUWqgoJvkfZypN0saSfLsJ8lKTMM+Bl6vSF6/EH6i0kGCgPY/tFZ7g3FGCWg6lA1NZEaqsJVo8RUEw62q8JV6MobX3o6Heq1HxBEl/4d8IiU8uXTO8TXJ/6iSed/CFJK8sNDZAeDPD+xiioiZWXHJggpwXeOkR5hjJBcX+D4KrbUgjmhkTQJisAwDfRwBCMSQzOOHafttd5fp6uLzOo1ZNasJv/8JvA8tJoaYsuvIL58OZELL0R5tejLR8K1S8YdB4N5ttT+QO2Z2h+krSgMTmwfr4fKVqhooRidzt7+GezaF6evy0MoEGp1OVjxAgc7nqa+E6qHS6ozTYOSpIgQTJp+Ec0LbsSzfeyii5Mv4OSLONkCdsHGKfq4rsDxVFz00Q+SwLejiPSzSJlB+lkUmScqPMIKRBSNiGqWLPCCOaWwNjGnji99LK+A5eawnTyOXaDgumQxGVLKSOuVSEBzMpQP7aMytZuq1MuErMEJMralqfTHw/THI/THI7iaClKi+hKv5HWvej5V2QLV2QLVmTxRy+FkZBSh64hwGEIm0tRxDQ3HEFgaFHVJXvXIqg5ZxWZYWKSFhWWApYGlg60HZT0aJxwrJxavIh6voqyslvJELRUV9dTEJ42SVUw/2s3gSEjfB887/toLPuDG1h74/lHroDyxrdHSgj5p0gnPf9zf6jSQjg/kRq5z/C5ASikTRx/1548zpPM/B9dxyAz0YeXz6KYZxHF7DQE8j0dIrm3jOC62pxxhmACGJtBNDSMUQjMjCM1k5542Zs+Zc0rX4A0NkX3mGTKr15BduxaZz6NEo8SWXUps+XJil16KOs6R9pSRTwXkkzowkYxS+4OUFiUknSnscq9hd+4SCm6MkOlQ1pLkYOV22g9up+KAQ1lODzKnjiyKwtzlK1l+y4cn5Ew6ElJKfFcy2JujbUsfB7b2k+wK5gJjYZsKLYnM7CSX7sPxQmiuge7qSCS2KvE0iaobmJpBSDcJaSYRLUJEixHVEoTVOMo4KduXPgUvR87NkvPy5D2LvGeT81wKvsRRJLoi0aWNrpuYlU0Y8Uo8d5j08B7Syb3Y2WCeSYrgJaaU3mSeJhBlYcprqpna1My0qiaqzbIgJE+xiJ8v4BcLyEIxyFJbLJTqishCsPaLBeQRddI6YRD7Y8JVSgSlBfdDlQIVgeILFBmMWfElorT8KTHpzn+m4l3vOqVjzwT8PEW8XklnJKaZ67q0tLTw4IMPUl5eTnt7O7Nnz2bWrFmjbT/+8Y9z00030dzcTFNTE2vXrh3dN3/+fFzX5eWXXz7Kz+eWW25h69atzJs3D4BzzjmHVatW0dzcTHNz82gUAs/zePvb385nPvMZQsdI6HXLLbewatUqamtrefnlYwvITz31FG9729toaWnB932uuXI5f/eRW1FUFd000UwT3QihmSaqdgqBJaUMslh6Fq5VwCkWsC0H2/XHJKGSddzBzi4a1n2G6sbJiHB5kOXSjIMRD8pG7Dh18WCtBVKNb1nkNmwgu3oNmTVr8JJJ0HWiF1xA/MrlxK64Ar3uT+Blb2VhsH1MQho8gJ88wKHDYXYNzOWAdT4+OtXafuriz3Kg/CBtg4LQIZ2wpyKRgdnAxhMAACAASURBVEpKQPnkJs67+s3Mvfxq1JMwG08nC7Rt6WffC330tQdJBGunxmnVnsQtfo+NiSzrjDADtk55Fmb5NZwnG5lRjKIOpEj1dGANZtAtFUEYP1YD0RqUSDV6qBI9XE7ISBAyygmpE6UAX3rk3cyoj9Lo4gyT94oUfQ8pdEAipQ1+jiArCgTJAEsGKABKJUKfggjVo4VrCYejRE2dSEhDN0A3xOhiGALdVMbqSmUjpKKpoAkXFRfVsxGui2/Z+AULadn4lo20bLyiRSGXJpsbJp8exB4exs1msKSHJTyKikcBhwIOeWzyWLiKxBfgC4GvBJk6fAVQFEw9hKlHCJkRQkaEsBEhZESJmDEiRixYh+JEzRhRM0HEiAbGPIoaBEAtrfWpU9Fra0/pMTxZ0jljS/hngnA4zNatWwG4+eab+da3vsUdd9wBwLRp00b3HYlMJsPhw4dpampi586dJzzH5MmTueuuu/jxj398zP1PPvkk1dXVZLNZPvjBD/KhD32I73//+0e1e9/73sdHP/pRbrrpphOeb+nSpaxatQoA3/MoZjM4loVjWViFwVH5WlEVNCM0joxMVF0/MREJEZCBZqCZcbQElGy5cB0bJ5/FLuSxixZFT+MHW6owX8qTMIeJ6xZxNR8sukVcs4nrFjHNQlOO8ZGmmmDGUIwYcTNOvDnGpA/VUeivI7MnS3bHVnrWr4fPfZ5Q6yTiF5xN/OLzMKZNQ4TigXHBCInp4dced8yMBYnRxiVHUwgmYZtdi2LnfvZu7GDXS/W8knwvypDHktgOpk59jL1iH7tS1WiDZQgpGDp8mNX338sf7v82iiqorq/hwhUrmXHpm1BCR6ecTlSFWXDVFBZcNYX0wAgB9fJc2yJgEfX9bXzaXEss+hybanWeam3lrv4tuL5L2bwyLmlcxqWNlxIy4qxvW8PmV36POrCfhrzJIqWF2V4dZVmB195HobcfP++jRKrxYnXYldPxEw3ooSrqIw2Ej7A686WH5acpeMNk3WEy9hBpe5icmybnDlH0cuMap5BWCmltxR4GCxgUKggdRTFQRAiBDhhIYSLQQGiAhhD6aBmhIYQGBHUqElX6aNJDkx6q76K5HprnoLo+qm2guuWoXhjVq0D1LMKeRaw0v6Z6Fqpvl8pFVGEhhI9UwVPBUwWu6uFoeRw1mIOzNCgqUFQlGS2QpFwVHDVYu6XjNE1F13V0XcfUDUzd4OzL38Giq/7mtT1/rxFnSOfPEIsXL2bbtm0n1fbGG2/kxz/+MZ/4xCd4+OGHefe7382DDz54zLZvfvObeeaZZ9i9e/cEyelIxGIx7rvvPpqamkilUlQekZr50ksvpb29/aSvBwIjgEjZmHOq9P1ATWZbOFYR17LIDw8xIpkLRQlIyDBHyUjTT85wQNMNtLJKwmXBuPuyBVbe+nd0791FeqCfdHKAruQAxdzRgV0j0QjxRIRYzCQe1YiHFeKmJG64ATGpBVQniygmiRgZIjOy1E7JYg8UyXSGyHTY9D/SQ/8jq9FjLvHJReKNRcJVdjBdooWCbK6R0hKuhEhVabtq3HbF2LYZPz5RaSahqbOZO3U2c28Mwtbseq6H3RvDHBicSziqcMl5Pq11B9jdvpbtO3uxBgWKVJAe9Hf0s+r+B5H3/wBF8WmohEvm1TJ55qwgM+3IEptEojrMgqunsODqgID2vdBH25Y4zx6cBpn3UZfcza0HnmXSRdex7bzFPN3xNM90PMNv9/8WTWicN+k8rl36AWJGjO392/nWodVknD2Um+VcNfUqrmm5hoWV8/AHkjg9vbi9PTi9vbg92xjqHmb/UIRhWYMdnYqph4gISZmvEiNBmV6DGpmY1E4SGF7kvDTDhQHSVgrbL+JIG9e3cbwijm/hSBvbG8L1i4CPKgUKAonAQ3Aig0Pn+LtAE8Ecm9BA6CUiU4EQEC3ldipZkaIAgZuBQKAggyDXEhQkKj6K76NIH1V6KJ6H5rsYvoviuChFF81zUFwHzXXRHBvdc9A8G8210bw8ivQZ0lbDGdJ5feHJ7/0nfQf3n9Y+a6e2cvn7PnhSbT3PY/Xq1XzgAx8YrWtra5sQHuaee+5h6dKlQJC75v3vfz+f+MQn+M1vfsNDDz10XNJRFIVPfvKTfPGLXzymBDMeiUSClpYW9u7dy4UXXnhSYz8SGzZs4Nxzz6WhoYG7776bs88+e3SfUBT0UAg9FCKwvgIpfVzbGSUhx7YopIfJjxCREIEkNEpGoZOyYFNUldnLlnP2suUT6u1igUxygExygGxpnUn2k0klGRro53BHP3bhCP8mIYiWVRCvmkG8qoZYdRXxqhrilVXEy2JURQ3MXJr8uvVk1m4k9dJuUrtiqPEwsXlNhOrDmCGBqdqoThqRfjmYzykMMnFqdfwF6EeQ0vEJqypSycXXVrH4rc0c2jXErg3dbN86wEteC1WNc5l/aRnVU+KEogV2736C3Wufxh3IIaRA+iqdA/DjNUnkmvVo6tNMjaZYVn2IyihBVIsSCSUqmlk4tZmF85sZ9lppe2mYfU90sD7zfvhvqHt6L/9n+Tv45JX/yH5/D08dfopnOp7ha1uC2MLV4WoubbqUylAlHZkOftP2G36656fUhmtZ0bKCa1uu5ewFK0fvbR0w8pnkJTvo/t6/cLBNsE9ZTNJtRDoCI5+ngX6q870kMv2YioYSqaIsUk15pAUlOu9VnlhwfBvXt0rrAo5fwKGIMCRaWMeMRonFylCjJr6p4Yc1ZEjBVXxcGRxrexauY+PaNq5dxLVt7GIRu2DhOg6u7eA5Lp7j4LkunhusfdfF9zx8L1g7vov0vVLepSPMv0s+0ceGAMzSMhGzq970qr/BH4szpPNngkKhwPz58+ns7GT27NkTEp6dSL1WVVVFRUUFjzzyCLNnzx7N1nk8vOc97+Guu+7iwIEDrzqmP2Y+cOHChRw8eJBYLMbvfvc7rrvuOvbuPXEMWSEC6UY3x/5ZpJTBP3BJLedaFoVMBukPl44RE6Qh3TADIjqJOGtGKExVYxNVjU3HbWPl82RTA2RKElI2NTBKVMnOw7RvexGnWDjqOqIVFcSbm4mdOx+zUETr6ELd14a29RCG52O4HqFYnND02ZjTp2O2tmJOqcesL0cNeYjiYGDtlk8F60KqVE5B/+6xbXnsCBCKUGkOV9AcqaI4r4G92UXsS85g1/oKnKeD14KizqKhdg7V88NEKl0O971Ix0vr8FLZIEKAp7M/XUdbuhYQ6CGYUZXk0rLniMnh0XOVAQujtSyc28KwU82+fSb7iktY/9gQ6x/byKSWBFcsuoG/vvRvyBnDPNv1LBu6NrC+cz1D1hAAMytmUh+tZ9ga5uFdD/Pgjgdpijexsnkl17Zcy/SK6aPnU6smM/nj9zJ5yw/giU9T9GN0zL6Lw4WzObQjRjsNEIFElUnjZI1JZQWq6YfUQRQjihIpQ4RjSC1EJlsgk86SS6UppIZx8za6YqILA10NoSsRQko5GiaaZWDYBmIoeLbU0gI+wayfUVriCENFhFQUU0UJaYiwilKuBknqtHEJ68aVhaqANlYeTVanBflypJBI4eGP/EkPx/WwLRvbtrFsB8e2cYoOdtHGKdrYlo1jObiWjWu7tJ7/p89Yc4Z0XiNOViI53RiZ08nn86xYsYJvfetb3HbbbSd17Dvf+U4+8pGP8L3vfe9V22qaxt///d/zb//2bydsl8lkaG9vZ+bME+cBOh4SiTHjx2uvvZZbb72VgYEBqqurX1M/Qgh0IyCTcMlITEqJ5zg4tlUioyLFXAY/PUZEqmEEx5kmruPgFIslqeq1wYxEMCNTqJo85Zj7pZSlfEJHSEvJJJlkPwNdHWSSA7i2BbWJYBkH3RlC37YRY/M6dM/DcH1MVSNcUUG0to5Y42TirXOIz5xFbGoz4XgcRVFHTh74AI0npFGCKhFWIUUon2Ku9zvmVg4gzUGGnWr6nRb63Vb6k63s75mGJePAHExmUT6pj5hxiJSzn+RgLxQ9BIFr1c7OMDs654EAsyzC3HNaWTK7HCPXAYPtlKW3c168g/Niv2DInURbcQn7Dl/MugOtrPvpXmLhAtXVGu9oXMFft7yXoVqPrc4LbOh+lvVd63F9F1MxaS5vxvEd7t9+P9/Z/h2ml0/n2pZrWdmykqZ4U6BuPO9maF1G6JcfYfr2DzB95jXIT32doXycwztTHNqRYu/OQXbaPopSTV1rK7V1CaoaY1RPjlFRH6FSnxhktZjN0rN/Lz379tDZtoeuPbsopMcIFiHQ0NEVAz+kky6DfFxBJsI0VE6lOTyVyXoDtVolpqsjLQ+/6CItD2fYDnL6uIEps3RPX3K6EboDAodazQQ1XCIuMUpo8UjVH32uV8MbnnSEECuBbxB8dHxXSvml/+Uh/VGIRCL8+7//O9dddx233nrrSR1z/fXX093dzYoVK+jq6nrV9u973/v48pe/TCaTOeb+bDbLrbfeynXXXUdFRcVrGv8Ienp6qKurQwjB888/j+/7VFWdngc+kG6MII12LGAiKSWe65bUcoF6zsrnKGTS5IcGued9N1JWW/eqFltHqeqO2D5Kkfcq7XUzRGVDI77vB+oT30f6XrDtecHi++RdB98tbUsf/CL0HAyWFyYm8RUEqlJF1VB0HVXTUTQ12FZVVG38ug6h1I+mXxYIUD0QDmguwthDwnsFz/FwLInjCIaHVfpdDV8aKDQhNYlGDk9m8L3AiVJIsIfyvLDuZTavk8GYVCiPtlIenxNImp4Nmd2Ycid1MkTOr8QuRGkfCLNnZ3j011SEx/mqwRLjfBzDI6sVSYlhcl6eVqrQFA1F5Nm++mG28zAJM8GkSB21kbpSZtfFYE+CJ3fBUzdC/VyINwBQ1ySxci75jE3PHptDL7njJHiJbqqjYYl0U8UwFRRdKUXegJqpLYF6LJ/DKuSx83msQh7XsyEHZm5MiVWgg23qWl7QfRxVIgyVsBYhooUJa2FCqhlcs+8jZZD8TZbKSBBSIHwlMHOXCooMZnfGl0f+BAqKVEbrBGpQEsFaFWpQFlppW0MRKoldSebMPzU/nZPFG5p0SvHjvkUQQ64D2CSE+LWUcsf/7sj+OCxYsIB58+bx8MMPs3Tp0qPmdG655ZYJUlA8Hucf/uEfTrp/wzC47bbb+Nu//dsJ9Zdffnngq+H7XH/99Xz2s5895vHvfve7eeqppxgYGGDy5Ml87nOf4wMf+AD33RekZfrwhz/Mz372M+699140TSMcDvPII4/8SUOKCCHQ9CBydYjACktKie+59GVyXPSOd5Lq7Agc7EqQR86hHLl5lHpRHrH/yFEcuf+oDo8a92ib0nqkhe95+K6LWyzg5nJ4hQKuVcS37UD/L12kcHCLRRwh8JVXn9dSVA2hKCiKglAUhBDBWlEQorQ2FYywgiEk4OK5MlgcDd8pHwuOJF2kzIG0RmrwPUilPZLpbEBCwieqRYJwSAAMYjCIoQikouNJA09quL6G42gUbR2JgopODVXUUYZUXFzVpaA4OIrAFz4iX6B78ADdHCCihYkbCWJ6DDU2B9KdcGAHhDoC4ilJhYoC0YQkmhB4no/nSDzXx3UkhbRPdnDs3iiKgmaoaKaKbqjopoYeimBGo6PX6jo2dqGAUyhg5XM4VjFIUe4pGF7JwTYHnlpgSM/SY3g4hkQ1DEJaiJARxtRChPVwaYkQ0SNoql66F0G665EyQimFOhy5byN1YuxeHnlcaR9CjJar58444XNyOvCG9tMpZT+9U0q5orT9aQAp5b8e75jXq5/OGfzp8Ea7v1JKvGQSa18b1r59WPv2Yu3dR35/G4VsFltTcTQFJxbDr6vBqyjHjUVxdB1XgOv7uJ4bWA4Wi9jF/7+9O4+PqjwXOP57Zp/sGyD7ouCGioIbWPcKVlvEalHLlVpbba1t0WrVq23v1bbaqrVa94VbubUCdbe91qVUWxtFsS6ACwYQTIhJyGSZJbOe9/5xTsKwJzjJkPB8P59x5rxzznvec86Yh/ecd4mTise7Jl3rDjuA+TDGg7E8dgA3HWASTr+grPJiD3QQ9HUwJNDOuGALVX4LT+VIvMlWvPEmvCTxiEXMqiKUGUNTaiwbM+NoTo0hnBm0KTN3G60FtawrqqepcAPNhbW0BpsQMRxWMo5ZY0/jpOYGCv91GxRUwVd+BxNO2enxJGIpmjdEaa6NsLEuQnNthOYNUdIJ55mZQOmgIFUjiqgcXtR1i664IoC4hHQySdO6tdTXrKLuo/fZ8NEHREIbt752LiEVFKKBNBuDMUJFSdqLUkQK0kSCGYoLShlaONR+FdnvexXuxbDCYQwtGkpFoGKzDrZ9STuHAiJyFjDDGPMtZ/k/gCONMZdusd5FwEUAo0aNmrxu3eZz1g20P0pqc3vK9TXGkAmFSHxcQ2J1DYmaGpJOYMq0tGy1vvj9uMvKcJeV4SotRcpKsYqLMUVFWIUFWAVBrIAfy+8j4/GQ8XhIC6QScbu/VbyDVDxOItpBtC1CRzhKItZBsqODTKoDuzdMz//+iMeF2yN43eBzGXxiIZYLK+MhbYIkTQlJyjD4EfFiiRD1dxAJhGkLttIWbKbU38jxyVaOjbdTWDUO75Q5eMYdg1SMxXi8m2qdxqnxGuMkma7bXZZlEW7uILQhSqg+Sqg+QmhDlHBzZ8MRg8fvpnxIAeVDCyjfy3kNKcDrdxOPRdi47hMa1q6m7qP3CdXVbtXoZKtr6BYyfqHDb9HuS9ASiBMNpokFLJJeC4/LQ4W/nEp/JRXOiNrlvnLK/KWU+8op9ZXiFfdWt++MsTCWYfi+B1A5YvsNZ3Z4XbRzaPcZY+4H7ge7ppPn4ijVK0QET2UlnspKCo/avJl7OhQiuXYt6VCITGvr5q8W+z1ds9pebmuDzKZWcYL9h8QD+F0u3CUlXcFqs9fIMV2fTVEpbakCQu1uPqtvY90HbxFpeAusduxgtH1WOoOVhoQYMi5IeSwsj4DfwmvSeNIR3Ok6JGXAGTYm2GF3Dt5UJxpELYP4I8BqYOlfsGdvya0EEG2E2uW5yU8yBk/MUByDYvwM30azZ1sL0EKKtTQCjd3M/5hvfHOXg053DfSgUwdkn8ERTppSKounogJPRfemhDaWhRWJbCM4tZDeIi3V0ED8o4/ItLZiOrb+V7wfGA2MKShAyspIVowkXrwvsUAVbZahNRqinThJqwWTsQNS5605txHcGfs5CYnOOpOFEUi5LSIFaZpLEmyojNNQHseNi+J4MVWxvRgUH0JxtITCjiKCySCYNJDGIkPKHcfyRHG7ohS4IlSYdippp0jC+FxxBIMzalAX6Zzv1ONDvEHwFiD+QvAGEW8BxhskSTGRRDGReJBozEsk4iIWxSm44HILwRI/BSV+gsU+591PQamfwlI/Xr8HEJLxDjra7QYwrU0NhDc2EWtroSMcJhXvwMpsu5k8AD4Pll+IByEcSNEQDNNQGKO9KEXGDXsNi7Brve66b6AHnTeB8SIyFjvYnAOcl98iKdW/SWdtpqQERm27qfi2WPE4mbY2Mi0t261NZdrbybStI9P0HsPa2sm0tWGSSSxxEfdX0FEwmGiwinBBGeGAn4g7RlJCYLUjzu06MeBLu6iI+KiI+Bi/IWvAVQG8CTJF9UQHNbJ+X4u1xTGsNi+l0SGUxCspTlRQEh9HcaIC0sWEAGemKdImSczfQtrfQsAXYi9XI+OsTxljfUKlqxG/RDY1ULScsWdS2PMtZT8Tc/pmpit8hNIjaE6NodnahzZGEG4rpLGplFQme6TyBIFggpIKDyVVhZQMHkb5iAJGHxKkuCpAcUUAt8d+lpNKJghv3Ehz7XoaP1lDqO5T2hobiLaGiEcipMMJCsJQAAyhGHDOj8tFkTTBId3/LeyKAR10jDFpEbkUeB67yfT8gT7zqVK7K1cggCsQ6PGgp1Y8Tmb1m1i/n00m+iGZlJAJjsE69BIy4XZSreWE21K0hw3hDjetaRetlkVU2roCEiRxHtBAMoM7lKEkBCUfwd5GQNIIdbipw5tJ4rcSuL2GVMBDTIKk3MVYrgoMFXg6KvHHK/CYUbRTwDtAZ9fstHSQ9LZgedvw+9up8LYy3NXAhMw6qlIf4vfENs2HCHi8LgYXRRjsWwueDfaQNyaDSSWIx6E97KM9XUV7ZgjtmcGEm4bQ9Nlg1mQGYbGpeb9gURhMUFKUpqRcKKnwUzK4iLH7HMohx51KYVWZ3RcHu6YaaQnRsHY1TevW0Fz7KW0NnxFtDTF0r9Gf9zLv1IBuSLArtPXankevbz/y9Pfh7QXOggtm3QOHbHsofmMM6XCEtvUhWj5toaU+THN9M02NTbS3riaR/AysMF0BaXtM139wGYMnnSGYSlMaS1AeTVOYKSYVqKQjUEm88z1ov1vuzZ+5eFIRgvFm/PFm/MkQgVSIYKqZolSIolQTXisGLpM1Wa8Bjxfx+hCPFzwexOMBt4sOdwkd7lJiUkLUVUpMSom6K4i5B9Hh3vxWqcukKDQhilytFHkiFPk6KAqkKSoyFJd48ZcUIYXlBA6dinfc9sdd3BFtvbaLNOjsefT69jPhRrj7SHtkBYDSUfC9N8AX3PF2W8ikLcLNcVobY7Q2xNhY20hzXT1tDavoaP2EdDJkN/MmRXdb2blcLrvTrddF2muR8GWIuwSsUjzpMnxWBR5TiZsKXFJpj1CdzerAZUWQTBR3JoI7E8WdjuBNR/Gkw/hSUXzJMP50FH8ijC8Vw2OMM/eOIBaIBZZxk/RVEA9WbSMYVpH2bj74qScdI9DRzMFjazn059f26Dx20tZrA8hll13G6NGjmTdvHgDTp09n5MiRPPjggwD86Ec/Yvjw4cyfP59bb721qyNoTU0Nw4cPJxgMcvDBB7NgwQLeeOMNrrjiChoaGigoKGDy5MnccccdLF68mGXLlnHnnXd27ff444/nlltuYcqUbf+OOufYcbvtDnbHHnssd9xxB9/4xjdYvHgxDQ0NXXPwzJs3j9tvv52mpiaqqqq65gfq9NRTT/Hxxx9z9dVXk0wm8fl83HzzzZx44omEw+GuAUwBamtrmTNnDr/97W9zeJZVv1E8GK5aC/+4FZZcD23r4Zd7wQnXwnE/7nY2bo+LsiEFlA0pgIMANn8+lU5miGwME/nHH4i8s4SwjKBl0Ak0tcVoq/+ARKweKx12OsBagN1x2kom7coT9h9YuytyM9CMyBrweMgEPMSKhFChRX2Ri5i3gKJEBYFUEYF0EYFUIYF0CcH0UAKpIrzWtlupGSzinhhxT5S4N0rcE3He7c8JT4Sku42kawNpV4SkO4JFHH864Dy/qqQwVUlhsoKCVCXWfmUc2tPr0UMadPqBadOmsXjxYubNm4dlWWzcuJH29vau76urq7ntttuYP38+06dPZ/r06cDWQaOhoYGzzz6bhQsXcvTRRwPw2GOPbXe4m+7onGNnS/vssw9PP/00c+bMwbIslixZwvDhw7u+z54fqFNLSwvPPvssw4YNY8WKFUyfPp26ujqKi4s3W3fy5MmceeaZu1xmNUAc+yM48rtwz9HQ+gn8/Rfw2t3wvaVQ/Pkny/P43JQNK6PsnEvhuGPh6e/BZw/DxFlw5c1QNIhkPE0klCDcEicSihMOxdlY+ylNa94mHPqEdLwZY8XorC0ZYzCpFKRSeMP26Nh2SWOA01m0c4QAtxvxuhGfh3TAa/eHCnhJBIMkg0Wk3QVkkl4k4cWV8OFJ+ilLDsYfDeJPFeI22/7znpGMHZyyAlU0GKW5uI5Yum2b2+SSBp0ean12NckN0Z2v2AO+YYWUfXnv7X4/depULrvsMgBWrlzJxIkTqa+vp6WlhYKCAj744IOt5rTZlrvuuou5c+d2BRyAs8466/MfwDacc845LFq0iDlz5vDyyy8zbdo0nnvuuR1uc+ihm/6NdeCBB9LR0UEikcCfNar0qlWraGxs3Kzmo/Zg/gKY9y68uwie+g7EW+DWCTDpfDjjd7nbz9CD4dtL4F+3wyu/gjWvwJduxjfxq1QMK6RiWPbtqr2B4wFnwNdo2g5KLQkioTitje3Uf/wuoQ3vE2/fQCbVWVtymjpnddwklbLjkUOwZ9vZ3tC04nLhcrsRrw+Xz48vUICvoBhPsBR3QSkmUE7SU0QyISSjftJRH1a8Cgn7cKWCDBq1PnfnbDs06PQDw4YNw+PxsH79eqqrqzn66KOpq6vjtddeo7S0lIMOOgifz7fTfFasWMHcuXO3+/2iRYt49dVXu5Zramp2mucJJ5zQdXtt7ty5XcFxwoQJPPPMM7S0tPDoo48yZ86czYJO51QNAGPHjuXJJ5/cLN/HH3+cww47bLOAA7Bw4UJmz57dq+O0qX7okNlw0Nnw4Mmw4S14ZwGs+BN8awnsdUBu9uH2wrFXwH6n27Wexy+EFU/AabdCydBtbiIiBIq8BIq8DBqZ1XSbTWUyliEWThJpSdDRniQWTtIRThJp7aCtsZ62z9YRa9tAItpMOtmOseLYHZPS2IHK0PnMyVgWGcsik0qRjEWJdTX23jF77DU3gebe7qWjQafHdlQj6U1Tp06lurqa6upqLr/8curq6qiurqa0tJRp06blZB+zZ8/e6pnOzmzv9hrAmWeeycKFC1m6dCn33XffZt9t6/Zap5UrV3LVVVfxwgsvbPXdwoULtzsJndrDuVxw0RL4pBr+9yuQ7oB7j4ZxJ8L5T+58++4avB9c+AK8fg8sucFu1DD9Rph0Xs+nGgfEJRQ6HUC3tnkDF3uqjDQd4SQd4ZTzniSW9bkjnCLS3ER7ywZS0XqM1YKx2jHO2HeYFHawsp9DdeZrMmkaVq/ucfl7SoNOPzFt2jSqq6tZvnw5EydOZOTIkdx6662UlJRwwQUXdCuPAw88kLfeeouZM2f2cmlts2fPZvLkycydOxdXNyZNA7uRwKxZs1iwYAF77715gH/3WBoPZQAAE19JREFU3XdJp9NMnjy5N4qrBooxU+G6Jlg0Bz78M6xZAtdXwrmLYfxJO9++O1xumHop7HsqPPN9ePoSWPE4fPl2KOu9YWREhEChl0Chl/JuzECQyVjEI05Aak911aK2GbDak0yb3fujTGvQ6SemTp3KLbfcwrhx43C73VRUVNDa2srKlSt54IEHiEQiO83j0ksv5YgjjuC0007rmmL6iSeeyFlNaUujR4/mF7/4BSeffHK31m9tbeW0007jpptu2maZHn30Uc4999xcF1MNRCJwziMQWgP3fgGSEXjkTBh8IFz8T3C7d55Hd1TuDXP/DMseghd/BncfBV+8HiZfYNe88sztdu2gFrU1Y/V+F5r8nxXVLQcddBAbN27kqKOO2iyttLS027NtDhkyhIULF3LFFVew7777sv/++/P88893NWveFSeccAKTJk1i0qRJnH/++Vt9f/HFF29VY9meO++8k5qaGq6//vquPBsbNw1VuHjxYg06qmcqxsF/1tmt3AAaV8INlfDW73O3D5cLjvg2XPIajJgCf7kcFnzFDnj9jOxk3qWc7EM7h25OO4fuefT67iFirXDX4fawz2BP4va9NyFQlLt9GANv/y88f6093tpJP4UjLuqaLG4g627nUK3pKKX2DAVlcOXHMONGQCC8AW4aDn+7IXf7EIHDzodLXocxX4C/Xg3/cyo0rcrdPvo5DTpqp4488siu212dr+XLczRBiFJ97ahL4NoGqHQemv/zFrhxFLTX524fpcPhvEVw5gOwcRXcewy8ehtk0rnbRz+lDQm6yRizx/YNWbp0ab6L0Gv09vIeyuuH7y+DD/4Mi8+HRBv8Zj+YNAfOuCs3+xCBg78G446Hv/wIXvovWPkUnHE3DDkwN/voh7Sm0w2BQIDm5mb9AzXAGGNobm4mENhe/2414O1/Ovy0GUY7rSXf+QP8ckRuaz1Fg2H2/8LZD0NbLdx3HLz8K0gnc7ePfkQbEmxhWw0JUqkUtbW1xOPxPJVK9ZZAIMCIESPwer07X1kNbOteg4dPtxsAAEz9AZySw+c9ANFm+znP8sUwZCLMvBOG9fYQm31DpzbYRdsKOkqpPYQx8NAXofZNezlYAT94F4Ilud3PR8/Bny+DSCNM+yEcdxV4+3eNW1uvKaVUT4nAt16CcxfZs6h1hOBXI+Gft+Z2P/ueardwm3QevPobuO8L8Okbud3HbkqDjlJKbWnfGfCTZhjk9N/62/Vw636QzOEt9mCZfXttzhOQ6oCHToGX/hsyqdztYzeUl6AjIjeLyIci8p6IPCkiZU76GBHpEJF3nNe9WdtMFpHlIlIjIneI05RMRCpE5EUR+dh5L3fSxVmvxtnPYfk4VqVUP+VywfdehzPuw+7XUw+/HALL5ud2P/ucZI9mcNh/2LWe+dP75WgG3ZWvms6LwERjzMHAKuCarO9WG2MmOa/vZKXfA3wbGO+8ZjjpVwN/M8aMB/7mLAOcmrXuRc72SinVM5POgWsboWy0vfzny+D2wyCTyd0+/MXwld/B1xZA82p7vLh3F9rPmAaYvAQdY8wLxpjOXlKvAyN2tL6IDAVKjDGvG7vlwwLgDOfrmcDDzueHt0hfYGyvA2VOPkop1TNeH8x7D6bfZC+3rLbHcFv+RG73c8BM+O6/YOgh8OTF8MS3Id77s3n2pd3hmc43gewpJceKyNsi8oqIdE4PORyozVqn1kkDGGKM6WxU/xmds7/a33+6nW2UUqrnjv4uXFMPhUMAA49fAPcck9taT+kImPssnHidPUncvQOrkUGvBR0ReUlEVmzjNTNrnWuBNPCIk1QPjDLGHApcDvxRRLrdVtGpBfW4PioiF4nIMhFZ1tTU1NPNlVJ7En8BXLkKjnXu5Dcsh58PgpoluduHyw3HXgnf/CtgYP4MeOVmsHIY3PKk14KOMeZkY8zEbbyeBhCRbwCnA193ggXGmIQxptn5/BawGpgA1LH5LbgRThpAQ+dtM+e9cyz8OmDkdrbZsqz3G2OmGGOmDBo06HMfu1JqD3DiNfDjTyBQBiYDf5gF80/N7T5GHgHfeRUmngl//zk8/GV7VIN+LF+t12YAPwa+YoyJZaUPEhG383kcdiOANc7ts3YROcpptXY+8LSz2TPAXOfz3C3Sz3dasR0FtGXdhlNKqc+voByuXmdPXwCwvtqepbQ2hx3MA6Xw1Qdh1v1Q/y7cM9Uew62fysuIBCJSA/iBZifpdWPMd0Tkq8D1QAp7Au+fGWOedbaZAvweCGI/A/q+McaISCWwGBgFrAO+ZowJOcHpTuxWbjHgAmPMTn8JOiKBUmqXtNbCXUdAKmovT5hhjzSdS6E18Pi3oO4tewqFGTeBrzC3+9hFOgzOLtKgo5T6XB6/CJY7wcbtt2+PDZqQu/wzKXj5Rvjnb6ByHzjrIbu1W57pMDhKKZUPX70fLnkDPH7IJOzZSp+4OHf5u732jKRzn4FkFB44CarvBMvK3T56kQYdpZTKtcH7wnWNMP4Ue/m9hfCLYdC+IXf7GHus3adnwnR44Vp45CwIN+Qu/16iQUcppXrL1/8E3/obuDz2s57f7A/P/Th3+RdUwOw/wOm/hXXVdiODVS/kLv9eoEFHKaV604gp8JONMPIoe3npfXDTaIi15iZ/EZhyAVz0MhTvBX88G567ClK75/xfGnSUUqq3icCFz8OcJ+0pE+Kt8OvR8PebcrePwfvZtaqjLoGl98KDJ0Hjh7nLP0c06CilVF/Z50T4aQiGHGQvv3Ij3DIBEh25yd8bgBk3wnl/gvBncP9x8OZDu9XAoRp0lFKqL4nAd1+FM+cDApEGuHEveP3enW7abRNOge9Ww+hp8JfLYdEciIVyl//noEFHKaXy4eCvwnVNUD7OXv7rVXBHDqdMKB4CX38Mpt8IH79gNzJY80pu8v4cNOgopVS+eLzww7fhtN/Yy6HV8PMqWPtqbvJ3ueDoS+wpuH1FsGAmvPRfeZ2dVIOOUkrl2+EXwjV1EKwAY8HDp8GiuTvfrruGHgIXvwKT58Krt9lTYzevzl3+PaBBRymldgf+IrhqLRwyx17+4Cn45QiIteQmf18hfPl2e3bS0Bq471h459E+b2SgQUcppXYns+6Ci16xO5Qmw/DrMfDa3bnLv2t20knw1HfsAUT7cHZSDTpKKbW7GTbJ7lA6eKK9/Pw1cNeRuRtfrXSEPXbbidfByifh3mP6bHZSDTpKKbU7EoFL/gWn3mwvN31oNzL49M3c5N81O+nzgNizk779h9zkvaPd9voelFJK7bojL4IfrwN/qT1d9UMn53bU6pGH29MvTDpv01A9vUiDjlJK7e4KyuCa9TDxLHv5vYVw0yiIt+cm/0AJzLwTqvbJTX47oEFHKaX6i7Megm++AOK2H/7fNBLe/J98l6pHNOgopVR/MupIu5FBlTMb6V/mwb1fyG+ZekCDjlJK9TcuF1z6JnzxBnv5s/fg+kqoX57fcnVDXoKOiPyXiNSJyDvO60tZ310jIjUi8pGITM9Kn+Gk1YjI1VnpY0VkqZO+SER8TrrfWa5xvh/Tl8eolFK9btoP4MrV9hA3VhruOwae+UG+S7VD+azp3GaMmeS8/g9ARA4AzgEOBGYAd4uIW0TcwF3AqcABwLnOugC/cvLaB2gBLnTSLwRanPTbnPWUUmpgKayC/6yD/U63l//9MPxqbO6mS8ix3e322kxgoTEmYYxZC9QARzivGmPMGmNMElgIzBQRAU4EHnO2fxg4Iyuvh53PjwEnOesrpdTAc84jdodPcUFHyJ4u4Z0/5rtUW8ln0LlURN4TkfkiUu6kDQc+zVqn1knbXnol0GqMSW+RvllezvdtzvpbEZGLRGSZiCxramr6/EemlFL5MPY4uG7jpukSnvouPHBSfsu0hV4LOiLykois2MZrJnAPsDcwCagHbu2tcnSHMeZ+Y8wUY8yUQYMG5bMoSin1+bjd9nQJJ1xnL9ctgxsGQeOq/JbL4emtjI0xJ3dnPRF5APizs1gHjMz6eoSTxnbSm4EyEfE4tZns9TvzqhURD1DqrK+UUgPfcVfCoXPgd4dBKgZ3Hw6HXwSn3ZzXYuWr9drQrMVZwArn8zPAOU7Ls7HAeOAN4E1gvNNSzYfd2OAZY4wB/g443XSZCzydlVfnhBRnAUuc9ZVSas9QMhSurYfxX7SX37wfbh4PqUTeipSvZzq/FpHlIvIecAJwGYAxZiWwGHgf+CvwPWNMxqnFXAo8D3wALHbWBbgKuFxEarCf2TzkpD8EVDrplwNdzayVUmqP8vXH4LzHABdEG+EXQ2DFk3kpiug//jc3ZcoUs2zZsnwXQymlci+dht9NgjanXdaoqfDN53KStYi8ZYyZsrP1drcm00oppXqLxwOXrYAvXGEvr6+GGwbDxrV9VgQNOkoptac56SfwwxXgCUAmAXdOghd+0ie71qCjlFJ7ovKRcF2D3bcHoPoOuHtqr+9Wg45SSu3J5j4Dsx+xRzIYd3yv767X+ukopZTqJ/Y/HX7W0ie70pqOUkqpPqNBRymlVJ/RoKOUUqrPaNBRSinVZzToKKWU6jMadJRSSvUZDTpKKaX6jAYdpZRSfUZHmd6CiDQB6/JdjjypAjbmuxB5tKcfP+g50OPf9eMfbYzZ6dTLGnRUFxFZ1p2hyQeqPf34Qc+BHn/vH7/eXlNKKdVnNOgopZTqMxp0VLb7812APNvTjx/0HOjx9zJ9pqOUUqrPaE1HKaVUn9Ggo5RSqs9o0BnARGSkiPxdRN4XkZUi8kMnvUJEXhSRj533cif96yLynogsF5FqETkkK68ZIvKRiNSIyNX5Oqae6uk5yNrucBFJi8hZWWlznfU/FpG5fX0su2JXjl9EjheRd5z1X8lK73e/gV34f6BURJ4VkXed9S/IymsgXf+znWVLRKZssc01zjX+SESmZ6Xn5vobY/Q1QF/AUOAw53MxsAo4APg1cLWTfjXwK+fzVKDc+XwqsNT57AZWA+MAH/AucEC+j683zkHW8S4B/g84y0mrANY47+XO5/J8H18v/AbKgPeBUc7y4P78G9iF4//PrM+DgJBzvAPt+u8P7Au8DEzJWv8A59r6gbHONXfn8vprTWcAM8bUG2P+7XwOAx8Aw4GZwMPOag8DZzjrVBtjOuesfR0Y4Xw+AqgxxqwxxiSBhU4eu72engPH94HHgcastOnAi8aYkHOOXgRm9HLxP7ddOP7zgCeMMeudbTrPQb/8DezC8RugWEQEKMIOOmkG2PU3xnxgjPloG5vMBBYaYxLGmLVADfa1z9n116CzhxCRMcChwFJgiDGm3vnqM2DINja5EHjO+Twc+DTru1onrV/pzjkQkeHALOCeLTbv9+egm7+BCUC5iLwsIm+JyPlO+p5y/Hdi1wI2AMuBHxpjLAbe8W/P9o4zZ8fv2ZWNVP8iIkXY/3KfZ4xpt/8RZzPGGBExW6x/AnbQOaZPC9qLenAOfgtcZYyxstfp73pw/B5gMnASEAReE5HX+7q8udaD458OvAOcCOwNvCgi/+zr8ubalsefz7Jo0BngRMSL/WN7xBjzhJPcICJDjTH1IjKUrNtIInIw8CBwqjGm2UmuA0ZmZTvCSesXengOpgALnT9KVcCXRCSNfbzHZ2U7Avt++G6vh8dfCzQbY6JAVET+ARzipPfL30APj/8C4CZjP+CoEZG1wH4MvOu/PTv6fz0n119vrw1gzn3ph4APjDG/yfrqGaCz9c1c4Gln/VHAE8B/GGNWZa3/JjBeRMaKiA84x8ljt9fTc2CMGWuMGWOMGQM8BlxijHkKeB44RUTKnZZOpzhpu7WeHr/zfoyIeESkADgS+zlAv/wN7MLxr8eu5SEiQ7Aftq9h4F3/7XkGOEdE/CIyFhgPvEEur3++W1foq/de2LfHDPAe9i2Dd4AvAZXA34CPgZeACmf9B4GWrHWXZeX1JeyWL6uBa/N9bL11DrbY9vc4rdec5W9iP1itAS7I97H11vEDV2K3YFuBfTum3/4GduH/gWHAC9jPc1YAcwbo9Z+FXXtNAA3A81nbXOtc44+w73jk9PrrMDhKKaX6jN5eU0op1Wc06CillOozGnSUUkr1GQ06Siml+owGHaWUUn1Gg45SeSS2V0Xk1Ky0s0Xkr/ksl1K9RZtMK5VnIjIR+BP2uFge4G1ghjFm9efI02OMSeeoiErljNZ0lMozY8wK4FngKuCnwAJjzGpn/pY3xJ7b5m4RcQGIyP0issyZD+WnnfmISK2I3CQib2N3/lNqt6Njrym1e/hv4N9AEpji1H5mAVONMWkRuR976JE/Ys8DExIRD/B3EXnMGPO+k0+jMebQfByAUt2hQUep3YAxJioii4CIMSYhIicDhwPLnMFHg2waWv5cEbkQ+//fYdgTb3UGnUV9W3KlekaDjlK7D8t5AQgw3xjzk+wVRGQ88EPgCGNMq4j8AQhkrRLtk5IqtYv0mY5Su6eXgK+JSBWAiFQ6o4CXAGGg3RmSf/oO8lBqt6M1HaV2Q8aY5SLy38BLTgOCFPAdYBn2rbQPgXXAv/JXSqV6TptMK6WU6jN6e00ppVSf0aCjlFKqz2jQUUop1Wc06CillOozGnSUUkr1GQ06Siml+owGHaWUUn3m/wF6iwNECsU+0AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.line_plot(color='model', legend=dict(loc='center left'))" ] @@ -279,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -288,63 +94,28 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.line_plot(color='model', legend=OUTSIDE_LEGEND['right'])" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.line_plot(color='model', legend=OUTSIDE_LEGEND['bottom'])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -363,7 +134,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.4" } }, "nbformat": 4, From 37b9c6ca34dbd53ea2d24f7717ff52e3d6614ba8 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Fri, 6 Dec 2019 14:33:16 +0100 Subject: [PATCH 16/34] update the ipcc-colors tutorial --- doc/source/tutorials/ipcc_colors.ipynb | 261 ++++--------------------- 1 file changed, 34 insertions(+), 227 deletions(-) diff --git a/doc/source/tutorials/ipcc_colors.ipynb b/doc/source/tutorials/ipcc_colors.ipynb index 7479a1eaf..dfd793ca1 100644 --- a/doc/source/tutorials/ipcc_colors.ipynb +++ b/doc/source/tutorials/ipcc_colors.ipynb @@ -6,224 +6,54 @@ "source": [ "# Using IPCC Color Palettes\n", "\n", - "`pyam` supports the use of explicit IPCC AR5 and AR6 color palettes by providing the RCP and/or SSP of interest via the `pyam.run_control()`.\n", - "\n", - "The full list of the IPCC color palette colors can be retrieved by the following code." + "**pyam** supports the use of explicit IPCC AR5 and AR6 color palettes by providing the RCP and/or SSP of interest via the `pyam.run_control()` feature." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    namecolor
    0AR6-SSP1-1.9#00AAD0
    1AR6-SSP1-2.6#003466
    2AR6-SSP2-4.5#EF550F
    3AR6-SSP3-7.0#E00000
    4AR6-SSP3-LowNTCF#E00000
    5AR6-SSP4-3.4#FFA900
    6AR6-SSP4-6.0#C47900
    7AR6-SSP5-3.4-OS#7F006E
    8AR6-SSP5-8.5#990002
    9AR6-RCP-2.6#003466
    10AR6-RCP-4.5#5492CD
    11AR6-RCP-6.0#C47900
    12AR6-RCP-8.5#990002
    13AR5-RCP-2.6#0000FF
    14AR5-RCP-4.5#79BCFF
    15AR5-RCP-6.0#FF822D
    16AR5-RCP-8.5#FF0000
    \n", - "
    " - ], - "text/plain": [ - " name color\n", - "0 AR6-SSP1-1.9 #00AAD0\n", - "1 AR6-SSP1-2.6 #003466\n", - "2 AR6-SSP2-4.5 #EF550F\n", - "3 AR6-SSP3-7.0 #E00000\n", - "4 AR6-SSP3-LowNTCF #E00000\n", - "5 AR6-SSP4-3.4 #FFA900\n", - "6 AR6-SSP4-6.0 #C47900\n", - "7 AR6-SSP5-3.4-OS #7F006E\n", - "8 AR6-SSP5-8.5 #990002\n", - "9 AR6-RCP-2.6 #003466\n", - "10 AR6-RCP-4.5 #5492CD\n", - "11 AR6-RCP-6.0 #C47900\n", - "12 AR6-RCP-8.5 #990002\n", - "13 AR5-RCP-2.6 #0000FF\n", - "14 AR5-RCP-4.5 #79BCFF\n", - "15 AR5-RCP-6.0 #FF822D\n", - "16 AR5-RCP-8.5 #FF0000" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import pyam\n", "import pandas as pd\n", - "\n", - "pd.DataFrame({'name': list(pyam.plotting.PYAM_COLORS.keys()), \n", - " 'color': list(pyam.plotting.PYAM_COLORS.values())})" + "import pyam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The full list of the IPCC color palette colors avaialable in **pyam** can be retrieved by the following code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colors = pyam.plotting.PYAM_COLORS\n", + "pd.DataFrame({'name': list(colors.keys()), 'color': list(colors.values())})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's pull out a few example scenarios from our tutorial dataset and plot them with the default color scheme." + "We use the scenario ensemble from the **first-steps tutorial** ([link](https://github.com/IAMconsortium/pyam/blob/master/doc/source/tutorials/pyam_first_steps.ipynb)).\n", + "Let's pull out two example scenarios (implemented by multiple modelling frameworks each) and plot them with the default color scheme." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Reading `tutorial_AR5_data.csv`\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "scenarios = ['AMPERE3-450', 'AMPERE3-RefPol']\n", + "scenarios = ['CD-LINKS_NoPolicy', 'CD-LINKS_NPi2020_400']\n", "\n", "df = (\n", - " pyam.IamDataFrame(data='tutorial_AR5_data.csv', encoding='utf-8')\n", + " pyam.IamDataFrame(data='tutorial_data.csv')\n", " .filter(variable='Emissions|CO2', region='World', scenario=scenarios)\n", ")\n", "\n", @@ -234,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As an example, let us say that each of these two sets of scenarios (both implemented by multiple modelling frameworks) correspond to categorizations in the AR6 context. We can utilize the specific colors by following two steps:\n", + "As an example, we assume that each of these two sets of scenarios correspond to categorizations in the AR6 context. We can utilize the specific colors by following two steps:\n", "\n", "1. Update `pyam.run_control()` telling it which scenario name maps to which AR6 color\n", "2. Call `line_plot` using that color mapping" @@ -251,13 +81,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "color_map = {\n", - " 'AMPERE3-450': 'AR6-SSP2-4.5', \n", - " 'AMPERE3-RefPol': 'AR6-SSP5-8.5',\n", + " 'CD-LINKS_NPi2020_400': 'AR6-SSP2-4.5', \n", + " 'CD-LINKS_NoPolicy': 'AR6-SSP5-8.5',\n", "}\n", "\n", "pyam.run_control().update({'color': {'scenario': color_map}})" @@ -281,32 +111,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.line_plot(color='scenario')" ] @@ -328,7 +135,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.4" } }, "nbformat": 4, From 4e25b4c29e7f20135ee325662fc25e0780b32604 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 11:24:10 +0100 Subject: [PATCH 17/34] remove output, harmonize formatting in "build the logo" tutorial --- doc/source/tutorials/pyam_logo.ipynb | 143 ++------------------------- 1 file changed, 10 insertions(+), 133 deletions(-) diff --git a/doc/source/tutorials/pyam_logo.ipynb b/doc/source/tutorials/pyam_logo.ipynb index 4b91565ce..f0fe86915 100644 --- a/doc/source/tutorials/pyam_logo.ipynb +++ b/doc/source/tutorials/pyam_logo.ipynb @@ -6,7 +6,7 @@ "source": [ "# Make our Logo!\n", "\n", - "The logo combines a number of fun `pyam` features, including\n", + "The logo combines a number of fun **pyam** features, including\n", "\n", "- line plots\n", "- filling data between lines\n", @@ -15,22 +15,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import itertools\n", "import pyam\n", @@ -44,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -67,128 +54,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " model scenario region variable unit year value\n", - "0 m1 s1 r v u 0 0.000000\n", - "12 m1 s1 r v u 1 0.060595\n", - "144 m1 s1 r v u 2 0.121124\n", - "276 m1 s1 r v u 3 0.181522\n", - "408 m1 s1 r v u 4 0.241722" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "df.filter(scenario='s2').line_plot(ax=ax, color='model', legend=False, title=False)\n", @@ -216,7 +93,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.4" } }, "nbformat": 4, From 82259af3903823e95c2f4cd2684b3383eab4d4b8 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 11:27:06 +0100 Subject: [PATCH 18/34] remove output, harmonize formatting in "aggregating & plotting" tutorial --- ...es_and_plotting_with_negative_values.ipynb | 549 ++---------------- 1 file changed, 33 insertions(+), 516 deletions(-) diff --git a/doc/source/tutorials/aggregating_variables_and_plotting_with_negative_values.ipynb b/doc/source/tutorials/aggregating_variables_and_plotting_with_negative_values.ipynb index 724ebfdb5..d6ce2459f 100644 --- a/doc/source/tutorials/aggregating_variables_and_plotting_with_negative_values.ipynb +++ b/doc/source/tutorials/aggregating_variables_and_plotting_with_negative_values.ipynb @@ -6,27 +6,14 @@ "source": [ "# Plotting aggregate variables\n", "\n", - "Pyam offers many great visualisation and analysis tools. In this notebook we highlight the `aggregate` and `stack_plot` methods of an `IamDataFrame`. " + "The **pyam** package offers many great visualisation and analysis tools. In this notebook, we highlight the `aggregate` and `stack_plot` methods of an `IamDataFrame`. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -36,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -55,108 +42,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    modelscenarioregionvariableunityearvalue
    0IMGa_scenWorldEmissions|CO2|AggMt CO2/yr20050.5
    1IMGa_scenWorldEmissions|CO2|AggMt CO2/yr2010-0.1
    2IMGa_scenWorldEmissions|CO2|AggMt CO2/yr2015-0.5
    3IMGa_scenWorldEmissions|CO2|AggMt CO2/yr2020-0.7
    4IMGa_scenWorldEmissions|CO2|CarsMt CO2/yr20051.6
    \n", - "
    " - ], - "text/plain": [ - " model scenario region variable unit year value\n", - "0 IMG a_scen World Emissions|CO2|Agg Mt CO2/yr 2005 0.5\n", - "1 IMG a_scen World Emissions|CO2|Agg Mt CO2/yr 2010 -0.1\n", - "2 IMG a_scen World Emissions|CO2|Agg Mt CO2/yr 2015 -0.5\n", - "3 IMG a_scen World Emissions|CO2|Agg Mt CO2/yr 2020 -0.7\n", - "4 IMG a_scen World Emissions|CO2|Cars Mt CO2/yr 2005 1.6" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pyam.IamDataFrame(pd.DataFrame([\n", " ['IMG', 'a_scen', 'World', 'Emissions|CO2|Energy|Oil', 'Mt CO2/yr', 2, 3.2, 2.0, 1.8],\n", @@ -181,44 +69,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.stack_plot(total=True);" ] @@ -232,22 +94,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.stack_plot(alpha=0.5, total={\"color\": \"grey\", \"ls\": \"--\", \"lw\": 2.0});" ] @@ -261,22 +110,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.filter(variable=\"Emissions|CO2|Energy*\").stack_plot(total=True);" ] @@ -290,22 +126,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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    " - ], - "text/plain": [ - " 2005 2010 2015 2020\n", - "model scenario region variable unit \n", - "IMG a_scen World Emissions|CO2|AFOLU Mt CO2/yr 0.6 0.7 0.6 0.2" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.filter(variable=\"Emissions|CO2|AFOLU\", region=\"World\").timeseries()" ] @@ -579,22 +218,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KfQFwD84PQhlwAPBlC552w/JVOEcJV+N0F/4O+KGqVkLjjVjeifG2DwS+cd+vyTjnXaxQ1S04J6mdhNP1uRSnGxGcs+8nA++LyBacE4t2ef3zbrgO5/P1tTjd1R/inimtqu8Af8M5wW0Z7o8vzs56O6r6Ec55Na/gHHUPBM5oSQAiMkCc67AHRGn2KdALJxloUIRzUtlMVa2JmH45zrkTxW7753DOl9gl9/3/Cc65N1U4Jznu6r2d7LYrU9XZEdNvxDnRrxon4Xi1mWWjuQanF2ILTgLX3I/+Gzg1/CJ3G/9p2sD9fE3EeT/W43zG7sA5KbLhpkHzdxHL32X7a+Zn7uZzaU4uzo/lZpwS2ac4CdxO9yVNnlfU7/AebnsHqvoyzj76525MZcAtOK99QxxH4BzRL3Bj+S1wjqo2vGcNvRITcRLFhtfxyKbbE5FDcI7YH3J7GRv+Tcb5Hp4Z7Ym1MJ4G83GSlYZ/F6hqOc4J3xfj7Pvn4XyGY9bDJNsnJ8aY9so9wpyHc3VMTI4cjGlrROR84BhVPT/O21VVTfylhHHQaXsMjOkIRORHIuIV5/4cdwBvWlJgjNkblhiYTkWcGwRtbebfw4mObQ9djHMuxXKcGmxCT1gzJg6KcMqd8XZjAraZEFZKMMYYY0wj6zEwxhhjTCNLDIwxxhjTqK2PGLbbsrOztaCgINFhGGOMMXEzc+bMSlXN2XXLXetwiUFBQQEzZsxIdBjGGGNM3IhI0zvk7jErJRhjjDGmkSUGxhhjjGlkiYExxhhjGlliYIwxxphGlhgYY4wxplFCEwMROUFEFovIMhG5vpn594lIkftviYhsSkScxhhjTGeRsMsV3XGlH8IZznYtMF1EJrtD5gKgqldFtL8cGB33QI0xxphOJJE9BgcBy1S1WFX9wAvApCjtzwSej0tkxhhjTCeVyBsc9QXWRDxeCxzcXEMRyQcKgSlxiMsYwuEwxcXFzJ49m8rKSoYPH86IESPo0qVLokMzxphWlcjEQJqZtrOhHs8AXlbVULMrErkIuAhgwIABsYnOdBq1tbXMnz+foqKi7/7Nns22rVt3aNs/v5CxY0YzdsxoRo0axahRo+jbty8izX2cjTGm/UlkYrAW6B/xuB+wfidtzwAu3dmKVPUR4BGAcePG2TjSZqfKy8uZPXv2dknAokWLCIfDACSnppOSU4hvv2PpkzeQpJ4FaGpX/JWr0MoVVJcV89YnX/P6a682rrNbjyxGjBjBgWPHNCYL++23HykpKYl6msYYs8cSmRhMBwaLSCGwDufH/6ymjURkCNAD+Cq+4Zn2LBQKsXz58u0SgFmziigtLWls4+veC092AVmH/5TU3gPRrHw83XsjsuOpN8ndesHAA8nA+TCG62vwV6wkXLmCYMVKpi1ZzhdfTkWDfqd9ipch++3PgWNHM3q007swcuRIunXrFqdXwBhj9oyoJu4AW0R+ANwPeIDHVPWvInITMENVJ7ttbgBSVXWHyxmbM27cOLVBlDqXmpoa5s6du10SMHvOHGpragAQj4fU7Hw8OYVk9hlIck4B9CzAk9Y1pnFoOERgwzqCFSsIV66krnQ5daXLCdVUN7bp2z+fMaNHbVeKGDBggJUijDF7RURmquq4mKwrkYlBa7DEoGMrLS3dvhegqIhlS5d+VwpIyyQlp5DU3H1Izx0IPfPxZA1AkhPTra+qhLZtJFBWjFatpL58BbUlywlsXAfudy+za7cdShFDhw7F6/UmJGZjTPtjiUEUlhh0DKFQiCVLlmx/QmDRbMrLyxrb+HrkkpxTSHreQHy9CtGeBXi69moXR99hfx2BipWEKlcSqlhBTely6stXoIF6AJKTUxg8ZD/GjRnN6NGjGksRWVlZCY7cGNMWWWIQhSUG7c/WrVuZM2fOdknA3HnzqKutBSDJk4yvVwHJOYVk5A0kOacQ6ZlPUmpmgiOPLQ2HCG4sIVBRTLhyFXVly6krLSa0dUNjm9y+/Rg7ejRj3GRh1KhRFBYWtotkyBjTeiwxiMISg7ZLVVm/fv0OlwUuX7aMhs9hSnpXknsVktp7IOm5+7ilgH6Ip/Oe4R/athF/+Qq0aiX+Mqd3IVC1FtQpn2RkduGAESMYO3pU44mOw4YNIzU1NcGRG2PixRKDKCwxaBsCgQCLFy/eoRRQVVXZ2CY1qw+enELS8/bB13sfNKsAT5dsO/ptgXCgnkDlKkIVK9xSRDH+8hWE/Q29LB4GDR7CuLGjGT3qu96F7OzsBEdujGkNlhhEYYlB/FVXV29XCpg9ezZz583DX+/Uy5OSvfh6FZCSU0BG3iA8DaUAX3qCI+9YVMMEN5USKC9GK1dSV1ZMXWkxwS3fJWO9cvMYPXo0YyNKEQMHDiQpyQZaNaY9s8QgCksMWo+qsmbNmu0SgFlFRawoLm5sk5LRjZRe+5Cauw9puQMhyy0FJHkSGHnnFqqpdnoTKlcSKC+mtrQYf9VqcK/kSE1LZ/iIERwYcQnl8OHDSU+3xM2Y9sISgygsMYgNv9/PwoULd0gCNm3c6DQQIbVnXzzZTi+At3ehUwrIzLJSQDugQT/+ytVOKaJyBbWlxdSXFROud+79kJSUROHAwYwbO5oxo0czcuRIRo0aRe/evRMcuTGmOZYYRGGJwe7buHFj422CG/6fP38+gUAAAE+KD2+vAlJy9iGjz0CSsgtI6plPkjctwZGbWFJVgtVl25ciyooJVpc3tumZ04vRo0czbsx3d3McPHgwHo/1CBmTSJYYRGGJwc6pKitXrtyhF2D1qlWNbbxdskjOKSTNLQVoz3w83ftYKaATC9VtJVBe3FiKqCktJlC5Gg0FAfClpjJ02AGNt38eOXIkBxxwAJmZHetyUmPaMksMorDEwFFfX984YmDjoEGzZ7O52rk9r0gSvuy+JGcXkt5nEN5e+zjnA2T2SHDkpj3QUIBA1RqC5SsIVa6ktnS5U4qoc0akFBHyCwc2liIaehfy8vKs1GRMK7DEIIrOmBhUVVVtnwAUFbFw4UKCQeeIzuNNw9urAG8vpxQgPQtI6jmApBS7zt3EjqoS2lKBv2wFWrWC+jLnRMfgptLGNt2zshk9aiTj3Ns/jxw5kiFDhpCcnMjx3Ixp/ywxiKIjJwbhcJji4uLtkoBZs4pYt25tYxtv12yScwpIyx1IWp47YmC3XCsFmIQJ129rclXEcvwVq9GQcw6L15fKfvsP3W4kyhEjRtClS5cER25M+2GJQRQdJTGora1l3rx52/UCFM2ezbatbldtkgdfdn+ScwrIzBtEcq9CZ8TAdBvW17R9GgoS2LCWoJsw1JYup66smHDt5sY2/fMLGTtm9HYjUfbt29dKEcY0wxKDKNpjYlBeXr59L0BREYsXLfpuxMDUdFJyCvH13of0PLcUkNWfpBRfgiM3JnZUldDWKuf2z5Ur8Jc5JzoGN65vbNO1ew9Gjhy53UiU++23HykpnfeW2caAJQZRteXEIBQKsWzZsu16AWbNKqK0tKSxja97LzzZBaTnDSS14TbB3XsjYnemM51TuL4Gf8UqwpXFBCtWUlOyHH/FSjToByA5xcuQ/fbfrhQxcuRIunWz3jPTeVhiEEVbSQxqamqYO3fudmMFzJ4zh9oa5wYy4vGQmp2PJ6eQzD4DSc4pcEoBaV0TG7gx7YCGQwQ3rHdHolxJXak7EmXNpsY2ffoPYOzo7UsRAwYMsFKE6ZAsMYgiEYlBaWnpdgnArKIili5Z0jhiYHJaJik5haTm7kN6w22Cew5Akq3705hYCm3diL+8GK1cQX35CmpLlhPYuA7c72Jm126MGDFiu1LE0KFD8Xq9CY7cmL1jiUEUrZkYhEIhlixZskMSUFH+3Z3hfD1yG28QlNp7H7RnAZ6uvewoxZgECfvrmoxEuZz68hVowBnkKzk5hUH7DuHAsWMY7Q4uNXLkSLKyshIcuTEtZ4lBFLFKDLZs2bJDKWDO3LnU19UBkORJxtergOScQjLyBpLcMGJgqt3tzZi2TsOhxpEow5UrqStzSxFbNzS2ye3bjzGjRm1XiigoKLCRKE2b1GESAxE5AXgA8ACPqurtzbQ5HbgBUGC2qp4VbZ27mxioKuvXr9+hF6B4+fLGUkBKeheSezk9AOm5A6GnO2Kgx0oBxnQkoW2bnKsiqlbgL3N6FwJVa0GdK4QyMrsw/IADGDfmuxMdhw0bRmqq3SzMJFaHSAxExAMsASYAa4HpwJmquiCizWDgReB7qrpRRHqpanmzK3RFSwwCgQCLFy/eLgkoKppNVdV349WnZvUhKaeAjLyB+BquCuiSbaUAYzqpcKCeQMRIlDUly50bNvlrAUjyeBg0eAhjx4za7vbPOTk5CY7cdCaxTAwSeR/Sg4BlqloMICIvAJOABRFtfgk8pKobAXaVFESqrq5mzpw52yUB8+bPx1/v1BWTkr34ehWQ0m8MeQcOwtNQCvDZGPTGmO8kpfjw5Q2GvMEAdAFUw24pYgVauZL1ZcspfvN9nn/uucbleuXmMWrUqMaRKEeNGsXAgQOtFGHavET2GJwGnKCqF7qPzwEOVtXLItq8jtOrcDhOueEGVX032np79OihPbKyWFFc3DgtJaMbyb0KndsEN1wVkNXPbhNsjImpUO3mxhs0Bcrd3oWq1eDerCw1LZ3hI0Zw2o9O4ZprrrHhqk3MdJQeg+b65ptmKcnAYOAYoB/wuYgMV9VNkY1E5CLgInC69QK5w+l17FH4ehcSzirAk5llpQBjTKvzpHUlLX8k5I8EoBugQT/+ytWEKlcSqljBvLVLmHH99Tz2wmu8+8ZLFA7on9igjWkikYnBWiDyG9EPWN9Mm69VNQCsEJHFOInC9MhGqvoI8AhAn0HD1Dvp+sZ51mlnjEkkSfbiyx0EuYMApxSxde5HLP3gH+w79ABuuOef/OGiM+zgxbQZifzdnA4MFpFCEfECZwCTm7R5HTgWQESygX2BYowxph3LPGA8uefeh6R140+XnM3g489jxorKXS9oTBwkLDFQ1SBwGfAesBB4UVXni8hNInKy2+w9oEpEFgAfA9eqalViIjbGmNjxZg8g99x7yRwxgeUfPM1RRx/D2ff/j3WbahMdmunkOtwNjvoMGqbe0+5MdBjGGNNiW+d/zIb3HkKSvfSZdA2X/OxUrhg/mC6pdq8U0zKxPPnQSvDGGJNgmcOOJe+8+/BkZrH2hb9w7603cOitH/DMV6sIhsKJDs90MpYYGGNMG5DSsz+559xD5sgT2DD1JZY9di03vvAZx9z1CZ8sbvEtXIzZa5YYGGNMG5GU4qPnCZeRfdK1+CtWUPzvy1g68zN+9cy3nPrPqSwq3ZzoEE0nYImBMca0MRlDjybvvPvxdOlJ+cs3sv79f/Ptigom/f1LfvvfIiq21Cc6RNOBWWJgjDFtUEpWX/LOuYfMUd9n87RXKXn2erZtKOXNOes56s6PeeDDJdQFQokO03RAlhgYY0wbJcleeh5/KdknX4e/chUlj19B9eKvqQ2EePjT5Rx620e8Nmst4XDHurrMJJYlBsYY08Zl7H8keec/QHK33lS8cjMbpjxKTV09G2sC/PG1eUy471OmrdiQ6DBNB2GJgTHGtAMpPfqQ+7O76DLmRLZMf53SZ68nWF1GjT/E8optnPfYNM57bBorK7clOlTTzlliYIwx7YQke8ma8CuyJ11PoGoNJY9fQc3SrwGoDYT4Ylklx9//GX9+fR7VNYEER2vaK0sMjDGmncnY7wintNAjj4pXb2HDh4+goQChsFIfDPPijDUcdvtHPPp5Mf6g3SDJ7B5LDIwxph1K6ZFH7tl30WXsSWyZOZnSZ39HYFMpAPXBMNv8Ie79YAlH3DGF9+aX0tFuf29ajyUGxhjTTklyClnHXUzOKX8gsGE9JU9cSc3iqY3za/whyrfUc9V/izjpwS+Yu7Y6gdGa9sISA2OMaefShxxG3vkPkJLVh4rXb2XDBw+jwe/OMajxh5i/fjM/eXgqv3pmJiXVNoKj2TlLDIwxpgNI6Z5L7tl30mXcJLZ8+xalz15LYGNJ43wF6oJhPlhQxrF3f8Lt7yxiW30wcQGbNssSA2OM6SDEk0LW+F+S8+M/EdxYQskTV7Jt0RfbtQmGlbpAmCemruBkbOMIAAAgAElEQVSQ2z7i+W9WE7IbJJkIlhgYY0wHkz74EPIueJCUnv2pfON2qt7/Jxr0b9emLhBmS12Qm99ewDF3f8znSysSFK1paywxMMaYDii5Wy9yz76drgf+iK2z3qb0mWsJbFi3Q7saf4g1G2q56KmZnP6vr1hatiUB0Zq2xBIDY4zpoMSTQo/v/YKcU/9CsLqckid/w7YFnzbbtjYQYsbKDZz04Bdc89JsKrfaCI6dlSUGxhjTwaUPOoi8C/6GN6eAyjfvouq9vxMO7PjDH1bnBMXJRes48o6PefCjpTaCYyeU0MRARE4QkcUiskxErm9m/vkiUiEiRe6/CxMRpzHGtHfJXXPofeZtdD34VLYWvUvp01cTqFrbbFt/SKkNhPjHJ8s57PYpvFG0zm6Q1IlIot5sEfEAS4AJwFpgOnCmqi6IaHM+ME5VL2vpevsMGqbe0+6McbTGGNNx1C6fTuXb96GhAD2Pv5SMocdEbZ/u9dCvRxq3/fgAxuZnxSdIs1tEZKaqjovFuhLZY3AQsExVi1XVD7wATEpgPMYY0ymkDTyQvPP/hrdXIZVv3k3VO39rtrTQoMYfYknZVs5+dBoXPD6N1VU1cYzWxFsiE4O+wJqIx2vdaU2dKiJzRORlEekfn9CMMaZjS+6a7ZQWDvkJW+e875YW1kRdpi4Q4rOllUy871NumDyP6lobwbEjSmRiIM1Ma1rXeBMoUNURwIfAk82uSOQiEZkhIjNqNm+McZjGGNMxSZKHHkefR6+f3Eho6wZKnryKrfOmRF0mFFbqgmGen+aM4Pj4lysIhGwEx44kkYnBWiCyB6AfsD6ygapWqWpD/9a/gbHNrUhVH1HVcao6Lr1rj1YJ1hhjOqq0fcY6Vy3kDqLq7Xup/N/9hAN1UZepD4bZVh/irncXc+QdH/PBgjI7QbGDSGRiMB0YLCKFIuIFzgAmRzYQkbyIhycDC+MYnzHGdBrJXbLpfcZf6XboT9k29yNKn/ot/srVu1yuJhCidHMdV74wi1Me+pJ562wEx/YuYYmBqgaBy4D3cH7wX1TV+SJyk4ic7Da7QkTmi8hs4Arg/MREa4wxHZ8keeh+1Dn0Ov0mQjWbKX3qKrbO/bBFy9b4Q8xZW81p/5zKZc99S2l19B4H03Yl7HLF1mKXKxpjzN4Lbt1A5Zt3Ub96LhnDx5M14VckeVNbtGxKkuDxCL84opBLjx1Euje5laM1HeVyRWOMMW1UcmYWvX96C90OP5Nt86ZQ+tRV+CtWtmjZgDuC43++WMEht37Ei9NtBMf2xBIDY4wxzZIkD92POJteZ9xCqG4LpU9dzZbZ77f4JMO6QJjNdUFueHMB37vnE75cVtnKEZtYsMTAGGNMVGn5I+lz/oP4+u7Hhnf/RtXb9xL217Z4+Rp/iFVVNVz45AzOfORrlpVvbcVozd6yxMAYY8wueTJ70Ov0m+h2xNlsW/ApJU9ehb98xW6tozYQ4psVVZz4t8+57pU5bNjmb6Vozd6wxMAYY0yLSJKH7oefSe8zbkHrt1H69NVsKXp3t+5fEFbnHgivfbuOI+6Ywj8+XkZ90EZwbEssMTDGGLNbUgeMIO+CB/H1G8aG9/5O5Zt3E67fvfET/KEwNf4QD05ZxmG3T+GtOevtBklthCUGxhhjdpsnozu9Tr+R7kedS82izyl58jf4y4t3ez21gRBVW/387uU5nPDA53y72m5rn2iWGBhjjNkjIkl0O/R0ep95Kxqoo+Spq9ky6397dORf4w+xuHQLZ/37ay58cjprNtgIjoliiYExxpi9ktp/OHkXPEjqgBFseP8fVE6+c7dLCw3qAmE+WVzBcfd+ys1vLWBznY3gGG+WGBhjjNlrnvRu9PrJ/9H96POpWfwlJU9cib9s+R6tKxhW6oNhnvl6FYfdNoUnp64kaCM4xo0lBsYYY2JCJIluh5xG77NuQ0MBSp6+mi3fvrXHJxXWB8NsrQ9yx7uLOOquj5myyEZwjAdLDIwxxsRUar9h5J3/AGn5o9jwwcNUvnE74fpte7y+Gn+I9ZvquPS5Wfz4n1NZWLI5htGapiwxMMYYE3Oe9G7knPYXuh9zATVLvqLkiSupL1m6V+us9YcoWrOJH/3jS658YRblm20Ex9ZgiYExxphWIZJEt4NPJffsO9BQiNJnrmXzjMl7VQ5QdU5Q/N/cEo6662PufX8xtX67QVIsWWJgjDGmVfn67k/eBX8jbZ8xbPzoESpev5Vw3d6NlxAIOSM4/vvzFRx620e8PHMNYRvBMSYsMTDGGNPqPGldyPnxn+lx7C+oXTaN9U9cSf36xXu93tpAiE21Af7yxnzG3/spXy2vikG0nZslBsYYY+JCROh60I/IPftOUKX02evYPP2NmFxpUOMPsaJyGz9/Yjo/e/RriitsBMc9ZYmBMcaYuPL1GeKUFgaOY+OUf1Px2l8J1W6JybprAyG+Wr6BHzzwOX94dS4bbQTH3WaJgTHGmLjzpGaS86M/0mP8L6ldPoOSJ66gft2imKw7pEpdMMzL367l8Dum8K9Pl9sIjrshoYmBiJwgIotFZJmIXB+l3WkioiIyLp7xGWOMaT0iQtdxk8j92Z0gSZQ+dx2bp70as5sY+YPOCI73f7iUw2+fwjtzS+wGSS2QsMRARDzAQ8D3gaHAmSIytJl2XYArgG/iG6Exxph48OXtS5/zHyB90MFs/PgxKl65iVBt7G5iVBsIUbnVz9UvzebEv33B7DWbYrbujiiRPQYHActUtVhV/cALwKRm2t0M3AnYnSyMMaaDSkrNJPuU39PjuIupXTmLksevpG7twphuo8YfYkHJZn76yFdc/PQM1m2qjen6O4pEJgZ9gTURj9e60xqJyGigv6q+Fc/AjDHGxJ+I0HXsSeT+7G7E46Hsueuo/uZlVGM7gFJdIMyHC8sYf/cn/PXtBWyxERy3k8jEQJqZ1lj8EZEk4D7g6l2uSOQiEZkhIjNqNm+MYYjGGGPizZc7iLzzHyB938PY9MkTlL98I6Ga6phuIxSGumCYp79axWG3T+GZr1bZCI6uRCYGa4H+EY/7AesjHncBhgOfiMhK4BBgcnMnIKrqI6o6TlXHpXft0YohG2OMiYckXwbZk64ja8KvqFs1m5LHr6Bu7fyYb6cuGGZLXZBb31nIMXd9wieLy2O+jfYmkYnBdGCwiBSKiBc4A5jcMFNVq1U1W1ULVLUA+Bo4WVVnJCZcY4wx8SQidBlzInnn3IOkeCl77vdUf/VizEsL4Jx/sHZTLb965ltO++dUFpfG5r4K7VHCEgNVDQKXAe8BC4EXVXW+iNwkIicnKi5jjDFti7f3QPLOe4D0IYez6bOnKH/phpiXFhrUBkJ8u3ojkx76gt/+t4iKLfWtsp22TKJd0+nW+eeo6vD4hbR3+gwapt7T7kx0GMYYY2JMVdk6+102fPgInrQuZJ/8O1L7t97PU4pHSE5K4pKj9+HioweSmuJptW3tLRGZqaoxuddP1B4DdfprZovIgFhszBhjjNlTIkKXUd8n79x7EG8aZc//geqp/22V0gI4IzjWBkI8/OlyDr3tI16btbZTjOAYtccAQESmAAcC04BtDdNVtU1291uPgTHGdHzh+hqq3n+ImgWfklowmuwf/hZPRuuefJ7u9ZDXLZXbfjyCgwqzWnVbuyuWPQYtSQyObm66qn4aiwBizRIDY4zpHFSVrXPeZ+OH/3KuYjjpWlLzR7T6dtNSPBxUmMWNJw+jIDuj1bfXEvFODC4DnlXVdnGDAEsMjDGmc/GXr6DijTsIblxPt8PPpNuhpyNJrXs+gCdJSE4STh/Xn2smDqFbekqrbm9X4naOgSsXmC4iL7qDHjV3YyJjjDEmIby9Csk77z4yhh5N9RfPUv7inwltbd1j2VBYqQ+GeXHGGg67/SMe/bwYf7Bj3CBplz0GAG4yMBG4ABgHvAj8R1WXt254u896DIwxpnNSVbbN/ZANHzyM+NLI/uE1pBWMisu2070eMn3J3HzKcCYO7U28j6Hj3WOAOtlDqfsvCPQAXhYR+wU2xhjTJogImSMmkHvuvXhSu1D+3z+z6fNn0XCo1bdd4w9RvqWeq/5bxEkPfsHcta1zn4V4aMk5BlcA5wGVwKPA66oacO9xsFRVB7Z+mC1nPQbGGGPC/jo2fPAw2+Z9iG/AAWSfdC3JmfG5kkAAX0oSxw7pxV9OGkpet7TW32acewyygR+r6vGq+pKqBqDxHgc/jEUQxhhjTCwleVPJPvE39PzBVfhLllDy+BXUrpgVl20rzgiOHywo49i7PuH2dxayrT4Yl23Hwk4TA3e0wgeAqUBZc21UNbaDZRtjjDExlHnAeHLPvQ9PelfKX/wLGz97Oi6lBYBgWKkLhnli6koOue0jnv9mNaF2cIOknZYSRCQZOAI4ATgWqMIZ1+AdVV0Stwh3k5USjDHGNBUO1LHxw0fYOud9fP2GkX3ytSR3yY5rDOleDz0zvNz64wM4cnBOTNcd1/sYRGw0D/g+TqIwGPhKVX8diyBiyRIDY4wxO7N1/sdseO8hJNlL9g+vJm2fsXGPIS3Fw4h+3bjllOEM7t0lJutMSGLQJIAk4FBV/TIWQcSSJQbGGGOiCVStoeKNOwhUrKTrIafR/chzWv2GSE0lCXg9SZw0sg/XfX8/sjN9e7W+uJx8KCIeEblYRG4WkcObzP5DW0wKjDHGmF1J6dmf3HPuIXPk8Wz++mXKnv89wc2VcY0hrFAXDPN60TqOvONjHvxoKXWB+Jz7sCvRrkr4F3A0zrkFfxOReyPm/bhVozLGGGNaUVKKj54nXE72SdfiL19ByRNXULt8etzjaBjB8R+fLOOw26fwRtE69qQnP5aiJQYHqepZqno/cDCQKSKviogP5zJNY4wxpl3LGHo0eefdj6dLT8pfvpGNHz+GhuJ/aWFtIMyGbX5+/+pcjr//M2au2hD3GBpESwy8DX+oalBVLwKKgClAZmsHZowxxsRDSlZf8s65h8xR32fztFcpe+56gpvLExJLjT/EkrKtnP3oNC54fBqrq2riHkO0xGCGiJwQOUFVbwIeBwpaMyhjjDEmniTZS8/jLyX75OvwV66i5PErqFn2TcLiqQuE+GxpJRPv+5QbJs+jujYQt23v0VUJbZldlWCMMWZvBDaup/KNO/CXLafLgafQ4+jzEE/ihlX2JSeR7BGumTiEnx2ST4pnx2P6uN0SWUR6iciNIvKyiLzk/t07Fht213+CiCwWkWUicn0z8y8RkbkiUiQiX4jI0Fht2xhjjGlOSo8+5P7sLrqMOZEt01+n9NnrCVYnprQAUB8Ms60+xF3vLebIOz7mgwVlrXqCYrTLFQ8HGk7RfAp4xv37m2YuX9xtIuIBHsK5adJQ4MxmfvifU9UDVHUUcCdwL8YYY0wrk2QvWRN+Rfak6wlUraHk8cupWfp1QmOq8Yco3VzHlS/M4pSHvmTeutYZwTFaj8E9wCmq+n+qOllV31DV/wNOITY/0AcBy1S1WFX9wAvApMgGqro54mEGztgUxhhjTFxk7HcEeec/QHKPPCpevYUNHz6ChuJX729OjT/EnLXVnPbwVC577ltKq+tiuv5oiUFXVd1hKCpVLQJicQ/HvsCaiMdr3WnbEZFLRWQ5To/BFc2tSEQucgd9mlFfXc45nvcZK4vJoDYGYRpjjOnMUnrkkXv2XXQZexJbZk6m9NnfEdhUmtCYGkZwfG9eKcfc/TGezKzYlfmjDKK0EDhMVTc2mZ4FTFXV/fZqwyI/AY5X1Qvdx+fg3Dvh8p20P8ttf1609Y4a2FtnnhOgFh8+rWcTmSwhn29C+zIvXMCCcD6lZGG3YjDGGLO7ahZPpfKdBwDI/v6VpA85LMEROdY/fnnAX1bs3XXLXUuOMu8+4H0RuQb41p02FrjDnbe31gL9Ix73A9ZHaf8C8M9drTQ5SfAQIpMaEMihmhzmcEjyfOrEhyccIEwSxdKXmaHBFIX2YYHms1z7EIz6chhjjOns0occRl7vfaicfAcVr99Kl7En0eOYnyPJibtqIdZ2+kuoqo+IyHrgZmAYTs/FAuAWVX0zBtueDgwWkUJgHXAGcFZkAxEZrKpL3YcnAkvZQx5CZGhNY0fBcIoZ5inmp8lphFTxaoASspmr+zA9NJgF4XwWaj5bSN/TTRpjjOmAUrrnknv2nWz85Am2zHiD+nULyT75OlJ65CU6tJhI6H0MROQHwP2AB3hMVf8qIjcBM1R1sog8ABwHBICNwGWqOj/aOscNztUZZ+/duQUB8VKvyfi0ns1ksIQBTIsoRawjGytFGGOMqVn6NVVv34eq0vP7V5Cx3xEJiSOWpYRo5xjcCRSr6sNNpl8F5KrqdbEIINZikRg0J0wStZJKkgZBYaX0YWZoMLNCA1moA1iq/QhYKcIYYzqdYHU5FW/cgb9kMZmjTyTre79AkmPyG91i8UoMFgDDVTXcZHoSMEdVh8cigFhrrcSgOQrUSyohFXxaTynZzNNCpoX2ZYHmsyA8gM02rIQxxnR4Ggqw6dOn2Dz9Nby9B5J98u9IydrhQrtWE6+TD7VpUuBODIuI9aPjFBNSta7xQT/K6SfljPd8i19TSNF6tpHGUrcUMTdcyAIdwBrthZUijDGm4xBPCj2+9wt8Aw6g6u37KHnyN/Q84XIy9j8q0aHttmiJQU2Tk/8A54RAsBsERJOiAVIIgICXrRzEAsYlL6JOUhENIRpmFXnMCg9iZngQC8L5LNO+1BPfridjjDGxlT7oILwXPEDl5LuonHwndavn0ON7vyQpxZfo0FosWmLwF+AdEbkFmOlOGwf8HvhNawfW0SQRJl3d4TMFhrCaIZ7VnJI8laAKqVpPOVksoJCvg04pYmF4ABvpmtjAjTHG7Jbkrr3ofeZtbPr8aTZ/8wr16xeTM+n6uJYW9kbUqxJEZDhwLdBwPsE84G5VnRuH2PZIPM8xaC1BUqgjBa/6qcXHMvozLbQvc8KFLNB8VmsvNPr4V8YYY9qA2uXTqXz7PjQUoOfxl5Ix9JhW2U5cTj5srzpCYtCcMEKdpIGG8GiINZJLUXgQM0JOKWKx9rdShDHGtEHBzZVUvnkn9WsXkDliIj2OuzjmpYV4nXxo2pAkdLtSxCDWMihpLSd5viKgSfjUTxXdWEiBW4ooYGE4n0q6JTZwY4zp5JK7ZrulhWfY/PVL1JcsIWfSdaT07L/rhRPAegw6oCDJ1ImXlLAfPykspz/TQoOZE3Zu/7xScwlbKcIYY+KutngmlW/dgwb9ZE38NZnDvxeT9ca1lCAih6vql7ua1lZYYtC870oRYZI1yFrpTVF4IDPdUsQi7U8tqYkO0xhjOrzglkoq37yb+jXzyDhgAlkTLiYpZe/2v/FODL5V1TG7mtZWWGKwe/ziw68efFrPBrqymHy+Ce7LfC1gfjifCrpj91wwxpjY0nCI6i+eo/qrF0nJ7k/2pOvxZg/Y4/XF5RwDETkUOAzIEZHfRszqijO2gekAvOqesijQm430ZiOHp8yjTrwkhwME8FBMP2aEvxuJcoXmEbKPgDHG7DFJ8tD9qHPw9R9O5Vt3U/rUVWRN+DWZB4xPdGhRTz70Aplumy4R0zcDp7VmUCaxPATJ0CAI+AgwgmUM9yynPjmVsCopGmAdvbYbiXKRDmAbaYkO3Rhj2pW0wtHkXfAglW/eRdX/7qNu9RyyJvyKJG/iSrstKSXkq+qqOMWz16yUEF+RpYhNZLKEfL52SxELwvmUkoWVIowxJjoNh6ie+gLVX75ASs9+ZE+6Dm9OQYuXj9cgSpOjLaiqJ8cigFizxCDxQnioEx+ecIAwSRTTl5kRpYjl2oegXSlrjDE7qF1ZROVbd6P1tWRNuJiMAybQkuGJ4pUYVABrgOeBb2hy2Keqn8YigFizxKBtCiPUSyphBa/6KSF7u1LEQs1nC+mJDtMYYxIutHUjlW/dTd2q2WQMO5asib8myRu9VBuvxMADTADOBEYAbwPPq+r8WGy4tVhi0L4ExEu9JuPTejaTwRIGMC00mHnhQhaE81lHNlaKMMZ0NhoOUf3Vi1R/+TzJPfqQc8r1UUsLcb8lsoj4cBKEu4CbVPXBWGy8NVhi0P41lCKSNAgKK6UPM0ODKQrvw4JwPku1HwErRRhjOoG61XOonHwX4fpt9Bh/EZkjj2+2tBC3xMBNCE7ESQoKgMnAY6q6LhYbbw2WGHRMCtRLKkEVfOqnjJ7M00KnFKH5LAjnU01mosM0xpiYC23bROVb91C3chbp+x9Nz+MvJcm3fek1XvcxeBJnVMV3gBtVdV4sNthkGycAD+DcF+FRVb29yfzfAhcCQaAC+Hl7ukLCxI4AqVrX+KAf5fSTcsZ7vqVenZEot5HKUgYwLbQvc8OFLNABrNFeWCnCGNOeeTK60+v0G9n89cts+vwZ/KVLndJCr31aZXvRzjEIA9vch5GNBFBV7bpXG3bOYViCcx7DWmA6cKaqLohocyzwjarWiMivgGNU9afR1ms9BiZMEnWSChoiScOsIo9Z4UHMDA9iYXgAS7WfjURpjGmX6tbMo3LynYRqt5A1/pdkjvo+IhKfHgNVbe1Rdg4ClqlqMYCIvABMAhoTA1X9OKL918DPWjkm0wEkEd5uJMohrGaIZzWnJE9tLEVU0IMFFLojUeazMDyAjexVrmuMMa0utf9w54ZIb93Dhvf/Qd3qufQ84fKYbiORZ3D1xbkcssFa4OAo7X+BU9YwZo9EliL6UEkfKjnGW0SdW4qoxccy+jMttC9zwoUs1AGs0t6ojURpjGlDPOnd6PWTG9j8zSts+uxp/GXLkL0chClSIhOD5gq/zdY1RORnwDjg6J3Mvwi4CGBATpedrNqYHSVrgEwCIOAlyFgWMTp5sTsSZQiPhlhNHq+HDuXl4FGUkZXokI0xBpEkuh3yE3z9hlL5xp0xTQxadLlia3AHabpBVY93H/8eQFVva9LuOOBB4GhVLd/Veu0cA9Ma/OJFw8p8Cnk8MJH3w+PsPAVjTJsQqqmm7L9/av1zDOJgOjBYRAqBdcAZwFmRDURkNPAv4ISWJAXGtBav+kFgDEvYz7eGO8OP8K4ezNOB8Xyrg7FeKmNMonjSu8V0fQlLDFQ1KCKXAe/hXK74mKrOF5GbgBmqOhnnhkqZwEvuDR1Wt9UxGkznka61IDBJvmSibzrbNI1nQuN5KXg0JfRMdHjGGLNXElZKaC1WSjCJEBAv4bCyiHweC0zkvfCB1OFLdFjGmE4ilpcr2unWxsRAivrxSYCRsozbfI8zy3cxf/M+xDhZxE7OqTXGmDbJbjhvTIw1lBp+KFMZ75tJjfp4LnQcLwaPYh05iQ7PGGOissTAmFaShJJBHRlSx69TJnOxZzLL6M9jgYm8Gz6IGmJ3eZExxsSKlRKMiYMUDZAqAYZLMX/1Pcm3vkv4h/dBDpaFCOFEh2eMMY2sx8CYOEtzSw0nyNcc7ZtFnXp5PvQ9/hs6mjXaO9HhGWM6OUsMjEmQyFLDxSlvc6HnbVbQl8eCE/lf6GC2kZboEI0xnZCVEoxpAxpKDfvLSm7yPs1M3yU87L2fQ5PmW6nBGBNX1mNgTBvTUGqYKNM50juXek3mv+FjeCF4LKs0N9HhGWM6OEsMjGmjnFJDLRkCv/S8ywVJ77KKPB4LHs//QgezhfREh2iM6YCslGBMO5CMU2oYIqu5wfs0032/4lHvvRyRNJckKzUYY2LIegyMaWfStA4EviczOdQ7n4B6eCl8DM8Hj6FY+yQ6PGNMO2eJgTHtVEOpAYELPO9yjud91mhvHg9O5K3QIWwmI9EhGmPaISslGNMBJBMkFT+DZQ1/8T7LdN+vecx7D0cnzbZSgzFmt1iPgTEdTKpbajhWZnKwdwFBTeKV8FE8FzyWZdov0eEZY9o4SwyM6aAEGksN53o+4EzPR6zXbB4PTuTN0GFUk5noEI0xbZCVEozpBJIJkoafgbKeP3lfYJrvUp703cmxSbPwEEp0eMaYNsR6DIzpZBpKDUdRxDjvYkIqvOqWGpZo/0SHZ4xJMEsMjOmkIksN53g+4KeeKZRpTx4LTmRy6DA20SXRIRpjEsBKCcYYPIRIw0+BlPBH7wt847uMZ3y3c1zSTJIJJjo8Y0wcJTQxEJETRGSxiCwTkeubmX+UiHwrIkEROS0RMRrT2fi0Hp8EOELm8ID3HxT5LuaWlMfZX1YlOjRjTBwkrJQgIh7gIWACsBaYLiKTVXVBRLPVwPnANfGP0BjTUGo40zOFUz2fUqE9eCI4gddDR7CBrokOzxjTChLZY3AQsExVi1XVD7wATIpsoKorVXUO2B1ajEmkhlLDACnjOu9LfOW7jOd9tzIxaTopVmowpkNJ5MmHfYE1EY/XAgcnKBZjTAv5tB4EDmUeI7zLUVXeDB/OM8HxzNd8nNMajTHtVSITg+b2HrpHKxK5CLgIYEBOl52s2hgTaw2lhtM9n3CK5wuqtCtPhibwWvBIKumW6PCMMXsgkaWEtUDkRdP9gPV7siJVfURVx6nquJxuNka9MfHmlBrq6ScVXJPyCl/6LudF3y2ckDQNL4FEh2eM2Q2J7DGYDgwWkUJgHXAGcFYC4zHGxEBDqeEgFjDcuwI0zFvhw3g6OJ65Woj16BnTtiUsMVDVoIhcBrwHeIDHVHW+iNwEzFDVySJyIPAa0AM4SURuVNVhiYrZGLN70t1Sw6mez/ihZyqbNJOnQhN4JXgkFfRIdHjGmGaI6h6V9duscYNzdcbZtYkOwxizE37xoeEQcxnE44GJfBgeQz3eRIdlTLu2/vHLA/6y4ph8keyWyMaYuPK6pYZxLGKobxWEw7yjh/B0YDxFOhArNRiTWJYYGGMSJl2dUsMp8jnf933DZk3n6dBxvBw8ijKyEh2eMZ2SjZVgjEk4D2HSqSNXNnBlyut85ruK13z/x8lJU/HhT3R4xnQq1mNgjGlTvNd+HGEAACAASURBVOoHgdEsZYhvLXeEH+E9PZinAuP5VgdjpQZjWpclBsaYNquh1HCyfMlE33S2ahrPhMbzUvBoSuiZ6PCM6ZAsMTDGtHlJbqkhXeq4PGUyl3reYBH5PB6YyLvhA6nDl+gQjekw7BwDY0y7kqJ+fBJgpCzjNt/j/9/efcdHUbQBHP9NekIKhN6rlJBAwIh0kCLSRZSirxQVFfVFUFQUBURBUZogr4CIKCX0qnQEUYNg6EgvgRR6IKSQwmXeP/ZyJJCEJFxygTzfz2c/uZvdnZ3n7nJ5MrO7wz7n15jq9C2PqaPk8K7qQohUpMdACPHAcjUPNXRUO2jtvIdY7cwCUxsW32pOOMVt3TwhHkgFIjFIcipMWP0PiPeqgpy4JMTDrS2KtkASDsRoF+JxJll+73OFRnP2ehJTd17jRkKyrZsjrKRAJAZh9T/Ao0oAlQo5oJR8QQhRUCSjQEM0blzRnsTiYusmPVS01hQteoP/AmO2X7V1c4SVFIjEIN6riiQFQhRAdmhQ4Eks7ioOre2IxINI7UFiwfj6y1VKKRzcPKlY+IqtmyKsqID8ZihJCoQowBRgjwZlojhRFCOKBBy5gidRuhDJch52jimlUDJU81ApIImBEEIYFBqlwJVEynKVslwlGjeuak9iZKhBCEmT88rNm/G06P4KJpOJkNAIfFs9B8C2oGBU2fr8ELjSsu3eQ0dRZeszfvrPlrKJM+ZRs/kz+LXuQd02PXln1ASSkpLyPA5rGjJyPJO/n2953u75N3hl6GjL83c/ncjEGfOyVaf7I03SLe83eCRLf9mc6b6jJkyn7KPt8G/by7Jcj4rO8rFHfP0dm7fvzFZ7g/cfZtAnX2Vrn3sZNWE6cxattjxf99tfBLR/gVotnqFm82cYOnqSZd3Mecuo2dwob9DxRf7ctdey7oW3hlOjWTd8Wz3HS++MsnzeQkIjaPnsgDTHXLHuN1TZ+hw9ecZSFhIagWvVRmlez8REo46V67dSp00Py2d65fqtlv3Se6+2BQXTqc+gNGVZfU9V2fqcPHPOUjZp5jxU2foE7z+MHZovp87CU8VS0e4iPuospVQkThjtjIuNYfSwwXRsUo9urRvRv3sHDuwNBuDi+XDeful5Ojd7lI5N6jFu5DCSEo3bN+/YvpVeHVrSvU1jenVoyc6/tqdp1w/fTuTXFYszbXtW/LPjT5r4VKBHu2b0aNeMV3s/nen2qxYvYOzH72X7OJFXrzDwP8/mtJniASOJQR6ZvWgVz7Rvhb29/V3r/GpVY9HqjZbnC1dtoK5Pdcvz6T8vZeP2Hfy95icOblnMP2vnUaKYNzfjE/Kk7bmlcUBdgoIPAJCcnMyVyOv8e+yUZX1Q8H6aPFY3S3WZTCartGnIgBfYt2mhZSns5ZHlfUe/N5A2zR/P1vEC6vow5bP3s9vMLDt09CRvfTyOeVM/58jvyzn022KqVCgLwC+btjNj3jL+XPkDR7cvZ/qXH/H8m8O5cMkYL36hW3uObl/OwS2LuRmfwKwFKzM8TuDKDTRt4M/CVRvSlFetWC7N6+nk5Mj+f48z9LNJrJo9iaPbl7P6x0kM/WwSBw4fz5XXwK9WNRauuv37tfTXLfhUr2J5PnbqbPNQQzIOKpni6gbVVTjVVThj33+DwoULs+aP3azYsoPPJv6P65FX0VozZEAfnmjXkTV/7Gb19mDiYmOZ+tVnABT2LsqU2YEs2xzEZxP/x/C3X0/TpqDtW2nUvFWW2n/r1q1M19dr0IjFG/5g8YY/mBmY8Xt0P7yLFqNYiZLs/efvXKlf5C8Fbihh8Iiv2WflLyB/n+pMHp15Fj5/+ToWTBub7roKZUpxIyaWi5evUqKYN+u3BtGhdVPL+jFTfmD78lmWP1JOTo4Me6v/Pdv1/fzlzJy/nMTEJKpVLs/cKZ/h5uqa7rZL1mzi00kzsbezw8vTne3Lf8BkMvHBmCls+H0HSsGA55/hvy/1YveBw7zz6URiYuMo5l2YOZM+pXTJ4rR8dgCP1/Nla1Aw16Oi+WHCCJo9Xj/D9jV5zJ8hoyYA8O+xU/jWqMb5S1e4dv0Gbq4uHDl5hnq+NdFa8/7nk1m3NQilFB8PepmeXduxLSiYTyfOpHTJYuz79xiHty2z1K215r8fj+O3v/6hcvmy6Pu48c2cRatZuWEbJpOJQ8dO8e5r/yEx8RZzl/2Ks5Mja+dOxbuIF/0Gj6RTm2Y826kNw8ZOYfXG33FwsOfJ5o0YP2JIuq/xtqBgxk//mV9+nkLktSheevdTTp8Lw83FhZlffUwdn+qMmjCdc+EXOH0unHPhFxj8yvMMerk3sXE36fHaB4Sdv4gpOZlP3n6Fnl3bpWn7V//7ieGDXqJmtcoAODg48Ea/HgCM+98cvv54MMW8iwBQ368WfZ/rxLQ5i/ns/TfSfAYb+Ncm7PzFdF+fmNg4/grex9bFM+nSfwij3n093e1SjJ/+Mx/99yUqmxOUyhXK8uFbL/H1dz8zd+rnOXuTMvF0uydYtXEbHw9+hdNnw/DydMfR0fjqGzZ2CjfjE/Bv24vaNaoy/9sxlqGG8JBTHNy7m+NTR3LT/jJXtCflK1aiXMVK7Pzzd5ydnXm65wsA2Nvb897IMXRo7M/Adz+klm8dy/Gr1ahFYkI8iQkJODk7ExN9g1tJiTg7O9O+cV1W/x6Mo6MjMdE3eO7JpqzevpvXn+9G3UcbsC94Jy3atqfva29lK+Ztm9bx/ZQJJCUlUriIN19MmUnR4iXSbLPxl5VMnzQOe3t73D08+XHZWkwmE998MYrgHX+RmJhAz76v8Nx/jO+aVu06snbFEuo91vB+3g7xAChwiYEtJCYmcfpcOJXKl8lwm2c7tmHJL5uo51uT+n41cXZyBCA6JpaYuDjLl2h2PNO+FQNeeAaAj8dN44fAVfz3pV7pbjt68vdsmD+NsqVLWLrPZ85bzpnQcPZuWICDgwOR16JISkrivx9/xaofJ1G8aBEWrdrA8HHTmD1xFAC3bpnY9etc1m75k08nzmTzoukZtq9MqeI4ONhzLvw8QcH7afRoHcIvXGLH7gN4ebpTp9YjODk5suzXLez79zj7Ny3kSuR1HuvwIs0bGgnHrn2HOPTbkrtenxXrfuPYqbMc3LKYi5cj8XmiOy/17HrP12zS9/OZt2wtAEW8PNm6dCYAh46dZO+GQOITEqnWpCvjPhrE3o2BDBk5np+X/sLgAS9Y6oi8FsWKdVs5un05SinL65nea5zayAnTqedbg5WzJ/Lbn7vo8/YI9m1aCMDRkyFsXTKT6NhYajR7hoF9nmX91iDKlCrOr3OnABB14+46jUTmxXRj/ff4aR6tUytNWUBdH35asiZNWVJSEnOXreWb0UPTrWfl+q081bIx1atWxLuwJ3sOHqG+n1HvqbNh+Lc1PnNNHqvLtLEf8u/x0wx9vU/a49bxYdqc++9aT4+nRyHKlynJoaMnWbVhGz07P8mPi42hli8/GsS3Py6yvM6p/Xv8NP61a+DoYIcDcRRS8WgN1/Ag5Pi/+Pj5p9ne3cOTUmXLERpymuq1fC3lm9eupqZvHZycjds2//3n7zRo0oJC7h4ENGzKH1s20uqpjqxfvZzW7bvg6Gj+3b8Rxeylv94zvr27dtCjXTMA2nbsyoBBQ6n/WCPmrd6EUorlgT/z43dTGDoibdI1Y/JXfDdvGSVLl+FGVBQAKxbOxd3DiwW//kZiQgJ9uz1Fo+atKFehIj51/Pn2a+snbiL/KXCJwb3+s88NVyKvU9gz8y7pHp3b0nPgMI6eDKH3008RFLwfMP7zTX1FxYZtQXwwZgrXb0Sz4NuxNM6kq/3QsVN8/NU0rt+IISY2jnYtGmW4bZOAuvQbMpIendvyTHuji3Pznzt5/cVncXAwPibeRbw4dPQkh46dom2vgQCYkpMpXaKYpZ5nOhj7PlqnFiFhEZnGDEavQVDwAYKCD/DOq/8h/MIlgoL34+XpTuMAI7Y/d+2l99PtsLe3p2TxorRoWJ9/9h/G070QDfx9002atv+9x7JPmVLFadXksXu2BYyhhDv/aAE80fgxPNwL4eFeCC8Pdzq3bQ4Y3dQHDp9Is62nRyFcnJ14ZehoOrZuSqc2xrbpvcap/blrH8u+/xqAVk0bcPValOWPfcfWTXF2dsLZ2YkSxYpw8XIkfjWrMfSzSXww5hs6tWmWae9MVt35eQN446Mvaf54vQzrD1y5gcEDngegV9d2BK5cb0kMUoYS7j5GesfNuF0ZXVWU1auNenVpx8JVG9jw+w62LJpuSQyyKmWoAQXFuEFxrhNtF4O3iiZKF8KUMiqrdZoz9E8eO8LksaOYPn+5pSxo2xa69jBer2d6v8ic76bQ6qmOrFq8gBHjJlu2a9f5mSy1rV6DRnw7Z1Gasovnw3nvjZe4cukCSUlJlC1f8a79/B97nBHvvsmTnZ6mdfvOgHFuxPEj/7J57SoAoqNvcO7MKcpVqIh3seJcvnghS20SDzY5xyAPuLo4E5+Q+fkApUoUw9HBgU3bd9K6aQNLuaeHO4VcXTlzLhyAdi0bs2/TQnxrVCXxHicf9hsykm8/H8bBLYsZOeRV4hMyntd++rjhfP7+G4RGXMT/yd5cjbxufFnfcRmS1pra1atYxowPblnMxsD/WdY7OzkBYG9vx61b9x73bxxQh6Dg/Rw8ehLfmlVpWN+PHbuNRKGJOTHQOuNhgEJuGZ9Fbs1LVFN6cADs7BTOzsZzO2XHrTvOb3BwcGDXr3Pp3qE1K9dv46kX3gTSf41TSy/OlBicnZ0sZfb29twymahetSK7183Hr2Y1PvziW0ZPmnnX/rWrV2H3wcPpxuTzSBV2HziSpmzPwaP4PHJ7/P3TiTO4fPUaE0e9m24dVyOv81vQP7wydDSVHu/I19/9zKLVmzJ9z2rXqELwgbRt2nPoSJrj3qloES+u3dHLEnk9imLehTPcJ7XObZszd9mvVChbCk8P9yztU7t6FfYfPk5ycto7+ik0vtWrsO/AIcqoq9RS56ikLkL0JS5EhFOukjFsc/F8OEMGvMjnk7+jvLkM4NC+3fj6PwpAvccaEhF2juAdf2EymXikpo9lO1c3tyy1Mz1fjviA3v1eYdnmID75chKJCfF3bfPJF5N4873hXIgIp0e7Zly/FonWmmGjx1nOWVgXtJ/GLYwkNjEhHmeX9IcixcPFpomBUuoppdQxpdRJpdSwdNY7K6UWmdfvVEpVyvtW3r8ihT0xmZKJv8fJgqOHDmTc8EF3naD44Vv9GfjhWEv3s9Y6zR/5PoM+YdfeQ3fVFx0TR+mSxUhKSmL+inWZHvtUSCiP1/dj9HsDKeZdmNCIizzZvBHT5y61nPwUeS2KGlUrcTnyGjvMPRpJSUlpThhMT/j5S7Tu8Vq665oE+PPL5j/wLuyJvb093kW8uH4jmh27D9AowBinbd6wPotWb8RkMnH56jW279xDA//amR6zecP6LFy1AZPJxPmLl9kaFGxZ9+EXU1mx7rdM978fMbFxREXH0KF1UyZ/OtRyTkt6r/GdbZ6/3HiftgUFU8y7cKZ/xCIuXMbN1YX/dO/I0NdfZM/Bo3dt897APoydOpvjp84CxkmeKVd6vP9GXz4YO8WSoOw7dIw5i9fwRl/jiplZC1awYdsOAqeNxc4u/a+Kpb9upk/3jpzdtZaQnb8SGryOyhXKpLm64U5DX+vDF1N/JCTU6FEKCY1g7NTZGQ55ADxSuQIRFy9z5MRpAM6GRbD/8HH8axsn6d7rPXV1dWHc8LcZPujlu9Y5Ojqke4VP1UrlCajjw8jx0y2JzonT51i1YRutmzUg7mY885aswU5p3JKjmfL5MPr36ERVt5skRF3hrb49eXvYiDRj8iePHaFS1UfS/I536t6LYW+9wtPmXoT0bFn3C998+WmG6+8UHX2DEqWMocvVSwLT3SY05Ax16gXw5tCPKOxdlAsR4TRu0Yolc2ffvgLl9Eni4mIBOHv6FNVq1Eq3LvFwsdlQglLKHpgGtAXCgH+UUqu11qn/lXgZuKa1rqaU6gWMA3rmfWvv35MtGvLnrn2ZnrWe0bDAwL7PERcfz+Od+uDs7Ii7mxtNHqtLPd8aABw4ciJNd36Kz94byOOd+lCxXGn8alYjOiYuw2O/9/lkTpwJRWtN66YNqFu7Or41q3L89FnqtOmJo4MDA17oxlv9e7F0xtcMGvEVUTdiuGUyMfiV56ldo2qGdZ+/dAUHh7uvxgCjK/5K5HWef/qp22U1HyEm9qblpLhu7VuxY/cB6rbthVKKr4a/TakSxTh6MiTDY3Zr34rf/voHv9Y9qF6lIi0aPmpZd/DICbqYhwLulPocA4CVsydmeIyMRMfE0vWld4hPSEBrmDTS+G87vdf49x27LfuNeuc1+r8zijpteuDm4sJPkzP/Q3Dw6Ane+3wydsoOR0cHvvvio7u2qeNTncmjhtL7zQ+JuxmPUoqO5pMKuzzZgvDzl2jctT9KKTzc3Zg39TNKlzQmH3p92FgqlitNoy79AGOYaMSQV9PUH7hqA8Pe7JemrHuH1ixYsZ4P7ihP4e9bg3HDB9G532CSkm7h6OjAV8Pfxt/8eQZ47YMxDB45HoDyZUqyY81PzJv6Of2HjCI+IRFHRwdmjR+Bl3mILrP3NEWvO07MTPHqC89Qp01P6vvVYv63Y9KsmzX+E94dPYlqTbri5upC0SJefP3xYJRSrJg1gTc++oLPJn9PstZ0aNWEL4e9iZOK5rufvics5DQ/fDOOmd8Yw0PfzV/OX1s306RlmzTH6NjtOaZ9PYanumZ8OWDY2TO4u2f9CpmBQ4YxdGA/SpQsTZ36AUSEnr1rm4ljRnDuzCm01jzetAU1fHypXqs2EaHn6NW+BVprihQtxuRZRiL5T9AfNGv9ZJbbIB5cKrMuv1w9sFKNgFFa63bm5x8CaK2/SLXNBvM2O5RSDsAFoLjOpNEBj5TSwS/cTFN2pN1ialUskcEeeWPvoaNMnDHP6mdd34iO4eV3R7NkpnWvhbemb39cSIWypenyZAtbNwUw7pewYcH/7r3hA2rUhOlUKleGfj275Er9IaER9Bsykm1Lv8+V+nMiv76nKXM1xOLCFe3J88+/wOeTvqN4yVKWbTb9uoqtG9cy9psZGdbz4aBXeW/kWLyL3v0PQF7p370D3/ywAM/Cdw/fXDx3mgGrz9ugVSJFxI//TUq8eNrp3lvemy1PPiwLhKZ6Hgbc+e+0ZRut9S2lVBRQFHjgbsxdz7cmTzQJwGQypXsvg5zy9HDP10kBwFv9078Swlby4x8QcX/y63uaMleDOzcppOLZsXAiUVpxTccRjxOffTKMP7ZuYcbPC3Ek4/sVjJ+SEl/m9zTILZFXr9BvwOsULeyebhscMFHmwftafqhcJNlqJ1XZMjFIL4g7ewKysg1KqVeBVwEqlPIG1yJ3bGAHynp/jHPqpd7dbd0ExnzzPUvWbEpT9lzntgx/e0AGe4gHTcvGDYyrYHLpM1/Yy4t+Pbrmi9+pB4UyL2gTXioOL2UM680f8ybwpnmrUJJR93HHjVxUHBp2qIPx/9vdktU1Nrvk/RVfwmCHpoHdDaudM/jwDSUEBOjg4OA0ZUeOHKFWLTlpRgiRj5huwa2bkHQTEmOMn6YkLNdt6uTM989Hjpy9RK0NPWzdjALNf3pM0r4Lpgd+KOEf4BGlVGUgHOgF3Hla7mqgL7ADeBb4LbOkQAghHhj2DmDvAc4egPkcKJ0MSfFGwpAYB4mxcCv+gUwWxIPLZomB+ZyBt4ANgD0wW2v9r1JqNBCstV4N/ADMVUqdBCIxkgchhHg4KTtwcjMWt6JGmdZgSjR6FFJ6F27FQ7LJ2F4nk84IqxA5ZtM7H2qt1wJr7ygbkepxPPBcXrdLCCHyDaXAwdlYXFNdEZB8y+hdSDL3LCTFPbBDESJ/kTsf5hF7e3v8/f3x9fWlc+fOXL9+/a51/v7+dOmS9hKzy5cv4+joyIwZaS9lmj17Nn5+ftSpUwdfX19WrTJuYdqvXz+WLl16z/2FEA84Owdwdgf3EuBdGUrWhtJ1oFh18CoPhYqBo6vRq4AynyxqvbuBiodXgZsrASDg801cicn49sDZVczdieCP22a6jaurK/v27QOgb9++TJs2jeHDh9+17k5LliyhYcOGBAYG8tprxt0Dw8LCGDNmDHv27MHLy4uYmBguX76c5f2FEA8pZWckA46ugLdRprXRk3DrptGrkBBrPJahCJGBAtljYM2kICf1NWrUiPDw8CxtGxgYyIQJEwgLC7Psc+nSJTw8PHB3N26X6+7uTuXKlbO8vxCiAFEKHJzAxQs8SkOxalDKz1i8q4BXWeMSb3tnjJ4FO3Mvgyio5N3PYyaTiS1btqQZMoiPjycgIICGDRuycuVKS3loaCgXLlygQYMG9OjRg0WLjBnU6tatS8mSJalcuTL9+/dnzZo1dx0ns/2FEAI7e2MoolBxKFIJSvpA6bqphiKKg6ObDEUUQJIY5JGbN2/i7+9P0aJFiYyMpG3b20MP586dIzg4mAULFjB48GBOnTImJVq4cCE9ehjXBvfq1YvAQGMyFHt7e9avX8/SpUupXr06Q4YMYdSoUXcdM6P9hRAiXUoZwxBu3uBVDorXgFJ1jKShSEXwKAXOnmDniKV3Qf6MPHTkHc0jKecRnD17lsTERKZNm2ZZV6aMMQtalSpVaNmyJXv3GjPTBQYGMmfOHCpVqkSXLl3Yv38/J06cAIzpeBs0aMCHH37IwoULWbZs2V3HzGx/IYTIEqXAPmUoohQUrQqlfI2hiKLVwKsMOBWCYo8YCYNTIXByR3oXHlySGOQxLy8vpkyZwvjx40lKSuLatWskJBjTMV+5coW//voLHx8fjh07RmxsLOHh4YSEhBASEmJJAiIiItizZ4+lzn379lGxYsU0x8lsfyGEuG929kYSUKi4cc+Ft4Lh44swYBt0/gYavwXlGhg9DPbOqXoaRH5XIK9KsLV69epRt25dFi5cSNWqVXnttdews7MjOTmZYcOG4ePjw6hRo+jWrVua/bp3706vXr3o06cPQ4cOJSIiAhcXF4oXL8706dPTbBsYGJjh/p988kmuxyiEKIDs7KF4dWPxSzWNdPRFuHgQzh+E0L/h/AGIvWQMWySbjKslRL5hs7kScktW5kqwxeWKQgjxsMrRfDSJcXDpCFw4AGHBEB4MkaeN+zMoe+MOj3IZZZY9LHMl2Iz8ERdCCBtzcoNyjxpLQH+jLDkZrp0xkoWIfRC600gekuLAwcW4FbTJupebi7sVyMRACCFEPmRnZ5zcWLQq1E41FBp7BS4cNJZzO4zEIfoCOLiClqEIa5PEQAghRP5WqBhUfcJYmgwyypLi4fIRI1kIC4awfyDylDEMYWdvzB8h80XkiCQGQgghHjyOLlCmnrHU72OUJSfD9bNGsnB+H5z72xiKSIw2ehdkKCJLJDEQQgjxcLCzMyaU8q4MPqkmpIuLhIuHzEMRfxtJw40II1kg2ehdEBaSGAghhHi4uXlD5ebG0uhNo+xWAlw+ZiQL4bshbBdcOWHc0MnOwbhqQpts224bkRsc5ZGcTLvcsmVL7rz0MsXbb79N2bJlSU6+PYY2Z84clFJs2bLFUrZixQqUUmmmYhZCiALPwdmYprreC9BpIrz+Jwy/AG/ugm4zoNlQI5FwK2rcmMnZ09inACiYPQZfP2LcXMNaCpWA9zK/1XBOp11OT3JyMitWrKB8+fJs376dli1bWtb5+fkRGBhI69atAWO+hLp162YzICGEKICUgsIVjKVmx9vlN6/DxX+N3oXQnRCxF6JCjUso0Q/dUETBTAysmRTkoL5GjRpx4MCBHB9u69at+Pr60rNnTwIDA9MkBs2aNeOPP/4gKSmJhIQETp48ib+/f46PJYQQBZ5rYajUxFgavm6UmZLgyvHbQxGhu4znOtmYWyIxDvQt27Y7hwpmYmBDKdMuv/zyy5aylGmXHRwcGDZsGE8//XSmdQQGBtK7d2+6du3KRx99RFJSEo6Oxj3IlVK0adOGDRs2EBUVRZcuXThz5kyuxiSEEAWOvSOUrG0sdXsZZVrDjXC4cAjO7zdu/3zhENyMNKawNiUaV0bkczZJDJRS3sAioBIQAvTQWl9LZ7v1QEPgT611p7xso7WlTLscEhLCo48+ete0y2XKlOH06dO0atUKPz8/qlatmm49iYmJrF27lkmTJuHh4cHjjz/Oxo0b6djxdrdXr169mDJlClFRUUyYMIGxY8fmenxCCFHgKWVMV+1VDmo8dbs8/oYxFHHxkDEUEb7HuKzSwcVYnxhLfrr9s616DIYBW7TWXyqlhpmff5DOdl8DbsBredm43JByHkFUVBSdOnVi2rRpDBpk3KgjvWmXM0oM1q9fT1RUFH5+fgDExcXh5uaWJjFo0KABhw4dwtXVlerVq+dyZEIIITLl4gkVGxlLgwFGmekWXD1h9ChE7DEShstHjUml7J0g6SYkJ9mkubZKDLoCLc2PfwK2kU5ioLXeopRqeWf5gyxl2uWuXbsycOBAYmJicHNzw9nZ2TLt8vvvv5/h/oGBgcyaNYvevXsDEBsbS+XKlYmLS3tL0C+++AIXF5dcjUUIIUQO2TtAiVrGUuc5o0xr41bPFw4at30+97fxOO6KMROl6RbcupnrTbNVYlBSa30eQGt9XilVwkbtsImsTLucomPHjpbzBxo1asSWLVuYMWOGZX2hQoVo2rQpa9asSXOM9u3b500wQgghrEMp8CxtLNWfvF2eEAOXDqeaiXI3RJ4BBydAWf2qiFybdlkptRkolc6q4cBPWuvCqba9prUukkE9LYGhmZ1joJR6FXgVoEKFCo+ePXs2zfq7pgS1weWKQgjxsMrRtMvi/iSb4OopI1k4v48Kz409fy4quYw1qs61HgOtdZuM1imlLiqlSpt7C0oD9/VXWms94anR2wAACEhJREFUE5gJEBAQcO9MR/6ICyGEeJDZ2UPx6sbi9yyhN8ZEWK1qa1WUTauBvubHfYFVNmqHEEIIIVKxVWLwJdBWKXUCaGt+jlIqQCk1K2UjpdQfwBKgtVIqTCnVziatFUIIIQoIm5x8qLW+CrROpzwYeCXV82ZWPCZKKWtVJ4QQAuO7VTxcCsQkSi4uLly9elU+wEIIYUVaa65evSqXRj9kCsQtkcuVK0dYWBiXL1+2dVOEEOKh4uLiQrly5WzdDGFFBSIxcHR0pHLlyrZuhhBCCJHvFYihBCGEEEJkjSQGQgghhLCQxEAIIYQQFrl2S2RbUUpdBs7ec0PrKwZcscFxbaUgxVuQYoWCFW9BihUKVrwFKVaAGlprD2tU9NCdfKi1Lm6L4yqlgrXWAbY4ti0UpHgLUqxQsOItSLFCwYq3IMUKRrzWqkuGEoQQQghhIYmBEEIIISwkMbCembZuQB4rSPEWpFihYMVbkGKFghVvQYoVrBjvQ3fyoRBCCCFyTnoMhBBCCGEhiUEGlFLllVJblVJHlFL/KqXeNpd7K6U2KaVOmH8WMZcrpdQUpdRJpdQBpVT9VHVVUEptNNd1WClVyTZRZczK8X5lruOIeZt8Na1lDmKtqZTaoZRKUEoNvaOup5RSx8yvwzBbxHMv1oo3o3ryE2u+t+b19kqpvUqpX/I6lqyw8me5sFJqqVLqqLm+RraIKSNWjnWIuY5DSqlApVS+mwUqB/G+YP4uPqCUClJK1U1VV/a+p7TWsqSzAKWB+ubHHsBxwAf4ChhmLh8GjDM/7gCsAxTQENiZqq5tQFvzY3fAzdbx5Va8QGPgL8DevOwAWto6vvuMtQTwGDAGGJqqHnvgFFAFcAL2Az62ji8X4023HlvHlxuxpqrvHWAB8IutY8vteIGfgFfMj52AwraOL5c+x2WBM4Cr+flioJ+t47NCvI2BIubH7bn9nZzt7ynpMciA1vq81nqP+XE0cATjA9UV4xcI88+nzY+7Aj9rw99AYaVUaaWUD+Cgtd5kritGax2Xl7FkhbXiBTTggvEBdAYcgYt5FkgWZDdWrfUlrfU/QNIdVTUATmqtT2utE4GF5jryFWvFm0k9+YYV31uUUuWAjsCsPGh6jlgrXqWUJ9Ac+MG8XaLW+nqeBJFF1nxvMe7h46qUcgDcgIhcbn625SDeIK31NXP530DKlJfZ/p6SxCALlNH1Xw/YCZTUWp8H443DyErBeMNCU+0WZi6rDlxXSi03d0l+rZSyz6u258T9xKu13gFsBc6blw1a6yN50/Lsy2KsGcnoPc+37jPejOrJl6wQ62TgfSA5l5poVfcZbxXgMvCj+XtqllKqUC42977cT6xa63BgPHAO4zsqSmu9MTfbe79yEO/LGD26kIPvKUkM7kEp5Q4sAwZrrW9ktmk6ZRojM20GDMXo1qoC9LNyM63mfuNVSlUDamFkq2WBVkqp5tZv6f3LRqwZVpFOWb69zMcK8Vq1ntx0v21USnUCLmmtd1u9cbnACu+JA1Af+E5rXQ+Ixeimznes8N4WwfiPuTJQBiiklPqPdVtpPdmNVyn1BEZi8EFKUTqbZfo9JYlBJpRSjhhvyHyt9XJz8UVzlznmn5fM5WFA+VS7l8PongoD9pq7cW4BKzF+AfMdK8XbDfjbPGQSg5G1NsyL9mdHNmPNSEavQb5jpXgzqidfsVKsTYAuSqkQjK7XVkqpebnU5Ptixc9ymNY6pQdoKfnwe8pKsbYBzmitL2utk4DlGOPz+U5241VK1cEY+uqqtb5qLs7295QkBhlQSimM8bYjWuuJqVatBvqaH/cFVqUq76MMDTG6p84D/wBFlFIpczi0Ag7negDZZMV4zwEtlFIO5g91C4yxsXwjB7Fm5B/gEaVUZaWUE9DLXEe+Yq14M6kn37BWrFrrD7XW5bTWlTDe19+01vnuv0orxnsBCFVK1TAXtSaffU9Z8ff2HNBQKeVmrrM1+ew7CrIfr1KqAka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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = plt.figure(figsize=(8, 4.5)).add_subplot(111)\n", "df.filter(\n", @@ -759,20 +287,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ True, True, True, True]])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "aviation_emms = df.filter(variable=\"*Aviation*\").timeseries()\n", "aggregate_emms = df.aggregate_region(\"Emissions|CO2|Fossil\")\n", @@ -797,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.4" } }, "nbformat": 4, From c879cf8ca9851d5458035bef41d30ec6c32ceb6f Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 11:30:33 +0100 Subject: [PATCH 19/34] remove output of "checking consistency" tutorial --- doc/source/tutorials/checking_databases.ipynb | 5720 +---------------- 1 file changed, 28 insertions(+), 5692 deletions(-) diff --git a/doc/source/tutorials/checking_databases.ipynb b/doc/source/tutorials/checking_databases.ipynb index e4461abfa..35710d9b1 100644 --- a/doc/source/tutorials/checking_databases.ipynb +++ b/doc/source/tutorials/checking_databases.ipynb @@ -11,22 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import time\n", "from pprint import pprint\n", @@ -47,132 +34,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Reading `tutorial_AR5_data.csv`\n" - ] - } - ], + "outputs": [], "source": [ "df = pyam.IamDataFrame(data='tutorial_AR5_data.csv', encoding='utf-8')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    0AIM-Enduse 12.1EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr200510540.74
    1AIM-Enduse 12.1EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr201013160.18
    2AIM-Enduse 12.1EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr202011899.38
    3AIM-Enduse 12.1EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr20309545.81
    4AIM-Enduse 12.1EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr20407355.07
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    " - ], - "text/plain": [ - " model scenario region variable unit year \\\n", - "0 AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2005 \n", - "1 AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2010 \n", - "2 AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2020 \n", - "3 AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2030 \n", - "4 AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2040 \n", - "\n", - " value \n", - "0 10540.74 \n", - "1 13160.18 \n", - "2 11899.38 \n", - "3 9545.81 \n", - "4 7355.07 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] @@ -199,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -219,1783 +92,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:`Emissions|CO2` - 1368 of 1522 rows are not aggregates of components\n" - ] - }, - { - "data": { - "text/html": [ - "
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    modelscenarioregionvariableunit
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    LAMEmissions|CO2Mt CO2/yr3285.003294.543367.622856.652207.361537.72NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.544238.913956.193490.812082.24NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.3311646.378272.304457.911625.18NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.423325.202991.241889.38960.75NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538321.7835588.6628531.6820287.4613367.27NaNNaNNaNNaNNaN
    EMF27-450-NoCCSASIAEmissions|CO2Mt CO2/yr10540.7413160.1111893.809478.337367.075513.79NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.683362.612837.111889.89899.63NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.494239.033619.252787.471671.29NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.1611659.298708.815488.863355.22NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.393322.953076.671977.781181.73NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538313.5935588.8528629.6520458.1013660.19NaNNaNNaNNaNNaN
    EMF27-550-LimBioASIAEmissions|CO2Mt CO2/yr10540.7413160.1114124.1714218.0813187.6610019.56NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.683445.633496.622986.081790.49NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.494368.484519.644294.832733.76NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.1612607.1711752.019749.336501.31NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.393826.803615.473258.313076.27NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538313.5939531.6138815.5434676.3825295.31NaNNaNNaNNaNNaN
    EMF27-Base-FullTechASIAEmissions|CO2Mt CO2/yr10540.7413160.1114149.8916559.1419658.6823071.34NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.683449.843660.683850.443866.20NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.494371.984751.635389.486082.37NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.1612642.7013332.2913742.9314150.35NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.393838.824220.974866.315615.39NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538313.5939612.6043835.4949027.8054552.86NaNNaNNaNNaNNaN
    EMF27-G8-EEREASIAEmissions|CO2Mt CO2/yr10540.7413152.5613415.9410147.897637.614435.80NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.523106.392825.271784.31899.06NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.024091.193977.503659.803336.85NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512750.8110276.068833.955845.243473.56NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953596.743453.293468.733376.253058.68NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538304.4135425.9630395.4323536.7116487.83NaNNaNNaNNaNNaN
    ................................................
    REMIND 1.5EMF27-450-NoCCSOECD90Emissions|CO2Mt CO2/yr15111.3915254.168082.872864.75369.53328.11299.06266.24255.25245.07226.35
    WorldEmissions|CO2Mt CO2/yr33837.4138224.9425524.607358.641691.051663.771616.521555.471553.001665.201883.11
    EMF27-550-LimBioASIAEmissions|CO2Mt CO2/yr10193.9813239.5514218.3711920.798135.325963.844486.533100.112246.061843.161570.02
    LAMEmissions|CO2Mt CO2/yr2926.603478.794413.411831.961357.42934.84712.03523.57418.39359.64107.38
    MAFEmissions|CO2Mt CO2/yr4035.324381.034504.493368.893582.703883.523663.913349.073064.562919.433153.50
    OECD90Emissions|CO2Mt CO2/yr15111.3915241.5613016.5210555.137238.064454.982745.801531.01766.12275.60-82.62
    WorldEmissions|CO2Mt CO2/yr33837.4137970.1137657.4128699.5020936.8315389.2011536.738368.716360.335299.364644.20
    EMF27-Base-FullTechASIAEmissions|CO2Mt CO2/yr10193.9813478.7820256.0124006.74NaNNaNNaNNaN34529.7928622.8323400.42
    LAMEmissions|CO2Mt CO2/yr2926.603508.405067.355464.434402.985424.515869.575988.956096.945152.734074.45
    MAFEmissions|CO2Mt CO2/yr4035.324381.095364.845862.75NaNNaNNaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr15111.3915234.63NaNNaN16610.2416943.5616515.9015922.4314587.2211864.629683.61
    WorldEmissions|CO2Mt CO2/yr33837.4138293.0848134.4253343.8259836.1070077.8977941.2182914.1584109.2375995.0968004.38
    WITCH_EMF27EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr9895.4513210.1813914.1212004.4910538.518767.497410.946299.163794.592865.462437.77
    LAMEmissions|CO2Mt CO2/yr4660.574644.171851.461537.681421.58658.62-161.02-1398.20-1659.55-1631.41-1586.79
    MAFEmissions|CO2Mt CO2/yr2508.312673.952224.441932.651907.501703.001588.401493.361413.901303.021118.21
    OECD90Emissions|CO2Mt CO2/yr12644.4012597.559780.38NaN4755.203257.792240.721399.83795.67359.17224.28
    REFEmissions|CO2Mt CO2/yr3870.584035.172381.752055.811733.111347.511064.51815.36611.13444.37383.41
    WorldEmissions|CO2Mt CO2/yr33579.3237161.0330152.1524091.3220355.9115734.4012143.568609.504955.743340.622576.88
    EMF27-550-LimBioASIAEmissions|CO2Mt CO2/yr9895.9813341.7617280.0618745.4116414.5212419.7210012.019373.388937.929270.479214.63
    LAMEmissions|CO2Mt CO2/yr4660.844612.382729.372515.922286.121309.501048.52677.15470.62-66.27-210.35
    MAFEmissions|CO2Mt CO2/yr2508.812621.972773.632885.032775.242552.542579.222758.682928.883067.603139.20
    OECD90Emissions|CO2Mt CO2/yr12645.7812542.8711852.60NaNNaN5310.744461.184405.414236.364016.453860.45
    REFEmissions|CO2Mt CO2/yr3871.044062.723764.233380.512617.251957.661686.231658.311628.481550.831510.95
    WorldEmissions|CO2Mt CO2/yr33582.4537181.7038399.8837802.0132002.0223550.1719787.1618872.9318202.2517839.0717514.87
    EMF27-Base-FullTechASIAEmissions|CO2Mt CO2/yr9893.4613378.3420016.5526248.4730889.3834562.4637566.0540325.6442647.5244874.7246657.52
    LAMEmissions|CO2Mt CO2/yr4659.584623.984524.394644.994937.365250.675698.256117.406522.666945.517358.61
    MAFEmissions|CO2Mt CO2/yr2506.452642.283291.194063.345028.416038.177017.408032.948851.509680.4910373.40
    OECD90Emissions|CO2Mt CO2/yr12639.2812598.8413097.9513835.6214969.1215784.5916540.1817249.2117924.8618566.2319180.64
    REFEmissions|CO2Mt CO2/yr3868.874077.284636.235039.145412.355886.806279.446439.806722.197040.237284.21
    WorldEmissions|CO2Mt CO2/yr33567.6437320.7245566.3053831.5661236.6267522.7073101.3278164.9882668.7487107.1790854.38
    \n", - "

    140 rows × 11 columns

    \n", - "
