leiv_andresen

Experience

Dribs

Cost function so we can compare algorithms:

def calculate_total_kwh_m(mst_table): return (mst_table['routes']*abs(mst_table['heat_total'])).sum()

6 months ago

  Iterative optimization with random perturbation of one distance.Reduction in total cost sum(abs(heat_total)*routes):

6 months ago

requirements.txt content extracted by github copilot:

numpy pandas geopandas pickle-mixin sparse xarray scipy matplotlib contextily

6 months ago