Feature: heat pump cop profiles #393
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| Original file line number | Diff line number | Diff line change |
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| from functools import partial | ||
| from multiprocessing import Pool | ||
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| import pandas as pd | ||
| import xarray as xr | ||
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| def group_gridcells( | ||
| gridded_data: xr.Dataset, | ||
| grid_weight: xr.DataArray, | ||
| annual_demand: xr.DataArray, | ||
| threads: int, | ||
| ) -> pd.DataFrame: | ||
| """Group gridded heat data into resolution-specific units, taking a weighted average of grid values. | ||
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| Also take a weighted average of hot water and space heat demand (using per-unit annual demands) to return a single "heat" timeseries. | ||
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| Args: | ||
| gridded_data (xr.Dataset): Gridded timeseries space heat and hot water data. | ||
| grid_weight (xr.DataArray): Weighted mapping from grid (a.k.a. "site") to units. | ||
| annual_demand (xr.DataArray): Per-unit annual space heating and hot water demands. | ||
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There was a problem hiding this comment. I find it hard to understand why this input is necessary. Can you explain? Is this a bias-correction? Or is it because the time series have no meaningful scale / are normed? There was a problem hiding this comment. It's doing two sets of weighted averages: weighted averaging "sites" into "IDs" (using population) and weighted averaging "space heat" and "hot water" demand into a general profile for "heat" (using annual demands). |
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| threads (int): Number of threads over which to undertake multiprocessing. | ||
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| Returns: | ||
| pd.DataFrame: Index: timeseries, Columns: unit IDs | ||
| """ | ||
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| partial_func = partial( | ||
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There was a problem hiding this comment. maybe rename function to "apply_weights" or similar? |
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| _site_weighted_ave, gridded_data=gridded_data, grid_weight=grid_weight | ||
| ) | ||
| # This is a slow operation, so we parallelise it. | ||
| with Pool(threads) as pool: | ||
| per_id_averages = pool.map(partial_func, grid_weight.id.values) | ||
| weighted_average_ds = xr.concat(per_id_averages, dim="id") | ||
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| weighted_average_da = ( | ||
| weighted_average_ds.to_array("end_use") | ||
| .groupby("time.year") | ||
| .apply(_end_use_weighted_ave, annual_demand=annual_demand) | ||
| ) | ||
| return weighted_average_da.astype("float32").to_series().unstack("id") | ||
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| def prepare_annual_demand(annual_demand: pd.Series) -> xr.DataArray: | ||
| """Restructure annual demand MultiIndex series into a multi-dimensional array. | ||
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| Result sums over all building categories and only contains hot water and space heating demands (not cooking). | ||
| """ | ||
| return ( | ||
| annual_demand.rename_axis(columns="id") | ||
| .stack() | ||
| .to_xarray() | ||
| .sel(end_use=["space_heat", "hot_water"]) | ||
| .sum("cat_name") | ||
| ) | ||
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| def _site_weighted_ave( | ||
| id: str, gridded_data: xr.Dataset, grid_weight: xr.DataArray | ||
| ) -> xr.Dataset: | ||
| """Multi-processing helper function to get the weighted average of all gridcells for a given spatial unit (id).""" | ||
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There was a problem hiding this comment. There is nothing "multi-processing" related in this function, right? Better remove from comment in that case. There was a problem hiding this comment. More that it only exists because it is needs to be its own function so that it can be called by the multiprocessor. |
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| id_grid_weight = grid_weight.sel(id=id).dropna("site") | ||
| return ( | ||
| gridded_data.reindex_like(id_grid_weight) | ||
| # we normalise the weights in case they aren't already | ||
| * (id_grid_weight / id_grid_weight.sum("site")) | ||
| ).sum(["site"]) | ||
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| def _end_use_weighted_ave( | ||
| one_year_profile: xr.DataArray, annual_demand: xr.DataArray | ||
| ) -> xr.DataArray: | ||
| """Take a weighted average of all heat energy end uses using annual demands per spatial unit as the weights.""" | ||
| year = one_year_profile.time.dt.year[0] | ||
| normalised_demand = annual_demand / annual_demand.sum("end_use") | ||
| demand = one_year_profile * normalised_demand.sel(year=year).drop("year") | ||
| return demand.sum("end_use") | ||
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| if __name__ == "__main__": | ||
| gridded_data = xr.open_dataset(snakemake.input.gridded_timeseries_data) | ||
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There was a problem hiding this comment. First use of this approach in the repository. The line will make history. :D |
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| grid_weights = xr.open_dataarray(snakemake.input.grid_weights) | ||
| annual_demand = pd.read_csv(snakemake.input.annual_demand, index_col=[0, 1, 2]) | ||
| annual_demand_ds = prepare_annual_demand(annual_demand) | ||
| resolution_specific_data = group_gridcells( | ||
| gridded_data, grid_weights, annual_demand_ds, snakemake.threads | ||
| ) | ||
| resolution_specific_data.to_csv(snakemake.output[0]) | ||
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It seems you've implemented most parts of #39, too. Is that correct? If yes, would add that too.