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# coding: utf-8 |
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# Copyright (c) 2023 Stefan Bender |
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# |
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# This file is part of pyeppaurora. |
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# pyeppaurora is free software: you can redistribute it or modify |
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# it under the terms of the GNU General Public License as published |
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# by the Free Software Foundation, version 2. |
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# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html. |
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"""Empirical model for auroral ionization rates |
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Implements the empirical model for auroral ionization, |
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derived from SSUSI UV observations [1]_. |
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.. [1] Bender et al., in prep., 2023 |
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""" |
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from os import path |
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from pkg_resources import resource_filename |
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import numpy as np |
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import xarray as xr |
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__all__ = [ |
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"ssusiq2023", |
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] |
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COEFF_FILE = "SSUSI_IRgrid_coeffs_f17f18.nc" |
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COEFF_PATH = resource_filename(__name__, path.join("data", COEFF_FILE)) |
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def ssusiq2023(gmlat, mlt, alt, sw_coeffs, coeff_ds=None, return_var=False): |
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u""" |
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Parameters |
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---------- |
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gmlat: float |
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Geomagnetic latitude in [degrees]. |
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mlt: float |
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Magnetic local time in [hours]. |
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alt: float |
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Altitude in [km] |
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sw_coeffs: array_like or `xarray.DataArray` |
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The space weather index values to use (for the requested time(s)), |
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should be of shape (N, M) with N = number of proxies, currently 5: |
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[Kp, PC, Ap, log(f10.7_81ctr_obs), log(v_plasma)]. |
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The `xarray.DataArray` should have a dimension named "proxy" with |
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matching coordinates: |
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["Kp", "PC", "Ap", "log_f107_81ctr_obs", "log_v_plasma"] |
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All the other dimensions will be broadcasted. |
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coeff_ds: `xarray.Dataset`, optional (default: None) |
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Dataset with the model coefficients, `None` uses the packaged version. |
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return_var: bool, optional (default: False) |
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If `True`, returns the predicted variance in addition to the values, |
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otherwise only the mean prediction is returned. |
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Returns |
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------- |
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q: `xarray.DataArray` |
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log(q), where q is the ionization rate in [cm⁻³ s⁻¹] |
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if `return_var` is False. |
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q, var(q): tuple of `xarray.DataArray`s |
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log(q) and var(log(q)) where q is the ionization rate in [cm⁻³ s⁻¹] |
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if `return_var` is True. |
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""" |
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coeff_ds = coeff_ds or xr.open_dataset(COEFF_PATH, decode_times=False) |
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coeff_sel = coeff_ds.sel( |
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altitude=alt, latitude=gmlat, mlt=mlt, method="nearest", |
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) |
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# prepare the coefficients (array) as a `xarray.DataArray` |
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if isinstance(sw_coeffs, xr.DataArray): |
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if "offset" in coeff_ds.proxy.values: |
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ones = xr.ones_like(sw_coeffs.isel(proxy=0)) |
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ones = ones.assign_coords({"proxy": "offset"}) |
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sw_coeffs = xr.concat([sw_coeffs, ones], dim="proxy") |
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else: |
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sw_coeffs = np.atleast_2d(sw_coeffs) |
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if "offset" in coeff_ds.proxy.values: |
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aix = sw_coeffs.shape.index(len(coeff_ds.proxy.values) - 1) |
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if aix != 0: |
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sw_coeffs = sw_coeffs.T |
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sw_coeffs = np.vstack([sw_coeffs, np.ones(sw_coeffs.shape[1])]) |
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else: |
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aix = sw_coeffs.shape.index(len(coeff_ds.proxy.values)) |
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if aix != 0: |
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sw_coeffs = sw_coeffs.T |
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extra_dims = ["dim_{0}".format(_d) for _d in range(sw_coeffs.ndim - 1)] |
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sw_coeffs = xr.DataArray( |
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sw_coeffs, |
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dims=["proxy"] + extra_dims, |
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coords={"proxy": coeff_ds.proxy.values}, |
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) |
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# Calculate model (mean) values from `beta` |
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# fill NaNs with zero for `.dot()` |
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coeffs = coeff_sel.beta.fillna(0.) |
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q = coeffs.dot(sw_coeffs) |
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if not return_var: |
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return q |
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# Calculate variance of the model from `beta_std` |
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# fill NaNs with zero for `.dot()` |
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coeffv = coeff_sel.beta_std.fillna(0.)**2 |
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q_var = coeffv.dot(sw_coeffs**2) |
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return q, q_var |
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st-bender /
pyeppaurora
