updated coefficients and climatology plots

model_vector_to_txt_test.py

@@ -0,0 +1,48 @@
+from pyamps.model_vector_to_txt import vector_to_df
+import numpy as np
+import time
+import pandas as pd
+external_parameters = ['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']
+
+# number of terms in the expansion of each spherical harmonic coefficient
+NTERMS = len(external_parameters)
+
+t = time.ctime().split(' ')
+date = ' '.join([t[1], t[-1]])
+header = '# Sherical harmonic coefficients for the Average Magnetic field and Polar current System (AMPS) model\n'
+header = header + '# Produced ' + date
+header = header + """
+#
+# Based on magnetic field measurements from CHAMP (2001-08 to 2010-09) and Swarm (2013-12 to 2023-12).
+# Reference: Laundal et al., "Solar wind and seasonal influence on ionospheric currents", Journal of Geophysical Research - Space Physics, doi:10.1029/2018JA025387, 2018
+#
+# Coefficient unit: nT
+# Apex reference height: 110 km
+# Earth radius: 6371.2 km
+#
+# Spherical harmonic degree, order: 65, 3 (for T) and 45, 3 (for V)
+# 
+# column names:
+# n m tor_c_const tor_s_const pol_c_const pol_s_const tor_c_sinca tor_s_sinca pol_c_sinca pol_s_sinca tor_c_cosca tor_s_cosca pol_c_cosca pol_s_cosca tor_c_epsilon tor_s_epsilon pol_c_epsilon pol_s_epsilon tor_c_epsilon_sinca tor_s_epsilon_sinca pol_c_epsilon_sinca pol_s_epsilon_sinca tor_c_epsilon_cosca tor_s_epsilon_cosca pol_c_epsilon_cosca pol_s_epsilon_cosca tor_c_tilt tor_s_tilt pol_c_tilt pol_s_tilt tor_c_tilt_sinca tor_s_tilt_sinca pol_c_tilt_sinca pol_s_tilt_sinca tor_c_tilt_cosca tor_s_tilt_cosca pol_c_tilt_cosca pol_s_tilt_cosca tor_c_tilt_epsilon tor_s_tilt_epsilon pol_c_tilt_epsilon pol_s_tilt_epsilon tor_c_tilt_epsilon_sinca tor_s_tilt_epsilon_sinca pol_c_tilt_epsilon_sinca pol_s_tilt_epsilon_sinca tor_c_tilt_epsilon_cosca tor_s_tilt_epsilon_cosca pol_c_tilt_epsilon_cosca pol_s_tilt_epsilon_cosca tor_c_tau tor_s_tau pol_c_tau pol_s_tau tor_c_tau_sinca tor_s_tau_sinca pol_c_tau_sinca pol_s_tau_sinca tor_c_tau_cosca tor_s_tau_cosca pol_c_tau_cosca pol_s_tau_cosca tor_c_tilt_tau tor_s_tilt_tau pol_c_tilt_tau pol_s_tilt_tau tor_c_tilt_tau_sinca tor_s_tilt_tau_sinca pol_c_tilt_tau_sinca pol_s_tilt_tau_sinca tor_c_tilt_tau_cosca tor_s_tilt_tau_cosca pol_c_tilt_tau_cosca pol_s_tilt_tau_cosca tor_c_f107 tor_s_f107 pol_c_f107 pol_s_f107
+"""
+model_vector = np.load(
+    '/Users/fasilkebede/Downloads/model_v1_iteration_13.npy')
+dataframes = [vector_to_df(m) for m in np.split(model_vector, NTERMS)]
+
+# add the external parameter to the column names:
+
+for m, param in zip(dataframes, external_parameters):
+    m.columns = [n + '_' + param for n in m.columns]
+# merge them all
+coefficients = pd.concat(dataframes, axis=1)
+
+# write txt file
+with open('/Users/fasilkebede/Downloads/SW_OPER_MIO_SHA_2E_00000000T000000_99999999T999999_0106.txt', 'w') as file:
+    # header:
+    file.write(header)
+    # data:
+    coefficients.to_string(buf=file, float_format=lambda x: '{:.7f}'.format(x),
+                           header=False, sparsify=False,
+                           index_names=False)