    " - ], - "text/plain": [ - " 2005 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 15111.39 \n", - " World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10193.98 \n", - " LAM Emissions|CO2 Mt CO2/yr 2926.60 \n", - " MAF Emissions|CO2 Mt CO2/yr 4035.32 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15111.39 \n", - " World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 10193.98 \n", - " LAM Emissions|CO2 Mt CO2/yr 2926.60 \n", - " MAF Emissions|CO2 Mt CO2/yr 4035.32 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15111.39 \n", - " World Emissions|CO2 Mt CO2/yr 33837.41 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 9895.45 \n", - " LAM Emissions|CO2 Mt CO2/yr 4660.57 \n", - " MAF Emissions|CO2 Mt CO2/yr 2508.31 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12644.40 \n", - " REF Emissions|CO2 Mt CO2/yr 3870.58 \n", - " World Emissions|CO2 Mt CO2/yr 33579.32 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9895.98 \n", - " LAM Emissions|CO2 Mt CO2/yr 4660.84 \n", - " MAF Emissions|CO2 Mt CO2/yr 2508.81 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12645.78 \n", - " REF Emissions|CO2 Mt CO2/yr 3871.04 \n", - " World Emissions|CO2 Mt CO2/yr 33582.45 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 9893.46 \n", - " LAM Emissions|CO2 Mt CO2/yr 4659.58 \n", - " MAF Emissions|CO2 Mt CO2/yr 2506.45 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12639.28 \n", - " REF Emissions|CO2 Mt CO2/yr 3868.87 \n", - " World Emissions|CO2 Mt CO2/yr 33567.64 \n", - "\n", - " 2010 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 13160.18 \n", - " LAM Emissions|CO2 Mt CO2/yr 3294.54 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.54 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.33 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.42 \n", - " World Emissions|CO2 Mt CO2/yr 38321.78 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 13160.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.16 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.39 \n", - " World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13160.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.16 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.39 \n", - " World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 13160.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.16 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.39 \n", - " World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 13152.56 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.52 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.02 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12750.81 \n", - " REF Emissions|CO2 Mt CO2/yr 3596.74 \n", - " World Emissions|CO2 Mt CO2/yr 38304.41 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 15254.16 \n", - " World Emissions|CO2 Mt CO2/yr 38224.94 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13239.55 \n", - " LAM Emissions|CO2 Mt CO2/yr 3478.79 \n", - " MAF Emissions|CO2 Mt CO2/yr 4381.03 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15241.56 \n", - " World Emissions|CO2 Mt CO2/yr 37970.11 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 13478.78 \n", - " LAM Emissions|CO2 Mt CO2/yr 3508.40 \n", - " MAF Emissions|CO2 Mt CO2/yr 4381.09 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15234.63 \n", - " World Emissions|CO2 Mt CO2/yr 38293.08 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 13210.18 \n", - " LAM Emissions|CO2 Mt CO2/yr 4644.17 \n", - " MAF Emissions|CO2 Mt CO2/yr 2673.95 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12597.55 \n", - " REF Emissions|CO2 Mt CO2/yr 4035.17 \n", - " World Emissions|CO2 Mt CO2/yr 37161.03 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13341.76 \n", - " LAM Emissions|CO2 Mt CO2/yr 4612.38 \n", - " MAF Emissions|CO2 Mt CO2/yr 2621.97 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12542.87 \n", - " REF Emissions|CO2 Mt CO2/yr 4062.72 \n", - " World Emissions|CO2 Mt CO2/yr 37181.70 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 13378.34 \n", - " LAM Emissions|CO2 Mt CO2/yr 4623.98 \n", - " MAF Emissions|CO2 Mt CO2/yr 2642.28 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12598.84 \n", - " REF Emissions|CO2 Mt CO2/yr 4077.28 \n", - " World Emissions|CO2 Mt CO2/yr 37320.72 \n", - "\n", - " 2020 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 11899.38 \n", - " LAM Emissions|CO2 Mt CO2/yr 3367.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 4238.91 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11646.37 \n", - " REF Emissions|CO2 Mt CO2/yr 3325.20 \n", - " World Emissions|CO2 Mt CO2/yr 35588.66 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 11893.80 \n", - " LAM Emissions|CO2 Mt CO2/yr 3362.61 \n", - " MAF Emissions|CO2 Mt CO2/yr 4239.03 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11659.29 \n", - " REF Emissions|CO2 Mt CO2/yr 3322.95 \n", - " World Emissions|CO2 Mt CO2/yr 35588.85 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 14124.17 \n", - " LAM Emissions|CO2 Mt CO2/yr 3445.63 \n", - " MAF Emissions|CO2 Mt CO2/yr 4368.48 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12607.17 \n", - " REF Emissions|CO2 Mt CO2/yr 3826.80 \n", - " World Emissions|CO2 Mt CO2/yr 39531.61 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 14149.89 \n", - " LAM Emissions|CO2 Mt CO2/yr 3449.84 \n", - " MAF Emissions|CO2 Mt CO2/yr 4371.98 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12642.70 \n", - " REF Emissions|CO2 Mt CO2/yr 3838.82 \n", - " World Emissions|CO2 Mt CO2/yr 39612.60 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 13415.94 \n", - " LAM Emissions|CO2 Mt CO2/yr 3106.39 \n", - " MAF Emissions|CO2 Mt CO2/yr 4091.19 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 10276.06 \n", - " REF Emissions|CO2 Mt CO2/yr 3453.29 \n", - " World Emissions|CO2 Mt CO2/yr 35425.96 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 8082.87 \n", - " World Emissions|CO2 Mt CO2/yr 25524.60 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 14218.37 \n", - " LAM Emissions|CO2 Mt CO2/yr 4413.41 \n", - " MAF Emissions|CO2 Mt CO2/yr 4504.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13016.52 \n", - " World Emissions|CO2 Mt CO2/yr 37657.41 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 20256.01 \n", - " LAM Emissions|CO2 Mt CO2/yr 5067.35 \n", - " MAF Emissions|CO2 Mt CO2/yr 5364.84 \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr 48134.42 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 13914.12 \n", - " LAM Emissions|CO2 Mt CO2/yr 1851.46 \n", - " MAF Emissions|CO2 Mt CO2/yr 2224.44 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 9780.38 \n", - " REF Emissions|CO2 Mt CO2/yr 2381.75 \n", - " World Emissions|CO2 Mt CO2/yr 30152.15 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 17280.06 \n", - " LAM Emissions|CO2 Mt CO2/yr 2729.37 \n", - " MAF Emissions|CO2 Mt CO2/yr 2773.63 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11852.60 \n", - " REF Emissions|CO2 Mt CO2/yr 3764.23 \n", - " World Emissions|CO2 Mt CO2/yr 38399.88 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 20016.55 \n", - " LAM Emissions|CO2 Mt CO2/yr 4524.39 \n", - " MAF Emissions|CO2 Mt CO2/yr 3291.19 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13097.95 \n", - " REF Emissions|CO2 Mt CO2/yr 4636.23 \n", - " World Emissions|CO2 Mt CO2/yr 45566.30 \n", - "\n", - " 2030 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 9545.81 \n", - " LAM Emissions|CO2 Mt CO2/yr 2856.65 \n", - " MAF Emissions|CO2 Mt CO2/yr 3956.19 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 8272.30 \n", - " REF Emissions|CO2 Mt CO2/yr 2991.24 \n", - " World Emissions|CO2 Mt CO2/yr 28531.68 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 9478.33 \n", - " LAM Emissions|CO2 Mt CO2/yr 2837.11 \n", - " MAF Emissions|CO2 Mt CO2/yr 3619.25 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 8708.81 \n", - " REF Emissions|CO2 Mt CO2/yr 3076.67 \n", - " World Emissions|CO2 Mt CO2/yr 28629.65 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 14218.08 \n", - " LAM Emissions|CO2 Mt CO2/yr 3496.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 4519.64 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11752.01 \n", - " REF Emissions|CO2 Mt CO2/yr 3615.47 \n", - " World Emissions|CO2 Mt CO2/yr 38815.54 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 16559.14 \n", - " LAM Emissions|CO2 Mt CO2/yr 3660.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4751.63 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13332.29 \n", - " REF Emissions|CO2 Mt CO2/yr 4220.97 \n", - " World Emissions|CO2 Mt CO2/yr 43835.49 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 10147.89 \n", - " LAM Emissions|CO2 Mt CO2/yr 2825.27 \n", - " MAF Emissions|CO2 Mt CO2/yr 3977.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 8833.95 \n", - " REF Emissions|CO2 Mt CO2/yr 3468.73 \n", - " World Emissions|CO2 Mt CO2/yr 30395.43 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 2864.75 \n", - " World Emissions|CO2 Mt CO2/yr 7358.64 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 11920.79 \n", - " LAM Emissions|CO2 Mt CO2/yr 1831.96 \n", - " MAF Emissions|CO2 Mt CO2/yr 3368.89 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 10555.13 \n", - " World Emissions|CO2 Mt CO2/yr 28699.50 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 24006.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 5464.43 \n", - " MAF Emissions|CO2 Mt CO2/yr 5862.75 \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr 53343.82 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 12004.49 \n", - " LAM Emissions|CO2 Mt CO2/yr 1537.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 1932.65 \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr 2055.81 \n", - " World Emissions|CO2 Mt CO2/yr 24091.32 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 18745.41 \n", - " LAM Emissions|CO2 Mt CO2/yr 2515.92 \n", - " MAF Emissions|CO2 Mt CO2/yr 2885.03 \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr 3380.51 \n", - " World Emissions|CO2 Mt CO2/yr 37802.01 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 26248.47 \n", - " LAM Emissions|CO2 Mt CO2/yr 4644.99 \n", - " MAF Emissions|CO2 Mt CO2/yr 4063.34 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13835.62 \n", - " REF Emissions|CO2 Mt CO2/yr 5039.14 \n", - " World Emissions|CO2 Mt CO2/yr 53831.56 \n", - "\n", - " 2040 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 7355.07 \n", - " LAM Emissions|CO2 Mt CO2/yr 2207.36 \n", - " MAF Emissions|CO2 Mt CO2/yr 3490.81 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4457.91 \n", - " REF Emissions|CO2 Mt CO2/yr 1889.38 \n", - " World Emissions|CO2 Mt CO2/yr 20287.46 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 7367.07 \n", - " LAM Emissions|CO2 Mt CO2/yr 1889.89 \n", - " MAF Emissions|CO2 Mt CO2/yr 2787.47 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 5488.86 \n", - " REF Emissions|CO2 Mt CO2/yr 1977.78 \n", - " World Emissions|CO2 Mt CO2/yr 20458.10 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13187.66 \n", - " LAM Emissions|CO2 Mt CO2/yr 2986.08 \n", - " MAF Emissions|CO2 Mt CO2/yr 4294.83 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 9749.33 \n", - " REF Emissions|CO2 Mt CO2/yr 3258.31 \n", - " World Emissions|CO2 Mt CO2/yr 34676.38 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 19658.68 \n", - " LAM Emissions|CO2 Mt CO2/yr 3850.44 \n", - " MAF Emissions|CO2 Mt CO2/yr 5389.48 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13742.93 \n", - " REF Emissions|CO2 Mt CO2/yr 4866.31 \n", - " World Emissions|CO2 Mt CO2/yr 49027.80 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 7637.61 \n", - " LAM Emissions|CO2 Mt CO2/yr 1784.31 \n", - " MAF Emissions|CO2 Mt CO2/yr 3659.80 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 5845.24 \n", - " REF Emissions|CO2 Mt CO2/yr 3376.25 \n", - " World Emissions|CO2 Mt CO2/yr 23536.71 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 369.53 \n", - " World Emissions|CO2 Mt CO2/yr 1691.05 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 8135.32 \n", - " LAM Emissions|CO2 Mt CO2/yr 1357.42 \n", - " MAF Emissions|CO2 Mt CO2/yr 3582.70 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 7238.06 \n", - " World Emissions|CO2 Mt CO2/yr 20936.83 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr 4402.98 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16610.24 \n", - " World Emissions|CO2 Mt CO2/yr 59836.10 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 10538.51 \n", - " LAM Emissions|CO2 Mt CO2/yr 1421.58 \n", - " MAF Emissions|CO2 Mt CO2/yr 1907.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4755.20 \n", - " REF Emissions|CO2 Mt CO2/yr 1733.11 \n", - " World Emissions|CO2 Mt CO2/yr 20355.91 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 16414.52 \n", - " LAM Emissions|CO2 Mt CO2/yr 2286.12 \n", - " MAF Emissions|CO2 Mt CO2/yr 2775.24 \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr 2617.25 \n", - " World Emissions|CO2 Mt CO2/yr 32002.02 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 30889.38 \n", - " LAM Emissions|CO2 Mt CO2/yr 4937.36 \n", - " MAF Emissions|CO2 Mt CO2/yr 5028.41 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 14969.12 \n", - " REF Emissions|CO2 Mt CO2/yr 5412.35 \n", - " World Emissions|CO2 Mt CO2/yr 61236.62 \n", - "\n", - " 2050 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 6119.50 \n", - " LAM Emissions|CO2 Mt CO2/yr 1537.72 \n", - " MAF Emissions|CO2 Mt CO2/yr 2082.24 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 1625.18 \n", - " REF Emissions|CO2 Mt CO2/yr 960.75 \n", - " World Emissions|CO2 Mt CO2/yr 13367.27 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 5513.79 \n", - " LAM Emissions|CO2 Mt CO2/yr 899.63 \n", - " MAF Emissions|CO2 Mt CO2/yr 1671.29 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3355.22 \n", - " REF Emissions|CO2 Mt CO2/yr 1181.73 \n", - " World Emissions|CO2 Mt CO2/yr 13660.19 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10019.56 \n", - " LAM Emissions|CO2 Mt CO2/yr 1790.49 \n", - " MAF Emissions|CO2 Mt CO2/yr 2733.76 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 6501.31 \n", - " REF Emissions|CO2 Mt CO2/yr 3076.27 \n", - " World Emissions|CO2 Mt CO2/yr 25295.31 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 23071.34 \n", - " LAM Emissions|CO2 Mt CO2/yr 3866.20 \n", - " MAF Emissions|CO2 Mt CO2/yr 6082.37 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 14150.35 \n", - " REF Emissions|CO2 Mt CO2/yr 5615.39 \n", - " World Emissions|CO2 Mt CO2/yr 54552.86 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 4435.80 \n", - " LAM Emissions|CO2 Mt CO2/yr 899.06 \n", - " MAF Emissions|CO2 Mt CO2/yr 3336.85 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3473.56 \n", - " REF Emissions|CO2 Mt CO2/yr 3058.68 \n", - " World Emissions|CO2 Mt CO2/yr 16487.83 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 328.11 \n", - " World Emissions|CO2 Mt CO2/yr 1663.77 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 5963.84 \n", - " LAM Emissions|CO2 Mt CO2/yr 934.84 \n", - " MAF Emissions|CO2 Mt CO2/yr 3883.52 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4454.98 \n", - " World Emissions|CO2 Mt CO2/yr 15389.20 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr 5424.51 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16943.56 \n", - " World Emissions|CO2 Mt CO2/yr 70077.89 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 8767.49 \n", - " LAM Emissions|CO2 Mt CO2/yr 658.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 1703.00 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3257.79 \n", - " REF Emissions|CO2 Mt CO2/yr 1347.51 \n", - " World Emissions|CO2 Mt CO2/yr 15734.40 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 12419.72 \n", - " LAM Emissions|CO2 Mt CO2/yr 1309.50 \n", - " MAF Emissions|CO2 Mt CO2/yr 2552.54 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 5310.74 \n", - " REF Emissions|CO2 Mt CO2/yr 1957.66 \n", - " World Emissions|CO2 Mt CO2/yr 23550.17 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 34562.46 \n", - " LAM Emissions|CO2 Mt CO2/yr 5250.67 \n", - " MAF Emissions|CO2 Mt CO2/yr 6038.17 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15784.59 \n", - " REF Emissions|CO2 Mt CO2/yr 5886.80 \n", - " World Emissions|CO2 Mt CO2/yr 67522.70 \n", - "\n", - " 2060 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 299.06 \n", - " World Emissions|CO2 Mt CO2/yr 1616.52 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 4486.53 \n", - " LAM Emissions|CO2 Mt CO2/yr 712.03 \n", - " MAF Emissions|CO2 Mt CO2/yr 3663.91 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 2745.80 \n", - " World Emissions|CO2 Mt CO2/yr 11536.73 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr 5869.57 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16515.90 \n", - " World Emissions|CO2 Mt CO2/yr 77941.21 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 7410.94 \n", - " LAM Emissions|CO2 Mt CO2/yr -161.02 \n", - " MAF Emissions|CO2 Mt CO2/yr 1588.40 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 2240.72 \n", - " REF Emissions|CO2 Mt CO2/yr 1064.51 \n", - " World Emissions|CO2 Mt CO2/yr 12143.56 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10012.01 \n", - " LAM Emissions|CO2 Mt CO2/yr 1048.52 \n", - " MAF Emissions|CO2 Mt CO2/yr 2579.22 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4461.18 \n", - " REF Emissions|CO2 Mt CO2/yr 1686.23 \n", - " World Emissions|CO2 Mt CO2/yr 19787.16 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 37566.05 \n", - " LAM Emissions|CO2 Mt CO2/yr 5698.25 \n", - " MAF Emissions|CO2 Mt CO2/yr 7017.40 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16540.18 \n", - " REF Emissions|CO2 Mt CO2/yr 6279.44 \n", - " World Emissions|CO2 Mt CO2/yr 73101.32 \n", - "\n", - " 2070 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 266.24 \n", - " World Emissions|CO2 Mt CO2/yr 1555.47 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 3100.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 523.57 \n", - " MAF Emissions|CO2 Mt CO2/yr 3349.07 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 1531.01 \n", - " World Emissions|CO2 Mt CO2/yr 8368.71 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr 5988.95 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15922.43 \n", - " World Emissions|CO2 Mt CO2/yr 82914.15 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 6299.16 \n", - " LAM Emissions|CO2 Mt CO2/yr -1398.20 \n", - " MAF Emissions|CO2 Mt CO2/yr 1493.36 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 1399.83 \n", - " REF Emissions|CO2 Mt CO2/yr 815.36 \n", - " World Emissions|CO2 Mt CO2/yr 8609.50 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9373.38 \n", - " LAM Emissions|CO2 Mt CO2/yr 677.15 \n", - " MAF Emissions|CO2 Mt CO2/yr 2758.68 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4405.41 \n", - " REF Emissions|CO2 Mt CO2/yr 1658.31 \n", - " World Emissions|CO2 Mt CO2/yr 18872.93 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 40325.64 \n", - " LAM Emissions|CO2 Mt CO2/yr 6117.40 \n", - " MAF Emissions|CO2 Mt CO2/yr 8032.94 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 17249.21 \n", - " REF Emissions|CO2 Mt CO2/yr 6439.80 \n", - " World Emissions|CO2 Mt CO2/yr 78164.98 \n", - "\n", - " 2080 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 255.25 \n", - " World Emissions|CO2 Mt CO2/yr 1553.00 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 2246.06 \n", - " LAM Emissions|CO2 Mt CO2/yr 418.39 \n", - " MAF Emissions|CO2 Mt CO2/yr 3064.56 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 766.12 \n", - " World Emissions|CO2 Mt CO2/yr 6360.33 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 34529.79 \n", - " LAM Emissions|CO2 Mt CO2/yr 6096.94 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 14587.22 \n", - " World Emissions|CO2 Mt CO2/yr 84109.23 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 3794.59 \n", - " LAM Emissions|CO2 Mt CO2/yr -1659.55 \n", - " MAF Emissions|CO2 Mt CO2/yr 1413.90 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 795.67 \n", - " REF Emissions|CO2 Mt CO2/yr 611.13 \n", - " World Emissions|CO2 Mt CO2/yr 4955.74 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 8937.92 \n", - " LAM Emissions|CO2 Mt CO2/yr 470.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 2928.88 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4236.36 \n", - " REF Emissions|CO2 Mt CO2/yr 1628.48 \n", - " World Emissions|CO2 Mt CO2/yr 18202.25 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 42647.52 \n", - " LAM Emissions|CO2 Mt CO2/yr 6522.66 \n", - " MAF Emissions|CO2 Mt CO2/yr 8851.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 17924.86 \n", - " REF Emissions|CO2 Mt CO2/yr 6722.19 \n", - " World Emissions|CO2 Mt CO2/yr 82668.74 \n", - "\n", - " 2090 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 245.07 \n", - " World Emissions|CO2 Mt CO2/yr 1665.20 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 1843.16 \n", - " LAM Emissions|CO2 Mt CO2/yr 359.64 \n", - " MAF Emissions|CO2 Mt CO2/yr 2919.43 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 275.60 \n", - " World Emissions|CO2 Mt CO2/yr 5299.36 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 28622.83 \n", - " LAM Emissions|CO2 Mt CO2/yr 5152.73 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11864.62 \n", - " World Emissions|CO2 Mt CO2/yr 75995.09 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2865.46 \n", - " LAM Emissions|CO2 Mt CO2/yr -1631.41 \n", - " MAF Emissions|CO2 Mt CO2/yr 1303.02 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 359.17 \n", - " REF Emissions|CO2 Mt CO2/yr 444.37 \n", - " World Emissions|CO2 Mt CO2/yr 3340.62 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9270.47 \n", - " LAM Emissions|CO2 Mt CO2/yr -66.27 \n", - " MAF Emissions|CO2 Mt CO2/yr 3067.60 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4016.45 \n", - " REF Emissions|CO2 Mt CO2/yr 1550.83 \n", - " World Emissions|CO2 Mt CO2/yr 17839.07 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 44874.72 \n", - " LAM Emissions|CO2 Mt CO2/yr 6945.51 \n", - " MAF Emissions|CO2 Mt CO2/yr 9680.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 18566.23 \n", - " REF Emissions|CO2 Mt CO2/yr 7040.23 \n", - " World Emissions|CO2 Mt CO2/yr 87107.17 \n", - "\n", - " 2100 \n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 226.35 \n", - " World Emissions|CO2 Mt CO2/yr 1883.11 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 1570.02 \n", - " LAM Emissions|CO2 Mt CO2/yr 107.38 \n", - " MAF Emissions|CO2 Mt CO2/yr 3153.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr -82.62 \n", - " World Emissions|CO2 Mt CO2/yr 4644.20 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 23400.42 \n", - " LAM Emissions|CO2 Mt CO2/yr 4074.45 \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr 9683.61 \n", - " World Emissions|CO2 Mt CO2/yr 68004.38 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2437.77 \n", - " LAM Emissions|CO2 Mt CO2/yr -1586.79 \n", - " MAF Emissions|CO2 Mt CO2/yr 1118.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 224.28 \n", - " REF Emissions|CO2 Mt CO2/yr 383.41 \n", - " World Emissions|CO2 Mt CO2/yr 2576.88 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9214.63 \n", - " LAM Emissions|CO2 Mt CO2/yr -210.35 \n", - " MAF Emissions|CO2 Mt CO2/yr 3139.20 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3860.45 \n", - " REF Emissions|CO2 Mt CO2/yr 1510.95 \n", - " World Emissions|CO2 Mt CO2/yr 17514.87 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 46657.52 \n", - " LAM Emissions|CO2 Mt CO2/yr 7358.61 \n", - " MAF Emissions|CO2 Mt CO2/yr 10373.40 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 19180.64 \n", - " REF Emissions|CO2 Mt CO2/yr 7284.21 \n", - " World Emissions|CO2 Mt CO2/yr 90854.38 \n", - "\n", - "[140 rows x 11 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.check_aggregate(\n", " \"Emissions|CO2\", \n", @@ -2021,19 +120,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:`Emissions|CO2` - 1368 of 1522 rows are not aggregates of components\n", - "INFO:root:cannot aggregate variable `Price|Carbon` because it has no components\n", - "INFO:root:cannot aggregate variable `Primary Energy|Coal` because it has no components\n" - ] - } - ], + "outputs": [], "source": [ "for variable in df.filter(level=1).variables():\n", " diff = df.check_aggregate(\n", @@ -2066,1215 +155,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:`Emissions|CO2` - 404 of 503 rows are not aggregates of subregions\n" - ] - }, - { - "data": { - "text/html": [ - "
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    20052010202020302040205020602070208020902100
    modelscenarioregionvariableunit
    AIM-Enduse 12.1EMF27-450-ConvWorldEmissions|CO2Mt CO2/yr34492.0538321.7835588.6628531.6820287.4613367.27NaNNaNNaNNaNNaN
    EMF27-450-NoCCSWorldEmissions|CO2Mt CO2/yr34492.0538313.5935588.8528629.6520458.1013660.19NaNNaNNaNNaNNaN
    EMF27-550-LimBioWorldEmissions|CO2Mt CO2/yr34492.0538313.5939531.6138815.5434676.3825295.31NaNNaNNaNNaNNaN
    EMF27-Base-FullTechWorldEmissions|CO2Mt CO2/yr34492.0538313.5939612.6043835.4949027.8054552.86NaNNaNNaNNaNNaN
    EMF27-G8-EEREWorldEmissions|CO2Mt CO2/yr34492.0538304.4135425.9630395.4323536.7116487.83NaNNaNNaNNaNNaN
    GCAM 3.0AMPERE3-450WorldEmissions|CO2Mt CO2/yr31473.4031678.1338660.7745110.9744768.1434990.0919397.621208.73-17387.30-37099.22-57844.17
    AMPERE3-450P-CEWorldEmissions|CO2Mt CO2/yr31473.4031678.1438603.9046071.5443844.7434636.4119108.641129.57-17399.41-37076.52-57817.45
    AMPERE3-450P-EUWorldEmissions|CO2Mt CO2/yr31473.4031678.1439487.8747419.6145118.7935081.5419182.911166.07-17384.21-37079.51-57832.34
    AMPERE3-550WorldEmissions|CO2Mt CO2/yr31473.4031678.1339660.5247541.0150744.1846992.9134172.7817064.62-2639.86-21628.98-42437.11
    AMPERE3-Base-EUbackWorldEmissions|CO2Mt CO2/yr31473.4031678.1341826.4152214.9563459.2975453.5081730.8386384.1789308.2992285.8196090.28
    AMPERE3-CF450P-EUWorldEmissions|CO2Mt CO2/yr31473.4031678.1341826.4152214.9549660.4436881.6319617.99995.01-17475.49-37146.77-57853.52
    AMPERE3-RefPolWorldEmissions|CO2Mt CO2/yr31473.4031678.1439787.4248131.2852770.9254537.5054976.5154792.5653594.2251287.9849551.47
    IMAGE 2.4AMPERE3-450WorldEmissions|CO2Mt CO2/yr34111.2935344.2736059.3732234.6619608.8815150.286668.57-677.70-3156.89-6273.64-7112.29
    AMPERE3-450P-CEWorldEmissions|CO2Mt CO2/yr34111.1435343.9338844.8540453.3531255.3821628.4213883.065169.80-2754.38-6409.44-8174.45
    AMPERE3-450P-EUWorldEmissions|CO2Mt CO2/yr34111.1435343.9340612.2246400.3837347.9725392.0817060.167214.90-2789.63-6821.32-7928.68
    AMPERE3-550WorldEmissions|CO2Mt CO2/yr34111.2935318.5037365.8637226.8232352.8930493.3722154.0712673.927180.83909.23-2504.16
    AMPERE3-RefPolWorldEmissions|CO2Mt CO2/yr34124.3235746.9840855.5446771.3448448.4451487.3048906.5143724.9640676.9436602.4232884.37
    MERGE_EMF27EMF27-450-ConvWorldEmissions|CO2Mt CO2/yr28501.2233303.1630506.6022718.8913174.434174.12-628.46-2784.94-4736.63-5526.81-6008.83
    EMF27-550-LimBioWorldEmissions|CO2Mt CO2/yr28501.2233303.1636019.8934031.5426705.7017872.9310598.827675.844542.866515.475797.86
    EMF27-Base-FullTechWorldEmissions|CO2Mt CO2/yr28501.2233303.1643021.9054681.2464214.8273116.8381405.9990072.9498476.95108882.84120493.31
    EMF27-G8-EEREWorldEmissions|CO2Mt CO2/yr28501.2233303.1631636.3627381.8419962.3414278.6810533.139599.279211.558061.248393.79
    MESSAGE V.4AMPERE3-450WorldEmissions|CO2Mt CO2/yr34474.5936035.6936941.4635238.7126747.9615173.324329.48-1304.69-5447.10-8728.73-11209.52
    AMPERE3-450P-EUWorldEmissions|CO2Mt CO2/yr34474.4936036.0240821.6546438.2338929.5427622.3112469.822786.95-2585.87-6268.49-8535.53
    AMPERE3-550WorldEmissions|CO2Mt CO2/yr34474.5136035.9639222.4142988.0740487.8034363.5620847.2610424.522479.89-2730.61-6474.82
    AMPERE3-RefPolWorldEmissions|CO2Mt CO2/yr34474.4636035.9840886.2347008.0451379.0953497.3950990.5746103.6441339.8334733.8927562.64
    EMF27-550-LimBioWorldEmissions|CO2Mt CO2/yr34491.0236087.0934675.7630326.5422000.7111312.709846.468570.057230.635834.564500.82
    EMF27-Base-FullTechWorldEmissions|CO2Mt CO2/yr34491.0236087.0942809.7248375.9955957.7764431.6871728.3275668.2877297.8277283.3575904.56
    REMIND 1.5AMPERE3-450WorldEmissions|CO2Mt CO2/yr33841.4937365.9135255.1831679.6925439.8416908.366524.72-910.93-5015.05-8196.08-10192.88
    AMPERE3-450P-CEWorldEmissions|CO2Mt CO2/yr33841.4937357.5839260.0443283.2534864.1720741.407005.88-622.25-4786.58-8275.44-10291.58
    AMPERE3-450P-EUWorldEmissions|CO2Mt CO2/yr33841.4937356.3741565.7047902.1237928.1621615.596958.30-563.45-4783.46-8277.63-10290.40
    AMPERE3-550WorldEmissions|CO2Mt CO2/yr33841.4937366.1238324.5139015.2736963.6631733.5122831.6613927.895749.59-327.77-3755.24
    AMPERE3-550P-EUWorldEmissions|CO2Mt CO2/yr33841.4937360.5841568.6347908.6746792.9637445.2524413.6214572.436804.2086.14-3693.90
    AMPERE3-Base-EUbackWorldEmissions|CO2Mt CO2/yr33841.4937371.8744020.6750296.8758575.0871744.5982786.7987993.1685663.0375402.5366716.49
    AMPERE3-CF450P-EUWorldEmissions|CO2Mt CO2/yr33841.4937365.9844028.8050295.9039726.7022501.376855.42-602.89-4769.69-8268.81-10300.93
    AMPERE3-RefPolWorldEmissions|CO2Mt CO2/yr33841.4937372.5341615.4448455.2255203.0561590.5764595.0464737.5962246.2056447.9951261.41
    EMF27-450-ConvWorldEmissions|CO2Mt CO2/yr33837.4137977.3129314.4913503.926281.743040.79787.74-526.27-1744.61-1641.29-1413.97
    EMF27-450-NoCCSWorldEmissions|CO2Mt CO2/yr33837.4138224.9425524.607358.641691.051663.771616.521555.471553.001665.201883.11
    EMF27-550-LimBioWorldEmissions|CO2Mt CO2/yr33837.4137970.1137657.4128699.5020936.8315389.2011536.738368.716360.335299.364644.20
    EMF27-Base-FullTechWorldEmissions|CO2Mt CO2/yr33837.4138293.0848134.4253343.8259836.1070077.8977941.2182914.1584109.2375995.0968004.38
    \n", - "
    " - ], - "text/plain": [ - " 2005 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 34492.05 \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 31473.40 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 31473.40 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 31473.40 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 31473.40 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 31473.40 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 31473.40 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 31473.40 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 34111.29 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 34111.14 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 34111.14 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 34111.29 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 34124.32 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 28501.22 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 28501.22 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 28501.22 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 28501.22 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 34474.59 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 34474.49 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 34474.51 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 34474.46 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 34491.02 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 34491.02 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 33841.49 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 33837.41 \n", - "\n", - " 2010 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 38321.78 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 38304.41 \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 31678.13 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 31678.14 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 31678.14 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 31678.13 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 31678.13 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 31678.13 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 31678.14 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 35344.27 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 35343.93 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 35343.93 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 35318.50 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 35746.98 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 33303.16 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 33303.16 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 33303.16 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 33303.16 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 36035.69 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 36036.02 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 36035.96 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 36035.98 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 36087.09 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 36087.09 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 37365.91 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 37357.58 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 37356.37 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 37366.12 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 37360.58 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 37371.87 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 37365.98 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 37372.53 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 37977.31 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 38224.94 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 37970.11 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 38293.08 \n", - "\n", - " 2020 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 35588.66 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 35588.85 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 39531.61 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 39612.60 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 35425.96 \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 38660.77 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 38603.90 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 39487.87 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 39660.52 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 41826.41 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 41826.41 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 39787.42 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 36059.37 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 38844.85 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 40612.22 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 37365.86 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 40855.54 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 30506.60 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 36019.89 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 43021.90 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 31636.36 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 36941.46 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 40821.65 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 39222.41 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 40886.23 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 34675.76 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 42809.72 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 35255.18 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 39260.04 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 41565.70 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 38324.51 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 41568.63 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 44020.67 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 44028.80 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 41615.44 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 29314.49 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 25524.60 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 37657.41 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 48134.42 \n", - "\n", - " 2030 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 28531.68 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 28629.65 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 38815.54 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 43835.49 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 30395.43 \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 45110.97 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 46071.54 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 47419.61 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 47541.01 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 52214.95 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 52214.95 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 48131.28 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 32234.66 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 40453.35 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 46400.38 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 37226.82 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 46771.34 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 22718.89 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 34031.54 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 54681.24 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 27381.84 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 35238.71 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 46438.23 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 42988.07 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 47008.04 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 30326.54 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 48375.99 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 31679.69 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 43283.25 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 47902.12 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 39015.27 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 47908.67 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 50296.87 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 50295.90 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 48455.22 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 13503.92 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 7358.64 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 28699.50 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 53343.82 \n", - "\n", - " 2040 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 20287.46 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 20458.10 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 34676.38 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 49027.80 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 23536.71 \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 44768.14 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 43844.74 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 45118.79 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 50744.18 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 63459.29 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 49660.44 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 52770.92 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 19608.88 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 31255.38 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 37347.97 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 32352.89 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 48448.44 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 13174.43 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 26705.70 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 64214.82 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 19962.34 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 26747.96 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 38929.54 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 40487.80 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 51379.09 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 22000.71 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 55957.77 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 25439.84 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 34864.17 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 37928.16 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 36963.66 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 46792.96 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 58575.08 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 39726.70 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 55203.05 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 6281.74 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1691.05 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 20936.83 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 59836.10 \n", - "\n", - " 2050 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 13367.27 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 13660.19 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 25295.31 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 54552.86 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 16487.83 \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 