lisa
3de9073b27
This program calculates the average flux (3D distribution) for each energy bin across all times and viewing directions. The resulting energy spectrum is then plotted on a double-logarithmic graph.
136 lines
2.8 KiB
Python
136 lines
2.8 KiB
Python
from rapid_fgm import *
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from datetime import datetime
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import numpy as np
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import matplotlib.pyplot as plt
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#---------USER INPUT------------------
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SC_ID = 'C3'
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TIME_PERIOD = (datetime(2002,6,27,23), datetime(2002,6,28,23))
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PARTICLE_TYPE = "electron" #'electron' oder "ion"
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#-----------------------------------------------------
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instrument = "IES" if PARTICLE_TYPE == "electron" else "IIMS"
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r = RAPID(sc_id=SC_ID, time_period=TIME_PERIOD, particle_type=PARTICLE_TYPE)
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data_rapid = r.load_rapid()
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r.get_rapid_var(r.data_rapid)
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print('rapid time:', r.time_rapid[0], r.time_rapid[-1])
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flow = FLOW(
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sc_id=SC_ID,
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time_period=TIME_PERIOD,
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particle_type=PARTICLE_TYPE
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)
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flow.get_flow_var(flow.data_flow)
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flow.load_flow()
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#font in Times New Roman
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plt.rcParams['font.family'] = 'Times New Roman'
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plt.rcParams['mathtext.fontset'] = 'stix'
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f = FGM(sc_id=SC_ID, time_period=TIME_PERIOD)
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f.load_fgm()
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f.get_fgm_var(f.data_fgm)
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p = PITCH_CSA(sc_id=SC_ID, time_period=TIME_PERIOD)
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p.load_pad()
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pad_data = p.get_pad_var(p.data_pad)
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w = WORKSPACE()
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sync = w.sync_all_by_resolution(r, f, flow)
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t_sync = sync['time']
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rapid_sync = sync['rapid']
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fgm_sync = sync['fgm']
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sc2gse = sync['sc2gse']
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sc_data = w.spacecrafts(
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sc_id=SC_ID,
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t_sync=t_sync,
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rapid_sync=rapid_sync,
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fgm_sync=fgm_sync,
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sc2gse=sc2gse,
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time_period=None
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)
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flux = sc_data['flux']
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time = sc_data['time'][:flux.shape[0]]
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mask = (time >= start) & (time <= end)
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flux = np.where(flux < 0, np.nan, flux)
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print(len(time))
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print(flux.shape)
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energy = r.energy_table
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mean_flux_energy = np.nanmean(flux, axis=(0, 2, 3))
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# Standardabweichung aus denselben Dimensionen
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flux_sd = np.nanstd(flux, axis=(0, 2, 3))
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plt.figure()
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color1 = '#0070C0'
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color2 = '#63A375'
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plt.errorbar(
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energy,
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mean_flux_energy,
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yerr = flux_sd,
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marker='o',
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markerfacecolor = color1,
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markeredgecolor = None,
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linestyle='None',
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color=color2,
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label=r'Flux $\pm$ SD',
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capsize=5
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)
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plt.xticks(fontsize=14)
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plt.yticks(fontsize=14)
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plt.xscale('log')
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plt.yscale('log')
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#plt.tick_params(fontsize = 14)
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gamma_ref = -1.5
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scale_factor = 2
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ref_line = mean_flux_energy[0] * scale_factor * (energy / energy[0])**gamma_ref
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plt.plot(
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energy,
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ref_line,
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linestyle='--',
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color='black',
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label=rf'$E^{{{gamma_ref}}}$'
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)
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plt.xlabel(r'Energy / $\mathrm{keV}$', fontsize = 12)
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plt.ylabel(r'Flux / $(\mathrm{cm^{2}\,s\,sr\,keV})^{-1}$',fontsize = 12)
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t0, t1 = (t.strftime('%Y-%m-%d') for t in TIME_PERIOD)
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plt.title(f'{SC_ID} – RAPID {instrument} Energy Spectrum – {t0} to {t1}', fontsize = 14)
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plt.grid(True, which='both')
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plt.legend()
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filename = 'C:/Users/lview/OneDrive/Dokumente/Desktop/C3_RAPID_20020628/'+ f'{SC_ID}_{instrument}_Power_law_fit_'+'20020627_20020629.pdf'
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print(filename)
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plt.savefig(filename)
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plt.show()
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