One advantage of transferring the legacy SSP web database to the new #ScenarioServices infrastructure...
You can can now query SSP scenarios directly via the #python package #pyam_iamc 🎉
Check out the tutorial at ssp.apps.ece.iiasa.ac.at/documentatio...
github.com/iamconsortiu...
🎉 Happy to announce release v3.2 of the open-source pyam package for analysis & plotting of #IntegratedAssessment & #EnergySystems scenarios!
Highlights:
- Streamline operations of timeseries data with non-SI units
- Explicit check for infinite values
- Fix for scenario categorization
#pyam_iamc
Over the past months, the #ScenarioServices team at @iiasa.ac.at has been busy improving our open-source packages for scenario analysis and database infrastructure...
🎉 Happy to announce release v3.1 of the pyam package for working with #IntegratedAssessment & #EnergySystems scenarios!
#pyam_iamc
Just in time before the end of the year…
🎉 Release 3.0 of our open-source Python package #pyam_iamc for scenario analysis & data visualization of emissions, energy systems and Integrated-Assessment models... 💡🔌
Highlights in the 🧵 …
Six years ago, @gidden.bsky.social & started the Python package pyam for scenario analysis & dataviz related to the energy transition, emissions and climate.
Now, we are getting ready for release v3.0, adding i/o with netcdf files, performance boosts and other improvements!
👀 Stay tuned
#pyam_iamc
🎉 Release 2.3 of our opensource python package pyam for dataviz and scenario analysis of Integrated Assessment, Energy Systems & Macro-Energy models..
Highlight: new options for data filtering!
#pyam_iamc
Finally got around to do some #python programming again...
🎉 Happy to announce release 2.2.4 of the pyam package for scenario analysis and dataviz
New option for filtering of IAMC-format timeseries data and improved documentation
#pyam_iamc
github.com/IAMconsortiu...
Thanks for the suggestion! I haven't thought about MAC curves, but a standardized method could fit well in the #pyam_iamc package...
We added a few methods over the years, like computing of annualized growth rates or implicit learning rates. Might be a useful starting point.
Check out the docs 👇
Github gist: Illustrative Python code for querying scenarios and projections from the SSP-Extensions ixmp4 platform hosted by IIASA ``` # Query scenario data from the SSP-Extensions ixmp4 platform as IamDataFrame # To avoid large queries, you can also filter by model, scenario or variable import pyam df = pyam.read_iiasa("ssp-extensions", region="World", ...) # To avoid large queries, you can also filter by model, scenario or variable # Explore the SSP-Extensions ixmp4 platform in more detail # You can use ixmp4 to connect to the database platform import ixmp4 platform = ixmp4.Platform("ssp-extensions") # For example, you can get a list of all "runs" (.e., scenarios or projections) platform.runs.tabulate() ... ```
I created a @github.com gist to illustrate how you can easily query timeseries from the #ixmp4 platform behind the #SSP-Extensions Explorer using #pyam_iamc...
💡🔌
👉 gist.github.com/danielhuppma...
2| Validation and categorization of scenarios is an important aspect of #IntegratedAssessment research. #pyam_iamc now offers a much more versatile and flexible interface for these tasks... Check out our tutorials on
#ReadTheDocs at pyam-iamc.readthedocs.io/en/stable/tu...
1| The #ScenarioServices team at #IIASA is hosting scenario databases for the #IPCC and #HorizonEurope projects. We are migrating to the a #opensource #ixmp4 database package, and #pyam_iamc now offers direct integration with platforms like the just-launched #SSP-Extensions Explorer!
Happy to announce...
🎉 Release 2.2 of our #opensource #python package #pyam_iamc for #dataviz and #scenario analysis of #IntegratedAssessment, #EnergySystems 💡🔌 & #MacroEnergy models.
Highlights in the thread...
For the #SSP-Extensions Explorer, multiple datasets on to inequality & governance were harmonized to the #IAMC-timeseries format and released under a #CreativeCommons CC-BY license.
This allows reuse of the data in scenario analysis & #IntegratedAssessment modeling using tools like #pyam_iamc!
Screenshot of the gist to query SSP data from the IIASA database infrastructure using the pyam package ``` import pyam # by default, you receive the latest SSP projections (2024 release) df = pyam.read_iiasa("ssp", region="World") # you can also query the data of the 2013 SSP projections release # we recommend to filter by model and/or scenario to avoid large queries df = pyam.read_iiasa("ssp", region="World", default_only=False) ```
And of course, the SSP projections of GDP and population (both the initial 2013 data and the 2024 update) are available via our open-source Python package pyam...
#pyam_iamc
gist.github.com/danielhuppma...
