Remove diagnostic functions

Author: JMGilbert

src/dscim/utils/functions.py

@@ -22,126 +22,6 @@ def mean_func(consumption, dims):
     return consumption.mean(dims)
 
 
-def constant_equivalent_discount_rate(scc, marginal_damages):
-    """
-    Calculate the constant equivalent discount rates given a series of damages
-    and the present discounted value (the SCC)
-    by solving for the r in this formula:
-    PDV = undiscountedcost (0) + undiscountedcost (1) / (1+r) + undiscountedcost (2) / (1+r)^2 …
-
-    Parameters
-    ----------
-    scc: float
-        the PDV in the equation above
-
-    marginal_damages: 1d array
-        the undiscountedcosts in the equation above
-
-    Returns
-    -------
-    r: float
-        the r in the equation above
-
-    """
-    # the marginal damages will be the coefficients
-    coeffs = marginal_damages.copy()
-    # calculate the constant term
-    coeffs[0] = coeffs[0] - scc
-    p = np.polynomial.Polynomial(coeffs)
-    # get the roots
-    roots = p.roots()
-    # get rid of the complex roots
-    real_roots = roots.real[abs(roots.imag) < 1e-5]
-    # get rid of negative roots
-    positive_real_root = real_roots[real_roots > 0]
-    # if we're only left with one root we're good to go!
-    assert len(positive_real_root) == 1
-    # root = 1/(1+r), calculate r by inversing it
-    r = 1 / positive_real_root - 1
-    return r
-
-
-def calculate_constant_equivalent_discount_rate(
-    folder,
-    recipe,
-    disc,
-    sel_dict,
-    eta,
-    rho,
-    mean_dims=("simulation",),
-    uncollapsed=False,
-):
-    """
-    calls constant_equivalent_discount_rate on a set of marginal damages and sccs
-
-    Parameters
-    ----------
-    folder: str
-        root directory to the menu results
-
-    recipe: str
-        one of "adding_up", "risk_aversion", "equity"
-
-    disc: str
-        discount type of the scc and marginal damages that we want to derive the
-        constant equivalent discount rate for
-
-    sel_dict : dict
-        dictionary of subsetting options
-
-    uncollapsed : boolean
-        if True, will use `mean` marginal damages combined with `uncollapsed_sccs` file
-
-    Returns
-    -------
-
-    """
-    # read files
-    if not uncollapsed:
-        sccs = xr.open_dataset(
-            f"{folder}/{recipe}_{disc}_eta{eta}_rho{rho}_scc.nc4"
-        ).sel(sel_dict)
-        marginal_damages = xr.open_dataset(
-            f"{folder}/{recipe}_{disc}_eta{eta}_rho{rho}_marginal_damages.nc4"
-        ).sel(sel_dict)
-    else:
-        sccs = (
-            xr.open_dataset(
-                f"{folder}/{recipe}_{disc}_eta{eta}_rho{rho}_uncollapsed_sccs.nc4"
-            )
-            .sel(sel_dict)
-            .mean(mean_dims)
-        )
-        marginal_damages = (
-            xr.open_zarr(
-                f"{folder}/{recipe}_{disc}_eta{eta}_rho{rho}_uncollapsed_marginal_damages.zarr"
-            )
-            .load()
-            .expand_dims({"fair_aggregation": ["uncollapsed"]})
-            .sel(sel_dict)
-            .mean(mean_dims)
-        )
-
-    # do stuff to the datasets so that they can be passed as parameters to the apply_ufunc
-    sccs = (
-        sccs.to_array().rename("constant_discrate").to_dataset().squeeze(dim="variable")
-    )
-    marginal_damages = (
-        marginal_damages.to_array()
-        .rename("constant_discrate")
-        .to_dataset()
-        .squeeze(dim="variable")
-    )
-    constant_discount_rates = xr.apply_ufunc(
-        constant_equivalent_discount_rate,
-        sccs,
-        marginal_damages,
-        input_core_dims=[[], ["year"]],
-        vectorize=True,
-    )
-    return constant_discount_rates
-
-
 def get_model_weights(rcp):
     # clean weights
     WEIGHT_FILE = (