Comments (4)
Thank you so much @hdrake, that did the trick!
from climatemargo.jl.
If you find yourself frequently doing the above to extract data from ClimateMARGO types and saving them as CSV files, I would recommend writing a function to do so. See examples of how we've done this with the JSON file structure here: https://github.com/ClimateMARGO/ClimateMARGO.jl/blob/master/src/IO/json_io.jl
from climatemargo.jl.
Thank you for your detailed response. I've tried adapting this solution to extract a time-series of the control deployment percentages but each control has the same values throughout the time-series in the resulting csv.
df = DataFrame(
year =t(m),
M = m.controls.mitigate,
R = m.controls.remove,
G = m.controls.geoeng,
A = m.controls.adapt
)
Would you be able to advise me on where I might be going wrong?
Kind regards,
Tilly
from climatemargo.jl.
Hi @txlauren, thanks for reaching out on here! I think the issue is that the example here has not yet been run through the optimization. So the controls are likely set to their defaults of zero at all times.
Try running the following commands to import the optimization functions and run the model through the control-optimizer.
using ClimateMARGO.Optimization
@time optimize_controls!(m);
You can see a more complete example in the documentation. Hope that helps!
from climatemargo.jl.
Related Issues (20)
- Consistent mitigation costs HOT 1
- Consistent and correct methods for changing the timestep on the fly. HOT 8
- Add optimization constraints as a submodel in ClimateModel or ClimateModelParameters? HOT 1
- Implement exact solution to two-box energy balance model HOT 2
- Towards a generic MARGO model structure (with abstract submodel types) HOT 1
- Add costs attributed to emissions (e.g. air pollution on human health) and CO2 (e.g. ocean acidification) HOT 3
- Inconsistency about whether `t.==present_year` should be included in `future_mask`. HOT 1
- Moving to ClimateMARGO github organization HOT 3
- TagBot trigger issue HOT 22
- Problems with T_adapt and optimization in 0.2.0 HOT 2
- Stochastic optimization
- Replace PyPlot calls with Plots.jl HOT 5
- Replace "tutorial.ipynb" with a Literate.jl script HOT 1
- Inconsistent naming "M" and "mitigate", nested and flat, struct and Dict HOT 4
- Experiment with converting to NLopt instead of JuMP
- Include example of the "policy response process" described in paper HOT 1
- What new feature or storyline would you like to see?
- Multiple equilibria HOT 3
- emissions to concentrations Q (unit_conversions.jl) HOT 2
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