Add functionality to run with technology shares

[camiloramirezgo]

Based on projected (desired) shares for certain technologies defined by the user, make OnStove select the regions where each technology should be prioritized. This can be done using each technology's net benefit calculation and prioritizing their shares, maximizing the overall total benefit.

One approach that was developed by Babak in https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove/tree/bk/conditional_opt. However, this approach is very computationally intensive and it can produce weird results having some cells sharing several technologies for a very few population.

An approach we can try is to use the net benefits as rankings, where we sort the dataframe by the value of the max benefit achieved and identify the threshold where at least one technology achieves its target population. Then we remove that technology from the remaining cells and recategorize them based on the maximum benefits achieved by the other technologies. Then we find again the next threshold until all desired technologies achieve their targets.

[camiloramirezgo]

@manuelsalslz we can develop this similarly to how we do urban calibration in OnSSET, see:

self.df.sort_values(by=[SET_POP_CALIB], inplace=True, ascending=False)
cumulative_urban_pop = self.df[SET_POP_CALIB].cumsum()
self.df[SET_URBAN] = np.where(cumulative_urban_pop < (urban_current * self.df[SET_POP_CALIB].sum()), 2, 0)
self.df.sort_index(inplace=True)

https://github.com/OnSSET/onsset/blob/7e46d876daedf8dd5fc8548603b95ac810786a78/onsset/onsset.py#L948-L951

We would sort by the max_net_benefit instead and get the cumsums of the different target technologies, and subsetting the sorted dataframe by the max_benefit_tech. After finding the first technology threshold, we clear the remaining cells and calculate again the max_benefit_tech using idxmax similar to what we do here https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove/blob/cbb63bb112e15b12d2ce9c2e902c270ebd235d2d/onstove/model.py#L2190C9-L2195C70. Then we sort the remainder and calculate the cumsums again until all targets are reached. If there is still population left, then we run the idxmax again with the remaining technologies. One challenge is how to handle partial access as we would have to create the duplicate cells with partial information in between this process.