34990.09 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 34636.41 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 35081.54 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 46992.91 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 75453.50 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 36881.63 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 54537.50 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 15150.28 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 21628.42 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 25392.08 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 30493.37 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 51487.30 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 4174.12 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 17872.93 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 73116.83 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 14278.68 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 15173.32 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 27622.31 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 34363.56 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 53497.39 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 11312.70 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 64431.68 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 16908.36 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 20741.40 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 21615.59 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 31733.51 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 37445.25 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 71744.59 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 22501.37 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 61590.57 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 3040.79 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1663.77 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 15389.20 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 70077.89 \n", - "\n", - " 2060 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 19397.62 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 19108.64 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 19182.91 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 34172.78 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 81730.83 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 19617.99 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 54976.51 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 6668.57 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 13883.06 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 17060.16 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 22154.07 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 48906.51 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -628.46 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 10598.82 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 81405.99 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 10533.13 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 4329.48 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 12469.82 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 20847.26 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 50990.57 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 9846.46 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 71728.32 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 6524.72 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 7005.88 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 6958.30 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 22831.66 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 24413.62 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 82786.79 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 6855.42 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 64595.04 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr 787.74 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1616.52 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 11536.73 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 77941.21 \n", - "\n", - " 2070 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr 1208.73 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 1129.57 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 1166.07 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 17064.62 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 86384.17 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr 995.01 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 54792.56 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -677.70 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr 5169.80 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 7214.90 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 12673.92 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 43724.96 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -2784.94 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 7675.84 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 90072.94 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 9599.27 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -1304.69 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr 2786.95 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 10424.52 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 46103.64 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 8570.05 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 75668.28 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -910.93 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -622.25 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -563.45 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 13927.89 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 14572.43 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 87993.16 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -602.89 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 64737.59 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -526.27 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1555.47 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 8368.71 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 82914.15 \n", - "\n", - " 2080 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -17387.30 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -17399.41 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -17384.21 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -2639.86 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 89308.29 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -17475.49 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 53594.22 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -3156.89 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -2754.38 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -2789.63 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 7180.83 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 40676.94 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -4736.63 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 4542.86 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 98476.95 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 9211.55 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -5447.10 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -2585.87 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 2479.89 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 41339.83 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 7230.63 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 77297.82 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -5015.05 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -4786.58 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -4783.46 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 5749.59 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 6804.20 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 85663.03 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -4769.69 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 62246.20 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -1744.61 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1553.00 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 6360.33 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 84109.23 \n", - "\n", - " 2090 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -37099.22 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -37076.52 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -37079.51 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -21628.98 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 92285.81 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -37146.77 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 51287.98 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -6273.64 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -6409.44 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -6821.32 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr 909.23 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 36602.42 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -5526.81 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 6515.47 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 108882.84 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 8061.24 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -8728.73 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -6268.49 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -2730.61 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 34733.89 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 5834.56 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 77283.35 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -8196.08 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -8275.44 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -8277.63 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -327.77 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr 86.14 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 75402.53 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -8268.81 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 56447.99 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -1641.29 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1665.20 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 5299.36 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 75995.09 \n", - "\n", - " 2100 \n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -57844.17 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -57817.45 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -57832.34 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -42437.11 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 96090.28 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -57853.52 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 49551.47 \n", - "IMAGE 2.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -7112.29 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -8174.45 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -7928.68 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -2504.16 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 32884.37 \n", - "MERGE_EMF27 EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -6008.83 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 5797.86 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 120493.31 \n", - " EMF27-G8-EERE World Emissions|CO2 Mt CO2/yr 8393.79 \n", - "MESSAGE V.4 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -11209.52 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -8535.53 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -6474.82 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 27562.64 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 4500.82 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 75904.56 \n", - "REMIND 1.5 AMPERE3-450 World Emissions|CO2 Mt CO2/yr -10192.88 \n", - " AMPERE3-450P-CE World Emissions|CO2 Mt CO2/yr -10291.58 \n", - " AMPERE3-450P-EU World Emissions|CO2 Mt CO2/yr -10290.40 \n", - " AMPERE3-550 World Emissions|CO2 Mt CO2/yr -3755.24 \n", - " AMPERE3-550P-EU World Emissions|CO2 Mt CO2/yr -3693.90 \n", - " AMPERE3-Base-EUback World Emissions|CO2 Mt CO2/yr 66716.49 \n", - " AMPERE3-CF450P-EU World Emissions|CO2 Mt CO2/yr -10300.93 \n", - " AMPERE3-RefPol World Emissions|CO2 Mt CO2/yr 51261.41 \n", - " EMF27-450-Conv World Emissions|CO2 Mt CO2/yr -1413.97 \n", - " EMF27-450-NoCCS World Emissions|CO2 Mt CO2/yr 1883.11 \n", - " EMF27-550-LimBio World Emissions|CO2 Mt CO2/yr 4644.20 \n", - " EMF27-Base-FullTech World Emissions|CO2 Mt CO2/yr 68004.38 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.check_aggregate_region(\n", " \"Emissions|CO2\",\n", @@ -3293,688 +176,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:`Emissions|CO2` - 404 of 503 rows are not aggregates of subregions\n", - "INFO:root:`Emissions|CO2|Fossil Fuels and Industry` - 239 of 239 rows are not aggregates of subregions\n", - "INFO:root:`Primary Energy` - 502 of 503 rows are not aggregates of subregions\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Fossil Fuels and Industry|Energy Supply` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Price|Carbon` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Primary Energy|Coal` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Primary Energy|Fossil|w/ CCS` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Temperature|Global Mean|MAGICC6|MED` to `World` because it does not exist in any subregion\n" - ] - }, - { - "data": { - "text/html": [ - "
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    20052010202020302040205020602070208020902100
    modelscenarioregionvariableunit
    AIM-Enduse 12.1EMF27-450-ConvWorldPrimary EnergyEJ/yr458.20518.89500.15521.23569.53581.44NaNNaNNaNNaNNaN
    EMF27-450-NoCCSWorldPrimary EnergyEJ/yr458.20518.81500.24493.64583.82614.23NaNNaNNaNNaNNaN
    EMF27-550-LimBioWorldPrimary EnergyEJ/yr458.20518.81544.28592.53639.70679.98NaNNaNNaNNaNNaN
    EMF27-Base-FullTechWorldPrimary EnergyEJ/yr458.20518.81545.24619.43715.12816.88NaNNaNNaNNaNNaN
    EMF27-G8-EEREWorldPrimary EnergyEJ/yr458.20518.64487.22463.48499.48555.22NaNNaNNaNNaNNaN
    GCAM 3.0AMPERE3-450WorldPrimary EnergyEJ/yr460.41504.35618.51743.09848.71935.921001.751091.211177.401281.611418.91
    AMPERE3-450P-CEWorldPrimary EnergyEJ/yr460.41504.35618.74753.34849.79942.161005.561092.561177.721281.711418.59
    AMPERE3-450P-EUWorldPrimary EnergyEJ/yr460.41504.35624.50769.43857.29943.231002.711089.481177.261282.191420.23
    AMPERE3-550WorldPrimary EnergyEJ/yr458.03501.89622.66751.83863.50956.221007.661064.331146.191221.761331.16
    AMPERE3-Base-EUbackWorldPrimary EnergyEJ/yr457.76501.61632.27788.66934.721073.331174.841261.381328.941395.491470.71
    AMPERE3-CF450P-EUWorldPrimary EnergyEJ/yr460.41504.35635.96793.04870.95942.831001.181087.411176.791282.691420.85
    AMPERE3-RefPolWorldPrimary EnergyEJ/yr457.76501.61622.68764.56884.66995.111080.881155.011214.591275.171325.32
    EMF27-450-ConvWorldPrimary EnergyEJ/yr458.82504.59556.01630.79694.18749.23791.67824.53834.82822.03794.94
    EMF27-450-NoCCSWorldPrimary EnergyEJ/yr458.82504.45546.22599.28628.39654.74684.87733.00787.27834.91NaN
    EMF27-550-LimBioWorldPrimary EnergyEJ/yr458.82504.45576.89663.16729.41777.90817.73865.14911.34939.80964.83
    EMF27-Base-FullTechWorldPrimary EnergyEJ/yr458.82504.45613.10730.03841.52952.451049.691139.911218.931276.801319.59
    IMAGE 2.4AMPERE3-450WorldPrimary EnergyEJ/yr441.25473.91544.13577.42638.17685.99751.05763.70778.29821.22863.04
    AMPERE3-450P-CEWorldPrimary EnergyEJ/yr441.25473.91580.13654.46676.13695.56750.32766.26792.33837.14879.08
    AMPERE3-450P-EUWorldPrimary EnergyEJ/yr441.25473.91598.26696.60692.53699.38747.23763.69791.18836.82879.48
    AMPERE3-550WorldPrimary EnergyEJ/yr441.25473.79559.87603.66689.49758.18815.96844.50862.31905.22942.64
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    " - ], - "text/plain": [ - " 2005 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr 458.20 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 458.20 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 458.20 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 458.20 \n", - " EMF27-G8-EERE World Primary Energy EJ/yr 458.20 \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 460.41 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 460.41 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 460.41 \n", - " AMPERE3-550 World Primary Energy EJ/yr 458.03 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 457.76 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 460.41 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 457.76 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 458.82 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 458.82 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 458.82 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 458.82 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 441.25 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 441.25 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 441.25 \n", - " AMPERE3-550 World Primary Energy EJ/yr 441.25 \n", - "\n", - " 2010 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr 518.89 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 518.81 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 518.81 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 518.81 \n", - " EMF27-G8-EERE World Primary Energy EJ/yr 518.64 \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 504.35 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 504.35 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 504.35 \n", - " AMPERE3-550 World Primary Energy EJ/yr 501.89 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 501.61 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 504.35 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 501.61 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 504.59 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 504.45 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 504.45 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 504.45 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 473.91 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 473.91 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 473.91 \n", - " AMPERE3-550 World Primary Energy EJ/yr 473.79 \n", - "\n", - " 2020 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr 500.15 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 500.24 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 544.28 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 545.24 \n", - " EMF27-G8-EERE World Primary Energy EJ/yr 487.22 \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 618.51 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 618.74 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 624.50 \n", - " AMPERE3-550 World Primary Energy EJ/yr 622.66 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 632.27 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 635.96 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 622.68 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 556.01 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 546.22 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 576.89 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 613.10 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 544.13 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 580.13 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 598.26 \n", - " AMPERE3-550 World Primary Energy EJ/yr 559.87 \n", - "\n", - " 2030 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr 521.23 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 493.64 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 592.53 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 619.43 \n", - " EMF27-G8-EERE World Primary Energy EJ/yr 463.48 \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 743.09 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 753.34 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 769.43 \n", - " AMPERE3-550 World Primary Energy EJ/yr 751.83 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 788.66 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 793.04 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 764.56 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 630.79 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 599.28 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 663.16 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 730.03 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 577.42 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 654.46 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 696.60 \n", - " AMPERE3-550 World Primary Energy EJ/yr 603.66 \n", - "\n", - " 2040 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr 569.53 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 583.82 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 639.70 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 715.12 \n", - " EMF27-G8-EERE World Primary Energy EJ/yr 499.48 \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 848.71 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 849.79 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 857.29 \n", - " AMPERE3-550 World Primary Energy EJ/yr 863.50 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 934.72 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 870.95 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 884.66 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 694.18 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 628.39 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 729.41 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 841.52 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 638.17 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 676.13 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 692.53 \n", - " AMPERE3-550 World Primary Energy EJ/yr 689.49 \n", - "\n", - " 2050 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr 581.44 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 614.23 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 679.98 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 816.88 \n", - " EMF27-G8-EERE World Primary Energy EJ/yr 555.22 \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 935.92 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 942.16 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 943.23 \n", - " AMPERE3-550 World Primary Energy EJ/yr 956.22 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 1073.33 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 942.83 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 995.11 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 749.23 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 654.74 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 777.90 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 952.45 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 685.99 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 695.56 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 699.38 \n", - " AMPERE3-550 World Primary Energy EJ/yr 758.18 \n", - "\n", - " 2060 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr NaN \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr NaN \n", - " EMF27-550-LimBio World Primary Energy EJ/yr NaN \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr NaN \n", - " EMF27-G8-EERE World Primary Energy EJ/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 1001.75 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 1005.56 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 1002.71 \n", - " AMPERE3-550 World Primary Energy EJ/yr 1007.66 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 1174.84 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 1001.18 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 1080.88 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 791.67 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 684.87 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 817.73 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 1049.69 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 751.05 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 750.32 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 747.23 \n", - " AMPERE3-550 World Primary Energy EJ/yr 815.96 \n", - "\n", - " 2070 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr NaN \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr NaN \n", - " EMF27-550-LimBio World Primary Energy EJ/yr NaN \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr NaN \n", - " EMF27-G8-EERE World Primary Energy EJ/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 1091.21 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 1092.56 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 1089.48 \n", - " AMPERE3-550 World Primary Energy EJ/yr 1064.33 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 1261.38 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 1087.41 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 1155.01 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 824.53 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 733.00 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 865.14 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 1139.91 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 763.70 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 766.26 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 763.69 \n", - " AMPERE3-550 World Primary Energy EJ/yr 844.50 \n", - "\n", - " 2080 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr NaN \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr NaN \n", - " EMF27-550-LimBio World Primary Energy EJ/yr NaN \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr NaN \n", - " EMF27-G8-EERE World Primary Energy EJ/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 1177.40 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 1177.72 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 1177.26 \n", - " AMPERE3-550 World Primary Energy EJ/yr 1146.19 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 1328.94 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 1176.79 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 1214.59 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 834.82 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 787.27 \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 911.34 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 1218.93 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 778.29 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 792.33 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 791.18 \n", - " AMPERE3-550 World Primary Energy EJ/yr 862.31 \n", - "\n", - " 2090 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv World Primary Energy EJ/yr NaN \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr NaN \n", - " EMF27-550-LimBio World Primary Energy EJ/yr NaN \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr NaN \n", - " EMF27-G8-EERE World Primary Energy EJ/yr NaN \n", - "GCAM 3.0 AMPERE3-450 World Primary Energy EJ/yr 1281.61 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 1281.71 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 1282.19 \n", - " AMPERE3-550 World Primary Energy EJ/yr 1221.76 \n", - " AMPERE3-Base-EUback World Primary Energy EJ/yr 1395.49 \n", - " AMPERE3-CF450P-EU World Primary Energy EJ/yr 1282.69 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 1275.17 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 822.03 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr 834.91 \n", - 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" AMPERE3-CF450P-EU World Primary Energy EJ/yr 1420.85 \n", - " AMPERE3-RefPol World Primary Energy EJ/yr 1325.32 \n", - " EMF27-450-Conv World Primary Energy EJ/yr 794.94 \n", - " EMF27-450-NoCCS World Primary Energy EJ/yr NaN \n", - " EMF27-550-LimBio World Primary Energy EJ/yr 964.83 \n", - " EMF27-Base-FullTech World Primary Energy EJ/yr 1319.59 \n", - "IMAGE 2.4 AMPERE3-450 World Primary Energy EJ/yr 863.04 \n", - " AMPERE3-450P-CE World Primary Energy EJ/yr 879.08 \n", - " AMPERE3-450P-EU World Primary Energy EJ/yr 879.48 \n", - " AMPERE3-550 World Primary Energy EJ/yr 942.64 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "for variable in df.variables():\n", " diff = df.check_aggregate_region(\n", @@ -4002,37 +206,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Reading `tutorial_check_database.csv`\n" - ] - } - ], + "outputs": [], "source": [ "consistent_df = pyam.IamDataFrame(data=\"tutorial_check_database.csv\", encoding='utf-8')" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:cannot aggregate variable `Primary Energy|Coal` because it has no components\n", - "INFO:root:cannot aggregate variable `Primary Energy|Gas` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CH4` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CF4` because it has no components\n" - ] - } - ], + "outputs": [], "source": [ "for variable in consistent_df.filter(level=1).variables():\n", " diff = consistent_df.check_aggregate(\n", @@ -4044,18 +229,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:cannot aggregate variable `Emissions|C2F6` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|CF4` to `World` because it does not exist in any subregion\n" - ] - } - ], + "outputs": [], "source": [ "for variable in consistent_df.filter(level=1).variables():\n", " diff = consistent_df.check_aggregate_region(\n", @@ -4078,68 +254,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking consistent data\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:cannot aggregate variable `Emissions|CO2|Cars` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Power` because it has no components\n", - "INFO:root:cannot aggregate variable `Primary Energy|Coal` because it has no components\n", - "INFO:root:cannot aggregate variable `Primary Energy|Gas` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CH4` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|C2F6` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|C2F6|Industry` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|C2F6|Industry` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|C2F6|Solvents` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|C2F6|Solvents` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|CF4` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CF4` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Aggregate Agg` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Aggregate Agg` to `World` because it does not exist in any subregion\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking AR5 subset\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:`Emissions|CO2` - 1390 of 1522 rows are not aggregates of components\n", - "INFO:root:`Emissions|CO2` - 404 of 503 rows are not aggregates of subregions\n", - "INFO:root:`Emissions|CO2|Fossil Fuels and Industry` - 1258 of 1258 rows are not aggregates of components\n", - "INFO:root:`Emissions|CO2|Fossil Fuels and Industry` - 239 of 239 rows are not aggregates of subregions\n", - "INFO:root:`Primary Energy` - 1522 of 1522 rows are not aggregates of components\n", - "INFO:root:`Primary Energy` - 503 of 503 rows are not aggregates of subregions\n", - "INFO:root:`Emissions|CO2|Fossil Fuels and Industry|Energy Supply` - 239 of 239 rows are not aggregates of components\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Fossil Fuels and Industry|Energy Supply` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity` because it has no components\n", - "INFO:root:cannot aggregate variable `Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Price|Carbon` because it has no components\n", - "INFO:root:cannot aggregate variable `Price|Carbon` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Primary Energy|Coal` because it has no components\n", - "INFO:root:cannot aggregate variable `Primary Energy|Coal` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Primary Energy|Fossil|w/ CCS` because it has no components\n", - "INFO:root:cannot aggregate variable `Primary Energy|Fossil|w/ CCS` to `World` because it does not exist in any subregion\n", - "INFO:root:cannot aggregate variable `Temperature|Global Mean|MAGICC6|MED` because it has no components\n", - "INFO:root:cannot aggregate variable `Temperature|Global Mean|MAGICC6|MED` to `World` because it does not exist in any subregion\n" - ] - } - ], + "outputs": [], "source": [ "# if all is good, None is returned\n", "print(\"Checking consistent data\"); time.sleep(0.5)\n", @@ -4152,1799 +269,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Emissions|CO2-aggregate',\n", - " 'Emissions|CO2-regional',\n", - " 'Emissions|CO2|Fossil Fuels and Industry-aggregate',\n", - " 'Emissions|CO2|Fossil Fuels and Industry-regional',\n", - " 'Primary Energy-aggregate',\n", - " 'Primary Energy-regional',\n", - " 'Emissions|CO2|Fossil Fuels and Industry|Energy Supply-aggregate']\n" - ] - } - ], + "outputs": [], "source": [ "pprint([k for k in errors.keys()])" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - 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    20052010202020302040205020602070208020902100
    modelscenarioregionvariableunit
    AIM-Enduse 12.1EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr10540.7413160.1811899.389545.817355.076119.50NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003294.543367.622856.652207.361537.72NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.544238.913956.193490.812082.24NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.3311646.378272.304457.911625.18NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.423325.202991.241889.38960.75NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538321.7835588.6628531.6820287.4613367.27NaNNaNNaNNaNNaN
    EMF27-450-NoCCSASIAEmissions|CO2Mt CO2/yr10540.7413160.1111893.809478.337367.075513.79NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.683362.612837.111889.89899.63NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.494239.033619.252787.471671.29NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.1611659.298708.815488.863355.22NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.393322.953076.671977.781181.73NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538313.5935588.8528629.6520458.1013660.19NaNNaNNaNNaNNaN
    EMF27-550-LimBioASIAEmissions|CO2Mt CO2/yr10540.7413160.1114124.1714218.0813187.6610019.56NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.683445.633496.622986.081790.49NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.494368.484519.644294.832733.76NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.1612607.1711752.019749.336501.31NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.393826.803615.473258.313076.27NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538313.5939531.6138815.5434676.3825295.31NaNNaNNaNNaNNaN
    EMF27-Base-FullTechASIAEmissions|CO2Mt CO2/yr10540.7413160.1114149.8916559.1419658.6823071.34NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.683449.843660.683850.443866.20NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.494371.984751.635389.486082.37NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512744.1612642.7013332.2913742.9314150.35NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953604.393838.824220.974866.315615.39NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538313.5939612.6043835.4949027.8054552.86NaNNaNNaNNaNNaN
    EMF27-G8-EEREASIAEmissions|CO2Mt CO2/yr10540.7413152.5613415.9410147.897637.614435.80NaNNaNNaNNaNNaN
    LAMEmissions|CO2Mt CO2/yr3285.003286.523106.392825.271784.31899.06NaNNaNNaNNaNNaN
    MAFEmissions|CO2Mt CO2/yr4302.214487.024091.193977.503659.803336.85NaNNaNNaNNaNNaN
    OECD90Emissions|CO2Mt CO2/yr12085.8512750.8110276.068833.955845.243473.56NaNNaNNaNNaNNaN
    REFEmissions|CO2Mt CO2/yr3306.953596.743453.293468.733376.253058.68NaNNaNNaNNaNNaN
    WorldEmissions|CO2Mt CO2/yr34492.0538304.4135425.9630395.4323536.7116487.83NaNNaNNaNNaNNaN
    ................................................
