Source code for pyamps.model_utils

import numpy as np
import pandas as pd
import os
from functools import reduce


basepath = os.path.dirname(__file__)

coeff_fn = os.path.abspath(os.path.join(basepath,'coefficients','model_coefficients.csv'))

# read coefficient file and store in pandas DataFrame (this line will be replaced when 
# there is a decision on new format, but the dataframe should be the same):
coeffs = pd.read_csv(coeff_fn, index_col=('n','m'))

# organize the coefficients in arrays that are used to calculate magnetic field values
names = ['const', 'sinca', 'cosca', 'epsilon', 'epsilon_sinca', 'epsilon_cosca', 
         'tilt', 'tilt_sinca', 'tilt_cosca', 'tilt_epsilon', 'tilt_epsilon_sinca', 
         'tilt_epsilon_cosca', 'tau', 'tau_sinca', 'tau_cosca', 'tilt_tau', 
         'tilt_tau_sinca', 'tilt_tau_cosca', 'f107']

m_matrix = np.array([np.hstack((coeffs['tor_c_' + ss].dropna().values,
                                coeffs['tor_s_' + ss].dropna().values,
                                coeffs['pol_c_' + ss].dropna().values,
                                coeffs['pol_s_' + ss].dropna().values)) for ss in names]).T

m_matrix_pol = np.array([np.hstack((coeffs['pol_c_' + ss].dropna().values,
                                    coeffs['pol_s_' + ss].dropna().values)) for ss in names]).T


[docs]def get_model_vectors(v, By, Bz, tilt, f107, epsilon_multiplier = 1.): """ tor_c, tor_s, pol_c, pol_s = get_model_vectors(v, By, Bz, tilt, F107) returns column vectors ((K,1)-shaped) corresponding to the spherical harmonic coefficients of the toroidal and poloidal parts, with _c and _s denoting cos and sin terms, respectively. This function is used by amps.AMPS class """ ca = np.arctan2(By, Bz) epsilon = v**(4/3.) * np.sqrt(By**2 + Bz**2)**(2/3.) * (np.sin(ca/2)**(8))**(1/3.) / 1000 * epsilon_multiplier # Newell coupling tau = v**(4/3.) * np.sqrt(By**2 + Bz**2)**(2/3.) * (np.cos(ca/2)**(8))**(1/3.) / 1000 # Newell coupling - inverse # make a dict of the 19 external parameters, where the keys are postfixes in the column names of coeffs: external_params = {'const' : 1 , 'sinca' : 1 * np.sin(ca), 'cosca' : 1 * np.cos(ca), 'epsilon' : epsilon , 'epsilon_sinca' : epsilon * np.sin(ca), 'epsilon_cosca' : epsilon * np.cos(ca), 'tilt' : tilt , 'tilt_sinca' : tilt * np.sin(ca), 'tilt_cosca' : tilt * np.cos(ca), 'tilt_epsilon' : tilt * epsilon , 'tilt_epsilon_sinca': tilt * epsilon * np.sin(ca), 'tilt_epsilon_cosca': tilt * epsilon * np.cos(ca), 'tau' : tau , 'tau_sinca' : tau * np.sin(ca), 'tau_cosca' : tau * np.cos(ca), 'tilt_tau' : tilt * tau , 'tilt_tau_sinca' : tilt * tau * np.sin(ca), 'tilt_tau_cosca' : tilt * tau * np.cos(ca), 'f107' : f107 } # The SH coefficients are the sums in the expansion in terms of external parameters, scaled by the ext. params.: tor_c = reduce(lambda x, y: x+y, [coeffs['tor_c_' + param] * external_params[param] for param in external_params.keys()]).dropna() tor_s = reduce(lambda x, y: x+y, [coeffs['tor_s_' + param] * external_params[param] for param in external_params.keys()]).fillna(0) pol_c = reduce(lambda x, y: x+y, [coeffs['pol_c_' + param] * external_params[param] for param in external_params.keys()]).dropna() pol_s = reduce(lambda x, y: x+y, [coeffs['pol_s_' + param] * external_params[param] for param in external_params.keys()]).fillna(0) pol_s = pol_s.ix[pol_c.index] # equal number of sin and cos terms, but sin coeffs will be 0 where m = 0 tor_s = tor_s.ix[tor_c.index] # return tor_c[:, np.newaxis], tor_s[:, np.newaxis], pol_c[:, np.newaxis], pol_s[:, np.newaxis], pol_c.index.values, tor_c.index.values