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icme_rate_web.py
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#!/usr/bin/env python
# coding: utf-8
# # ICME rate web plots
#
# Adapted from the code for the paper Möstl et al. 2020 ApJ https://iopscience.iop.org/article/10.3847/1538-4357/abb9a1
#
# https://github.com/helioforecast/Papers/tree/master/Moestl2020_PSP_rate
#
# makes a prediction of the ICME rate in solar cycle 25, and solar cycle tracking plots, for the webpage
# https://helioforecast.space/solarcycle
#
# Main author: C. Möstl, Austrian Space Weather Office, GeoSphere Austria
#
# part of https://github.com/cmoestl/heliocats
#
# uses environment 'envs/env_helio4.yml'
#
#
# ---
#
# **MIT LICENSE**
#
# Copyright 2020-2024, Christian Moestl
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to the following
# conditions:
#
# The above copyright notice and this permission notice shall be included in all copies
# or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
# OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# In[49]:
debug_mode=0
# -----CHECK LIST
## update every year
last_year=2024 #2024 means last date is 2023 Dec 31
##update the data files, which are independent in folder /data/icme_rate_data_files for the yearly update
#needed:
#stereoa_2007_now_rtn.p
#stereoa_beacon_rtn_last_300days_now.p
#wind_1995_now_rtn.p
#solo_2020_now_rtn.p
#NOTE: set cutoff date for STEREO-A science data when loading data consistent with ICMECAT
#manually set latest solo kernel file for spiceypy
solo_kernel_file='solo_ANC_soc-orbit-stp_20200210-20301120_312_V1_00350_V01.bsp'
#----------
from scipy import stats
import scipy.io
from matplotlib import cm
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import datetime
from datetime import timedelta
import astropy.constants as const
from sunpy.time import parse_time
import sunpy.time
import time
import pickle
import seaborn as sns
import os
import sys
import urllib
import json
import warnings
import copy
import importlib
import astropy
import astrospice
import spiceypy
from sunpy.coordinates import HeliocentricInertial, HeliographicStonyhurst, HeliocentricEarthEcliptic
from astropy.constants import au
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
from heliocats import stats as hs
from heliocats import data as hd
from heliocats import cats as hc
#########################
#Convert this notebook to a script with:
os.system('jupyter nbconvert --to script icme_rate_web.ipynb')
##### check for system type
#server
if sys.platform == 'linux':
print('system is linux')
matplotlib.use('Agg')
from config_server import kernels_path
#mac
if sys.platform =='darwin':
print('system is mac')
get_ipython().run_line_magic('matplotlib', 'inline')
from config_local import kernels_path
#warnings.filterwarnings('ignore') # some numpy mean-of-empty-slice runtime warnings
########### make directories first time
resdir='results'
if os.path.isdir(resdir) == False: os.mkdir(resdir)
datadir='data'
if os.path.isdir(datadir) == False: os.mkdir(datadir)
resdir='results/icme_rate'
if os.path.isdir(resdir) == False: os.mkdir(resdir)
#where the in situ data files are located is set as data_path
data_path=datadir+'/icme_rate_data_files/'
if os.path.isdir(data_path) == False: os.mkdir(data_path)
#set output directory
outputdirectory=resdir
t0all = time.time()
print('results path', resdir)
print('path for data files', data_path)
# ## 1 Settings and load data
# In[2]:
t0 = time.time()
print('----------------------- ')
print('icme_rate main program.')