    REMIND 1.5EMF27-450-NoCCSOECD90Emissions|CO2Mt CO2/yr15111.3915254.168082.872864.75369.53328.11299.06266.24255.25245.07226.35
    WorldEmissions|CO2Mt CO2/yr33837.4138224.9425524.607358.641691.051663.771616.521555.471553.001665.201883.11
    EMF27-550-LimBioASIAEmissions|CO2Mt CO2/yr10193.9813239.5514218.3711920.798135.325963.844486.533100.112246.061843.161570.02
    LAMEmissions|CO2Mt CO2/yr2926.603478.794413.411831.961357.42934.84712.03523.57418.39359.64107.38
    MAFEmissions|CO2Mt CO2/yr4035.324381.034504.493368.893582.703883.523663.913349.073064.562919.433153.50
    OECD90Emissions|CO2Mt CO2/yr15111.3915241.5613016.5210555.137238.064454.982745.801531.01766.12275.60-82.62
    WorldEmissions|CO2Mt CO2/yr33837.4137970.1137657.4128699.5020936.8315389.2011536.738368.716360.335299.364644.20
    EMF27-Base-FullTechASIAEmissions|CO2Mt CO2/yr10193.9813478.7820256.0124006.7428404.7833016.6635977.3536397.9234529.7928622.8323400.42
    LAMEmissions|CO2Mt CO2/yr2926.603508.405067.355464.434402.985424.515869.575988.956096.945152.734074.45
    MAFEmissions|CO2Mt CO2/yr4035.324381.095364.845862.758659.6112865.8217680.1322674.4127242.6029150.2929981.48
    OECD90Emissions|CO2Mt CO2/yr15111.3915234.6315486.5716326.5916610.2416943.5616515.9015922.4314587.2211864.629683.61
    WorldEmissions|CO2Mt CO2/yr33837.4138293.0848134.4253343.8259836.1070077.8977941.2182914.1584109.2375995.0968004.38
    WITCH_EMF27EMF27-450-ConvASIAEmissions|CO2Mt CO2/yr9895.4513210.1813914.1212004.4910538.518767.497410.946299.163794.592865.462437.77
    LAMEmissions|CO2Mt CO2/yr4660.574644.171851.461537.681421.58658.62-161.02-1398.20-1659.55-1631.41-1586.79
    MAFEmissions|CO2Mt CO2/yr2508.312673.952224.441932.651907.501703.001588.401493.361413.901303.021118.21
    OECD90Emissions|CO2Mt CO2/yr12644.4012597.559780.386560.694755.203257.792240.721399.83795.67359.17224.28
    REFEmissions|CO2Mt CO2/yr3870.584035.172381.752055.811733.111347.511064.51815.36611.13444.37383.41
    WorldEmissions|CO2Mt CO2/yr33579.3237161.0330152.1524091.3220355.9115734.4012143.568609.504955.743340.622576.88
    EMF27-550-LimBioASIAEmissions|CO2Mt CO2/yr9895.9813341.7617280.0618745.4116414.5212419.7210012.019373.388937.929270.479214.63
    LAMEmissions|CO2Mt CO2/yr4660.844612.382729.372515.922286.121309.501048.52677.15470.62-66.27-210.35
    MAFEmissions|CO2Mt CO2/yr2508.812621.972773.632885.032775.242552.542579.222758.682928.883067.603139.20
    OECD90Emissions|CO2Mt CO2/yr12645.7812542.8711852.6010275.137908.885310.744461.184405.414236.364016.453860.45
    REFEmissions|CO2Mt CO2/yr3871.044062.723764.233380.512617.251957.661686.231658.311628.481550.831510.95
    WorldEmissions|CO2Mt CO2/yr33582.4537181.7038399.8837802.0132002.0223550.1719787.1618872.9318202.2517839.0717514.87
    EMF27-Base-FullTechASIAEmissions|CO2Mt CO2/yr9893.4613378.3420016.5526248.4730889.3834562.4637566.0540325.6442647.5244874.7246657.52
    LAMEmissions|CO2Mt CO2/yr4659.584623.984524.394644.994937.365250.675698.256117.406522.666945.517358.61
    MAFEmissions|CO2Mt CO2/yr2506.452642.283291.194063.345028.416038.177017.408032.948851.509680.4910373.40
    OECD90Emissions|CO2Mt CO2/yr12639.2812598.8413097.9513835.6214969.1215784.5916540.1817249.2117924.8618566.2319180.64
    REFEmissions|CO2Mt CO2/yr3868.874077.284636.235039.145412.355886.806279.446439.806722.197040.237284.21
    WorldEmissions|CO2Mt CO2/yr33567.6437320.7245566.3053831.5661236.6267522.7073101.3278164.9882668.7487107.1790854.38
    \n", - "

    140 rows × 11 columns

    \n", - "
    " - ], - "text/plain": [ - " 2005 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 10540.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 3285.00 \n", - " MAF Emissions|CO2 Mt CO2/yr 4302.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12085.85 \n", - " REF Emissions|CO2 Mt CO2/yr 3306.95 \n", - " World Emissions|CO2 Mt CO2/yr 34492.05 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 15111.39 \n", - " World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10193.98 \n", - " LAM Emissions|CO2 Mt CO2/yr 2926.60 \n", - " MAF Emissions|CO2 Mt CO2/yr 4035.32 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15111.39 \n", - " World Emissions|CO2 Mt CO2/yr 33837.41 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 10193.98 \n", - " LAM Emissions|CO2 Mt CO2/yr 2926.60 \n", - " MAF Emissions|CO2 Mt CO2/yr 4035.32 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15111.39 \n", - " World Emissions|CO2 Mt CO2/yr 33837.41 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 9895.45 \n", - " LAM Emissions|CO2 Mt CO2/yr 4660.57 \n", - " MAF Emissions|CO2 Mt CO2/yr 2508.31 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12644.40 \n", - " REF Emissions|CO2 Mt CO2/yr 3870.58 \n", - " World Emissions|CO2 Mt CO2/yr 33579.32 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9895.98 \n", - " LAM Emissions|CO2 Mt CO2/yr 4660.84 \n", - " MAF Emissions|CO2 Mt CO2/yr 2508.81 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12645.78 \n", - " REF Emissions|CO2 Mt CO2/yr 3871.04 \n", - " World Emissions|CO2 Mt CO2/yr 33582.45 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 9893.46 \n", - " LAM Emissions|CO2 Mt CO2/yr 4659.58 \n", - " MAF Emissions|CO2 Mt CO2/yr 2506.45 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12639.28 \n", - " REF Emissions|CO2 Mt CO2/yr 3868.87 \n", - " World Emissions|CO2 Mt CO2/yr 33567.64 \n", - "\n", - " 2010 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 13160.18 \n", - " LAM Emissions|CO2 Mt CO2/yr 3294.54 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.54 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.33 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.42 \n", - " World Emissions|CO2 Mt CO2/yr 38321.78 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 13160.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.16 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.39 \n", - " World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13160.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.16 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.39 \n", - " World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 13160.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12744.16 \n", - " REF Emissions|CO2 Mt CO2/yr 3604.39 \n", - " World Emissions|CO2 Mt CO2/yr 38313.59 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 13152.56 \n", - " LAM Emissions|CO2 Mt CO2/yr 3286.52 \n", - " MAF Emissions|CO2 Mt CO2/yr 4487.02 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12750.81 \n", - " REF Emissions|CO2 Mt CO2/yr 3596.74 \n", - " World Emissions|CO2 Mt CO2/yr 38304.41 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 15254.16 \n", - " World Emissions|CO2 Mt CO2/yr 38224.94 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13239.55 \n", - " LAM Emissions|CO2 Mt CO2/yr 3478.79 \n", - " MAF Emissions|CO2 Mt CO2/yr 4381.03 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15241.56 \n", - " World Emissions|CO2 Mt CO2/yr 37970.11 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 13478.78 \n", - " LAM Emissions|CO2 Mt CO2/yr 3508.40 \n", - " MAF Emissions|CO2 Mt CO2/yr 4381.09 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15234.63 \n", - " World Emissions|CO2 Mt CO2/yr 38293.08 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 13210.18 \n", - " LAM Emissions|CO2 Mt CO2/yr 4644.17 \n", - " MAF Emissions|CO2 Mt CO2/yr 2673.95 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12597.55 \n", - " REF Emissions|CO2 Mt CO2/yr 4035.17 \n", - " World Emissions|CO2 Mt CO2/yr 37161.03 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13341.76 \n", - " LAM Emissions|CO2 Mt CO2/yr 4612.38 \n", - " MAF Emissions|CO2 Mt CO2/yr 2621.97 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12542.87 \n", - " REF Emissions|CO2 Mt CO2/yr 4062.72 \n", - " World Emissions|CO2 Mt CO2/yr 37181.70 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 13378.34 \n", - " LAM Emissions|CO2 Mt CO2/yr 4623.98 \n", - " MAF Emissions|CO2 Mt CO2/yr 2642.28 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12598.84 \n", - " REF Emissions|CO2 Mt CO2/yr 4077.28 \n", - " World Emissions|CO2 Mt CO2/yr 37320.72 \n", - "\n", - " 2020 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 11899.38 \n", - " LAM Emissions|CO2 Mt CO2/yr 3367.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 4238.91 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11646.37 \n", - " REF Emissions|CO2 Mt CO2/yr 3325.20 \n", - " World Emissions|CO2 Mt CO2/yr 35588.66 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 11893.80 \n", - " LAM Emissions|CO2 Mt CO2/yr 3362.61 \n", - " MAF Emissions|CO2 Mt CO2/yr 4239.03 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11659.29 \n", - " REF Emissions|CO2 Mt CO2/yr 3322.95 \n", - " World Emissions|CO2 Mt CO2/yr 35588.85 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 14124.17 \n", - " LAM Emissions|CO2 Mt CO2/yr 3445.63 \n", - " MAF Emissions|CO2 Mt CO2/yr 4368.48 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12607.17 \n", - " REF Emissions|CO2 Mt CO2/yr 3826.80 \n", - " World Emissions|CO2 Mt CO2/yr 39531.61 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 14149.89 \n", - " LAM Emissions|CO2 Mt CO2/yr 3449.84 \n", - " MAF Emissions|CO2 Mt CO2/yr 4371.98 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 12642.70 \n", - " REF Emissions|CO2 Mt CO2/yr 3838.82 \n", - " World Emissions|CO2 Mt CO2/yr 39612.60 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 13415.94 \n", - " LAM Emissions|CO2 Mt CO2/yr 3106.39 \n", - " MAF Emissions|CO2 Mt CO2/yr 4091.19 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 10276.06 \n", - " REF Emissions|CO2 Mt CO2/yr 3453.29 \n", - " World Emissions|CO2 Mt CO2/yr 35425.96 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 8082.87 \n", - " World Emissions|CO2 Mt CO2/yr 25524.60 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 14218.37 \n", - " LAM Emissions|CO2 Mt CO2/yr 4413.41 \n", - " MAF Emissions|CO2 Mt CO2/yr 4504.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13016.52 \n", - " World Emissions|CO2 Mt CO2/yr 37657.41 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 20256.01 \n", - " LAM Emissions|CO2 Mt CO2/yr 5067.35 \n", - " MAF Emissions|CO2 Mt CO2/yr 5364.84 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15486.57 \n", - " World Emissions|CO2 Mt CO2/yr 48134.42 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 13914.12 \n", - " LAM Emissions|CO2 Mt CO2/yr 1851.46 \n", - " MAF Emissions|CO2 Mt CO2/yr 2224.44 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 9780.38 \n", - " REF Emissions|CO2 Mt CO2/yr 2381.75 \n", - " World Emissions|CO2 Mt CO2/yr 30152.15 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 17280.06 \n", - " LAM Emissions|CO2 Mt CO2/yr 2729.37 \n", - " MAF Emissions|CO2 Mt CO2/yr 2773.63 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11852.60 \n", - " REF Emissions|CO2 Mt CO2/yr 3764.23 \n", - " World Emissions|CO2 Mt CO2/yr 38399.88 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 20016.55 \n", - " LAM Emissions|CO2 Mt CO2/yr 4524.39 \n", - " MAF Emissions|CO2 Mt CO2/yr 3291.19 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13097.95 \n", - " REF Emissions|CO2 Mt CO2/yr 4636.23 \n", - " World Emissions|CO2 Mt CO2/yr 45566.30 \n", - "\n", - " 2030 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 9545.81 \n", - " LAM Emissions|CO2 Mt CO2/yr 2856.65 \n", - " MAF Emissions|CO2 Mt CO2/yr 3956.19 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 8272.30 \n", - " REF Emissions|CO2 Mt CO2/yr 2991.24 \n", - " World Emissions|CO2 Mt CO2/yr 28531.68 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 9478.33 \n", - " LAM Emissions|CO2 Mt CO2/yr 2837.11 \n", - " MAF Emissions|CO2 Mt CO2/yr 3619.25 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 8708.81 \n", - " REF Emissions|CO2 Mt CO2/yr 3076.67 \n", - " World Emissions|CO2 Mt CO2/yr 28629.65 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 14218.08 \n", - " LAM Emissions|CO2 Mt CO2/yr 3496.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 4519.64 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11752.01 \n", - " REF Emissions|CO2 Mt CO2/yr 3615.47 \n", - " World Emissions|CO2 Mt CO2/yr 38815.54 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 16559.14 \n", - " LAM Emissions|CO2 Mt CO2/yr 3660.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 4751.63 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13332.29 \n", - " REF Emissions|CO2 Mt CO2/yr 4220.97 \n", - " World Emissions|CO2 Mt CO2/yr 43835.49 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 10147.89 \n", - " LAM Emissions|CO2 Mt CO2/yr 2825.27 \n", - " MAF Emissions|CO2 Mt CO2/yr 3977.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 8833.95 \n", - " REF Emissions|CO2 Mt CO2/yr 3468.73 \n", - " World Emissions|CO2 Mt CO2/yr 30395.43 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 2864.75 \n", - " World Emissions|CO2 Mt CO2/yr 7358.64 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 11920.79 \n", - " LAM Emissions|CO2 Mt CO2/yr 1831.96 \n", - " MAF Emissions|CO2 Mt CO2/yr 3368.89 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 10555.13 \n", - " World Emissions|CO2 Mt CO2/yr 28699.50 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 24006.74 \n", - " LAM Emissions|CO2 Mt CO2/yr 5464.43 \n", - " MAF Emissions|CO2 Mt CO2/yr 5862.75 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16326.59 \n", - " World Emissions|CO2 Mt CO2/yr 53343.82 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 12004.49 \n", - " LAM Emissions|CO2 Mt CO2/yr 1537.68 \n", - " MAF Emissions|CO2 Mt CO2/yr 1932.65 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 6560.69 \n", - " REF Emissions|CO2 Mt CO2/yr 2055.81 \n", - " World Emissions|CO2 Mt CO2/yr 24091.32 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 18745.41 \n", - " LAM Emissions|CO2 Mt CO2/yr 2515.92 \n", - " MAF Emissions|CO2 Mt CO2/yr 2885.03 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 10275.13 \n", - " REF Emissions|CO2 Mt CO2/yr 3380.51 \n", - " World Emissions|CO2 Mt CO2/yr 37802.01 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 26248.47 \n", - " LAM Emissions|CO2 Mt CO2/yr 4644.99 \n", - " MAF Emissions|CO2 Mt CO2/yr 4063.34 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13835.62 \n", - " REF Emissions|CO2 Mt CO2/yr 5039.14 \n", - " World Emissions|CO2 Mt CO2/yr 53831.56 \n", - "\n", - " 2040 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 7355.07 \n", - " LAM Emissions|CO2 Mt CO2/yr 2207.36 \n", - " MAF Emissions|CO2 Mt CO2/yr 3490.81 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4457.91 \n", - " REF Emissions|CO2 Mt CO2/yr 1889.38 \n", - " World Emissions|CO2 Mt CO2/yr 20287.46 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 7367.07 \n", - " LAM Emissions|CO2 Mt CO2/yr 1889.89 \n", - " MAF Emissions|CO2 Mt CO2/yr 2787.47 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 5488.86 \n", - " REF Emissions|CO2 Mt CO2/yr 1977.78 \n", - " World Emissions|CO2 Mt CO2/yr 20458.10 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 13187.66 \n", - " LAM Emissions|CO2 Mt CO2/yr 2986.08 \n", - " MAF Emissions|CO2 Mt CO2/yr 4294.83 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 9749.33 \n", - " REF Emissions|CO2 Mt CO2/yr 3258.31 \n", - " World Emissions|CO2 Mt CO2/yr 34676.38 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 19658.68 \n", - " LAM Emissions|CO2 Mt CO2/yr 3850.44 \n", - " MAF Emissions|CO2 Mt CO2/yr 5389.48 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 13742.93 \n", - " REF Emissions|CO2 Mt CO2/yr 4866.31 \n", - " World Emissions|CO2 Mt CO2/yr 49027.80 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 7637.61 \n", - " LAM Emissions|CO2 Mt CO2/yr 1784.31 \n", - " MAF Emissions|CO2 Mt CO2/yr 3659.80 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 5845.24 \n", - " REF Emissions|CO2 Mt CO2/yr 3376.25 \n", - " World Emissions|CO2 Mt CO2/yr 23536.71 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 369.53 \n", - " World Emissions|CO2 Mt CO2/yr 1691.05 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 8135.32 \n", - " LAM Emissions|CO2 Mt CO2/yr 1357.42 \n", - " MAF Emissions|CO2 Mt CO2/yr 3582.70 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 7238.06 \n", - " World Emissions|CO2 Mt CO2/yr 20936.83 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 28404.78 \n", - " LAM Emissions|CO2 Mt CO2/yr 4402.98 \n", - " MAF Emissions|CO2 Mt CO2/yr 8659.61 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16610.24 \n", - " World Emissions|CO2 Mt CO2/yr 59836.10 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 10538.51 \n", - " LAM Emissions|CO2 Mt CO2/yr 1421.58 \n", - " MAF Emissions|CO2 Mt CO2/yr 1907.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4755.20 \n", - " REF Emissions|CO2 Mt CO2/yr 1733.11 \n", - " World Emissions|CO2 Mt CO2/yr 20355.91 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 16414.52 \n", - " LAM Emissions|CO2 Mt CO2/yr 2286.12 \n", - " MAF Emissions|CO2 Mt CO2/yr 2775.24 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 7908.88 \n", - " REF Emissions|CO2 Mt CO2/yr 2617.25 \n", - " World Emissions|CO2 Mt CO2/yr 32002.02 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 30889.38 \n", - " LAM Emissions|CO2 Mt CO2/yr 4937.36 \n", - " MAF Emissions|CO2 Mt CO2/yr 5028.41 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 14969.12 \n", - " REF Emissions|CO2 Mt CO2/yr 5412.35 \n", - " World Emissions|CO2 Mt CO2/yr 61236.62 \n", - "\n", - " 2050 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 6119.50 \n", - " LAM Emissions|CO2 Mt CO2/yr 1537.72 \n", - " MAF Emissions|CO2 Mt CO2/yr 2082.24 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 1625.18 \n", - " REF Emissions|CO2 Mt CO2/yr 960.75 \n", - " World Emissions|CO2 Mt CO2/yr 13367.27 \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr 5513.79 \n", - " LAM Emissions|CO2 Mt CO2/yr 899.63 \n", - " MAF Emissions|CO2 Mt CO2/yr 1671.29 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3355.22 \n", - " REF Emissions|CO2 Mt CO2/yr 1181.73 \n", - " World Emissions|CO2 Mt CO2/yr 13660.19 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10019.56 \n", - " LAM Emissions|CO2 Mt CO2/yr 1790.49 \n", - " MAF Emissions|CO2 Mt CO2/yr 2733.76 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 6501.31 \n", - " REF Emissions|CO2 Mt CO2/yr 3076.27 \n", - " World Emissions|CO2 Mt CO2/yr 25295.31 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 23071.34 \n", - " LAM Emissions|CO2 Mt CO2/yr 3866.20 \n", - " MAF Emissions|CO2 Mt CO2/yr 6082.37 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 14150.35 \n", - " REF Emissions|CO2 Mt CO2/yr 5615.39 \n", - " World Emissions|CO2 Mt CO2/yr 54552.86 \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr 4435.80 \n", - " LAM Emissions|CO2 Mt CO2/yr 899.06 \n", - " MAF Emissions|CO2 Mt CO2/yr 3336.85 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3473.56 \n", - " REF Emissions|CO2 Mt CO2/yr 3058.68 \n", - " World Emissions|CO2 Mt CO2/yr 16487.83 \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 328.11 \n", - " World Emissions|CO2 Mt CO2/yr 1663.77 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 5963.84 \n", - " LAM Emissions|CO2 Mt CO2/yr 934.84 \n", - " MAF Emissions|CO2 Mt CO2/yr 3883.52 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4454.98 \n", - " World Emissions|CO2 Mt CO2/yr 15389.20 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 33016.66 \n", - " LAM Emissions|CO2 Mt CO2/yr 5424.51 \n", - " MAF Emissions|CO2 Mt CO2/yr 12865.82 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16943.56 \n", - " World Emissions|CO2 Mt CO2/yr 70077.89 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 8767.49 \n", - " LAM Emissions|CO2 Mt CO2/yr 658.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 1703.00 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3257.79 \n", - " REF Emissions|CO2 Mt CO2/yr 1347.51 \n", - " World Emissions|CO2 Mt CO2/yr 15734.40 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 12419.72 \n", - " LAM Emissions|CO2 Mt CO2/yr 1309.50 \n", - " MAF Emissions|CO2 Mt CO2/yr 2552.54 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 5310.74 \n", - " REF Emissions|CO2 Mt CO2/yr 1957.66 \n", - " World Emissions|CO2 Mt CO2/yr 23550.17 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 34562.46 \n", - " LAM Emissions|CO2 Mt CO2/yr 5250.67 \n", - " MAF Emissions|CO2 Mt CO2/yr 6038.17 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15784.59 \n", - " REF Emissions|CO2 Mt CO2/yr 5886.80 \n", - " World Emissions|CO2 Mt CO2/yr 67522.70 \n", - "\n", - " 2060 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 299.06 \n", - " World Emissions|CO2 Mt CO2/yr 1616.52 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 4486.53 \n", - " LAM Emissions|CO2 Mt CO2/yr 712.03 \n", - " MAF Emissions|CO2 Mt CO2/yr 3663.91 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 2745.80 \n", - " World Emissions|CO2 Mt CO2/yr 11536.73 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 35977.35 \n", - " LAM Emissions|CO2 Mt CO2/yr 5869.57 \n", - " MAF Emissions|CO2 Mt CO2/yr 17680.13 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16515.90 \n", - " World Emissions|CO2 Mt CO2/yr 77941.21 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 7410.94 \n", - " LAM Emissions|CO2 Mt CO2/yr -161.02 \n", - " MAF Emissions|CO2 Mt CO2/yr 1588.40 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 2240.72 \n", - " REF Emissions|CO2 Mt CO2/yr 1064.51 \n", - " World Emissions|CO2 Mt CO2/yr 12143.56 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 10012.01 \n", - " LAM Emissions|CO2 Mt CO2/yr 1048.52 \n", - " MAF Emissions|CO2 Mt CO2/yr 2579.22 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4461.18 \n", - " REF Emissions|CO2 Mt CO2/yr 1686.23 \n", - " World Emissions|CO2 Mt CO2/yr 19787.16 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 37566.05 \n", - " LAM Emissions|CO2 Mt CO2/yr 5698.25 \n", - " MAF Emissions|CO2 Mt CO2/yr 7017.40 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 16540.18 \n", - " REF Emissions|CO2 Mt CO2/yr 6279.44 \n", - " World Emissions|CO2 Mt CO2/yr 73101.32 \n", - "\n", - " 2070 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 266.24 \n", - " World Emissions|CO2 Mt CO2/yr 1555.47 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 3100.11 \n", - " LAM Emissions|CO2 Mt CO2/yr 523.57 \n", - " MAF Emissions|CO2 Mt CO2/yr 3349.07 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 1531.01 \n", - " World Emissions|CO2 Mt CO2/yr 8368.71 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 36397.92 \n", - " LAM Emissions|CO2 Mt CO2/yr 5988.95 \n", - " MAF Emissions|CO2 Mt CO2/yr 22674.41 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 15922.43 \n", - " World Emissions|CO2 Mt CO2/yr 82914.15 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 6299.16 \n", - " LAM Emissions|CO2 Mt CO2/yr -1398.20 \n", - " MAF Emissions|CO2 Mt CO2/yr 1493.36 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 1399.83 \n", - " REF Emissions|CO2 Mt CO2/yr 815.36 \n", - " World Emissions|CO2 Mt CO2/yr 8609.50 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9373.38 \n", - " LAM Emissions|CO2 Mt CO2/yr 677.15 \n", - " MAF Emissions|CO2 Mt CO2/yr 2758.68 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4405.41 \n", - " REF Emissions|CO2 Mt CO2/yr 1658.31 \n", - " World Emissions|CO2 Mt CO2/yr 18872.93 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 40325.64 \n", - " LAM Emissions|CO2 Mt CO2/yr 6117.40 \n", - " MAF Emissions|CO2 Mt CO2/yr 8032.94 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 17249.21 \n", - " REF Emissions|CO2 Mt CO2/yr 6439.80 \n", - " World Emissions|CO2 Mt CO2/yr 78164.98 \n", - "\n", - " 2080 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 255.25 \n", - " World Emissions|CO2 Mt CO2/yr 1553.00 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 2246.06 \n", - " LAM Emissions|CO2 Mt CO2/yr 418.39 \n", - " MAF Emissions|CO2 Mt CO2/yr 3064.56 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 766.12 \n", - " World Emissions|CO2 Mt CO2/yr 6360.33 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 34529.79 \n", - " LAM Emissions|CO2 Mt CO2/yr 6096.94 \n", - " MAF Emissions|CO2 Mt CO2/yr 27242.60 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 14587.22 \n", - " World Emissions|CO2 Mt CO2/yr 84109.23 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 3794.59 \n", - " LAM Emissions|CO2 Mt CO2/yr -1659.55 \n", - " MAF Emissions|CO2 Mt CO2/yr 1413.90 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 795.67 \n", - " REF Emissions|CO2 Mt CO2/yr 611.13 \n", - " World Emissions|CO2 Mt CO2/yr 4955.74 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 8937.92 \n", - " LAM Emissions|CO2 Mt CO2/yr 470.62 \n", - " MAF Emissions|CO2 Mt CO2/yr 2928.88 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4236.36 \n", - " REF Emissions|CO2 Mt CO2/yr 1628.48 \n", - " World Emissions|CO2 Mt CO2/yr 18202.25 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 42647.52 \n", - " LAM Emissions|CO2 Mt CO2/yr 6522.66 \n", - " MAF Emissions|CO2 Mt CO2/yr 8851.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 17924.86 \n", - " REF Emissions|CO2 Mt CO2/yr 6722.19 \n", - " World Emissions|CO2 Mt CO2/yr 82668.74 \n", - "\n", - " 2090 \\\n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 245.07 \n", - " World Emissions|CO2 Mt CO2/yr 1665.20 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 1843.16 \n", - " LAM Emissions|CO2 Mt CO2/yr 359.64 \n", - " MAF Emissions|CO2 Mt CO2/yr 2919.43 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 275.60 \n", - " World Emissions|CO2 Mt CO2/yr 5299.36 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 28622.83 \n", - " LAM Emissions|CO2 Mt CO2/yr 5152.73 \n", - " MAF Emissions|CO2 Mt CO2/yr 29150.29 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 11864.62 \n", - " World Emissions|CO2 Mt CO2/yr 75995.09 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2865.46 \n", - " LAM Emissions|CO2 Mt CO2/yr -1631.41 \n", - " MAF Emissions|CO2 Mt CO2/yr 1303.02 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 359.17 \n", - " REF Emissions|CO2 Mt CO2/yr 444.37 \n", - " World Emissions|CO2 Mt CO2/yr 3340.62 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9270.47 \n", - " LAM Emissions|CO2 Mt CO2/yr -66.27 \n", - " MAF Emissions|CO2 Mt CO2/yr 3067.60 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 4016.45 \n", - " REF Emissions|CO2 Mt CO2/yr 1550.83 \n", - " World Emissions|CO2 Mt CO2/yr 17839.07 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 44874.72 \n", - " LAM Emissions|CO2 Mt CO2/yr 6945.51 \n", - " MAF Emissions|CO2 Mt CO2/yr 9680.49 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 18566.23 \n", - " REF Emissions|CO2 Mt CO2/yr 7040.23 \n", - " World Emissions|CO2 Mt CO2/yr 87107.17 \n", - "\n", - " 2100 \n", - "model scenario region variable unit \n", - "AIM-Enduse 12.1 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-450-NoCCS ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - " EMF27-G8-EERE ASIA Emissions|CO2 Mt CO2/yr NaN \n", - " LAM Emissions|CO2 Mt CO2/yr NaN \n", - " MAF Emissions|CO2 Mt CO2/yr NaN \n", - " OECD90 Emissions|CO2 Mt CO2/yr NaN \n", - " REF Emissions|CO2 Mt CO2/yr NaN \n", - " World Emissions|CO2 Mt CO2/yr NaN \n", - "... ... \n", - "REMIND 1.5 EMF27-450-NoCCS OECD90 Emissions|CO2 Mt CO2/yr 226.35 \n", - " World Emissions|CO2 Mt CO2/yr 1883.11 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 1570.02 \n", - " LAM Emissions|CO2 Mt CO2/yr 107.38 \n", - " MAF Emissions|CO2 Mt CO2/yr 3153.50 \n", - " OECD90 Emissions|CO2 Mt CO2/yr -82.62 \n", - " World Emissions|CO2 Mt CO2/yr 4644.20 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 23400.42 \n", - " LAM Emissions|CO2 Mt CO2/yr 4074.45 \n", - " MAF Emissions|CO2 Mt CO2/yr 29981.48 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 9683.61 \n", - " World Emissions|CO2 Mt CO2/yr 68004.38 \n", - "WITCH_EMF27 EMF27-450-Conv ASIA Emissions|CO2 Mt CO2/yr 2437.77 \n", - " LAM Emissions|CO2 Mt CO2/yr -1586.79 \n", - " MAF Emissions|CO2 Mt CO2/yr 1118.21 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 224.28 \n", - " REF Emissions|CO2 Mt CO2/yr 383.41 \n", - " World Emissions|CO2 Mt CO2/yr 2576.88 \n", - " EMF27-550-LimBio ASIA Emissions|CO2 Mt CO2/yr 9214.63 \n", - " LAM Emissions|CO2 Mt CO2/yr -210.35 \n", - " MAF Emissions|CO2 Mt CO2/yr 3139.20 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 3860.45 \n", - " REF Emissions|CO2 Mt CO2/yr 1510.95 \n", - " World Emissions|CO2 Mt CO2/yr 17514.87 \n", - " EMF27-Base-FullTech ASIA Emissions|CO2 Mt CO2/yr 46657.52 \n", - " LAM Emissions|CO2 Mt CO2/yr 7358.61 \n", - " MAF Emissions|CO2 Mt CO2/yr 10373.40 \n", - " OECD90 Emissions|CO2 Mt CO2/yr 19180.64 \n", - " REF Emissions|CO2 Mt CO2/yr 7284.21 \n", - " World Emissions|CO2 Mt CO2/yr 90854.38 \n", - "\n", - "[140 rows x 11 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "errors[\"Emissions|CO2-aggregate\"]" ] @@ -5966,7 +302,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" } }, "nbformat": 4, From 6944e082f169fe906679059fa055459beed8c98a Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 11:35:18 +0100 Subject: [PATCH 20/34] rename the "checking consistency" tutorial --- ...{checking_databases.ipynb => checking_consistency.ipynb} | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) rename doc/source/tutorials/{checking_databases.ipynb => checking_consistency.ipynb} (95%) diff --git a/doc/source/tutorials/checking_databases.ipynb b/doc/source/tutorials/checking_consistency.ipynb similarity index 95% rename from doc/source/tutorials/checking_databases.ipynb rename to doc/source/tutorials/checking_consistency.ipynb index 35710d9b1..d00d947a7 100644 --- a/doc/source/tutorials/checking_databases.ipynb +++ b/doc/source/tutorials/checking_consistency.ipynb @@ -4,9 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Checking a database\n", + "# Checking consistency of a scenario ensemble\n", "\n", - "It has happened ocassionally that the reported data is not internally consistent. Here we show how to make the most of pyam's tools to check a database. We apply these tools to the sample AR5 data. " + "It has happened ocassionally that the reported data is not internally consistent. Here we show how to make the most of **pyam** to check that a scenario ensemble is complete and that timeseries data \"add up\" across regions and along the variable tree (i.e., that the sum of values of the subcategories such as `Primary Energy|*` are identical to the values of the category `Primary Energy`).\n", + "\n", + "We apply these tools to the sample AR5 data." ] }, { From d916b12e72466477b0beb5ef412028e5f17c12b1 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 20:45:08 +0100 Subject: [PATCH 21/34] rewrite "checking consistency" tutorial, remove deprecated data file --- .../tutorials/checking_consistency.ipynb | 215 ++++++------------ .../tutorials/tutorial_check_database.csv | 81 ------- 2 files changed, 69 insertions(+), 227 deletions(-) delete mode 100755 doc/source/tutorials/tutorial_check_database.csv diff --git a/doc/source/tutorials/checking_consistency.ipynb b/doc/source/tutorials/checking_consistency.ipynb index d00d947a7..28a2301e8 100644 --- a/doc/source/tutorials/checking_consistency.ipynb +++ b/doc/source/tutorials/checking_consistency.ipynb @@ -6,9 +6,16 @@ "source": [ "# Checking consistency of a scenario ensemble\n", "\n", - "It has happened ocassionally that the reported data is not internally consistent. Here we show how to make the most of **pyam** to check that a scenario ensemble is complete and that timeseries data \"add up\" across regions and along the variable tree (i.e., that the sum of values of the subcategories such as `Primary Energy|*` are identical to the values of the category `Primary Energy`).\n", + "It has happened in previous model comparison exercises that the reported data was not internally consistent. This can be due to incomplete variable hierarchies, reporting templates incompatible with model specifications, or user error.\n", "\n", - "We apply these tools to the sample AR5 data." + "In this tutorial, we show how to make the most of **pyam** to check that a scenario ensemble (or just a single scenario) is complete and that timeseries data \"add up\" across regions and along the variable tree (i.e., that the sum of values of the subcategories such as `Primary Energy|*` are identical to the values of the category `Primary Energy`).\n", + "\n", + "
    \n", + " This feature of the pyam package currently only supports \"consistency\"\n", + " in the sense of a strictly hierarchical variable tree\n", + " (with subcategories summing up to the category value)\n", + " and subregions of depth 1 adding up the \"World\" region.\n", + "
    " ] }, { @@ -17,30 +24,21 @@ "metadata": {}, "outputs": [], "source": [ - "import time\n", - "from pprint import pprint\n", - "\n", - "import pyam\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" + "import pyam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We start with the tutorial data, it contains only a fraction of the AR5 data so is not internally consistent and is hence the perfect dataset to start with." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pyam.IamDataFrame(data='tutorial_AR5_data.csv', encoding='utf-8')" + "We start with a hypothetical tutorial data set, which is constructed to highlight the individual validation features below.\n", + "\n", + "The scenario below has two inconsistencies:\n", + "\n", + "1. In year `2010` and regions `region_b` & `World`, the values of coal and wind do not add up to the total `Primary Energy` value\n", + "2. In year `2020` in the `World` region, the value of `Primary Energy` and `Primary Energy|Coal` is not the sum of `region_a` and `region_b`
    \n", + " (but the sum of wind and coal in that region to `Primary Energy ` is correct)" ] }, { @@ -49,7 +47,21 @@ "metadata": {}, "outputs": [], "source": [ - "df.head()" + "tutorial_df = pd.DataFrame([\n", + " ['World', 'Primary Energy', 'EJ/y', 7, 15],\n", + " ['World', 'Primary Energy|Coal', 'EJ/y', 4, 11],\n", + " ['World', 'Primary Energy|Wind', 'EJ/y', 2, 4],\n", + " ['region_a', 'Primary Energy', 'EJ/y', 4, 8],\n", + " ['region_a', 'Primary Energy|Coal', 'EJ/y', 2, 6],\n", + " ['region_a', 'Primary Energy|Wind', 'EJ/y', 2, 2],\n", + " ['region_b', 'Primary Energy', 'EJ/y', 3, 6],\n", + " ['region_b', 'Primary Energy|Coal', 'EJ/y', 2, 4],\n", + " ['region_b', 'Primary Energy|Wind', 'EJ/y', 0, 2],\n", + "],\n", + " columns=['region', 'variable', 'unit', 2010, 2020]\n", + ")\n", + "\n", + "df = pyam.IamDataFrame(data=tutorial_df, model='model_a', scenario='scen_a')" ] }, { @@ -58,7 +70,8 @@ "source": [ "## Summary\n", "\n", - "With the `pyam.IamDataFrame.check_internal_consistency` method, we can check the internal consistency of a database. If this method returns `None`, the database is internally consistent (i.e. the total variables are the sum of the sectoral breakdowns and the regional breakdown \n", + "With the [check_internal_consistency()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_internal_consistency) feature, we can check the internal consistency of a scenario ensemble (i.e., an `IamDataFrame` instance).\n", + "If this method returns `None`, the database is internally consistent (i.e. the total variables are the sum of the sectoral breakdowns and the regional breakdown).\n", "\n", "In the rest of this tutorial, we give you a chance to better understand this method. We go through what it is actually doing and show you the kind of output you can expect." ] @@ -67,9 +80,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Checking variables are the sum of their components\n", + "## Checking that variables are the sum of their components\n", "\n", - "We are going to use the `check_aggregate` method of `IamDataFrame` to check that the components of a variable sum to its total. This method takes `np.is_close` arguments as keyword arguments, we show our recommended settings here." + "We are going to use the [check_aggregate()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_aggregate) method of the `IamDataFrame`\n", + "to check that the components of a variable add up to its total.\n", + "This method takes [np.is_close()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.isclose.html) arguments as keyword arguments. We show our recommended settings here." ] }, { @@ -79,9 +94,9 @@ "outputs": [], "source": [ "np_isclose_args = {\n", - " \"equal_nan\": True,\n", - " \"rtol\": 1e-03,\n", - " \"atol\": 1e-05,\n", + " 'equal_nan': True,\n", + " 'rtol': 1e-03,\n", + " 'atol': 1e-05,\n", "}" ] }, @@ -89,7 +104,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using `check_aggregate` on the `IamDataFrame` allows us to quickly check if a single variable is equal to the sum of its sectoral components (e.g. is `Emissions|CO2` equal to `Emissions|CO2|Transport` plus `Emissions|CO2|Solvents` plus `Emissions|CO2|Energy` etc.). A returned `DataFrame` will show us where the aggregate is not equal to the sum of components." + "The [check_aggregate()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_aggregate) function allows us to quickly verify whether a given variable is equal to the sum of its sectoral components (e.g. is `Primary Energy` should be equal to `Primary Energy|Coal` plus `Primary Energy|Coal`). The validation is performed separately for each region.\n", + "\n", + "This section illustrates the first constructed inconsistency in this scenario. The returned [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) indicates where the aggregate is not equal to the sum of components." ] }, { @@ -98,17 +115,14 @@ "metadata": {}, "outputs": [], "source": [ - "df.check_aggregate(\n", - " \"Emissions|CO2\", \n", - " **np_isclose_args\n", - ")" + "df.check_aggregate('Primary Energy', **np_isclose_args)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we are missing most of the sectoral data in this subset of AR5, the total variables are mostly not equal to their components. The data table above shows us which model-scenario-region combinations this is the case for. As a user, we would then have to examine which sectors we have for each of these model-scenario-region combinations in order to determine what is missing." + "In practice, it would now be up to the user to determine the cause of the inconsistency (or confirm that this is expected for some reason)." ] }, { @@ -117,7 +131,7 @@ "source": [ "### Checking multiple variables\n", "\n", - "We can then wrap this altogether to check all or a subset of the variables in an `IamDataFrame`." + "We can now construct a loop over all variables in this `IamDataFrame`." ] }, { @@ -126,107 +140,34 @@ "metadata": {}, "outputs": [], "source": [ - "for variable in df.filter(level=1).variables():\n", - " diff = df.check_aggregate(\n", - " variable, \n", - " **np_isclose_args\n", - " )\n", - " # you could then make whatever summary you wanted\n", - " # with diff" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output tells us where there are issues as well as where it is not possible to actually check sums because no components have been reported. " + "for variable in df.variables():\n", + " df.check_aggregate(variable, **np_isclose_args)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Checking that regions sum to aggregate regions\n", - "\n", - "Similarly to checking that the sum of a variable's components give the declared total, we can check that summing regions gives the intended total.\n", - "\n", - "To do this, we use the `check_aggregate_regions` method of `IamDataFrame`. By default, this method checks that all the regions in the dataframe sum to World. \n", + "The log tells us the same message as in the previous example, and it shows that the other two variables (coal and wind) cannot be assessed because they have no subcategories.\n", "\n", - "Using `check_aggregate_regions` on the `IamDataFrame` allows us to quickly check if a regional total for a single variable is equal to the sum of its regional contributors. A returned `DataFrame` will show us where the aggregate is not equal to the sum of components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.check_aggregate_region(\n", - " \"Emissions|CO2\",\n", - " **np_isclose_args\n", - ")" + "
    \n", + "Note that the detailed output (i.e., where the aggregation validation fails) is not shown in a notebook when calling the function within a loop.\n", + "
    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Again, as the AR5 snapshot is incomplete, all World sums are not equal to the regions provided.\n", - "\n", - "Once again, we can repeat this analysis over all the variables of interest in an `IamDataFrame`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for variable in df.variables():\n", - " diff = df.check_aggregate_region(\n", - " variable, \n", - " **np_isclose_args\n", - " )\n", - " # you could then make whatever summary you wanted\n", - " # with diff\n", - " if diff is not None:\n", - " eg = diff\n", + "## Checking that timeseries subregions sum to aggregate regions\n", "\n", - "eg.head(20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## An internally consistent database\n", + "Similarly to checking that the sum of a variable's components give the declared total shown above, we can check that summing over subregions returns the value of a region.\n", "\n", - "If we have an internally consistent database, the returned `DataFrame` will always be none. \n", + "To do this, we use the [check_aggregate_region](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_aggregate_region) function. By default, this method checks that all the regions in the dataframe sum to `World`. \n", "\n", - "Repeating the same analysis as above can then confirm that all is well with the database as well as give us some insight into which variables do not have regional or sectoral breakdowns reported." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "consistent_df = pyam.IamDataFrame(data=\"tutorial_check_database.csv\", encoding='utf-8')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for variable in consistent_df.filter(level=1).variables():\n", - " diff = consistent_df.check_aggregate(\n", - " variable, \n", - " **np_isclose_args\n", - " )\n", - " assert diff is None" + "Using this function allows us to quickly check if a regional total for a single variable is equal to the sum of its regional values.\n", + "This section illustrates the second constructed inconsistency in this scenario. \n", + "The returned [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) indicates where the timeseries at the `region='World'` level is not equal to the sum of regional components." ] }, { @@ -235,38 +176,22 @@ "metadata": {}, "outputs": [], "source": [ - "for variable in consistent_df.filter(level=1).variables():\n", - " diff = consistent_df.check_aggregate_region(\n", - " variable, \n", - " **np_isclose_args\n", - " )\n", - " assert diff is None" + "df.check_aggregate_region('Primary Energy', **np_isclose_args)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Putting it altogether\n", + "## Checking complete internal consistency of a scenario (ensemble)\n", "\n", - "Finally, we provide the `check_internal_consistency` method which does all the above for you and returns a dictionary with all of the dataframes which document the errors.\n", + "The previous sections illustrated two functions to validate specific variables across their subcategories or regional breakdown. These two functions are combined in the [check_internal_consistency()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_internal_consistency) feature.\n", "\n", - "Note: at the moment, this method's regional checking is limited to checking that all the regions sum to the World region. We cannot make this more automatic unless we start to store how the regions relate, see [this issue](https://github.com/IAMconsortium/pyam/issues/106). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# if all is good, None is returned\n", - "print(\"Checking consistent data\"); time.sleep(0.5)\n", - "assert consistent_df.check_internal_consistency() is None\n", + "If we have an internally consistent scenario ensemble (or single scenario), the function will return `None`; otherwise, it will return a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) indicating all detected inconsistencies.\n", "\n", - "# otherwise we get a dict back\n", - "print(\"Checking AR5 subset\"); time.sleep(0.5)\n", - "errors = df.check_internal_consistency()" + "
    \n", + " Note that at the moment, this method assumes that all the regions sum to the World region. See this issue for more information.\n", + "
    " ] }, { @@ -275,16 +200,14 @@ "metadata": {}, "outputs": [], "source": [ - "pprint([k for k in errors.keys()])" + "df.check_internal_consistency()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "errors[\"Emissions|CO2-aggregate\"]" + "The output of this function reports both types of illustrative inconsistencies in the scenario constructed for this tutorial." ] } ], diff --git a/doc/source/tutorials/tutorial_check_database.csv b/doc/source/tutorials/tutorial_check_database.csv deleted file mode 100755 index d7ac517e7..000000000 --- a/doc/source/tutorials/tutorial_check_database.csv +++ /dev/null @@ -1,81 +0,0 @@ -model,scenario,region,variable,unit,2005,2010 -IMAGE,a_scenario,R5ASIA,Primary Energy,EJ/y,1.0,6.0 -IMAGE,a_scenario,R5ASIA,Primary Energy|Coal,EJ/y,0.75,5.0 -IMAGE,a_scenario,R5ASIA,Primary Energy|Gas,EJ/y,0.25,1.0 -IMAGE,a_scenario,R5ASIA,Emissions|CO2,Mt CO2/yr,3.0,8.0 -IMAGE,a_scenario,R5ASIA,Emissions|CO2|Cars,Mt CO2/yr,1.0,3.0 -IMAGE,a_scenario,R5ASIA,Emissions|CO2|Power,Mt CO2/yr,2.0,5.0 -IMAGE,a_scenario,R5REF,Primary Energy,EJ/y,0.3,0.6 -IMAGE,a_scenario,R5REF,Primary Energy|Coal,EJ/y,0.15,0.4 -IMAGE,a_scenario,R5REF,Primary Energy|Gas,EJ/y,0.15,0.2 -IMAGE,a_scenario,R5REF,Emissions|CO2,Mt CO2/yr,1.0,1.4 -IMAGE,a_scenario,R5REF,Emissions|CO2|Cars,Mt CO2/yr,0.6,0.8 -IMAGE,a_scenario,R5REF,Emissions|CO2|Power,Mt CO2/yr,0.4,0.6 -IMAGE,a_scenario,World,Primary Energy,EJ/y,1.3,6.6 -IMAGE,a_scenario,World,Primary Energy|Coal,EJ/y,0.9,5.4 -IMAGE,a_scenario,World,Primary Energy|Gas,EJ/y,0.4,1.2 -IMAGE,a_scenario,World,Emissions|CO2,Mt CO2/yr,4.0,9.4 -IMAGE,a_scenario,World,Emissions|CO2|Cars,Mt CO2/yr,1.6,3.8 -IMAGE,a_scenario,World,Emissions|CO2|Power,Mt CO2/yr,2.4,5.6 -IMAGE,a_scenario_2,R5ASIA,Primary Energy,EJ/y,1.4,6.4 -IMAGE,a_scenario_2,R5ASIA,Primary Energy|Coal,EJ/y,0.95,5.2 -IMAGE,a_scenario_2,R5ASIA,Primary Energy|Gas,EJ/y,0.45,1.2 -IMAGE,a_scenario_2,R5ASIA,Emissions|CO2,Mt CO2/yr,3.4,8.4 -IMAGE,a_scenario_2,R5ASIA,Emissions|CO2|Cars,Mt CO2/yr,1.2,3.2 -IMAGE,a_scenario_2,R5ASIA,Emissions|CO2|Power,Mt CO2/yr,2.2,5.2 -IMAGE,a_scenario_2,R5REF,Primary Energy,EJ/y,0.7,1.0 -IMAGE,a_scenario_2,R5REF,Primary Energy|Coal,EJ/y,0.35,0.6 -IMAGE,a_scenario_2,R5REF,Primary Energy|Gas,EJ/y,0.35,0.4 -IMAGE,a_scenario_2,R5REF,Emissions|CO2,Mt CO2/yr,1.4,1.8 -IMAGE,a_scenario_2,R5REF,Emissions|CO2|Cars,Mt CO2/yr,0.8,1.0 -IMAGE,a_scenario_2,R5REF,Emissions|CO2|Power,Mt CO2/yr,0.6,0.8 -IMAGE,a_scenario_2,World,Primary Energy,EJ/y,2.1,7.4 -IMAGE,a_scenario_2,World,Primary Energy|Coal,EJ/y,1.3,5.8 -IMAGE,a_scenario_2,World,Primary Energy|Gas,EJ/y,0.8,1.6 -IMAGE,a_scenario_2,World,Emissions|CO2,Mt CO2/yr,4.8,10.2 -IMAGE,a_scenario_2,World,Emissions|CO2|Cars,Mt CO2/yr,2.0,4.2 -IMAGE,a_scenario_2,World,Emissions|CO2|Power,Mt CO2/yr,2.8,6.0 -MESSAGE-GLOBIOM,a_scenario,R5ASIA,Primary Energy,EJ/y,0.8,5.8 -MESSAGE-GLOBIOM,a_scenario,R5ASIA,Primary Energy|Coal,EJ/y,0.65,4.9 -MESSAGE-GLOBIOM,a_scenario,R5ASIA,Primary Energy|Gas,EJ/y,0.15,0.9 -MESSAGE-GLOBIOM,a_scenario,R5ASIA,Emissions|CO2,Mt CO2/yr,2.8,7.8 -MESSAGE-GLOBIOM,a_scenario,R5ASIA,Emissions|CO2|Cars,Mt CO2/yr,0.9,2.9 -MESSAGE-GLOBIOM,a_scenario,R5ASIA,Emissions|CO2|Power,Mt CO2/yr,1.9,4.9 -MESSAGE-GLOBIOM,a_scenario,R5REF,Primary Energy,EJ/y,0.1,0.4 -MESSAGE-GLOBIOM,a_scenario,R5REF,Primary Energy|Coal,EJ/y,0.05,0.3 -MESSAGE-GLOBIOM,a_scenario,R5REF,Primary Energy|Gas,EJ/y,0.05,0.1 -MESSAGE-GLOBIOM,a_scenario,R5REF,Emissions|CO2,Mt CO2/yr,0.8,1.2 -MESSAGE-GLOBIOM,a_scenario,R5REF,Emissions|CO2|Cars,Mt CO2/yr,0.5,0.7 -MESSAGE-GLOBIOM,a_scenario,R5REF,Emissions|CO2|Power,Mt CO2/yr,0.3,0.5 -MESSAGE-GLOBIOM,a_scenario,World,Primary Energy,EJ/y,0.9,6.2 -MESSAGE-GLOBIOM,a_scenario,World,Primary Energy|Coal,EJ/y,0.7,5.2 -MESSAGE-GLOBIOM,a_scenario,World,Primary Energy|Gas,EJ/y,0.2,1.0 -MESSAGE-GLOBIOM,a_scenario,World,Emissions|CO2,Mt CO2/yr,3.6,9.0 -MESSAGE-GLOBIOM,a_scenario,World,Emissions|CO2|Cars,Mt CO2/yr,1.4,3.6 -MESSAGE-GLOBIOM,a_scenario,World,Emissions|CO2|Power,Mt CO2/yr,2.2,5.4 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Primary Energy,EJ/y,-1.4,-6.4 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Primary Energy|Coal,EJ/y,-0.95,-5.2 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Primary Energy|Gas,EJ/y,-0.45,-1.2 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Emissions|CO2,Mt CO2/yr,-3.4,-8.4 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Emissions|CO2|Cars,Mt CO2/yr,-1.2,-3.2 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Emissions|CO2|Power,Mt CO2/yr,-2.2,-5.2 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Primary Energy,EJ/y,-0.7,-1.0 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Primary Energy|Coal,EJ/y,-0.35,-0.6 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Primary Energy|Gas,EJ/y,-0.35,-0.4 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Emissions|CO2,Mt CO2/yr,-1.4,-1.8 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Emissions|CO2|Cars,Mt CO2/yr,-0.8,-1.0 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Emissions|CO2|Power,Mt CO2/yr,-0.6,-0.8 -MESSAGE-GLOBIOM,a_scenario_2,World,Primary Energy,EJ/y,-2.1,-7.4 -MESSAGE-GLOBIOM,a_scenario_2,World,Primary Energy|Coal,EJ/y,-1.3,-5.8 -MESSAGE-GLOBIOM,a_scenario_2,World,Primary Energy|Gas,EJ/y,-0.8,-1.6 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|CO2,Mt CO2/yr,-5.0,-10.6 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|CO2|Cars,Mt CO2/yr,-2.0,-4.2 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|CO2|Power,Mt CO2/yr,-2.8,-6.0 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|CO2|Aggregate Agg,Mt CO2/yr,-0.2,-0.4 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|CF4,kt CF4/yr,54.0,56.0 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|C2F6,kt C2F6/yr,32.0,27.0 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|C2F6|Solvents,kt C2F6/yr,30.0,33.0 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|C2F6|Industry,kt C2F6/yr,2.0,-6.0 -MESSAGE-GLOBIOM,a_scenario_2,World,Emissions|CH4,Mt CH4/yr,322.0,217.0 -MESSAGE-GLOBIOM,a_scenario_2,R5REF,Emissions|CH4,Mt CH4/yr,30.0,201.0 -MESSAGE-GLOBIOM,a_scenario_2,R5ASIA,Emissions|CH4,Mt CH4/yr,292.0,16.0 From e0fa74449dc7d2027016b88d49b4b0ef539ee55c Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 20:46:45 +0100 Subject: [PATCH 22/34] remove output of "iiasa db" tutorial --- doc/source/tutorials/iiasa_dbs.ipynb | 319 +++------------------------ 1 file changed, 27 insertions(+), 292 deletions(-) diff --git a/doc/source/tutorials/iiasa_dbs.ipynb b/doc/source/tutorials/iiasa_dbs.ipynb index c9ff44931..826c8f42d 100644 --- a/doc/source/tutorials/iiasa_dbs.ipynb +++ b/doc/source/tutorials/iiasa_dbs.ipynb @@ -11,22 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pyam" ] @@ -40,20 +27,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['IXSE_SETNAV', 'IXSE_SR15', 'IXSE_SR15_QA', 'IXSE_TEST_PUBLIC']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "conn = pyam.iiasa.Connection()\n", "conn.valid_connections" @@ -98,17 +74,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:You are connected to the IXSE_SR15 scenario explorer hosted by IIASA. If you use this data in any published format, please cite the data as provided in the explorer guidelines: https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/about.\n" - ] - } - ], + "outputs": [], "source": [ "df = pyam.read_iiasa(\n", " 'IXSE_SR15',\n", @@ -130,22 +98,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df.filter(variable='Emissions|CO2').line_plot(\n", " color='category', \n", @@ -162,22 +117,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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    0MESSAGEix-GLOBIOM 1.0WorldCD-LINKS_INDCiMt CO2/yr31667.90819Emissions|CO22000
    1MESSAGEix-GLOBIOM 1.0WorldCD-LINKS_INDCiMt CO2/yr35933.06970Emissions|CO22005
    2MESSAGEix-GLOBIOM 1.0WorldCD-LINKS_INDCiMt CO2/yr38542.01816Emissions|CO22010
    3MESSAGEix-GLOBIOM 1.0WorldCD-LINKS_INDCiMt CO2/yr39615.22255Emissions|CO22020
    4MESSAGEix-GLOBIOM 1.0WorldCD-LINKS_INDCiMt CO2/yr40671.28065Emissions|CO22030
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    " - ], - "text/plain": [ - " model region scenario unit value \\\n", - "0 MESSAGEix-GLOBIOM 1.0 World CD-LINKS_INDCi Mt CO2/yr 31667.90819 \n", - "1 MESSAGEix-GLOBIOM 1.0 World CD-LINKS_INDCi Mt CO2/yr 35933.06970 \n", - "2 MESSAGEix-GLOBIOM 1.0 World CD-LINKS_INDCi Mt CO2/yr 38542.01816 \n", - "3 MESSAGEix-GLOBIOM 1.0 World CD-LINKS_INDCi Mt CO2/yr 39615.22255 \n", - "4 MESSAGEix-GLOBIOM 1.0 World CD-LINKS_INDCi Mt CO2/yr 40671.28065 \n", - "\n", - " variable year \n", - "0 Emissions|CO2 2000 \n", - "1 Emissions|CO2 2005 \n", - "2 Emissions|CO2 2010 \n", - "3 Emissions|CO2 2020 \n", - "4 Emissions|CO2 2030 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = conn.query(\n", " model='MESSAGEix*', \n", @@ -488,22 +236,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jKNVOjLiBJcA6s878FPu3Na8pZ4wgYcxjuSoezWQTxu/3ghgBlKPN9KtEni2UUvHAWmCmiFQXIwi6sYjkudC/B8aIiLuI1AQmFLK7b4EXRcRHjPiPtzG6Kwpz7V8LWzBi/HZYpW0z0xKUUnndxnFmnmnmuWuFMTLva4rHYqC3GHFMjhiBq0W1P99gxCN2xYgxzKMakAykiEgz4DkbZQujGoaXK90U34Nt5Jlovvk3x7ivbI2M+groIyI9zevV2WwTbsYg2uIc8xWY4icXmEnRXqHrJRRoIkZwvYP5aS9XdrFZ25QDLAXeNH+fZvzdpublOY4h8r8ElhTkNVZKXQAmAXNFpJ+5PwcxYkRnmNm+BV4XkbpixB9NwvjdwTinacB5EXEB3vgH50FTxtxQMWQ+ZB/DGI5+BiPYuo9SKtPsa34QIxg0CSO+aKlV2V0YcUMfmtujsDEiBUBE7hCRlGLadE4ptT5f95w1t8vV82O0x+gS6QiEiTGK4VdgH4bAA+MmmInh/j0DjMIYXRWN0R32mTJGhyXkfcxjGyIi9mY3T3eMhv4v0z28BuNBPKcY9lmfj+oY3pHRSqkT5r4XAJ/l825YMwpDTLwHnMMIjP0Pxu9yzCrfx+ax5n0+M9MHABtNm1MwGogFwBUjUDCCOFPM3+tL4HWl1OoCbBqNEYfylbnPvPMxwFbmYvy21niZ9ucJsTQgMm+jGKMjXzP3m4khaEOA8xjB6f1U4fES+QnBEF4HMK7nxfzdfTIfQyxFYIjnnzBeFHJs7GchxnnbAhzFeJPNf45tUsz7ZDNG9902q7RtZlp+kfko4I3xZrwMI27jl+LYooyRgaMwBE48xjk5Xmgh40F0J7DBqrsZjEDlwRj36HxsC5XCGAlMEZGLGA+3/FM3gHFeojBE/7vKiMG5AlMgPoDhmUrE8HK8jNmuishrIlLQtV7aFOeYbfEF0JK/BUBhnM/XTo0rqoDZ7XoPRmzPSYz2dDpGd31BjAZqmHm/xLhOMvLl+dy0u1ARp5R6DxiH0d2e9xuO5m9v0lsYcWARwF7gDzMNjJCJShjt/68Y7ZWmnCLFe45oNJqSQkTuBf6rlCoqcF2jKVVEJAQYppS6aSchFJHpQH2llPUAlq4YAs5bGbGreelvAiil3ixlMzU3OeVhcjiNpkJhdkveZ3avumO41wsb3qvRlDpixHWNBD4pa1usMUMYWpld3x0wumuXWW13wJhW5FNrIaTRFIYWQxpN6SMYQ3STMLrJDmJ0XWg0NwUi0hOj2+gUVwav3wxUwwixuITR3TcTYyoAzFij8xhd0rNslN1kfjSaK9DdZBqNRqPRaG5ptGdIo9FoNBrNLc3NvojhTUGdOnWUt7d3WZuh0Wg05Yrdu3efUUrVLWs7NJqiqDBiyJwnZxfGLNe9RcQHY60eF4zhkI8rpTLNuWW+ANpizEA6UBlT+heIt7c3u3Zd7zJeGo1Gc2siIrbWVNNobjoqUjfZGIxA1DymA+8rpfwwAlWfNtOfBpKUUr4Y60VNL1UrNRqNRqPR3FRUCDFkzvh6P8aEfXnLgHTHmOgOjAm48qZcf4C/F/ZbDPQoZGJCjUaj0Wg0FZwKIYYwhlCO5+9VsGsD562WLDjO3wtKumMu7Gduv4CNNZJEZJiI7BKRXYmJiSVpu0aj0Wg0mjKk3IshEekNnFZK7bZOtpFVFWPb3wlKfaKUaqeUale3ro7/02g0Go2molIRAqiDgL4ich/gDFTH8BTVNNcAy1vhOG+l4eMYqxwfFxF7jDVuzpW+2RqNRqPRaG4Gyr1nSCn1qlLKQynljbHY3wal1BCMRUQfMrM9gTlDKbDS/I65fUMxF/rUaDQajUZTASn3YqgQJgDjRCQKIyZogZm+AKhtpo8DXikj+zQajUaj0dwEVIRusssopTZhrjujlIoGOtjIkw48XKqGaTQazS3CpfNJJMZEk3gspqxN0WiKTYUSQxqNRqMpHXKyszh34jiJsUc5HXuUM8diSIw9SuqF82VtmkZzzWgxpNFoNJpCSU2+QGLMURJjo0mMPUrisRjOHo8jN8eYvcTOwYHaHg3xadOOug19qOvlQ10vb/71fY0ytlyjKR5aDGk0Go0GgJzsbJLiTxiCx+pz6XzS5TxVarlQ18sH78DbTNHjg4ubBxY7uzK0XKP5Z2gxpNFoNLcgaReTSYyNuUL0nD1xjJysLAAsdvbU9vDEq1UbQ/Q09KGutw+Vq2tvj6biocWQRqPRVGByc3NIOnmSxGNXentSzp29nKdyjZrU9fKhTcs+pvDxxsXdAzt7h+uoMAdO7oGjm2/gUWg0JYsWQxqNRlOByEi9xIGtGzl91IjvORsXS3ZWJgAWOztc3DzwDGh5uYurrpcPVWrWuv4KlYLESEP8RG+GmG2QceEGHY1GUzpoMaTRaDQVhFPRUfw46x0unErAuVp16nl5E3jPvdT1amTE9rh7Yu9wHd6e/JyP+1v8HN0CKQlGek0vaP4A+HQDn64w2fWf16XRlAJaDGk0Gk05RylF+C+r2fT5J1SqUZOBk6fj3jQAEVtLMV4HqecM0RO9yRBB56KN9Cp1DdHj0w0adYNa3jemPo2mlNFiSKPRaMoxmWmprJ03h8idW/Fp3ZZeo8b98yDnjBQ4tvNv8ZOwD1DgWA28g6D9s4b4qRcAN0pwaTRliBZDGo1GU05JjD3Kj++/w/mEeLoMCqHDAw8hlutYZSk7E07sMru9NsPxXZCbBXaO4NkRgv9tiB+3NmB3A7rZNJqbDC2GNBqNppyhlGLvhrVs/GweTlWr8vCkqXgGtCz+DnJz4dTev8VP7E7IugQIuLWG20cZ4sezEzhWLrHj0GhuFrQY0mg0mnJEVno66z6dy4GtG2nYsjX3jX6p6NFgShlxPnndXke3Qto5Y1udJtB6sCF+vLtApX8wskyjKadoMaTRaDTlhLPHj/Hj++9w9kQctz80mE4DBmKxFDDz88WEvz0/R7fAhTgjvbo7NOlliB+frlDdrfQOQKO5SdFiSKPRaMoB+zevZ92Cj3B0rsRD//4PXi1b2854MBQ2vAWJB43vzjUN0dNlLPjcCbUb66BnjSYfWgxpNBrNTUxWZgYbFs5j38a1eAS04P4XxlO1lsvVGbMz4JdJEPZfcG0Bd002vD/1W0FB3iONRgNoMaTRaDQ3LedOniD0/WkkHouhY/9H6PzwENsLop47Cj8Mhfg90PE5uHsy2DuVur0aTXlFiyGNRqO5CTm0fTNrP/kQOwcHHnx1Mj6t29rOuH85rHze6Poa+DX49y5dQzWaCkC5F0Mi4gxsAZwwjmexUuoNEfkf0A3IWyRnqFJqjxhTsn4A3Aekmul/lL7lGo1GczXZmZls+uJTwn/5Cbcm/tw/ZjzV69S9OmNWOqz9N/z+Kbi3g4cWQi2v0jdYo6kAlHsxBGQA3ZVSKSLiAGwTkdXmtpeVUovz5b8X8DM/HYGPzb8ajUZTppxPiOfH99/hdMwR2vV5kC6DQrCzt9FMnz0CPzwBCXvh9tHQ4w2wdyx9gzWaCkK5F0NKKQWkmF8dzI8qpMgDwBdmuV9FpKaINFBKxZewqRqNRlMgh8N2sObjWVgsFvqNn0jjtgW8o+1dDD+OMWaCfnQRNO1VuoYWk5SM7LI2QaMpNtcxb/vNh4jYicge4DTwi1IqzNw0VUQiROR9EcmLJnQH4qyKHzfT8u9zmIjsEpFdiYmJJWq/RqO5dcnJzmLj/z5h5Xtv4+LmzmPvfGBbCGWlGSJoydPg2hxGbLsphVByehYfbjhMl+kbytoUjabYlHvPEIBSKgdoLSI1gWUi0gJ4FUgAHIFPgAnAFMDWBBtXeZKUUp+Y5WjXrl1hniaNRqO5LpITT/PjrHdIiPqLNvf2odtjT2Fnb2Ptr8S/jNFip/dDlxeNtcJusjXCLqRmsXD7UT7bfpTk9Gx6NKtHeFkbpdEUkwohhvJQSp0XkU1AL6XUu2Zyhoh8BvzL/H4c8LQq5gGcLD0rNRqNBo7sDmPN3PfJzc2lz7hXadIxyHbG8O8gdBw4OMOQJeB3V+kaWgTnLmWyYFs0n++IJSUjm57NXXm+ux8t3Guw8Mmytk6jKR7lXgyJSF0gyxRClYC7gOl5cUDm6LF+wD6zyEpgtIh8hxE4fUHHC2k0mtIiJzubbd99wa4fl1LPuzF9XnyFmvUbXJ0x8xL8NB72fAVeQTDg05tq6YzEixl8ujWaL3+NJS0rh/taNuD57r40q1+9rE3TaK6Zci+GgAbA5yJihxED9b1SKlRENphCSYA9wAgz/08Yw+qjMIbW63cXjUZTKlw8e4bQWdM5+ddBAu++lztDnsXe0cYosNMHjW6xxEjo+jJ0ewXsbo7m+nRyOvO2RPN1WCyZ2bn0DXRjdHdffOtVK2vTNJrr5ua4u/4BSqkIoI2N9O4F5FfAqJK2S6PRaKw5umc3P304k5ysLO574WX8g7pdnUkp2PM1rPoXOFWFx5dCY5tNWalz8nwa8zYf4dvf48jJVfRv486oYF986lQpa9M0mn9MuRdDGo1GczOTm5PDjh++IWzZIuo09KbPi6/g4uZxdcaMFFj1EkR8B953GN1i1eqXvsH5iDuXysebj/DDrjiUgofaejDyTl8a1q5c1qZpNDcMLYY0Go2mhEhJOseq2TM4fmAfLYLvpvuTw3Fwcr46Y8I+WPwknDkMd75qdI2V8eKqsWcvMXdjFEv/OIFFhIHtPRnRrTEetbQI0lQ8tBjSaDSaEiB27x5+mvMumWlp9Br5Is279bg6k1Kw+3+w5hVwrgFPrASfrqVuqzVHElOYuyGKFeEnsbcIj3XyYni3RjSoUalM7dJoShIthjQajeYGkpubQ9jS79mx+Btc3Dx4eOJU6njaWDMsPRlCx8K+JdAoGB6cD1VtrEFWSvx16iIfbojix4iTONvb8VSQN892bUS9ajY8WRpNBUOLIY1Go7lBpF44z6o573Js7x787wjmrmdG4uhsw6MSH26MFkuKge4Tocs4sJTNggAHTibz4cbD/LQ3gcqOdgzv2phn7vChTlWnogtrNBUELYY0Go3mBvBX2HbWL/iYjNRL3D3seVp2vwdjmjMrlDJWmf/5NahcG54IBe8CJlssYfYev8DsDYf55cApqjnZ83x3X54K8qFWFb3gq+bWQ4shjUaj+QdcOp/E+oUfczhsB/W8G/PQv/9DXS+fqzOmX4CVz8OBFeB7N/T/L1SpU+r2/nEsiTnrD7MxMpHqzva8eFcThgZ5U6PSzbW8h0ZTmmgxpNFoNNeBUooDWzaw6fP5ZGVm0GVQCO36PIidvY1m9cQfxmix83Fw12To/EKpd4v9HnOO2esPs/XwGWpVduDlnk0Jud2Las5aBGk0WgxpNBrNNZJ8JpF18z/k6J7duDXx554RL1Db3fPqjEpB2DxY+zpUdYUnV0NDGyvSlxBKKX6NNkTQzuiz1KnqyKv3NuOxTl5UcdLNv0aTh74bNBqNppio3Fwi1q9hy9efkZubS/DQYbTueT8WW3MCpSXBitFwKBSa3Av9PoLKLqVjp1JsizrD7PWH+T0mibrVnJjYO4DBHRpSybFs5y/SaG5GtBjSaDSaYpCUcJK182Zz/MA+GrYI5O5hz1PTtYAZoo/vgh+ehIsnoefb0Gkk5A+mLiHC487z5o/7+fPYeRrUcGZy3+YMbO+Js4MWQRpNQWgxpNFoNIWQm5vDH6tWsP37r7HY2RU8UgyMbrGdc2HdG1DNDZ76GTzalYqd2Tm5fLTpCB+sP0zdqk5M7d+Ch9p64GSvRZBGUxRaDGk0Gk0BnImL5ef/fkBC1F80atuBu54ZSTWXAkaAXToLK0bCX2ugWW944EOoVKtU7Iw5c4kXv9/Dn8fO06+1G5MfaKFHh2k014AWQxqNRpOPnOwsflu+mF+XLsKpcmXue+FlmnXuatsbBBC9GZYOg7Rz0Gs6dBxeKt1iSim++z2O/4QewN4izHm0DX0C3Uq8Xo2moqHFkEaj0VhxKjqKnz+eReKxGJp27kr3J4dTuXoN25lzsmDTNNj6HtT2hSE/QINWpWLnmZQMXlmyl3UHTxHkW5t3Hw7U64dpNNeJFkMajUYDZGVmsHPxt+z6cSmVa9TkgZcn4tvHomowAAAgAElEQVSukGHwSTGw5Bk4/ju0eRzunQ6OVUrF1nUHTjFhSQQXM7KZ2DuAJzt7Y7GUToC2RlMRKfdiSEScgS2AE8bxLFZKvSEiPsB3gAvwB/C4UipTRJyAL4C2wFlgoFIqpkyM12g0NwXHD+1n7X9nkxR/ghbBd9Pt8adxrlK14AL7lsCPYwGBhz6DFg+Wip2XMrJ5a9UBvv0tDv8G1flmYGua1q9WKnXfKHbv3l3P3t7+U6AFUDYLsmluRXKBfdnZ2c+0bdv2dP6N5V4MARlAd6VUiog4ANtEZDUwDnhfKfWdiPwXeBr42PybpJTyFZFBwHRgYFkZr9Foyo7M9DS2fvM5e9auonqdugz493/wbtWmkAKXYPV4+PMr8GgPAxZALRsr0pcAfxxLYtyiPcSeS2VEt8a8eLdfuRwpZm9v/2n9+vX969atm2SxWFRZ26O5NcjNzZXExMSAhISET4G++beXezGklFJAivnVwfwooDsw2Ez/HHgTQww9YP4PsBj4UETE3I9Go7lFiIn4k18++ZDkM6dp07M3XR4Nsb3CfB7x4bD4aTgbBXf8C+58BexKfsRWVk4uc9Yf5sONUTSoUYnvnu1Ex0a1S7zeEqSFFkKa0sZisai6deteSEhIaGFre7kXQwAiYgfsBnyBucAR4LxSKtvMchxwN/93B+IAlFLZInIBqA2cybfPYcAwgIYNG5b0IWg0mlIi/VIKm79cwL6Nv1DLzYNBb07HvVlAwQWUgrD/wi+TzJXmV4JP11Kx9UhiCuMW7SH8+AUevM2dN/s2p3r5X0vMooWQpiwwrzubXbMVQgwppXKA1iJSE1gG+NvKZv61FWV41Y2plPoE+ASgXbt2+sbVaCoAUb//yroFH5F64TwdHniI2x8ajL2jY8EFLp2B5SPh8M/Q9D7o+yFUKXmvjFKKr8KOMXXVAZwd7PhoyG3c17JBider0dyqVAgxlIdS6ryIbAI6ATVFxN70DnkAJ81sxwFP4LiI2AM1gHNlYa9GoykdUpMvsGHhf4ncuZW6Db3pP34Sro18Cy8UvQmWDjfWGLvvXWj/TKnMHXT6YjrjF0ewKTKRrk3q8n8PtcK1unOJ16vR3MqU+0h+EalreoQQkUrAXcBBYCPwkJntCWCF+f9K8zvm9g06XkijqZgopTi4bROfjXuOw7/tpPMjQxgy7f3ChVBOFqx7E77oB8414NkN0OHZUhFCa/Yl0PP9Lew8cpbJfZvz+ZPttRAqAY4dO2bfu3fvRp6eni0aN27cvFu3br4RERFOzs7Ot/n7+wc0atSoecuWLf3nzJlToBuwQ4cOTbds2VLZOi00NLRacHCwL8Ds2bNrWyyWtmFhYZcD0fz8/JpHRkY6Ari7u7eMj4+3B9i6dWtld3f3ltu3b68UFxdnHxwc7Nu0adOAPNsKsiEyMtLRz8+veV7dItL2m2++uTwpVnBwsG9oaGi1PHu9vb1bNGnSJMDHx6d5SEhIwzNnzlyOwC/onMTExDj06tWr0bWe4/JGRfAMNQA+N+OGLMD3SqlQETkAfCcibwF/AgvM/AuAL0UkCsMjNKgsjNZoNCXLxXNnWDd/LtF//E593yb0HDGGOp5FjPw6dxSWPA0ndkPbodBzGjhWLrzMDSAlI5vJK/fzw+7jtHCvzqyBrfGtV76GzJcXcnNz6du3r+/gwYPPhoaGRgPs2LGj0smTJx08PT0zDh48eADgwIEDjg8++KBvbm4uY8aMOXs9dbm6umZOmTKlwapVq6ILyhMWFlZp0KBBjb/66qsjQUFBaYMHD/bq3r178sSJE0/nbb+G+rKmT5/eYPDgwRdsbf/iiy+iu3btmpqeni7PP/+8+7333uv7+++/RxZ2Tlq1apWyZs2aAu2vKJR7MaSUigCuGgurlIoGOthITwceLgXTNBpNGaCUYu+GtWz+cgG5OTl0e/xpbruvLxZLEcPQ9y425g6yWODhz6F5v1Kxd1fMOV78fg8nktIYHezLCz38cLQv9077YvHy4nDPvxIu3lC12aR+tdT/eygwrqDtoaGh1ezt7dX48eMT89I6d+6cluexySMgICBzxowZcRMmTPC8XjHUo0ePC2FhYdXCw8OdAgMDM/JvDw8Pd37mmWd8Fi5ceDQ4ODgVICEhweGee+65LGY6duyYVtz6/P39U7OysmTZsmXV+/fvn1xQPmdnZ/Xxxx8f9/Lyarlz585KiYmJ9rbOCRjep969e/sdPnx4f3HtKI/cGnecRqO5JbhwOoHFb73OL5/MwdWnMSH/N4d2vfsXLoQyUowg6SVPg2tzGLGtVIRQZnYu//fzIR6ZtxOA74ffzr96Nr1lhFBZERERUSkwMDC1OHk7d+6cevTo0evup7RYLIwZMyZh8uTJNqPfBw4c6Dtz5sxjPXv2zJsehlGjRp1+/vnnvTt27NhkwoQJ9WNiYq5p+ODrr78e//bbbxcZbW9vb4+/v3/qvn37nK/lnFRUyr1nSKPRaFRuLn/+HMrWbz/HYrFw1zOjaNWjJ2IpQlic3AOLn4Kko9BtAnQdD3Yl3yxGnb7I2EV72HcimUfaeTCpT3OqOt16zXFhHpybgRsRTjp8+PCz7777boNDhw5dNWwxKCgoecGCBXUGDBhwwd7e+P0HDBiQ3KVLl73Lli2rsWbNmhpt27YN2Lt37343N7fsq3Zug169eqVMnDiRNWvWFDKFuoEOl/0b/Qqi0WjKNVnp6ax87202/u8TPANa8sS7HxF4972FC6HcXNg5Fz69C7LS4IkfIfi1EhdCubmK/20/yv2zt3HyfDrzHm/LjIcCb0khVFa0bNkyLTw8vFhdczt37qzcqFGjNIAuXbr4NWvWLGDgwIHXNOW4g4MDo0ePTpgyZUr9/Nvmz59/DCAkJOSKfbq6uuaMGDHi3PLly4+2atXq0tq1a4sUNta8+uqr8VOnTi3UO5SdnU1kZGTlVq1apV/LOamoaDGk0WjKLRfPneG7NydwZNdvBD/xLP0nvEH1OnULL5SSCN88Aj+/Bk16wnPbwbtLidt6KjmdJz77jTd/PEDnxrVZM/YOeja/6vmoKWH69OlzMTMzU2bOnFknL23z5s2Vo6KirvDcREZGOr7yyisew4cPPw2wbdu2w4cOHTqwaNGi2Gutc/To0We3bdtW/dy5c1eoXovFwooVK6KjoqKcx44d6wawcuXKahcvXrQAJCUlWWJjY518fHwyr6W+Bx98MPnChQt2Bw8etClwMjIyZPTo0R4NGjTI7NixY1pB52TVqlXXJMLKM/p1RKPRlEtORUexfMYUMtLS6Dd+Io1ua190oSMbjLmD0i/A/TOh3dOlMmT+p73xvLZsL+lZObzVrwVDOjZESqFezdVYLBZWrlx5ZOTIkZ6zZs2q7+TkpDw8PDLmzJkTFxcX5+Tv7x+QkZEhVapUyR0+fPjpwoKn+/fv72dvb68AbrvttpRRo0Yl2srn7Oyshg0bdnrixIme+bdVqlRJrV69OiooKKjptGnTslJTUy0vvvhiQzs7O6WUkscff/xMt27drjmeZ8KECfGPPfbYFcPyQ0JCGjk6OuZmZmZa7rjjjuTVq1dHFXVOrrXe8oroPsOiadeundq1a1dZm6HRaEwO/76Tn+a8S6Wq1ek/YRJ1vXwKL5CdCRvfgu0fQN1m8NBCI1i6hElOz+LNFftZ+ucJAj1q8P7A1jSqe8u8bCMiu5VS7azTwsPDYwIDA88UVEajKUnCw8PrBAYGeudP154hjUZTblBKsevHpWz55n/Ub+xHv5cnUqVmrcILnYs2Flg9+Qe0ewrumVoqcweFRZ9l3PfhJCSnM6aHH6O7++JgpyMTNJqbES2GNBpNuSAnO4t1n37Mvo1raXL7HfQaORYHR6fCC0V8D6HjjLmDHvkSAvqWuJ0Z2Tm898tffLIlGi+XyiwecTttGhYh2DSaQvjtt98qhYSEXOH+dHR0zI2IiDhUVjZVNLQY0mg0Nz1pKRf58b1pxO2PoNODA+n88JDCR4tlXISfXobwb6FhZ3jwE6h5VbjGDScywRgyfzA+mUc7NOT1+/2pokeKaf4hHTp0SDt06NCBsrajIqPvUo1Gc1OTFH+CZdOnkJx4intHjSOga/fCC5z805w7KAbufBXu+FeJD5k/m5LBB+sP83XYMWpVduDTkHbcFeBaonVqNJobhxZDGo3mpiXuwF5WznwbRHho4lQ8mhUS9JybC7/OhXWToaorDF0FXp1L1L70rBw+3xHDhxuiSM3KYXCHhoy9y4/aVYvovtNoNDcVWgxpNJqbkn2b1vHLJx9S07U+/Se8Qc36hcwhl3Ialo2AI+vBvw/0mQ2VXUrMNqUUoRHxTF9ziONJaXRvVo/X7mumF1fVaMopWgxpNJqbCpWby7bvvuC3FYtp2CKQPuNexblKIcPR/1oLK0YacUK934e2T5bo3EG7Y5OYuuoAfxw7j3+D6nz9TCuCfOsUXVCj0dy06HGeGo3mpiErI50f33+H31YsptVdvXjw1ckFC6GsdFg9Ab552OgWG7bZGDpfQkIo7lwqo7/5gwEf7+B4UhozBrQi9PkuWgiVQ44dO2bfu3fvRp6eni0aN27cvFu3br4RERFOzs7Ot/n7+wc0atSoecuWLf3nzJlTu6B9dOjQoemWLVuumKMhNDS0WnBwsC/A7Nmza1sslrZhYWGV8rb7+fk1j4yMdARwd3dvGR8fbw+wdevWyu7u7i23b99eKS4uzj44ONi3adOmAXm2FWRDZGSko4i0nTp1ar28tJCQkIazZ8+uDTBgwABvd3f3ls2aNQsICAjwX7duXRWAsWPHui1fvly7Ma3QniGNRnNTkHLuLMv/7z+cOnqEO0Oe4bb7Hih4lubTB425g07vh47PwV1vgsN1Ly5eKMnpWczdGMVn22KwWOCFHn4M79pIjxIrp+Tm5tK3b1/fwYMHnw0NDY0G2LFjR6WTJ086eHp6Zhw8ePAAwIEDBxwffPBB39zcXAqbhbowXF1dM6dMmdJg1apV0QXlCQsLqzRo0KDGX3311ZGgoKC0wYMHe3Xv3j154sSJp/O2F1aHi4tL9rx58+q99NJLic7OzlfNovzWW28df/LJJ5OWLl1afeTIkV5//fXXgVmzZp28nuOpyOi7WaPRlDmnY6JZNmMKGSkp9Hv5dRq37Wg7o1KwawH8/G9wqgZDFoPf3SViU1ZOLt/+doxZ6w6TlJrJgNs8+Nc9Talfo2RE1y3J8lGenD5wY2fArBeQSr+5BS4jERoaWs3e3l6NHz/+8tIZnTt3Tsvz2OQREBCQOWPGjLgJEyZ4Xq8Y6tGjx4WwsLBq4eHhToGBgRn5t4eHhzs/88wzPgsXLjwaHBycCpCQkOBwzz33XMjL07Fjx7TC6nBxcclu3759yty5c2u/9NJLBc7s3atXr4tDhgxxAsNj1Lt37wtPPvlk0vUcV0VEd5NpNJoyJWpXGN9NGg/AoCkzChZCl87Cd4Nh1UvGwqrP7SgRIaSUYv3BU/SatYVJK/bT1LUaP47uwrsPB2ohVAGIiIioFBgYWKy1vjp37px69OjR6/7RLRYLY8aMSZg8ebLN6P+BAwf6zpw581jPnj1T8tJGjRp1+vnnn/fu2LFjkwkTJtSPiYlxKKqeSZMmxX/44Yeu2dnZBeb57rvvavr5+RUqrG5lyr1nSEQ8gS+A+kAu8IlS6gMReRN4FshT/68ppX4yy7wKPA3kAC8opX4udcM1mlscpRS7Q5ex+evPcPXxpd/4iVStVcAIsCMbjdFiaeeg1zvQYbgxq/QNZv/JC0xddZAdR87SqE4V5oe04y7/enpR1ZKiEA/OzcCNWLtz+PDhZ999990Ghw4dcsy/LSgoKHnBggV1BgwYcMHe3ngcDxgwILlLly57ly1bVmPNmjU12rZtG7B37979bm5uBSqdZs2aZbZu3frSvHnzrrqBXn/9dY/p06c3cHFxyVqwYEHMPz6gCkpF8AxlAy8ppfyBTsAoEQkwt72vlGptfvKEUAAwCGgO9AI+EhG7sjBco7lVycnOZt38uWz+aiF+HW5n4JvTbAuh7ExY+zp82Q+ca8CzG6DTczdcCJ1KTuflH8LpPWcbB+OTmdy3OT+/2JW7A1y1EKpgtGzZMi08PLxYXXM7d+6s3KhRozSALl26+DVr1ixg4MCBXtdSn4ODA6NHj06YMmVK/fzb5s+ffwwgJCTkin26urrmjBgx4tzy5cuPtmrV6tLatWuLXN130qRJCbNmzWqQm5t7Rfpbb711/NChQwd27NhxuH379unXYvutRLkXQ0qpeKXUH+b/F4GDgHshRR4AvlNKZSiljgJRQIeSt1Sj0QCkp6SwdNobRKxfQ4d+D9Nn7Cs4ONnoiThzGBbcBTvmQLunYdgmqN/yhtqSmpnNrHV/cef/bWLFnpM8e0cjNr0czBOdvfWiqhWUPn36XMzMzJSZM2deHga4efPmylFRUVd4biIjIx1feeUVj+HDh58G2LZt2+FDhw4dWLRoUey11jl69Oiz27Ztq37u3LkremMsFgsrVqyIjoqKch47dqwbwMqVK6tdvHjRApCUlGSJjY118vHxySyqjjZt2qT7+fmlrV+/vsa12qepAN1k1oiIN9AGCAOCgNEiEgLswvAeJWEIpV+tih3HhngSkWHAMICGDRuWqN0aza3C+YR4lk6fzIVTCfQa+SLNu/W4OpNS8McXsOYVsHeGQd9As/tvqB05uYolfxxn5tpITiVncH/LBkzo1YyGtUt+NXtN2WKxWFi5cuWRkSNHes6aNau+k5OT8vDwyJgzZ05cXFyck7+/f0BGRoZUqVIld/jw4acLC57u37+/n729vQK47bbbUkaNGpVoK5+zs7MaNmzY6YkTJ161QF6lSpXU6tWro4KCgppOmzYtKzU11fLiiy82tLOzU0opefzxx89069atWDFOEydOjA8KCgooOqcmP3Ij+kRvBkSkKrAZmKqUWioirsAZQAH/ARoopZ4SkbnATqXUV2a5BcBPSqklBe27Xbt2ateuXSV/EBpNBeb4wX2smPk2KMUDL/0bj4AWV2dKPQc/joGDK8GnG/SfB9ULmXn6OtgRdYa3Vh3kQHwyrT1rMrG3P229Sm626lsZEdmtlGpnnRYeHh4TGBhY4KgnjaYkCQ8PrxMYGOidP71CeIZExAFYAnytlFoKoJQ6ZbV9PhBqfj0OWKtzD0DPuaDRlCD7N69n7bw51KjnSv9X3qBWfberM8Vsg6XDIOUU3D0Fbn/+hsYGRZ1O4Z3VB1l38DTuNSsx+9E29GnVQMcEaTSa8i+GxGjJFgAHlVLvWaU3UErFm1/7A/vM/1cC34jIe4Ab4Af8VoomazS3DCo3l+3ff0XYsu9p2KIVfV58Deeq+WJBc7Jg0zTY+h64NIKnfwH3226YDdYryld2sGNCr2Y8GeSNs4MeN6EpH/z222+VQkJCfKzTHB0dcyMiIg6VlU0VjXIvhjBigx4H9orIHjPtNeBREWmN0U0WAwwHUErtF5HvgQMYI9FGKaVySt1qjaaCk5WRzpq57/NX2HZa9uhJj6eew84+X5NzLhqWPAMndkObx6DXdHAqcuBMsdArymsqCh06dEg7dOjQgbK2oyJT7sWQUmobYMvP/VMhZaYCU0vMKI3mFufS+SSWz5hCQnQU3R57ira9+1/ZHaUURCwyJlC02MHD/4Pm/W9I3XpFeY1Gc62UezGk0WhuLk7HRLN8xn9IS0nmgZf+jW/7TldmSL8AoeNg32Jo2Bke/ARqXjXI5rrQK8prNJrrQYshjUZzwziy+zdWzf4/nCpXZtDkGbj6NL4yw7EwWPoMXDgBwa/DHeMMz9A/5OiZS8xcG0loRDz1qjkxY0ArBrT1wM6ig6M1Gk3RaDGk0Wj+MUop/vhpJZu/XEA9n0b0e3kiVV1q/50hJxu2zoTN70ANT3jqZ/Bs/4/rjTlzidkbDrP8zxM42lv0ivIajea60FOsajSaf0ROdjbrF3zEpi/m07hdRwa+8c6VQuj8Mfjf/bDpbWj5MIzY9o+FUMyZS7z0fTg93tvMT3vjebqLD1vHd2fc3U20ENIUybFjx+x79+7dyNPTs0Xjxo2bd+vWzTciIsLJ2dn5Nn9//4BGjRo1b9mypf+cOXNqF7SPDh06NN2yZcsVs3SGhoZWCw4O9gWYPXt2bYvF0jYsLKxS3nY/P7/mkZGRjgDu7u4t4+Pj7QG2bt1a2d3dveX27dsrxcXF2QcHB/s2bdo0IM+2gmyIjIx0FJG2U6dOrZeXFhIS0nD27Nm1wVid3t3dvWWzZs0CAgIC/NetW1cFYOzYsW7Lly+vBtC3b18fb2/vFn5+fs0ffvhh74yMDAHIzc1l6NChng0bNmzRpEmTgG3btlUG2LFjR6XWrVs38/X1bd6kSZOA+fPn18qr+9ChQ46tWrVq5uXl1eL+++9vlJ6eXqBrdvXq1VUDAgL87e3t23722We1rLfNmTOntpeXVwsvL68W1r/B1q1bKzdp0iSgYcOGLYYOHeqZt/TIqVOn7Dp37uzn5eXVonPnzn6JiYnX7G7WYkij0Vw3yYmnWTJ1IuG/rKb9Aw/Rd9yrODhbLa2xdzF83AVO7YcH5xvxQc7Vr7u+2LOX+NcPhggKjTjJk5292TI+mH/fH0DdanqUmKZocnNz6du3r2/Xrl0vxsXF7Tty5Mj+adOmnTh58qSDp6dnxsGDBw9ER0fvX7Ro0ZG5c+e6fvDBBwUKoqJwdXXNnDJlSqGzhoaFhVUaNGhQ46+++upIUFBQ2oQJE9y7d++eHBkZeeDIkSP7Z8yYcaKw8i4uLtnz5s2rV5DwyFub7K233joxcuRIL4BZs2ad7Nev30WAIUOGnIuOjt4XGRm5Pz09XWbNmlUH4IcffqgRHR3tHBMTs+/jjz+OHTlyZEOAqlWr5n755ZdHo6Ki9q9du/bwa6+95nnmzBk7gHHjxnmMHj36VGxs7L4aNWpkf/DBBwUG7DVq1Cjzs88+i+nTp88VM3yfOnXKbvr06W6//fbbwV27dh2cPn26W564GTlypNdHH30UGxMTsy86Otp58eLF1QHeeOONBnfeeefF2NjYfXfeeefFSZMmXbUOXFHoVyiNRnPNKKWIWLeGzV8tBLh6aY2Mi/DTeAj/BjzaG0LIxaeAvRVN7NlLfLghiqV/nsDeIgzt7M3wbo2oV83GmmaacsPE7RM9o5KibugaKL61fFP/E/SfuIK2h4aGVrO3t1fjx4+/vHRG586d0/I8NnkEBARkzpgxI27ChAmehS3JURg9evS4EBYWVi08PNwpMDAwI//28PBw52eeecZn4cKFR4ODg1MBEhISHO65554LeXk6duyYVlgdLi4u2e3bt0+ZO3du7ZdeeqnAmb179ep1cciQIU5geIx69+594cknn0waOHDg5bratWt36fjx444AK1asqDlkyJCzFouFHj16XEpOTraPjY11aNWq1eXj8Pb2znJxccmOj4+3d3Fxydm5c2e1FStWRAM89dRTZ9988023CRMm2FyipGnTpplgLI9izfLly2t07do12dXVNQega9euyUuXLq3Rq1eviykpKZa77rrrEsCQIUPOLl++vNYjjzySvGbNmpqbN2+OBBg+fPjZbt26NQUKFZH50WJIo9FcE8lnTrN23hxiI/6kYYtA7hn+AjXquf6d4fhuWPI0nI+FruOh23iwc7iuuo6dTeXDjYdZ8ochgp643ZsRd2oRpLl+IiIiKgUGBhZrra/OnTunHj169LovNovFwpgxYxImT57cYOnSpTH5tw8cOND3k08+OdqzZ8+UvLRRo0adHjp0aKOPP/449c4770x+7rnnznp7e2cVVs+kSZPi7733Xr8xY8YUKIa+++67mn5+fgUKq4yMDFm0aFHt9957Lw4gPj7ewdvb+/ICsQ0aNMiMjY118PLyumzLxo0bK2dlZUlAQEDGqVOn7KtVq5bj4GDc697e3pmnTp1yvKqiIjhx4oSDh4fH5Xrd3d0zT5w44RAbG+vQoEGDy3V7eXllxsfHOwCcPXvWPs8uLy+vrPwL4hYHLYY0Gk2xUEqxd8PPbP5yASpXcdczI2l1171/zx+UmwPbP4CNU6FqfRi6Crw6X1ddcedSmbPBEEF2FiHkdi+e69aYetW1CKpIFObBuRm4EWt3Dh8+/Oy7777b4NChQ1cJg6CgoOQFCxbUGTBgwAV7c0LSAQMGJHfp0mXvsmXLaqxZs6ZG27ZtA/bu3bvfzc0tu6A6mjVrltm6detL8+bNu2qRvddff91j+vTpDVxcXLIWLFgQU9A+nnjiiYadOnVK6dWrVwrYPnbrucJiY2MdnnzyyUYLFiw4amdnV1D+az6BBdVblD3/FB0zpNFoiiT5TCJL3p7EL598SP3Gfjzx7lwC777v78bowgn44gFYPxma9Ybntl2XEIo7l8qExREEv7uJ5XtOEnK7F1vHB/NGn+ZaCGluCC1btkwLDw8vVtfczp07Kzdq1CgNoEuXLn7NmjULGDhwoNe11Ofg4MDo0aMTpkyZclUcy/z5848BhISEXLFPV1fXnBEjRpxbvnz50VatWl1au3ZtkdOyT5o0KWHWrFkN8oKK88iLGdqxY8fh9u3bp9sq+9JLLzU4c+aM/fz58y+LUzc3t6yYmJjLAi4+Pt6xYcOGWQDnzp2z3Hvvvb6TJk060aNHj0sA9evXz7548aJdVpbhvImJiXGsV69eoR4tW3h4eGTlddUBnDhxwtHNzS3L29s7K88TBBAbG+tYv379LIDatWtnx8bGOpjpDi4uLgUKx4LQYkij0RSI4Q1ay+f/GsXJyIP0eOo5Hvr3W1d2ix1YAR93hhN/wANzjdmkK9UqcJ+2iDuXyitLDBG0bM8JHuv0twhy1SJIcwPp06fPxczMTJk5c+bl4N7NmzdXjoqKusJzExkZ6fjKK694DB8+/DTAtm3bDvYYqlYAACAASURBVB86dOjAokWLYq+1ztGjR5/dtm1b9fzdNxaLhRUrVkRHRUU5jx071g1g5cqV1S5evGgBSEpKssTGxjr5+Phk2tqvNW3atEn38/NLW79+fY1rse29996rs2HDhhrLly+PtrP7exBW3759z3/99de1c3NzWb9+fZVq1arleHl5ZaWnp8v999/vO2jQoLNPPfVUkvWxdOrU6WLeyLCFCxfW7t279/lrsQWgX79+FzZv3lw9MTHRLjEx0W7z5s3V+/Xrd8HLyyurSpUquevXr6+Sm5vL119/XfuBBx44D9CzZ8/z8+bNqw0wb9682r169brmenU3mUajscnFs2dY+8kcYvbsxjOgJfeMGENNV6uX25REWP0y7F8GDVrDgAVQp8BRwDaJO5fKR5ui+GHXcSwW4bFOXozo1pj6NbQA0pQMFouFlStXHhk5cqTnrFmz6js5OSkPD4+MOXPmxMXFxTn5+/sHZGRkSJUqVXKHDx9+urDg6f79+/vZ29srgNtuuy1l1KhRNoOFnZ2d1bBhw05PnDjxqqnWK1WqpFavXh0VFBTUdNq0aVmpqamWF198saGdnZ1SSsnjjz9+plu3bsWKcZo4cWJ8UFBQQHHPBcD48eO9GjRokNGuXTt/gN69eye9++678Y888siFVatW1fDy8mpRqVKl3E8//TQGYOHChbV+//33qklJSfbffPNNHTPtaOfOndNmzpx5fODAgY3feust9+bNm6cWFsO0efPmyo888ohvcnKy3fr162tOnTrVLSoqar+rq2vOyy+/fLJt27b+pn0n84KpP/roo9inn37aJz09XYKDg5MffvjhCwCTJ0+O79+/f2MvL686bm5umcuXLz9yLecAQG5En2hFp127dmrXrl1lbYZGUyoopdi/aR0bP59Pbm4OXQcPpfU99yN5oz6Ugn1L4KeXITPFCJAOGntNQdLHk1KZu/EIP+yKwyLCox08ee5OXy2CKhgislsp1c46LTw8PCYwMLDAh6RGU5KEh4fXCQwM9M6frj1DGo3mMhfPneGXTz7k6J+78PBvQc8RY6hZ32qalOR4Y3HVyFXg3tboFqvnX+z954mgxbvjEITBHRv+P3vnHRbF1YXxd3YX2KUtLAgsvZeliIqogAqaKAYUFRVLxJLESmIhEZMvEjWmmNhLEjViSTRqjILBWLFiwRpRERCkKb2zhWXL/f5YQFRANCqo83uefXbmzp07d/eB2XfOOfccTPe3A5/LefrJNDQ0NC+JDiOGKIpiAviEELKyvedCQ/O2QQjB7dMJOLVtExRyOQImTkGXgcGPWoP+3Qkc+RyQS4F3vwZ6zgCYbbuFPKiUYP3JDPx5RSWCxnjTIoiGpq1cunSJEx4e/kiiLnV1dWVycnJqe82prURFRZnExcU9ssotJCSkfOnSpYXtNafm6FBuMoqiThFC/Nt7Ho9Du8lo3mRqyktxfNN63Lt2GWbOrhg4fRb0TUwfdqjMA/6eBWQmAJa9gCHr2hwb9KBSgp9OZmBPvQgK626B6f52MNWjRdDbAO0mo+lovC5usnMURa0DsBuAqKGREHKt/aZEQ/NmQghBypkTOLltIxQyOQImfIQugYMfWoOUSuDqFuBYtMoyNOhHoPuHAOPpi1DzKyX46VQGdl9WrdQN626BGf72tAiioaHpkHQ0MdSQmGRxkzYCoF9LJ1AUZQFgOwATAEoAGwkhqymK4kElqqwBZAMYRQipoFSJUVYDeA+AGMBEWmzRvG0Iy8twbNM63Lt2GaZOAgROnwV9vtnDDuX3gAOfANlnAZu+wJA1gL71U8fNr5Tg51OZ2H05DwQEo7wsMCPAHma0CKKhoenAdDQx9A4hRPGM58gBRBJCrlEUpQPgKkVRxwBMBJBACPmeoqj5AOYDiAIwCIBD/asHgJ/r32lo3ngIIbhz9iRObN0ARZ0M/uEfosugwWAw6vOLKBXApY1AwmKAYgKDVwNdJwBPyfRaUCXBTycfiqCRXhaYSYsgGhqa14SOJoYyKIraCyCGEHKnLScQQgoAFNRv11AUdQeAGYAQAP713bYBOAWVGAoBsJ2ogqUuUhSlR1EUv34cGpo3FlFlBY5tWofMK0kwdXTBwOmzwTNtYg0qSQcORAB5SYDDACB4JcA1b31MqRwrjqXjtws5jSJohr8dzPVfaO1NGhoampdKR8tA7QEgHcBmiqIuUhQ1haIo3baeTFGUNYAuAJIAGDcInPp3o/puZgCa1sO5X9/2+FhTKIq6QlHUlZKSZvNo0dC8FjRYg7bOnY6cG9fRd/wHCFv0/UMhpJADiSuBX/yAkjRg2AZg7J6nCqHT6SUYsPIMYs5lYVgXM5z81B/fDnOnhRBNhyc3N5cVHBxsa2Fh4WZnZ+fat29f++TkZA02m93VxcVFYGtr6+ru7u6ydu1ag5bG8Pb2djpz5swjf+zx8fE6AQEB9gCwZs0aAwaD0S0pKanRPOrg4OCalpamDgBmZmbuBQUFLAA4e/asppmZmfu5c+c4eXl5rICAAHsnJydBw9xamkNaWpo6RVHdvvnmm4bfN4SHh1uuWbPGAFBVpzczM3N3dnYWCAQCl+PHj2sBwOzZs01jY2N1AGDIkCE21tbWbg4ODq4jR460lkqlFAAolUpMnDjRwtLS0s3R0VGQmJioCQDnz5/neHp6Otvb27s6OjoKNm3a1JhuPjU1Vd3Dw8PZysrKLSgoyLa2trZFk/KhQ4e0BQKBC4vF6taQtbqBu3fvqvv6+jrY2tq62tnZNX5nLY0vkUiooKAgW0tLSzcPDw/nhv7PQocSQ4SQGkLIJkKID4B5AL4CUEBR1DaKolpdvkJRlDaAvwDMJoRUt9a1uUs3M5eNhBAvQohXp06dnuFT0NB0HESVFTiw/Bv8s2459E3NMP6HNfAKHvbQLVZ0G9j8DnB8IeDwLjDzEtB5dKtusQpRHebu+RcTYi6BrcbA3mm9sHSEBy2CaF4LlEolhgwZYt+nT5+avLy8W5mZmbe/++67B/n5+WoWFhbSO3fupNy7d+/27t27M9evX2+8evXqFgXR0zA2Nq5bvHgxv7U+SUlJnNGjR9v9/vvvmb6+vpKoqCizfv36VaelpaVkZmbe/uGHHx60dj6Px5Nv2LDBqCXh0VCbbMmSJQ9mzJhhBQCrVq3KHzp0aA0AjBs3rvzevXu30tLSbtfW1lKrVq0yBIA///yTe+/ePXZ2dvatn3/+OWfGjBmWAKCtra387bffsjIyMm4fPXr07hdffGFRWlrKBIC5c+eaR0REFOXk5Nzicrny1atXGzY3JwCwtbWt27JlS/bgwYOfyPA9btw4m08//bTo3r17t69du3anoUhtS+OvXr3akMvlynNzc29FREQUzZ07t/UnuWboUG6y+lxDQQAmQRX4vBzADgC9AfwDwLGF89SgEkI7CCH76puLGtxfFEXxARTXt98H0DQlujmA/Bf8UWho2hVCCFLPn8GJmF8gk9aiz/uT0S0o5KEIkteprEFnfgTYXGDEFsB1WKsiiBCCf24W4qsDt1ApluHjfvaYGWAPthqzxXNoaFoj/4v/WUjv3n2hKlrDwUFs+u03eS0dj4+P12GxWGTevHmNJn8fHx/J49YEgUBQ98MPP+RFRUVZtFaSozX69+9flZSUpHPjxg2Nzp07Sx8/fuPGDfaHH35oExMTkxUQECAGgMLCQrUBAwZUNfTp0aOHpLVr8Hg8effu3YXr1683iIyMbDFlQWBgYM24ceM0AJXFKDg4uGrSpEkVYWFhjdfy8vISNRRJjYuL0xs3blwZg8FA//79RdXV1aycnBw1Dw+Pxs9hbW0t4/F48oKCAhaPx1NcuHBBJy4u7h4ATJ48uWzhwoWmUVFRzbpWnJyc6gBVeZSmXL16la1QKDBs2LBqAOByuUpAJWJbGj8+Pl5v4cKF+QAwadKkiqioKEulUvnE2K3RoSxDAO5CFdPzIyGkCyFkBSGkiBCyF8Dh5k6oXx22GcAdQsiKJocOAJhQvz0BQFyT9nBKRU8AVXS8EM2bhKiyAn+v+A7/rPkR+iamGL90DboPHv5QCOVfBzYFAKe+BQQhwMwkwG14q0KoqLoWU367ipk7r4HP5eBAhB8iBzjRQojmtSM5OZnTuXPnNtX68vHxEWdlZT13jRgGg4FZs2YVLlq0qFnrUFhYmP3y5ctzBw4cKGxomzlzZvHHH39s3aNHD8eoqCiT7Ozsp9a5iY6OLli3bp2xXN5ysfZdu3bpOTg4tCispFIptXv3boOgoKAqACgoKFCztrZuLBDL5/PrGirDN3Dy5ElNmUxGCQQCaVFREUtHR0ehpqbqYm1tXVdUVPTM7qqUlBS2rq6uYsCAAXYuLi6CqVOnmsvlcrQ2flFRkXpDMVs1NTVoa2srioqKnsnY0yEsQxRFjQFwFIAHIUTYXB9CyCctnO4LYDyAmxRF/Vvf9gWA7wHsoSjqAwC5AEbWH/sHqmX1GVAtrZ/0Qj4EDU07QwhB2oWzSIj5BbJaCXqPnQivwU1cYrJa4PRS4NxqQKsTMHon4Bz01DF3X87DN//cQZ1cic8HOeMDPxuwmB3tOYrmdaQ1C05H4EUkJZ46dWrZsmXL+KmpqU8IA19f3+rNmzcbhoaGVrFYqp/j0NDQaj8/v5v79+/nHj58mNutWzfBzZs3bze4iprD2dm5ztPTU7Rhwwbe48e+/PJL86VLl/J5PJ5s8+bN2S2NMWHCBMuePXsKAwMDhUDzn51q8sCUk5OjNmnSJNvNmzdnMZnMlvo/8xcol8upK1euaCclJaU4ODjUBQcH261du9YwLCzsiUr0DeO/iGt3lDuaFYA/ARyiKGohRVE9KOopa3nrIYQkEkIoQogHIcSz/vUPIaSMENKfEOJQ/15e358QQmYSQuwIIe6EEDq1NM1rj7iqEn+v/A4HV/8APWMTjP9+NbxDRjwUQnmXgQ19gMQVQOcxwMyLTxVCOWUijPs1CfP33YSAr4sjs/tgal87WgjRvNa4u7tLbty40SbX3IULFzRtbW0lAODn5+fg7OwsCAsLs3qW66mpqSEiIqJw8eLFJo8f27RpUy4AhIeHPzKmsbGxYtq0aeWxsbFZHh4eoqNHj2o/7TrR0dGFq1at4iuVykfaG2KGzp8/f7d79+61zZ0bGRnJLy0tZW3atKlRnJqamsqys7MbBVxBQYG6paWlDADKy8sZgwYNso+Ojn7Qv39/EQCYmJjIa2pqmDKZDACQnZ2tbmRkJHvavB/H0tKyzsXFRSIQCOrU1NQwZMiQimvXrmm2Nr6JiUldVlaWOgDIZDIIhUKmkZHRM6Xp6RB3NULI94SQflBZbG4AmAzgGkVROymKCqcoyrh9Z0hD03FJu5CIrZEzcO/qJfiNmYAxi3+Egbml6mCdGDjyP2Dzu0CdCHj/L2DoeoCj3+J4CiXBpjP3MHDVGdy8X4Vvh7njj496wtpQ6xV9Ihqal8fgwYNr6urqqOXLlzcG954+fVozIyPjEctNWlqa+vz5882nTp1aDACJiYl3U1NTU3bv3p3zrNeMiIgoS0xM1C0vL3/EG8NgMBAXF3cvIyODPXv2bFMAOHDggE5NTQ0DACoqKhg5OTkaDS6g1ujSpUutg4ODJCEhgfssc1uxYoXhiRMnuLGxsfeYzIdu7yFDhlTu2LHDQKlUIiEhQUtHR0dhZWUlq62tpYKCguxHjx5dNnny5Iqmn6Vnz541DSvDYmJiDIKDg5+w5jyNvn37iqqqqpj5+fksADh58qSuQCCQtDZ+UFBQZUxMjAEAbNmyRb9Xr141zxIvBHQQN1kDhJAaAPvrX6AoSgBVksTtAAa249RoaDocZffzcP7PHUi/mAhjWwcEzpgNQ4smD5jZiUBcBFCRBXh9ALyzEGC3nqkitbAaUXuTceN+Fd5xMcKSoe4w4T53yAQNTYeDwWDgwIEDmTNmzLBYtWqViYaGBjE3N5euXbs2Ly8vT8PFxUUglUopLS0t5dSpU4tbC54eNmyYA4vFIgDQtWtX4cyZM5sNFmaz2WTKlCnFCxYssHj8GIfDIYcOHcrw9fV1+u6772RisZgxZ84cSyaTSQgh1Pjx40v79u3bphinBQsWFPj6+gra+l0AwLx586z4fL7Uy8vLBQCCg4Mrli1bVjBq1KiqgwcPcq2srNw4HI7y119/zQaAmJgY/cuXL2tXVFSwdu7caVjfluXj4yNZvnz5/bCwMLslS5aYubq6imfNmtViQPfp06c1R40aZV9dXc1MSEjQ++abb0wzMjJus1gsfP/99/f9/f0dAcDd3V08Z86cUgBoafxZs2aVhoaG2lhaWrpxuVzF7t27M5/lOwA6SKFWiqK6tnJYCiC3Xii1C3ShVpqOgqxOivQLibh54ggepKaAyWKh14ix6D4kFIyGpzppjWqp/OVfVSU0hqwFbPq0Oq5UrsD6Exn46VQmuBw1LBziimAPPtroraahaRa6UCtNR6OjF2pd3soxFgBLiqLWE0J+eFUToqHpSJTkZiP5+GHcSTwJqUgEfb4p+oybBNe+/aHJ1XvYMfMEcGAWUJUH9JwB9PsSUG/dvXU1pwJRfyUjo1iIYV3MsCBYAJ7WMy8CoaGhoXlt6RBiiBAS0NpxiqI0AFwHQIshmreGuloJ0s6fxc2EIyjISAOTxYJDD1949B8Ic4H7o1YbSSVw9Evg+m+AgQMw+Qhg2XrJPZFUjmVH07D1fDb4umxsmdQdAU5GrZ5DQ0Pz6rl06RInPDzcpmmburq6Mjk5ObW95tRWoqKiTOLi4h5Z5RYSElK+dOnSwvaaU3N0FDfZvAarD0VRIwkhfzY59i0h5AuKoroRQq62x/xoNxnNq6ToXgaSEw4j9dxp1EkkMDC3hHu/gRD0CQBHp5mYn7TDQPxsQFgE+HwC+H8OqLUe53MmvQSf77uJB5UShPeywrxAZ2hrdIhnI5o3CNpNRtPR6OhustF4aPX5HKpl9g0EAviivYQQDc2rQCoWI/XcaSQnHEZxViZYaupw8ukN934DYerk0nzsTkkacGYZcHMPYCQARu8AzLq1ep1KcR2WHLyDvVfvw7aTFv6c1gvdrZ9ITUJDQ0PzVtFRxBDVwnZz+zQ0bwSEEBRmpKusQOfPQC6VopOlNfpNngYXP3+wtZpJLSIqBW79Bdz4Q5VJmsEC+s4HekcCrNbjfA7dLMCCuNuoENdhZoAdPu7nQGeQpqGhoUHHEUOkhe3m9mloXmtqhULcSTyJ5IQjKM3NhpoGGy6+feHefyBM7ByftALJaoH0w8CNXUDGMUApB0w8gIHfAm4jAJ3W03AVV9diQdwtHLldBFdTXWyb3B2ups+UioSGhobmjaajiKHOFEVVQ2UF4tRvo36fTnJC89pDCMGDtBTcPH4Y6RfPQS6rg7GtA979KALOvn2gztF8/AQg9yKQvAu4tR+QVgE6fNUKsc6jAWPXNl3zzyv3seRgCqRyJaICnfFRb7qUBg0NDc3jdIi7IiGESQjRJYToEEJY9dsN+08tUkdD01GR1FTj6sFYbI2cgd1fRSHjykW4+r+D979fjfe/WwmPdwIfFULl94CT3wFrPIEtgUDyHsBpEDB+PzDnNjDg6zYJodwyMd7fnIR5fyXDma+LQ7N6Y7o/XUqDhgYAcnNzWcHBwbYWFhZudnZ2rn379rVPTk7WYLPZXV1cXAS2trau7u7uLmvXrjVoaQxvb2+nM2fOPPIUEx8frxMQEGAPAGvWrDFgMBjdkpKSOA3HHRwcXNPS0tQBwMzMzL2goIAFAGfPntU0MzNzP3fuHCcvL48VEBBg7+TkJGiYW0tzSEtLU6coqts333zTuAw0PDzccs2aNQaAqjq9mZmZu7Ozs0AgELgcP35cCwBmz55tGhsbqwMAQ4YMsbG2tnZzcHBwHTlypLVUKqUAVZX4iRMnWlhaWro5OjoKEhMTNQHg/PnzHE9PT2d7e3tXR0dHwaZNmxrT2aempqp7eHg4W1lZuQUFBdnW1tY+Ncxly5Yt+hRFdWv6XX7++ecmlpaWbtbW1m5//fVX46qRvXv36lpbW7tZWlq6ffHFF0+UN/kvdBTLEA3NGwMhBHm3b+LmiSO4m3QOCrkcfEdnDJw2C069ekON/ZixU1IB3N6vcoPlJQGgVEkS+84HXAYDGk8tS9SIQkmw5VwWlh9NB5NBYclQN4z1tgSDQYfe0XQ8ErbfsSh/IGxTnbC2wjPTFvcPd2mxAKxSqcSQIUPsx44dWxYfH38PUP3A5+fnq1lYWEjv3LmTAgApKSnqw4cPt1cqlWgtC3VrGBsb1y1evJh/8ODBey31SUpK4owePdru999/z/T19ZWMHTvWql+/ftULFiwobjje6ufl8eQbNmwwioyMLGGz2U+ElSxZsuT+pEmTKvbt26c7Y8YMq/T09JRVq1blNxwfN25ceWxsbBYAhISE2KxatcowKiqq5M8//+Teu3ePnZ2dfevkyZNaM2bMsExOTk7V1tZW/vbbb1nu7u7S7Oxste7du7sMGzas2tDQUDF37lzziIiIoilTplSMHTvWcvXq1YZRUVHNZuUGVOVG1q9fb+Th4SFqaLt69Sp73759vLS0tNs5OTlq7777rmNISMgtAJgzZ47lkSNH0m1tbWWdO3d2CQ0NrezWrVuz9daeFfoxkYbmBSGqrMCluL2ImT0Ff379BbL+vQKPdwdhwo/rMPbrZXALePehEJLXAan/ALvHA8scgfg5QG2VqmTGnNvAhAOA55hnEkJphTUI/fk8lhy8g152Bjg2tw/e72lFCyEamibEx8frsFgsMm/evMYfaR8fH8nj9b8EAkHdDz/8kPfLL788d23M/v37V6Wnp3Nu3Lih0dzxGzdusENDQ+1jYmKyAgICxABQWFioZmFh0TiXHj16SFq7Bo/Hk/v5+dWsX7++RSsWAAQGBtbk5eVpACqLUUONr7CwsCoGgwEGgwEvLy/R/fv31QEgLi5Ob9y4cWUMBgP9+/cXVVdXs3JyctQ8PDyk7u7uUgCwtraW8Xg8eUFBAUupVOLChQs6kyZNqgCAyZMnl/399996Lc0HACIjI80iIyMLNTQ0GkXc3r179YYPH17O4XCIs7NznZWVlfTUqVNap06d0rKyspIKBII6NptNhg8fXr53795Wx38WaMsQDc1/gCiVyLn5L5ITDiPzShKUCgXMnF3Ra8RYOPTwgZp6k3sgIUD+NZUF6OZeQFIOaBqq6oZ1Hg3wOwPPUf6iTq7E+pMZ+OlUBnTYalg92hNDOpvSpTRoOjytWXBeFsnJyZzOnTu3qdaXj4+POCsr67njVhkMBmbNmlW4aNEi/r59+7IfPx4WFma/cePGrIEDBwob2mbOnFk8ceJE259//lns7+9fPX369DJra+tWq79HR0cXDBo0yKG1WmC7du3Sc3BwaFFYSaVSavfu3QYrVqzIA4CCggI1a2vrRlHG5/PrcnJy1KysrBrncvLkSU2ZTEYJBAJpUVERS0dHR6Gmpopssba2risqKmpxieu5c+c4Dx48UB8zZkzVypUrG11eDx48UO/Zs2fj92FqalqXl5fX4FpsnI+5uXldUlJS258WnwIthmhonhG5TIaqokLcvXQeN08cRXVJEdg6uugyaAjc+w2AgdljtRgr84Dk3SoRVHYXYGoAzu8BnccAdv0A5vOHxV3PVZXSSC8SIsTTFNHBAhhoN/sQSkND84y8iKTEU6dOLVu2bBk/NTX1CWHg6+tbvXnzZsPQ0NAqFkv1cxwaGlrt5+d3c//+/dzDhw9zu3XrJrh58+ZtU1NTeUvXcHZ2rvP09BRt2LDhiaRhX375pfnSpUv5PB5Ptnnz5uyWxpgwYYJlz549hYGBgUKg+c/e9AErJydHbdKkSbabN2/OYjKZLfVv9gtUKBSYM2eO5W+//Zb1+LGWxlEqlU883bU0/vNAiyEammaQ19WhqrgQFQX5qCzMR2VRASoKC1BZWICa0hIQogQAWLp1Ru+xE2DfvRdYak1ETW01cOeASgBln1W1WfoAPh8DghCA8/zW3fsVYhy5XYQjtwpxOaccJrpsxEz0Qj/n57bm09C8Nbi7u0tiY2P1n94TuHDhgqatra0EAPz8/BxKS0vVOnfuLNq9e3dOW6+npqaGiIiIwsWLFz8R8Ltp06bcSZMmWYWHh1vt3LmzcUxjY2PFtGnTyqdNm1YeEBBgf/ToUe2JEydWtnad6OjowlGjRtn16NHjkaLmDTFDrZ0bGRnJLy0tZR05cqSx2rupqaksOzu7UcAVFBSoW1paygCgvLycMWjQIPvo6OgH/fv3FwGAiYmJvKamhimTyaCmpobs7Gx1IyOjZi1alZWVzLt377L79evnBAClpaVqI0aMsN+7d2+Gubl5oyUIAPLz89XNzc1lgMpq1NB+//59dVNT01YtZs/Cay+GKIqKARAMoJgQ4lbfthDARwAafMJfEEL+qT/2OYAPACgAfEIIOfLKJ03TIZBJa1FZVIjKwnyV6ClSiZ3KwgLUlJeq3Fr1sLV1oGfCh6mjM/T79oOeiSlMHZyhZ8J/OKBCDtw7pUqImHoQkEsAni0Q8D/AY5SqgvxzklEsxJHbhTh8qxA3H1QBAJxNdDDnHUdM8rWGDptedElD0xYGDx5cs2DBAmr58uWGkZGRpQBw+vRpTaFQ+EgMbVpamvr8+fPNp06dWgwAiYmJd5/3mhEREWWOjo4mIpHokSynDAYDcXFx9/r27es4e/Zs01WrVuUfOHBAJyAgQKSjo6OsqKhg5OTkaDwez9QcXbp0qXVwcJAkJCRwvb29RU/r38CKFSsMT5w4wT179mwak/lwekOGDKn86aefjD766KPykydPauno6CisrKxktbW1VFBQkP3o0aPLJk+e3CiyGAwGevbsWbNlyxb9KVOmVMTExBgEBwc3K+AMDAwUFRUVNxr2vb29nZYtW5bXp08fsaampnLcwhr8tgAAIABJREFUuHG20dHRRTk5OWrZ2dlsf39/ESEE2dnZ7NTUVHVra2vZvn37eDt27GgxMP1Zee3FEICtANYB2P5Y+0pCyLKmDRRFCaAq/eEKwBTAcYqiHAkhilcxUZpXT51E/FDwFBaorDz178KK8kf6cnS50DPhw0LgBj0TU+jxTaFvzAfXhA+Otk7LFym8WR8H9KeqPhhbD/Acq3KDmXs9VxwQIQS386tx+FYhDt8uREaxyoXexVIPnw9yxkBXE1gbtl6NnoaG5kkYDAYOHDiQOWPGDItVq1aZaGhoEHNzc+natWvz8vLyNFxcXARSqZTS0tJSTp06tbi1lWTDhg1zYLFYBAC6du0qnDlzZrMrp9hsNpkyZUrxggULLB4/xuFwyKFDhzJ8fX2dvvvuO5lYLGbMmTPHkslkEkIINX78+NK+ffu2KcZpwYIFBb6+voK2fhcAMG/ePCs+ny/18vJyAYDg4OCKZcuWFYwaNarq4MGDXCsrKzcOh6P89ddfswEgJiZG//Lly9oVFRWsnTt3Gta3Zfn4+EiWL19+PywszG7JkiVmrq6u4tZimFrCy8urdujQoeWOjo6uTCYTK1asyGlwIS5fvjw3MDDQUaFQYOzYsaVeXl4vZCUZ0EEKtf5XKIqyBhD/mGVI2IwY+hwACCHf1e8fAbCQEHKhtfHpQq0dG6lYhMrCAlQ0Cp0CVBaprD3iqkcfTLT09KFnwoeesanq3YQPfRPVtoZmG8WFvA6oyFZlhU7eDRTdAhhqgONAwCNM9c569rgdpZLgWm5FowC6XyEBgwJ62Bgg0M0EA1yNwee2usqWhqZDQRdqpelodPRCrS+DCIqiwgFcARBJCKkAYAbgYpM+9+vbaF4DpGIRCtJTUZCZjsqCfFTUu7Uk1VWP9NPW50GPbwrbrt71YoevsvQYmzyZ6bklZLVAZY4qCWL5PaAs8+F2VR5QHzMEMy/gvWWAWyig+ewFT2UKJS7eK8PhW4U4mlKEkhop1JkM+DkY4pN+DnhHYAyeVus1x2hoaGho/htvqhj6GcDXUNU1+xrAcgCT0XzR12ZNYxRFTQEwBQAsLS1fzixpWoQQgqqiQuSn38GDtBTkp6eiNC+nMY5Hx6AT9Ez4sO/es9Gyo2diCj0jkyeTGrZEnVhl4WkQOeX3gPJMoDwLqLqPR/40NLiAga3K7eURpooFMu8OGLaYHLZFamUKnEkvweHbhUi4U4wqiQya6kwEOBlhoJsJApw60TFANE+FEAJFZSXkxcWqV1ERZMXFkBcVg8hk4IaEQNO7O51i4Q3g0qVLnPDwcJumberq6srk5OTU9ppTW4mKijKJi4t75EkxJCSkfOnSpYXtNafmeCPdZC0do91kHRe5TIbirAzkp93Bg7Q7yE+/0+jiUudowtTRGaaOLjB1cgHf3rHtFh6pEKjIekzwZKksPTX5j/bl8FQix8BO9d70xdF/rtifBmpqZTiZVoIjtwpxMq0Y4joFuBw1vONijEA3E/R2MKQryNM0opRIIC8uhqyoCPLiEsiLilT7xY/uk7on42qZ+vogcjmUNTXQELjAYMIE6A4aBEr91VsYaTcZTUfjrXKTURTFJ4QU1O8OA3CrfvsAgJ0URa2AKoDaAcCldpjiW4+4qhL56amNVp+ie3ehkKlWSeoZ82Ht0QWmTgKYOrnAwNwCDEYrQqG2+kmx07AtfOzhQ6sTwLMDbP3rhY7Nw3dOm1bbtplyUR2OpxTh8O1CJN4tRZ1CCUNtDQzrYoZANxP0tDWAGl0r7K2CyOWQl5U9Yclp2JeXFENWVAxldfUT51IcDtSMjMAyNgbH0xMsY6PGfZaRMVhGRmAZdQJDXR3K2lpUHTiA8m3bkR81H8XLlkP//fehHzYKTL0XlrSXhuaN4bUXQxRF/QHAH4AhRVH3AXwFwJ+iKE+o/BzZAKYCACHkNkVRewCkAJADmEmvJHv5EKUSZQ/ykF9v8XmQloLKQpVWZbJYMLK1h+fAYJg5ucDU0QVaei2IEoUMuH8FyEl8NIZH9NgCDh2+SuA4vPOodUffBmDrNj/2C6KwqrZxCXxSVhmUBDDX5yC8lxUC3UzQxVIfTLo8xhuLrKgI0rsZTYTNo5YceWkpoFQ+ehKTCZahIVjGxlCzsoJmd+96gWMENWMjlcgxNgZDW7vNLi8Gmw39UaOgN2IEROfOoXzLVpSsXInSn38Gd9hQ8MLDoWFj8/SBaGjeEt4IN9nLpj3dZHVSKY7MXwm+whJCeSXKUQi2ox6MHeygqacHTV0utLh60OTqga2j07oF5VXNuVaCwox0lcsr/Q4K0lMhFavSXnB0uTB1dGkUPsa29mC1Zr4vvwdkngAyTwJZZwBp/ROzrnkTq07Tlw2g/mqXnGeXilQC6HYhrueqXHsORtoIdDPBQFcTuJrq0nEbbzBKkQjVR4+hKjYW4qSkR44xudxGMaN6f9ya0wksAwNQzJf/f1ublo7y7dtQfeBvEJkM2v7+4E2cCM0e3i/t75N2k9F0NFpyk9FiqA20lxj6Z/FKmJabw5BtArFcCHUGGywGC4QQVNYVo6Q2D8W1uSipzUOdshYUxQBHVxeaulxo1gskTa5KMKmEk16jcNLk6rUuQp6B6tLiR2J9SnKyQOqffg3MLWFW7+4ydXKBnjG/9RtvbbUqY3NGgkoEVdRna9ezBOz6q8pX2PT5Txmc/yt1ciXuFFTjZFoxDt8qRGqhKuGrhzkXA11VAsje6IWVzKHpgBClEuJLl1C1PxbVx46BiMVQs7AAd2gItLwfWnYYbQ3mf4XIS0tR8ccuVPzxBxTl5dBwdgZv4gRw33vvhccV0WKIpqNBi6H/wKsWQ2e37gTrqgwWWraoVYiRLkmG36LJkNdJcXlrHNTzNaDPNIG+hjaYFKVaeSWrQan0PioYBVAaKlEnl0BcVQlxdSXqJM3X5lPncOrFUoNA4kKTqw9Nbr21SVevUURpaGmBoigo5HKU5GTVu7tU4kdYprqvqWmwwXdwrA90FoBv7wS29lNEgVIB5P9bb/1JAPIuAUQBqGsD1r0B+3oBxLP9TwHMzwshBA8qJfg3rxLXcyvxb14lbj6oQp1cCYoCulvxMNDNBANdjWGu38agbprXlrrsbFTGxaEqLg7y/AIwtLWhOygQ3KFDwena9bWyACqlUlT//TfKtm5FXUYmWJ06QX/cWOiFhYGl/2Li5zqqGMrNzWXNmDHD8saNG5rq6uqNSRe9vb1dbWxsahuSLk6ZMqX4448/bjbpYtOsyQ1t8fHxOsuXLzc+efJkxpo1awxmz55tfeHChZSGyvMODg6u8fHxd52cnOrMzMzcr1y5cofP58vPnj2rOXr0aLs9e/ZkWFpaysLDw63z8/PV5XI5ZW5uLj19+nRGc3NIS0tTd3Z2dl+yZEne//73v2IACA8Pt/Ty8hJ98sknLSaLDA0Ntb548aKOjo6OgsFgkDVr1uS+8847LWatDg0NtQ4ODq6aNGlSRVhYmNW8efOKunXr9sISHr5K3qoA6teV1AsXUfD7Ndhqu4BwCNKqb8DmIz+81+Wzxj4DP5/euF2cm4tr2w5Ao1gLPBYfNtousKcEIGKCGrkUFVQRJHYiCEb2g7YBF6KqCoirqlQiqeFVXQVxVQUqC/PxIC0FkprqR8pQNMBgsqDJ5aJWJIRcKgUA6Bh2Ull96t1enaxswGiLub/qfr34OaEqXyGpAEABpp6A32yV+DH3BlivfvWLSCrHjfuVj4ifkhrV59VgMeBuxsWEXlbwtNCHtw0PnXTooqhvOorqalQfOoyq2FhIrl8HGAxo+fjAaG4kdPr3A4PzeibCZGhoQG/ECHBDQyFKPIfybdtQsmo1Sn/ZAG5ICHgTwqFha/tS53Dk51UWpXk5L/QpwtDCSjxw+uy8lo4rlUoMGTLEfuzYsWXx8fH3AOD8+fOc/Px8NQsLC+mdO3dSACAlJUV9+PDh9kqlEq1loW4NY2PjusWLF/MPHjzYYtmIpKQkzujRo+1+//33TF9fX8nYsWOt+vXrV71gwYLihuOtXYPH48k3bNhgFBkZWcJms9ts3WioWbZv3z7dGTNmWKWnp6e05bxnqcv2OkGLoQ5AZXEJLizaDkdNd9hruyFXlAFmHy76j4po9TwjS0sELnjY50FGOm7sOAxOqS70WXyYsy3BFFFQbCnAfXkWKuTFkBpJ4Dn+PfAsm881qVQqIKmuVomkSpVlqUE4iaoqoc7hNAogHQPDtn3AOhGQc14lfjISgNI0VbsOH3B6TyV+bP0BrTaO94JQKgkySoT4N7cS1/MqcD23EulFNVDW305sDbXQ294QXSz14GmhD2e+Dr366y2ByOUQnT+PqthY1BxPAKmrg7qdHYw+jYTu4MFQM35ziuJSFAXt3n7Q7u0H6d27KN++HVX796Ny925o9+0L3sQJ0OzZ87WyerVGfHy8DovFIvPmzWtceeHj4yNJS0t75OlLIBDU/fDDD3lRUVEWzyuG+vfvX5WUlKRz48YNjc6dO0sfP37jxg32hx9+aBMTE5MVEBAgBoDCwkK1AQMGNGaSbbAqtQSPx5N3795duH79eoOGWmsNnD9/njN9+nQriUTCsLKyku7cuTO7U6dOjywaCgwMrBk3bpxGW/s3tYjt3btXNzo62kyhUFA8Hk+emJiYbmtr63bhwoVUU1NTuUKhgI2NjVtSUlIqn8+XP/s3+OqgxVA70hAcbc9whbuON4olD1DML0Hg0o+fazwze0eYfeXYuJ975zaS/zgGTrke9NXqxVENBdH6TBTKb6NCUQQZX4Yu4UPA5auECIPBhJaevmpF1/PmmlQqVSUqGqw/uRcARR3AYgNWvkDXcJUAMnJ5pa6vUqEU/9Zbe67nVSA5rwo1UtX/py6bBU9LfQx0NYGnpR48zfWgT2d+fuuoTU9HVWwcqv4+AEVJKZhcrsp6Mmwo2G5ub4wgaAkNBwfwv/4anWbPbowryp00GRpOTuBNmADd4CAwXmBcUWsWnJdFcnIyp3Pnzm2q9eXj4yPOysp67sAvBoOBWbNmFS5atIi/b9++7MePh4WF2W/cuDFr4MCBwoa2mTNnFk+cONH2559/Fvv7+1dPnz69zNrautXq7NHR0QWDBg1yeLwW2MSJE21WrlyZGxQUJJw9e7ZpVFSUaUxMzCPf+a5du/QcHBwkbe3fQH5+PisiIsL61KlTqc7OznVFRUVMJpOJESNGlP3666+86Ojo4ri4OF0XFxdJRxdCAC2G2o1/lqwGv5SPLmxfVNeV47r8PAb+MBvqGi/O7WLp4grLxa6N+xnXr+Lm3jPQruKpxJGGFZgVFKpXpeCBvBYViiIozJXoGj4Y2kbPWFpCWPxQ/GSeBETFqnYjV8B7iir2x7IXoPZqXApSuQIp+dWNrq7reRXIK1c9YDEZFFz4OgjpYoouFvrwtNSDjYEWGPSS97cSeUUFqv+OR1VsLGpTUgAWC9p9+oA7NATa/v4v9Mf/dYFlYIBOETNh8NGHqI6PR/nWbSj44gsUr1gB3rix0Bs9+oXFFXVkXkRM7dSpU8uWLVvGT01NfeIPydfXt3rz5s2GoaGhVQ3FSENDQ6v9/Pxu7t+/n3v48GFut27dBDdv3rxtamraoqBwdnau8/T0FG3YsKHxxl1WVsasqalhBgUFCQHgo48+Khs5cmSj3/PLL780X7p0KZ/H48k2b96c/bT+j3Pq1Cktb2/vGmdn5zoAMDY2VgDA9OnTS4cMGWIfHR1dHBMTYzhx4sTXIlieFkOvmMTfd4GRVAsPra6oVRMjWXgRfosmQ2AQ8tKvbd+lG+y7dGvcv3MhEZlxSdAWdoKeer04KqVQsfwWcuUSVCiLQKwY8JowFGz9xwKhZbVA3sV619cJoOimql3TQGX1sesH2AYAuvyX/rkIIbhfIcG13IrGWJ+U/GrUKVQr2vhcNjwt9DC+pyrWx92MC456+6cgoGk/SF0dhGfOoHJ/LISnTwNyOTQELjD+4nPoBgeDxXv2OnNvIgwNDeiFhoI7fDhE58+jfOs2lKxe82hckZ1de0/zmXB3d5fExsa2SclduHBB09bWVgIAfn5+DqWlpWqdO3cWPUvcjJqaGiIiIgoXL15s8vixTZs25U6aNMkqPDzcaufOnY1jGhsbK6ZNm1Y+bdq08oCAAPujR49qT5w4sfLx85sSHR1dOGrUKLsePXrUtGVeDTFDDftlZWXPdFMkhDRrKbW3t5cZGhrKDxw4oHP9+nWt2NjYFuOlOhK0GHpF3L10GXnbLsNOSxUcnV6TDKvJPfFet8+efvJLwqWXH1x6+TXuJ585ieyDV6ErNIaeugnM1a3BLKRQ8v011MglqCBFULPOhz37PLhFSWAqaqFkqEFo1A3V3eahxqwPpIauYDAYYFAUKCHAEFWDQVFgUKrYBIpC4z7jkf3m+1BN+jb0l8qVuPWgCtebiJ8ykaosAVuNAQ9zPUzytW6M9THhdrzlzTSvHkIIam+noCo2FtXx8VBUVoJpaAje+PHgDh0KtpPj0wd5S6EoCtq+vtD29YU0IwPl27ajKi4OlXv2QKtPbxhMnAjNXr1eCzfi4MGDaxYsWEAtX77csCHG5vTp05pCofCRgMC0tDT1+fPnm0+dOrUYABITE+8+7zUjIiLKHB0dTUQi0SOCg8FgIC4u7l7fvn0dZ8+ebbpq1ar8AwcO6AQEBIh0dHSUFRUVjJycHA0bG5sn6648RpcuXWodHBwkCQkJXG9vb5GBgYFCV1dXcfjwYe3AwEDh5s2bDXr16iVs6fxn7R8QECCKjIy0Sk1NVW9wkzVYhyZPnlzy4Ycf2oSGhpY1WLw6Oq/HLF9j6qRSHI9cA0eOBxx13JEjzADlp41+o2e299SewKNPADz6BDTuXz12GPeP3IKuxAT69eJIed8K6ZknkWnVCwnsbrioFECczVbl+UY1gFbLvL1w7Dppwd/JqF746MHZRAcsOsiZpgmy4mJU//03qmJjIb2bAUpdHdr9+0Fv6FBo+fqCek1u1h0FDXt78L9ejE5zZqNi1y5U7PwDuZM/gIajoyquaHBwh3YtMhgMHDhwIHPGjBkWq1atMtHQ0GhcWp+Xl6fh4uIiaFhaP3Xq1OLWgqeHDRvmwGKxCAB07dpVOHPmzJLm+rHZbDJlypTiBQsWWDx+jMPhkEOHDmX4+vo6fffddzKxWMyYM2eOJZPJJIQQavz48aV9+/ZtU4zTggULCnx9fQUN+1u2bMmaPn261SeffMKwtLSU/vHHH9mtnf8s/U1NTeVr1qzJHjZsmL1SqYSBgYHs/PnzdwFgzJgxVREREcwpU6Y8V+B5e0DnGWoDz5tnKPPqVZRuvQ0zLRsUS/JRxC/CoPmfvIQZvhpO//YXrG51Qk1dLUjCF1AOGAjJyPFQ8AyhJARKQkAI6rdRv/9wW0lUT+dt6aNUNj3+aH8mg4KAr4vO5nrgatLV3WmeRFlbi5qEBFTFxkF07hygVILj6Qnu0KHQHRQIJpfb3lN8Y1DW1aE6/iDKt26FND0dTEND6I8ZDf0xY6BmYNAh8wzRvFzOnDmjOWfOHIurV6+mtfdcHofOM/SKOb56A0xz+eBrWuF29RUErJyBri8wOLo96Ds+FDf3XIP+NQopfT6F+aFvwD52EPpjxsDgow/BMjBo7ynSvMUQQiC5fl2VFfrwYShrasDi82Ew5SNwQ0LoWlwvCYa6OvSGDwN32FCIL15E2datKF27DmUbNrb31GjagS+++MJk69atnbZs2ZLV3nN5FmjLUBt4VstQ/KylcNPoDhmpQ5rGLQQv+fQlzu7VQghB6vKL0CqRYY/aZQyWZEN56AQoNhu8cePAmzzprVhlQtNxUAhFqNr3F8p37IAsJxcUhwPdAQPAHTYUmt7eoBi02/RVI713D+XbtsN08SLaMvQCuHTpEic8PPwRNa+urq5MTk5Oba85va7Q5Tj+A20VQ2X5Bbj1zQHY6QhQWlsIWR91dB/+8leJvWoUYhlyvr2IOinBctP9iOo3DJztB1D9zz9gaGqCN2ECeBMngKn7civE07zdyIqKUfH776jYvRvK6mpwunaF3siR0B3wLhhar7ZYL03zdNRyHDRvL7Sb7CVzcc9f4JwnsNMRIKPmNjy/CoWeUaf2ntZLgampBpOJbij79RYm5A1B1MVN+DF6EWymTkHpuvUo/eknlP/+OwwmT4L+++PB1KZ/mGheHNK7d1EWswVV8fGAQgHtd99F4ZDuOKJXAiXugZW2GQyKASaDCRbFAoNigMVggUkxG7cZFANMivlwm8EEk2I+2la/3/RYs9uPtelr6L8Wq6poaGgeQouhF8DfX/wAF1lnMNVZuF57DoPXz2/vKb10NO31IfE1hfW5fITdGYePYyOxbugK2KxZjdqUFJSsXYeSVatRvm07DD78EPpjx7y2NZxo2h9CCMRJSSiLiYHozFlQHA6YQwNxykcHe4SnUXQ/Aax8leBRKBWQk/ZLeOvMc8bCXgvhauj69M40NDQdAloM/QdqRSKcmbcJnbV7QKioRIFtMQZHvPlCqAHeezYoyKhE90KC/OSxmKo2HRuDfoG1QACLn3+CJDkZJWvWovjHH1G2dQsMP5oCvbBRYLzmgeQ0rw4ik6H6yFGUx8SgNiUFDAMeCsYFYIdTKS5JDoFZwoSPqQ8+7f4pAiwCoMF8+LelJEoolAooiOolV8pVbUTxsL2l448da65/c9timRi/pfyGsf+MxTiXcYjwjICm2gutQ0pDQ/MSoGOG2kBzMUOpFy6i+o8MmGpaIU90D6ZTusLK3b2dZth+yEslKFh1DWW1cvyllorznn8iZtBmWOlaNfYRX72KktVrIL50CSwTExhOmwq94cNBdeBcJDTti0IoQuXeP1G+fTvk+QWQWxjjXN9O2GKWATFDDgd9B4TYhSDINgiGnFdb4Pdp1NTVYPW11didthumWqb4X8//oY95n/aeVrvQUWOGNDU1u4jF4usvYqw1a9YYfPXVV+bGxsaN9cN27Nhxr1u3brUvYvxnIT4+XmfMmDF25ubmdRKJhGFoaCiLjIwsHDNmTNXTz375NC3y2l5zeGNjhiiKigEQDKCYEOJW38YDsBuANVTpAEcRQioolSN/NYD3AIgBTCSEXHvWax5dvh4W+VYw4ZjjVvVl9Fs584XWFHudYBlywBtqB2rvXfQWO6H6bj9MpiYjJjCmURBpdusGq+3bILp4ESWr16Bw4SKUbfoVhjOmgxsSQie9o2lEVlSkCoretRvKmhqUOhtjtz8XZyxLoc8hGG4zGiH2IXDmObf3VFtER10HX/b8EkG2QVh0fhFmJszEIOtBmOc9r8MJN5oXw+DBgyu2b9+e+yLHlMlkUFN79jxqXl5ewpMnT2YAqir0I0eOtNfU1MwOCQlpU5mOt5U34VdoK4B1ALY3aZsPIIEQ8j1FUfPr96MADALgUP/qAeDn+vc2c/DjH+DK8UIdJcVN9lUELY18AR/h9UazmzFq0yrgcrMUJQW9UKVZgslHJmPLwC2w1LVs7KfVsyc0e/SAKDERJavXoOB/X6Js4yYYRsyE7nvvgWLS9cLeVmrT0lG+RRUUTZQK3HTTxh9dmMg1r4a/hT/W2oXAx8wHaozXJ8lmF6Mu2DN4Dzbf2oxNyZtwLv8cPvX6FEPth9IB1vWU7023kBWKXqgfUc1ES8wb4dhspfXWSE9PV58wYYJ1WVkZy8DAQL59+/ZsGxubOmtra/fc3Nyb5eXlTCMjI8+DBw+mDRo0SNitWzenbdu2Zbc0Xnx8vM7ixYtNeTyeLC0tjePu7i6OjY3NYjAYOHv2rObcuXMtxGIxQ19fX75jx45sKysrmbe3t5O3t7cwKSlJ+7333qscNWpU5dixY20UCgX1zjvvVG3cuNFYLBZfHzp0qM2IESMq3n///UoAGDJkiE1YWFg5l8tVNp2Dj4+P5LPPPstft26dUUhISE1+fj5r0qRJVg8ePFAHgBUrVuQOGDBANHfuXNPs7Gz1oqIitezsbPa3336bd+HCBe0TJ07oGhsby44fP56hoaFBPv30U/7hw4f1pFIpw8vLS7hjx44cBoMBb29vp27dugkTExN1a2pqmL/88kt2YGCgUCgUUqNHj7ZJT09nOzg41NbW1nbYP/zXPgEHIeQMgPLHmkMAbKvf3gZgaJP27UTFRQB6FEW1qZJoSW4uzkRsQmetXiiXlqDGHwhaRAshQFW3SH+YPZhcdfTQU0OvjMHQKzXD5COTkVed90Rf7d69Yf3nHpj/tB4Um438z+bhXkgIqg8fBlEqW7gKzZsGIQSiCxeQ/cEHyAoJQck/B3DIU4mIqQwcmOiAMcMW4OSok1jhvwJ9Lfq+VkKoAXWmOqZ3no69Q/bCXs8e0eej8cHRD5BT3eY6nzSviGnTplmOHTu2LD09PSUsLKxs+vTpFiwWCzY2NrXXrl1jHzt2TFsgEIhPnTqlLZFIqMLCQnU3NzcpAPz999/6zs7OgoaXUCikAODOnTuc9evX52VkZNzOzc3VOHbsmLZUKqU++eQTy7i4uMzbt2/fmTBhQumnn35q1jCPyspK5uXLl9MWLVpUFBERYTFjxoziW7du3TE1NW10w3300UclW7duNQBUBVavXr2qPWrUqGZdYd7e3uLMzEw2AEydOtVi7ty5Rbdu3bqzf//+zGnTplk39MvJydE4ceJExt69ezOmTZtm069fv+r09PQUNput3LNnDxcAPvvss+Jbt27duXv37m2JRMLYtWtXYyp3uVxO3bx5887SpUvzFi9ebAoAy5YtM+JwOMr09PSU6OjogpSUlA67tPhNsAw1hzEhpAAACCEFFEUZ1bebAWj663y/vq3g8QEoipoCYAoAWJnwUbDyMmy1nZFefRNeX4dBl862/AgMTTUYhDlBufEmuulrAGmTEKexGpOPTkbMwBhY6DxakoeiKOj06wdtf3/UHD1iO5M+AAAgAElEQVSKkrXr8GD2HGg4O6PTxxHQ7tePfnp+QyEyGaoOHcKDjT+BkZGDSm0K//RlINnXBP3dQrDZbjBsuG9Wtmhbri22BG7Bvrv7sOLKCgyPG45pnadhoutEqDFfP5H3ongeC87L4vr161qHDh3KBIDp06eXL1q0yBwAfHx8ahISEnSysrI0Pvvss4LNmzd3OnPmjLBz586ihnNbcpO5u7uL7OzsZADg6uoqzszMVOfxePK7d+9y+vXr5wgASqUSnTp1ahQ6Y8aMaXy4v379uvbRo0czAODDDz8sW7hwoTkABAUFCWfPnm314MED1o4dO/SDgoIqWnKpNY0LPnfunO7du3cbl/UKhUJmRUUFAwDeeeedKg0NDeLt7S1RKBTUiBEjquvnLcnKylIHgEOHDumsWLHCpLa2llFZWckSCAQSAFUAMHLkyIr670v02WefqQNAYmKi9ieffFIMAD169JA4Ojq2W6zQ03hTxVBLNPfr2mwEOSFkI4CNAOBu4ki01bi4Xnceg3+Kepnze63RsNWDjr8FcCoPZmosjMicg9+cv8YHRz5AzMAYmOuYP3EOxWBANzAQOu++i+p//kHJunW4PzMCbDc3dPrkY2j17k2LojcEhVCInN82oer3nWCXCZFvABwezIbmewMR7DwMi0y6g0G99sbqFmFQDIxwHIG+5n3x3aXvsOb6GvyT9Q8W+SyCRyeP9p4eTQv4+/sLf/rpp05FRUXqK1aseLBy5UqThIQEHT8/v6fG4GhoaDT+vjCZTMjlcooQQtnb20v+/fffZrNH6+jotMk8PmrUqLJff/2V99dff/FiYmKyW+p3+fJlTXt7+1pAJYyuXLlyR1tb+4nfvYa5MplMsFgswqjP3M5gMCCXyymxWExFRkZaJSUlpdjb28vmzp1rWltb2/gPy2azCQCwWCwoFIrGm/brcv9+U+88RQ3ur/r34vr2+wCamijMAeQ/bTAlUSLXIQ+DV9BC6GnovmsJNXNteHKYoKqU+KhgIcRSCT448gEeCB+0eB7FZII7eDDsDh4E/5tvoCgvR96UqcgZOw6iixdf4SegedFU5mYi8YspuOnXE9LVG5GhLcJfU1wg3vItvv72HL4O+B49+D3eaCHUlE6anbDCfwXWBKxBTV0N3v/nfXyX9B1EMtHTT6Z5aXTp0kX066+/6gPAhg0beF5eXkIA8Pf3F127dk2bwWAQTU1N4urqKt6+fXungIAA4fNcx8PDo7a8vJx1/PhxLQCQSqXUlStX2M319fT0FG7dulUfAGJiYnhNj02bNq10w4YNxgDg5eXV7Mq1pKQkzo8//mg6c+bMYgDw8/OrXrp0aYOnBOfPn29z8jexWMwAABMTE3lVVRXj77//fmrdJT8/P+Hvv//OA4DLly+z09PTO2yeiTf17nMAwIT67QkA4pq0h1MqegKoanCntQazkwb8P5r0cmb6hkExGeCNdgYFggBrHVTcq0Nk7Q8Q1gkx+fBk5Atb154UiwW90OGwO3wIJgu/giw/H7kTJyEnfALEV6++ok9B819RKBVIOrMb8RPfRV5gMPT2n0WKIwfXvx8L37+O48u5+xDiOOytzsETYBmA2JBYjHEegz9S/0BIbAhO5Z1q72m9FdTW1jKMjY09Gl4LFy40/vnnn3N/++03Q0dHR8Eff/xh8NNPP+UBAIfDISYmJnVeXl4iAOjdu7dQJBIxvL29JQ3jPR4zdOzYsRZjY9hsNtm1a1fm/PnzzZ2cnASurq6C06dPazfXd+3atXlr1641dnd3dykoKFDT1tZWNByzsLCQ29nZ1b7//vtlTc+5cuWKtouLi8Da2tptxowZlj/++GNuw0qyjRs35l27dk3L0dFRYGdn57pu3bo2l0kwNDRUjBs3rkQgELgOGjTIvqmbsCU+/fTTYpFIxHR0dBR8++23Ju7u7h1W8b/2eYYoivoDgD8AQwBFAL4CEAtgDwBLALkARhJCyuuX1q8DEAjV0vpJhJCnFh171kKtNMD/27vz+Kiq+//jrzNrkpksE0ISEghBBRRByIK4dKFFQeqvxb3254KtUvdqv/3Wot/Hr4u/r7a1m7Xfr18XRHGnWvcWK2JdWmRJAhGRLSBIWBKSmcmeyczc8/3jTibDHiDJZDKf58N5zM2dc++cOdw475x7zr1tq/fi+8sW/KPS+WCdl1MuTOcXrXeQ4chg4ayFFLgLerUfIxDAv/jPNDz2GOGGBlznnsvwO35A6hlyWmEwqvHVsPz1/8H98jJOqwnQaYedXxtP0fW3MvmM8xKmy3ygVe+r5ufLf06Nv4aZo2cy/8z5DE9L/Nv5DNbrDCWKlpYWi8vlMiwWC4899phn8eLF2cuWLdva/dqECRMmrF27dsOwYcPCR9uXMMmNWk+AhKFjp7XG+/xGOtY3siU3jQ2b/Ey8Jov5O24jw5HBk7OeZIS7VxP5ADA6OvA9/wKNjz9O2O8n7cwzyb5uLu7p0+Wu5HHm7/SzZPObfP7qM5Qs20lxPbRlOui6dCZTbriLtOzE/1IfCEEjyFOfPsUj1Y/gtDr5YfkPuXTspQl9+lDC0Il5++233XfccUeR1pqMjIzwU089tX3ixImB1157Lf3mm28uvvnmm+t++tOf1h99T6KbhKETIGHo+BjtQer+WAVWCx8FDPyNnUyel8kPq28h05nJkxc8Sb4r/5j2GW5tw7/4RbzPPkdozx7so4vIvvZasi66SO5UPsB2Nu/khZWP0v7qm5y3uothLdA+Koe8G+ZRcPGVWOQK48dlR/MO7v34XlbtXUVpbik/O+dnnJR5UryrdVwkDInBRsLQCZAwdPwC2/zse3wd9ok5/K26EZvDwoQbXNy2/CY8KR4Wzlp4zIEIzOnZLUuX0rhoEZ3Vn2DJyMBzxeV4rroK+4je9ziJY/dJfTV/e+tBPH9dyVkbNY4wUDaJUTfdjutLX5JTYX1Aa81rNa/x24rf0hHqYN4Z87hh4g0JNw3/MGFo26RJk3wWi0W+fMSAMgxDrVu3zjN58uSD/rqQMNQLEoZOTNPb22l5fyfW80fz+mvbyC1K56Srbdz8DzMQPTnrSfJcece9//Y1a/AuepqWd94BpciYNYvs6+bKuKI+ZGiDD7csperZBzn1g+2MqYNQih33nG9ScPV1OMeOjXcVh6SGjgYeWPUAS7Yv4eTMk/nZOT+jJLck3tXqtcOEoTfy8/MnDB8+vEkCkRgohmGoffv2Ze7du/ezyZMnf+vA1yUM9YKEoROjQwb1j1QTauyk42uj+Pvzmxl/Vj453+jipndvIic1hydmPnFCgQigq3YXvmefxf/yyxitraSWlJA9dy7p582Q+58dp65wF+98sJC9zy6ipNJPWgDaR+cycu4NDJ9ziZyaHCAf1n7If674T/a07eHb47/NHaV3kO5Ij3e1jupQYaiysjLXZrMtACYydGc0i8HHAD4NhUI3lJWVHTTOSsJQL0gYOnHBhg7qH6rCMTKdz0e4Wf3X7Zx98clYSnzRQLRw1kJy03KPvrOjCLe20fTKX/A+/QzB2lrsBQV4rrmGrMsuxZo++L9ABgN/ayP/eP5XWF55h3HbuwhZFR1fKeHUG+4kvbRcToXFQXuwnT+t+RPPb3yenJQc7pl2DzNGz4h3tY7oUGFIiMFIwlAvSBjqG93T7TMuKObjmiZqKuuZfeMkmgt2cePSG8lNy2XhrIV9NqVYh8O0vPce3kWL6KioxOJykXXZpXiuuQbHyIOvhi2gdms1FY//ktx3P8HTqmnKduK49JtMuu5O7HILmkHh04ZP+fnyn7PJt4kZRTO4+8y7T7hXtb9IGBKJQsJQL0gY6hux0+2HzZvEW4u34N3dyiX/XkZtSg03Lr2RvLQ8nrzgSXJSc/r0vTs+XY930SKalywBwyB9xgxzXFFpadL3cmjDYOM7f2bHokcZuXYvSsPuSXmMmjuP8bOvRFmt8a6iOEDQCPLMZ8/w8NqHsVvs3Fl6J5ePv3zQTcOXMCQShYShXpAw1He6p9sruxX33An85fdrALhsfjmbOtdz07s3ke/KZ+GshX0eiACCdXX4nnse3+LFGE1NpEycSPbcuWRcMAt1mBsdDlUhv5+1i/5A4OU3yN7XSUuaou7rkyj9/nwKxyXOIN1ktrN5J/euuJcVe1YwZfgUfnb2zzjFc0q8qxUlYUgkCglDvSBhqG91bvXTsGAdrvJ8Qmfm88pvKske4eLiH5VS7VvLze/ezAjXCJ6Y9US/BCIAo72dptdfx7voabq2b8eWl4fn6qvwXHEF1szMfnnPwaLlk7V89vjvSHm/EkdQs7XIgXHx+Xz1mvlkuvunvUX/0Vrz5rY3+c3q39AabOX6idcz74x5OK3OeFdNwpBIGBKGekHCUN9revtzWt6vJfuq09gb1ix5dB2nlOYy84bTqair4NZlt1LgKmDBrAX9FojAPEXU+uGHeBctov3jFajUVLIuvgjPNdfgHDOm3953oBkdHdS/+So7n16Au2YPnXaoLs0i76prOW9G4l2/RhzM2+nlN6t/w1vb3qI4o5h7pt3D2QVnx7VOEoZEopAw1AsShvpe7HT7vDtL+WTFXj5+dStTLyzmzG+exOq9q7l12a0UugtZMHMBw1L7f/Bu56ZNeBc9TfObb6KDQdzTp5N93VzSpk1L2HFFXdu3s+uZJ2l+7XUcbQF25sCm6WMoueZOzh13fsJ+LnF4/9r1L+5beR87W3Zy/ujz+XH5j4/p1jd9ScKQSBQShnpBwlD/CDZ0UP/HKhyj0hl2/UT+8cxGNq7Yy8zrT2fs1DxW7VnFrctuZWT6SJ6Y9QTZKdkDUq9QQwO+F17E98ILhL1enKeeSva115Lxfy5MiFtM6FCI1vffZ9fTT6BXrSVkgVXjLfi+MY3ZF/0bpw+fGO8qin4WCAdYtH4Rj3/yOADzzpjHdadfh8M6sMevhCGRKCQM9YKEof4TO93edW4hr/9xDfXbW7joRyXkj8lk5Z6V3LbsNkZljGLBzAUDFogAjECA5jffxLtoEYEtNVhzcvB850o83/kOtuyBq0dvBevr8b/0MvUvPodln5eGDPig1IHrkou4/Ox5jEyXywkkm92tu/ltxW9ZumMpRelF/OTMn/CVkV8ZsPeXMCQShYShXpAw1H+01nif20DHZ15yb5lMONPJy7+uINhlcPn8ctKzU1ixZwW3LbuN0RmjWTBzAZ4Uz4DXsW35cryLFtH24Ucoh4PMOd/C9aUvo+w28+rWVivKZkfZrOZU9O7l6Gsx5ex2lDVSLnb5OOvWvmo13uefo+Xdd1Fhg7VjFB+flcHEb13Htyd8h0zn0B4QLo5u+a7l/HLVL9nevJ3po6Zz19S7GJU+qt/fV8KQSBQShnpBwlD/ip1un/uDEnwNHbzyQCXpOalc8u+lOFJsfLz7Y25/73aKM4pZMHMBWSlZcalrYOtWvE8/Q9Prr6M7O/tux0qBzWYGo8jzfkHJbkNZI4HKZo0uBxsbCH2xk7ZUC+9N0mz48igu/Oo8vnnyNwfFbCIxeATDQZ7Z8AyPVD9C2Ahz/aTr+d7E75FiS+m395QwJBKFhKFekDDU/2Kn23suHcuO9Y389b+qKT4jh9k3TkJZFMt3LTcDUWYx151+HWV5ZRS4C+JS33BzM8E9e9ChEIRC6HAYHQxBuGdZhyOvhcL7L4ci5SLLhy0XDkX231NOh8K0dzThb/fS0uGnMeTno3EG7V8p4eqS7zF91PRBd+E9MbjUtdXxu8rfseTzJRS6C7lr6l18bdTX+mUwvYQhkSiGdBhSSm0HWoAwENJalyulsoHFQDGwHbhCa+070n4kDA2M2On2aZNyqH5vJ//88xZKZhZxziXmheT+tetfzP9oPv6AH4ARrhGU5pVSlldGWW4ZYzLHDKkZUlprtjVtY/Xe1azeu5qKugq8nV4A8tLymDZiGpePu5wpuVPiXFORaFbvXc39K++nxl/DuYXncveZdzM6Y3SfvoeEIZEokiEMlWutG2LWPQB4tda/UkrNBzxa658caT8ShgaGDhnU/081Ia853d6a4eCD5zex/qPdfP3a0zjtHHN6cNgIU+OvobKuksq6Sqrqq2joMP+Js1OyKcktoSyvjNK8UsZ7xmOzJM4d6w1tsNW/lYq6ClbvXU1lXWU0/OS78pmaN5Wp+VMpzy9npHvkkAp+YuAFjSAvbnyRh9c+TCAcYO7pc5k3aR5p9rQ+2b+EIZEokjEMbQKma633KKVGAO9rrccfaT8ShgZOcF879Q+twVGUTs71kzC05q0/VbN7i585d5ZQMPbgsUJaa75o+YKquioq6iqoqquitrUWAJfdxZThU6LhaGLOxEE1lsbQBjX+Gir2VlBRV0HF3gp8AbOjcoRrhBl88sqZmj+VQnehhB/RLxo6GvhD5R94Y+sb5KXl8eOpP2bm6JknfLxJGBKJYqiHoc8BH6CBR7XWjyml/FrrrJgyPq31QdOTlFLfB74PUFRUVLZjx46BqnbS655unzm7mPSvjqKzLchfHqikszXIZfPLyRyeetR91LXVUVVfFe09qvHXAOCwOJiYM9E8rZZXxpTcKbjsrv7+SFGGNtji2xINPhV1FdFTfgWuAsrzzeDTHX4O1NkWxLu7lYbaNhp3t9LS0EGK24Hb48TtSYk8m8upbjvKIuFJ9N6a+jXcv/J+Nno3Mm3ENO4+825Ozjr5uPcnYUgkiqEehgq01ruVUrnAUuB24I3ehKFY0jM0sA6cbu8YmY6/rp2Xf11BWoaDS39SjjP12E59+Tv9VNVXUVVnBqQN3g2EdRiLsnBq9qnRMUcleSV9ei2j7vDTPd6noq6CpkATAIXuwmivT3l++X7hJxwy8Ne107irNfJoo3FXK62+QLSM02UjMyeVzrYgrb4ARnj/32WLTeHOMoORK8tJerYTV5YEJnFkYSPMS5tf4qE1D9ER7OCq067ipsk34Xa4j3lfEoZEohjSYSiWUurnQCswDzlNNugZ7UHqHqxCOczp9haHldqNXt58qJrC8VmUnD8al8eJO8uJ4xiDEUB7sJ21+9ZGw9G6hnUEwmbQOCnzpOhptfK8cvJd+b2vtzbY7Ntshp9Iz09zVzNghp/uXp/yvHIK3AXmNYz8XTTubqWxtif4+Pa2RcONxarw5LsYNtLFsAI3w0a6GVbgxpXliJ7G0IamozVIq6+TVl8g8uiMPrf5AxKYxDHxdnp5qOohXtnyCjmpOfxb+b9x4ZgLj+nUmYQhkSiGbBhSSrkAi9a6JbK8FLgXmAE0xgygztZa33WkfUkYio/odPup+XguGQvA+o928f5zm/YrZ0+x4s5y4spyRp9dWeaXefdyWrrjiF/oXeEu1jeuj55WW1u/ltZgK2CevuoOR2V5ZRRnFEe/EMJGOBp+VtetpqquKhp+RrpH7hd+cuy5NO5uxburjYZdkfCzu5VAWyhaD7fHybBCd+ThYlihm6y8NKy2E58u36+BKd0u45mGqHX71nHfyvtY37ie0txS7pl2D+Ozj/j3Y5SEIZEohnIYOgl4NfKjDXhea32fUmoY8GegCPgCuFxr7T3SviQMxU/Tks9p+aCWYVefRupE8+71Ld5Omhs6zC9vf4A2X6Bn2R+grakLbRzwhW5VpGU6cGel9IQmT094cnucuDKdWO1m6OgOOd2z1WJndWWnZFOaW0pIh6isq6SlqwWAovQiyvPLKRtezmn2M7B4U6M9PQ27Wmlu6DBHrwF2p5VhhS6yC93kRIJPdoGbFFd87x5/vIHJk59G2exixpbnYrHKdY6GGkMbvLrlVR6sepCWrhauPPVKbplyCxmOjCNuJ2FIJIohG4b6koSh+Omebh/2dZJ3RynWzKPPBDMMTUdLF62RkNT9BR4bmFr9AUKB8EHbprjt0R6l2F4mV5aTJlsDGwOfstZfSdW+KmwWG1Mzz+J0VUpe52iCDRYaa1vx7m4jFDQA88LSmblp+/X05Ix0k56dkrCnng4MTM0NHWz8eA+Nu9rIyEmhdNZoTj1rRDRYiqGjKdDEn9b8iZc2v0SWM4s7S+9kzilzDnuhTwlDIlFIGOoFCUPxdeB0+74IEVprujrD0d6O2MAUG5o6WoIHbWtzmqflujpDtDd1RdenpttjTnFFentGuLA5ju++Y4lEG5rt6xqoWLKD+u3NuLKclJxfxIQvFWB3Dv3Pn2w2NG7gvpX3Ub2vmjOGn8E90+7h9GGnH1ROwpBIFBKGekHCUPy1rdqL75UtZM4eQ/pXB+7u6+GgQVtTTI9STGCy2S3mYOZI+EnLcAxYvQYrrTW1G31ULtnOrs1+Utx2Js8YxaTpI495BqAY3Axt8Na2t/h9xe/xdnq5fNzl3F5y+373DZQwJBKFhKFekDAUf1prvM9uoGOjl9xbpuAoPPZpvmJg7anxU7FkB1+sb8SRamPS9EImzxhFqltC41DS0tXCw2sf5oWNL+B2uPlByQ+4dOylWC1WCUMiYUgY6gUJQ4ND7HT7rDkn4yzORMm4lEFv3xctVL69na1r9mGzWzj9y4WUnF+EK2vwXAlcnLjNvs3cv/J+KusqmTBsAvdMu4cpuVMkDImEIGGoFyQMDR6dW/00PrUeHTRQdguOMZmkjPOQMs6DbXiqTO8exLx72qh6ewebV9ehLHDa2SMonTWajJyjX1FcJAatNUs+X8LvKn5HfUc9n173qYQhkRAkDPWChKHBxegKE9jWRGCzj87NPkINHQBYs5ykjPXgHJdFyslZWNLiO01dHFrTvg7WvLODDR/vQRswbmoepReMJnvEwN0WRfSvtmAbj1Y/yo+m/kjCkEgIEoZ6QcLQ4BbydtK5xQxGgRo/OhAGBY5R6aSM8+Ac58ExMj1hp7IPVa2+AGvf/YL1H+0iFDQ4ecpwymYXM7woPd5VE31ExgyJRCFhqBckDCUOHTbo2tlC52YfnVv8BGtbQINKtZFySlY0HNl6cb0iMTA6WruoXraTde/voqsjRNHpwyifPZoRp2QdfWMxqEkYEolCwlAvSBhKXOG2IIEafyQc+TCazesC2XLTomONnGMyUHa5Fk68BTpCrHu/luplO+lsDVIwNovy2cWMPM0jY8ESlIQhkSgkDPWChKGhQWtNqK49GowCnzdBSIPNgnNMRs9A7Nw0+fKNo2AgzGf/3M2ad3bQ1tRF7uh0ymYXM+aMHDnVmWAkDIlEIWGoFyQMDU1GV5iuz5ui4ShUHxmIneHAGQlGKafIQOx4CQcNNq7YQ9Xfd9Dc0El2gYuy2aM5pSwPi4SihCBhSCQKCUO9IGEoOYT8AXOG2hZzvJHuDJkDsUem4xxrjjdyjMpAWeWLeCAZYYMtFfVULtmOb287mcNTKb1gNOOn5WO1yXWmBjMJQyJRSBjqBQlDyUeHNV27WqLT97t2RgZip1hJOTkL5zgPztEZWD0pWOTeWwNCG5pt1fuoXLKDfV+04PY4KZlZxIRzC5Li/m+JSMKQSBQShnpBwpAw2oN0bvUT2GwOxg43BaKvWVw2rJ4UbNnmI7rsScGa5URJ70Wf0lrzxWdeKpdsZ09NE6npdqacV8TErxTikPufDSoShkSikDDUCxKGRCytNaF9HQT3tBHydRL2dsY8B8CI+Z1SYM10xgQkJ9bsnuBkcTtkUPAJ2L3FR8WSHez8zIvNbiE9JxW3x4k7y4mr+znLiduTgtvjxJlmk8HxA0jCkEgU8meUEMdIKYU9Nw17btpBr2lDE24OmMHIG+gJSZELQ3ZP7Y+yKbMH6ZA9S04ZvH0UBWM9fGush7rtzWxetZdWX4BWXwDvrlbamrvggL/1bHaLGZI8TtxZKQcEJjM0pbrtElCFSDIShoToQ8qisGWlYMtKwXnSwa/roEHI3xOQYnuU2ne2oDtC++8vxYotEo6shwhLcn0kU15xBnnFGfutC4cN2pu6aPObAcl87qTVH6DNF2D3Fj9t/gCGsX9islhVTziK6VnqCUxO0jIcWKxy+lOIoSIpw5BS6gLgj4AVWKC1/lWcqySShLJbsA9Pwz784F4lAKMjRMjbSdi3f1gK1rfTsckHIWO/8ha3HYvTirJbUQ4LymFF2c1nS8yyclj2K2M5ynqsKuFPJ1mtFtKzU0jPTjlsGW1o2lsODEwBWv2dtPkC1O9oobW6gXBw/3ZXCtIyzWDkyoo5LedxkpJmx+a0YndYsTvNh/mzRQKUEINU0oUhpZQV+G/gfKAWWK2UekNr/Vl8ayYEWFJtOArdUOg+6DVtaIzW4H6n3sJNAYxAGB000F1hdFcYoy1oPncZ6GAY3WXsP46pVxUhEpLMoGQ5RNhSdosZuBwWsFrMU0sKsESClAVQylwfu6zMcodcbznE9getj90+sty9ProN0fc51M/RbRSkuR2kpTsYXpR+yACotSbQFqI10rPUE5gCtPk68e1pY+cGL8HO8NGb1aaiIckWG5YcVuxOC3aHNRqkbM6e1+0Oy/7rJWgJ0aeSLgwBZwI1WuttAEqpF4E5gIQhMagpi8Ka4cCa4YDRGUffIIYOGdHAZMQEp54QZWBEgtPR1kfDVkyZYw5bg5XikIGp+2e7UngUeGKDlkNBXioaMzhpbT5j/tezHHktuhwOQ1sI3Wq+dc+2QHfZCA0EI48j1V3Fhr9jMET+9YQ4bskYhgqBnTE/1wLTDiyklPo+8H2AoqKigamZEP1E2SzmFP9UG/0xykhrDQZ0f5vr7mVDo41IKjD0fuX2W39QmUiAMCLpwKBn/X7bx5SLvn9PADncz9GwEVuf/V4/1LrD7Ns4YN+Hb6TDv3SYlUbYwDA0OqzNZwOM6LKBETbbwYg8esoNgngzCKogRG8lYxg61N9MB/3aaq0fAx4Dc2p9f1dKiESmlDJH4EV+vRJ7tJHoMz+NdwWE6J1kPMlcC4yK+XkksDtOdRFCCCFEnCVjGFoNjFVKjVFKOYArgTfiXCchhBBCxEnSnSbTWtdVDdAAAAbaSURBVIeUUrcBf8fs2F+otV4f52oJIYQQIk6SLgwBaK3/Bvwt3vUQQgghRPwl42kyIYQQQogoCUNCCCGESGoShoQQQgiR1CQMCSGEECKpKX2Eq6IKk1KqBdgU73oMEjlAQ7wrMUhIW/SQtughbdFjvNY6Pd6VEOJoknI22XHYpLUuj3clBgOlVIW0hUnaooe0RQ9pix5KqYp410GI3pDTZEIIIYRIahKGhBBCCJHUJAz1zmPxrsAgIm3RQ9qih7RFD2mLHtIWIiHIAGohhBBCJDXpGRJCCCFEUpMwJIQQQoiklpRhSCk1Sin1D6XUBqXUeqXUHZH12UqppUqpLZFnT2S9Uko9pJSqUUp9opQqjdnX3Ej5LUqpufH6TMfrONriqkgbfKKUWq6UmhyzrwuUUpsi7TQ/Xp/peB1rW8RsN1UpFVZKXRazLqmOi8hr05VSayPlP4hZn1THhVIqUyn1plKqOlL+uzH7GqrHxeWRnw2lVPkB29wd+bffpJSaFbM+oY8LMcRorZPuAYwASiPL6cBmYALwADA/sn4+8OvI8jeAJYACzgJWRtZnA9siz57Isifen6+f2+Kc7s8IzI5pCyuwFTgJcADVwIR4f77+bIuYz/0e8DfgsiQ+LrKAz4CiyM+5yXpcAPfELA8HvJHPPpSPi9OA8cD7QHlM+QmRf3MnMCZyLFiHwnEhj6H1SMqeIa31Hq11VWS5BdgAFAJzgEWRYouAiyLLc4CntWkFkKWUGgHMApZqrb1aax+wFLhgAD/KCTvWttBaL498VoAVwMjI8plAjdZ6m9a6C3gxso+EcRzHBcDtwF+A+ph1SXdcAP8XeEVr/UVkm+72SMbjQgPpSikFuDHDUIghfFxorTdorQ91lf45wIta64DW+nOgBvOYSPjjQgwtSRmGYimlioESYCWQp7XeA+YvPZAbKVYI7IzZrDay7nDrE1Iv2yLW9Zg9ZpCEbaGUKgQuBh45YPOkawtgHOBRSr2vlKpUSl0bWZ+MbfFfmD0lu4F1wB1aa4Oh3RaHkxT/7xSJL6lvx6GUcmP+VX+n1rrZ/EPu0EUPsU4fYX3COYa26C7/Ncww9KXuVYcoNtTb4kHgJ1rr8AFlkrEtbEAZMANIBT5WSq0gOdtiFrAW+DpwMrBUKfURQ7gtjlT0EOs0h/5DPCHbQgwNSdszpJSyY/4yP6e1fiWyui5y+ovIc3dXfy0wKmbzkZh/9R1ufUI5xrZAKXUGsACYo7VujKxOxrYoB15USm0HLgMeVkpdRHK2RS3wtta6TWvdAHwITCY52+K7mKcMtda6BvgcOJWh3RaHM6T/3ymGjqQMQ5Fz+U8AG7TWv4956Q2ge4bHXOD1mPXXKtNZQFOkW/zvwEyllCcyk2RmZF3CONa2UEoVAa8A12itN8eUXw2MVUqNUUo5gCsj+0gYx9oWWusxWutirXUx8DJwi9b6NZLwuIg8f1kpZVNKpQHTMMeTJN1xAXyB2UOGUioPc2DxNob2cXE4bwBXKqWcSqkxwFhgFUPguBBDTLxHcMfjgXlqRwOfYHZnr8WcMTYMWAZsiTxnR8or4L8xZz+sY//ZEt/DHBRYA3w33p9tANpiAeCLKVsRs69vYM4u2Qr8R7w/W3+3xQHbPkVkNlkyHheRbX6MOaPsU8zTJ0l5XAAFwDuR/1d8ClydBMfFxZi9PQGgDvh7zDb/Efm33wTMHirHhTyG1kNuxyGEEEKIpJaUp8mEEEIIIbpJGBJCCCFEUpMwJIQQQoikJmFICCGEEElNwpAQQgghkpqEISEGmcj1rP6plJods+4KpdTb8ayXEEIMVTK1XohBSCk1EXgJ895PVszruVygtd56Avu0aa1DfVRFIYQYMiQMCTFIKaUeANoAF9Citf7/Sqm5wK2AA1gO3Ka1NpRSjwGlmPcFW6y1vjeyj1rgUcy7oz+otX4pDh9FCCEGtaS+UasQg9wvgCqgCyiP9BZdDJyjtQ5FAtCVwPPAfK21VyllA/6hlHpZa/1ZZD9tWutz4/EBhBAiEUgYEmKQ0lq3KaUWA61a64BS6jxgKlARuWN6KrAzUvw7SqnrMX+nC4AJmLfGAFg8sDUXQojEImFIiMHNiDzAvEfeQq31/4stoJQaC9wBnKm19iulngVSYoq0DUhNhRAiQclsMiESx7vAFUqpHACl1DClVBGQAbQAzUqpEcCsONZRCCESjvQMCZEgtNbrlFK/AN5VSlmAIHATUEHP3eK3Af+KXy2FECLxyGwyIYQQQiQ1OU0mhBBCiKQmYUgIIYQQSU3CkBBCCCGSmoQhIYQQQiQ1CUNCCCGESGoShoQQQgiR1CQMCSGEECKp/S8MHZ1WT0tkmwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df = pyam.IamDataFrame(df)\n", "ax = df.filter(variable='Primary Energy|Coal').line_plot(\n", @@ -529,7 +264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.4" } }, "nbformat": 4, From dbc4a19216824bc2bddcbde98bd26cc135c7840e Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 20:54:05 +0100 Subject: [PATCH 23/34] extend the readme --- README.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 6469b2aa1..fe018a4be 100644 --- a/README.md +++ b/README.md @@ -39,19 +39,24 @@ for more information on the IAMC format and the ``pyam`` data model. | ... | ... | ... | ... | ... | ... | ... | ... | -Tutorial --------- +Tutorials +--------- An introduction to the basic functions is shown in [the "first-steps" notebook](doc/source/tutorials/pyam_first_steps.ipynb). -More tutorials are available in the folder [doc/source/tutorials](doc/source/tutorials). +All tutorials are available in rendered format (i.e., with output) as part of +the [online documentation](https://pyam-iamc.readthedocs.io/en/stable/tutorials.html). +The source code of the tutorials notebooks is available +in the folder [doc/source/tutorials](doc/source/tutorials) of this repository. Documentation ------------- -The documentation pages can be built locally. -See the instruction in [doc/README](doc/README.md). +The complete documentation is hosted on [Read the Docs](https://pyam-iamc.readthedocs.io). + +The documentation pages can be built locally, +refer to the instruction in [doc/README](doc/README.md). Authors ------- From 0142624ec3e800639788ba9e2441ec21846d6c08 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Tue, 10 Dec 2019 20:58:09 +0100 Subject: [PATCH 24/34] rename consistency-tutorial in the test --- tests/test_tutorials.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_tutorials.py b/tests/test_tutorials.py index a7389b7af..a6af555b3 100644 --- a/tests/test_tutorials.py +++ b/tests/test_tutorials.py @@ -68,8 +68,8 @@ def test_pyam_first_steps(capsys): @pytest.mark.skipif(not jupyter_installed, reason=jupyter_reason) @pytest.mark.skipif(not pandoc_installed, reason=pandoc_reason) -def test_checking_databases(): - fname = os.path.join(tut_path, 'checking_databases.ipynb') +def test_checking_consistency(): + fname = os.path.join(tut_path, 'checking_consistency.ipynb') nb, errors = _notebook_run(fname) assert errors == [] From 66f770c348bd56f11319f2272f3a0ade8932072e Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Thu, 12 Dec 2019 08:38:47 +0100 Subject: [PATCH 25/34] implement review comments by @znicholls --- doc/source/tutorials/checking_consistency.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/source/tutorials/checking_consistency.ipynb b/doc/source/tutorials/checking_consistency.ipynb index 28a2301e8..033f956c5 100644 --- a/doc/source/tutorials/checking_consistency.ipynb +++ b/doc/source/tutorials/checking_consistency.ipynb @@ -38,7 +38,7 @@ "\n", "1. In year `2010` and regions `region_b` & `World`, the values of coal and wind do not add up to the total `Primary Energy` value\n", "2. In year `2020` in the `World` region, the value of `Primary Energy` and `Primary Energy|Coal` is not the sum of `region_a` and `region_b`
    \n", - " (but the sum of wind and coal in that region to `Primary Energy ` is correct)" + " (but the sum of wind and coal to `Primary Energy` in each sub-region is correct)" ] }, { @@ -104,7 +104,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The [check_aggregate()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_aggregate) function allows us to quickly verify whether a given variable is equal to the sum of its sectoral components (e.g. is `Primary Energy` should be equal to `Primary Energy|Coal` plus `Primary Energy|Coal`). The validation is performed separately for each region.\n", + "The [check_aggregate()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.check_aggregate) function allows us to quickly verify whether a given variable is the sum of its sectoral components (e.g. `Primary Energy` should be equal to `Primary Energy|Coal` plus `Primary Energy|Wind`). The validation is performed separately for each region.\n", "\n", "This section illustrates the first constructed inconsistency in this scenario. The returned [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) indicates where the aggregate is not equal to the sum of components." ] From dbb9134046586775bf52edd200f86d8ba53323c9 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Thu, 12 Dec 2019 10:13:02 +0100 Subject: [PATCH 26/34] add to release notes --- RELEASE_NOTES.md | 1 + 1 file changed, 1 insertion(+) diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index dc39b5239..7926473fb 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,6 +1,7 @@ # Next Release +- [#302](https://github.com/IAMconsortium/pyam/pull/302) Rework the tutorials - [#297](https://github.com/IAMconsortium/pyam/pull/297) Add `empty` attribute, better error for `timeseries()` on empty dataframe - [#295](https://github.com/IAMconsortium/pyam/pull/295) Include `meta` table when writing to or reading from `xlsx` files - [#292](https://github.com/IAMconsortium/pyam/pull/292) Add warning message if `data` is empty at initialization (after formatting) From 0cc109e27d73cbfca8c384104e6750be76dfe9c2 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Thu, 12 Dec 2019 11:01:54 +0100 Subject: [PATCH 27/34] implement review comments by @jkikstra --- doc/source/tutorials/pyam_first_steps.ipynb | 37 ++++++++++++++++++--- 1 file changed, 32 insertions(+), 5 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index 84e8431a5..d856c5cf4 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -300,8 +300,12 @@ "\n", "Filtering for **years** can be done by one integer value, a list of integers, or the Python class [range](https://docs.python.org/3/library/stdtypes.html#ranges).\n", "\n", - "**Note**: the last year of a range is not included, so ``range(2010, 2015)``\n", - "is interpreted as ``[2010, 2011, 2012, 2013, 2014]``." + "
    \n", + " The last year of a range is not included, so range(2010, 2015)\n", + " is interpreted as [2010, 2011, 2012, 2013, 2014].\n", + "
    \n", + "\n", + "See the next subsection for an illustration (it makes more sense to show this feature when we know how to look at the data)." ] }, { @@ -311,7 +315,12 @@ "### Displaying timeseries data\n", "\n", "As a next step, we want to view a selection of the timeseries data.\n", - "The [timeseries()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.timeseries) function returns the data as a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) in the standard IAMC format, i.e., _wide format_ where years are shown as columns." + "\n", + "
    \n", + " The timeseries() function\n", + " returns the data as a pandas.DataFrame in the standard IAMC template.
    \n", + " This is wide format table where years are shown as columns.\n", + "
    " ] }, { @@ -324,6 +333,22 @@ "display_df.timeseries()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell shows an illustration of filtering by **year** (discussed above)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display_df.filter(year=[2010, 2030, 2050]).timeseries()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -333,7 +358,9 @@ "When developing **pyam**, we followed the syntax of the Python package **pandas** ([read the docs](https://pandas.pydata.org)) closely where possible. In many cases, you can use of similar functions directly on the ``IamDataFrame``.\n", "\n", "In the next cell, we illustrate this parallel behaviour. The function [pyam.IamDataFrame.head()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.head) is similar to [pandas.DataFrame.head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html): \n", - "it returns the first n rows of the ``data`` table in *long format* (i.e., columns are in year/value format)." + "it returns the first n rows of the ``data`` table in **long format** (columns are in year/value format).\n", + "\n", + "Similar to the [timeseries()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.timeseries) function shown above, the returned object of [head()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.head) is a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)." ] }, { @@ -720,7 +747,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If the filter arguments passed to [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) do not yield a unique value (in this case without a specific year), we can pass a `method` to aggregate or select a specific value (e.g., the maximum using the **numpy** package)." + "If the filter arguments passed to [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) do not yield a unique value (in this case without a specific year), we can pass a `method` to aggregate or select a specific value (e.g., the maximum using the [numpy](https://numpy.org) package)." ] }, { From 3a7c58e6d7c5f8be3710fec82061d56e1906f7f9 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Thu, 12 Dec 2019 11:09:37 +0100 Subject: [PATCH 28/34] add link to "tips & tricks" when working with notebooks (per @znicholls) --- doc/source/tutorials/checking_consistency.ipynb | 3 ++- doc/source/tutorials/pyam_first_steps.ipynb | 6 ++++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/doc/source/tutorials/checking_consistency.ipynb b/doc/source/tutorials/checking_consistency.ipynb index 033f956c5..e20879684 100644 --- a/doc/source/tutorials/checking_consistency.ipynb +++ b/doc/source/tutorials/checking_consistency.ipynb @@ -151,7 +151,8 @@ "The log tells us the same message as in the previous example, and it shows that the other two variables (coal and wind) cannot be assessed because they have no subcategories.\n", "\n", "
    \n", - "Note that the detailed output (i.e., where the aggregation validation fails) is not shown in a notebook when calling the function within a loop.\n", + "Note that the detailed output (i.e., where the aggregation validation fails) is not shown in a notebook when calling the function within a loop.
    \n", + " Read this page for helpful tips and tricks when working with Jupyter notebooks.\n", "
    " ] }, diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index d856c5cf4..05571c6fa 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -830,6 +830,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
    \n", + " New to Jupyter notebooks?\n", + " Read this page for helpful tips and tricks when working with Jupyter notebooks.\n", + "
    \n", + "\n", + "\n", "## Questions?\n", "\n", "Take a look at the next tutorials - then join our [mailing list](https://groups.io/g/pyam)!" From 2a3e56be251ae4d72528b40693a793f25e6779a6 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Thu, 12 Dec 2019 18:31:53 +0100 Subject: [PATCH 29/34] implement review comments by @francescolovat --- doc/source/tutorials/pyam_first_steps.ipynb | 110 ++++++++++++++------ 1 file changed, 76 insertions(+), 34 deletions(-) diff --git a/doc/source/tutorials/pyam_first_steps.ipynb b/doc/source/tutorials/pyam_first_steps.ipynb index 05571c6fa..ed9f9f460 100644 --- a/doc/source/tutorials/pyam_first_steps.ipynb +++ b/doc/source/tutorials/pyam_first_steps.ipynb @@ -292,22 +292,6 @@ "df.filter(level=1).variables()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filtering by year\n", - "\n", - "Filtering for **years** can be done by one integer value, a list of integers, or the Python class [range](https://docs.python.org/3/library/stdtypes.html#ranges).\n", - "\n", - "
    \n", - " The last year of a range is not included, so range(2010, 2015)\n", - " is interpreted as [2010, 2011, 2012, 2013, 2014].\n", - "
    \n", - "\n", - "See the next subsection for an illustration (it makes more sense to show this feature when we know how to look at the data)." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -319,7 +303,7 @@ "
    \n", " The timeseries() function\n", " returns the data as a pandas.DataFrame in the standard IAMC template.
    \n", - " This is wide format table where years are shown as columns.\n", + " This is a wide format table where years are shown as columns.\n", "
    " ] }, @@ -337,7 +321,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following cell shows an illustration of filtering by **year** (discussed above)." + "### Filtering by year\n", + "\n", + "Filtering for **years** can be done by one integer value, a list of integers, or the Python class [range](https://docs.python.org/3/library/stdtypes.html#ranges).\n", + "\n", + "
    \n", + " The last year of a range is not included, so range(2010, 2015)\n", + " is interpreted as [2010, 2011, 2012, 2013, 2014].\n", + "
    \n", + "\n", + "The next cell shows the same down-selected `IamDataFrame` as above, but further reduced to three timesteps." ] }, { @@ -355,10 +348,10 @@ "source": [ "### Parallels to the *pandas* data analysis toolkit\n", "\n", - "When developing **pyam**, we followed the syntax of the Python package **pandas** ([read the docs](https://pandas.pydata.org)) closely where possible. In many cases, you can use of similar functions directly on the ``IamDataFrame``.\n", + "When developing **pyam**, we followed the syntax of the Python package **pandas** ([read the docs](https://pandas.pydata.org)) closely where possible. In many cases, you can use similar functions directly on the `IamDataFrame`.\n", "\n", "In the next cell, we illustrate this parallel behaviour. The function [pyam.IamDataFrame.head()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.head) is similar to [pandas.DataFrame.head()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html): \n", - "it returns the first n rows of the ``data`` table in **long format** (columns are in year/value format).\n", + "it returns the first n rows of the `data` table in **long format** (columns are in year/value format).\n", "\n", "Similar to the [timeseries()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.timeseries) function shown above, the returned object of [head()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.head) is a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)." ] @@ -396,7 +389,7 @@ "source": [ "## Visualize timeseries data using the plotting library\n", "\n", - "This section provides an illustrative example of the plotting features of the **pyam** package. Please look at the [plotting gallery](https://pyam-iamc.readthedocs.io/en/stable/examples/index.html) for more examples.\n", + "This section provides an illustrative example of the plotting features of the **pyam** package.\n", "\n", "In the next cell, we show a simple line plot of global CO2 emissions. The colours are assigned randomly by default, and **pyam** deactivates the legend if there are too many lines." ] @@ -410,16 +403,62 @@ "df.filter(variable='Emissions|CO2', region='World').line_plot()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most functions of the plotting library also take some intuitive keyword arguments for better styling options or using the same colors across groups of scenarios. For example, `color='scenario'` will use consistent colors for each scenario name (most of them implemented by multiple modeling frameworks).\n", + "There are now less than 13 colors used, so the legend will be shown by default." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.filter(variable='Emissions|CO2', region='World').line_plot(color='scenario')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The section on categorization will show more options of the plotting features, as well as a method to set specific colors for different categories. For more information, look at the other tutorials and the [plotting gallery](https://pyam-iamc.readthedocs.io/en/stable/examples/index.html)." + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform scenario diagnostic and validation checks\n", "\n", - "When analyzing scenario results, it is often useful to check whether certain timeseries data exist or the values are within a specific range. For example, it may make sense to ensure that reported data for historical periods are close to established reference data or that near-term developments are reasonable.\n", + "When analyzing scenario results, it is often useful to check whether certain timeseries data exist or the values are within a specific range.\n", + "For example, it may make sense to ensure that reported data for historical periods are close to established reference data or that near-term developments are reasonable.\n", "\n", + "Before diving into the diagnostics and validation features, we need to briefly introduce the `meta` table.\n", + "This attribute of an `IamDataFrame` is a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html), which can be used to store categorization information and quantitative indicators of each model-scenario.\n", + "Per default, a new `IamDataFrame` will contain a column `exclude`, which is set to `False` for all model-scenarios.\n", + "\n", + "The next cell shows the first 10 rows of the `meta` table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.meta.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "The following section provides three illustrations of the diagnostic tools:\n", - "0. Verify that a timeseries `Primary Energy` exists in each scenario (in at least one year and in the last year of the horizon).\n", + "0. Verify that a timeseries `Primary Energy` exists in each scenario\n", + " (in at least one year and, in a second step, in the last year of the horizon).\n", "1. Validate whether scenarios deviate by more than 10% from the `Primary Energy` reference data reported in the *IEA Energy Statistics* in 2010.\n", "2. Use the `exclude_on_fail` option of the validation function to create a sub-selection of the scenario ensemble.\n", "\n", @@ -490,9 +529,9 @@ "source": [ "### Use the `exclude_on_fail` feature to create a sub-selection of the scenario ensemble\n", "\n", - "Per default, the functions above only report how many scenarios or which data points do not satisfy the validation criteria above. However, they also have an option to `exclude_on_fail`, which marks all scenarios failing the validation. This feature can be particularly helpful when a user wants to perform a number of validation steps and then efficiently remove all scenarios violating any of the criteria as part of a scripted workflow.\n", - "\n", - "Any scenario (by a particular model) failing the validation criteria is then marked as `exclude=True`. This \"exclusion flag\" is implemented in the `meta` table of the `IamDataFrame`, which can be used categorization and quantitative indicators (more below). \n", + "Per default, the functions above only report how many scenarios or which data points do not satisfy the validation criteria above.\n", + "However, they also have an option to `exclude_on_fail`, which marks all scenarios failing the validation as `exclude=True` in the `meta` table.\n", + "This feature can be particularly helpful when a user wants to perform a number of validation steps and then efficiently remove all scenarios violating any of the criteria as part of a scripted workflow.\n", "\n", "We illustrate a simple validation workflow using the CO2 emissions. The next cell shows the trajectories of CO2 emissions across all scenarios." ] @@ -547,8 +586,11 @@ "outputs": [], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(8, 4), sharey=True)\n", - "df_world.filter(variable='Emissions|CO2').line_plot(ax=ax[0])\n", - "df_world.filter(exclude=False, variable='Emissions|CO2').line_plot(ax=ax[1])" + "\n", + "df_world_co2 = df_world.filter(variable='Emissions|CO2')\n", + "\n", + "df_world_co2.line_plot(ax=ax[0])\n", + "df_world_co2.filter(exclude=False).line_plot(ax=ax[1])" ] }, { @@ -728,9 +770,9 @@ "\n", "In the previous section, we classified scenarios in distinct groups by their end-of-century warming outcome. In other use cases, however, it may be of interest to easily derive quantitative indicators and use those for more detailed scenario assessment.\n", "\n", - "In this section, we illustrate two ways to add quantitative indicators. These are stored in the `meta` table of the `IamDataFrame`, which was already mentioned in relation to the `exclude_on_fail` feature described above.\n", - "\n", - "First, we add two indicators derived directly from timeseries data: the warming at the end of the century (`end-of-century-temperature`) and the peak temperature over the entire model horizon (`peak-temperature`). For the end-of-century indicator, we can pass the year of relevant as a filter argument to the [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) function." + "In this section, we illustrate two ways to add quantitative indicators.\n", + "First, we add two indicators derived directly from timeseries data: the warming at the end of the century (`end-of-century-temperature`) and the peak temperature over the entire model horizon (`peak-temperature`).\n", + "For the end-of-century indicator, we can pass the year of relevant as a filter argument to the [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) function." ] }, { @@ -747,7 +789,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If the filter arguments passed to [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) do not yield a unique value (in this case without a specific year), we can pass a `method` to aggregate or select a specific value (e.g., the maximum using the [numpy](https://numpy.org) package)." + "If the filter arguments passed to [set_meta_from_data()](https://pyam-iamc.readthedocs.io/en/stable/api.html#pyam.IamDataFrame.set_meta_from_data) do not yield a unique value (in this case without a specific year), we can pass a `method` to aggregate or select a specific value (e.g., the maximum using the **numpy** package - [read the docs](https://numpy.org))." ] }, { @@ -793,7 +835,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As a last step of this illustrative example, we show the first rows (i.e., the 'head') of the `meta` table for the scenarios in the `IamDataFrame`. This table now includes the `exclude` column, the categ three new metadata indicators." + "As a last step of this illustrative example, we again display the first 10 rows of the `meta` table for the scenarios in the `IamDataFrame`. In addition to the `exclude` column seen in cell 20, this table now also includes columns with the three quantitative indicators." ] }, { @@ -802,7 +844,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.meta.head()" + "df.meta.head(10)" ] }, { From 9d560909a1cb296770ce0aca5787330ef3a8f517 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 23 Dec 2019 03:05:04 +0100 Subject: [PATCH 30/34] try downgrading `matplotlib-base` for building the docs on travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 3249ad32a..34387a5b9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -37,7 +37,7 @@ script: - make test # only test docs once to make sure everything works on most recent python - cd doc - - if [[ "${PYENV}" == "py37" && "${TRAVIS_OS_NAME}" != 'windows' ]]; then conda install --yes kealib==1.4.7; make html; fi + - if [[ "${PYENV}" == "py37" && "${TRAVIS_OS_NAME}" != 'windows' ]]; then conda install --yes kealib==1.4.7 "matplotlib-base<3.1.2"; make html; fi - cd .. after_success: From 24e786c4595100304b19dd38f5a7bdc5a69b107f Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 23 Dec 2019 06:59:02 +0100 Subject: [PATCH 31/34] try removing `kealib` for building docs on travis --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 34387a5b9..cd11bf94b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -37,7 +37,6 @@ script: - make test # only test docs once to make sure everything works on most recent python - cd doc - - if [[ "${PYENV}" == "py37" && "${TRAVIS_OS_NAME}" != 'windows' ]]; then conda install --yes kealib==1.4.7 "matplotlib-base<3.1.2"; make html; fi - cd .. after_success: From 889e55616476134eef9c78b3fe534ce1bfc7b28b Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 23 Dec 2019 07:02:25 +0100 Subject: [PATCH 32/34] try remove outdated region-plotting dependencies from appveyor tests --- appveyor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appveyor.yml b/appveyor.yml index 71ea6cf49..5d0d030e5 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -26,7 +26,7 @@ install: - conda --version - activate testing - conda install -y numpy pandas pyyaml xlrd xlsxwriter seaborn==0.9.0 six requests jupyter nbconvert proj4==5.2.0 pywin32 - - conda install -y -c conda-forge matplotlib==3.0.3 libiconv gdal fiona "geopandas<0.5.0" cartopy cython pyproj==1.9.6 + - conda install -y -c conda-forge matplotlib==3.0.3 pyproj==1.9.6 build: false From 11b2078ded4bd2b10c822ba582a9f396e85b0d44 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 23 Dec 2019 07:48:05 +0100 Subject: [PATCH 33/34] try re-inserting the building docs on travis --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index cd11bf94b..cb8d22ef9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -37,6 +37,7 @@ script: - make test # only test docs once to make sure everything works on most recent python - cd doc + - if [[ "${PYENV}" == "py37" && "${TRAVIS_OS_NAME}" != 'windows' ]]; then make html; fi - cd .. after_success: From 408029f52977f5fbd188dea3049c5340487fe7c9 Mon Sep 17 00:00:00 2001 From: Daniel Huppmann Date: Mon, 23 Dec 2019 07:48:22 +0100 Subject: [PATCH 34/34] remove unused imports from `plotting` --- pyam/plotting.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/pyam/plotting.py b/pyam/plotting.py index 93c4136e6..978a9390c 100644 --- a/pyam/plotting.py +++ b/pyam/plotting.py @@ -10,12 +10,7 @@ import pandas as pd from collections import defaultdict, Iterable -from contextlib import contextmanager -try: - from functools import lru_cache -except ImportError: - from functools32 import lru_cache from pyam.run_control import run_control from pyam.utils import requires_package, IAMC_IDX, SORT_IDX, isstr

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