#constants:
#solar radius
Rs_in_AU=float(const.R_sun/const.au)
#define AU in km
AU_in_km=const.au.value/1e3
#set for loading
load_data=1
get_new_sunspots=1
get_new_sunspots_ms=1
get_new_sunspots_mean=1
get_new_omni=0
if load_data > 0:
print('load data')
print('')
#####################
print('get RC ICME list')
#download richardson and cane list
rc_url='http://www.srl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm'
try: urllib.request.urlretrieve(rc_url,data_path+'rc_list.htm')
except urllib.error.URLError as e:
print('Failed downloading ', rc_url,' ',e)
#read RC list into pandas dataframe
rc_dfull=pd.read_html(data_path+'rc_list.htm')
rc_df=rc_dfull[0]
##################
print('get sunspot number from SIDC')
#get daily sunspot number from SIDC
#http://www.sidc.be/silso/datafiles
#parameters
#http://www.sidc.be/silso/infosndtot
#daily sunspot number
if get_new_sunspots==1:
ssn=pd.read_csv('http://www.sidc.be/silso/DATA/SN_d_tot_V2.0.csv',sep=';',names=['year','month','day','year2','spot','stand','obs','prov'])
ssn_time=np.zeros(len(ssn))
for k in np.arange(len(ssn)):
ssn_time[k]=mdates.date2num(datetime.datetime(ssn.year[k],ssn.month[k], ssn.day[k]))
print('time convert done')
ssn.insert(0,'time',ssn_time)
#set all standard deviations < 0 to nan
snan=np.where(ssn.stand< 0)[0]
for k in snan: ssn.at[k,'stand']=np.nan
fileout='ssn.p'
pickle.dump(ssn, open(data_path+fileout, "wb"))
#also get preliminary data for current month for plotting
#download
ssn_prelim_url='http://www.sidc.be/silso/DATA/EISN/EISN_current.csv'
try: urllib.request.urlretrieve(ssn_prelim_url,data_path+'EISN_current.csv')
except urllib.error.URLError as e:
print('Failed downloading ', ssn_prelim_url,' ',e)
ssn_prelim_raw = np.loadtxt(data_path+'EISN_current.csv', delimiter=',',usecols=(0,1,2,4,5))
ssn_p_int=ssn_prelim_raw.astype(int)
#with one entry do this different:
if ssn_p_int.size == 5:
ssn_p=pd.DataFrame([ssn_p_int],columns=['year','month','day','spot','stand'])
if ssn_p_int.size > 5:
ssn_p=pd.DataFrame(ssn_p_int,columns=['year','month','day','spot','stand'])
ssn_p_time=np.zeros(len(ssn_p))
for k in np.arange(len(ssn_p)):
ssn_p_time[k]=mdates.date2num(datetime.datetime(ssn_p.year[k],ssn_p.month[k], ssn_p.day[k]))
ssn_p.insert(0,'time',ssn_p_time)
fileout='ssn_prelim.p'
pickle.dump(ssn_p, open(data_path+fileout, "wb"))
if get_new_sunspots_ms==1:
#ssn_ms=pd.read_csv('data/SN_ms_tot_V2.0.csv',sep=';')
ssn_ms=pd.read_csv('http://www.sidc.be/silso/DATA/SN_ms_tot_V2.0.csv',sep=';',names=['year','month','year2','spot','stand','obs','check'])
ssn_ms_time=np.zeros(len(ssn_ms))
for k in np.arange(len(ssn_ms)):
ssn_ms_time[k]=mdates.date2num(datetime.datetime(ssn_ms.year[k],ssn_ms.month[k], 1))
ssn_ms.insert(0,'time',ssn_ms_time)
fileout='ssn_13ms.p'
pickle.dump(ssn_ms, open(data_path+fileout, "wb"))
if get_new_sunspots_mean==1:
ssn_m=pd.read_csv('http://www.sidc.be/silso/DATA/SN_m_tot_V2.0.csv',sep=';',names=['year','month','year2','spot','stand','obs','check'])
ssn_m_time=np.zeros(len(ssn_m))
for k in np.arange(len(ssn_m)):
ssn_m_time[k]=mdates.date2num(datetime.datetime(ssn_m.year[k],ssn_m.month[k], 1))
ssn_m.insert(0,'time',ssn_m_time)
fileout='ssn_m.p'
pickle.dump(ssn_m, open(data_path+fileout, "wb"))
file='ssn.p'
ssn=pickle.load(open(data_path+file, "rb"))
file='ssn_13ms.p'
ssn_ms=pickle.load(open(data_path+file, "rb"))
file='ssn_m.p'
ssn_m=pickle.load(open(data_path+file, "rb"))
#make 13 month running mean
runmean_months=13.0
ssn_mean_13=hs.running_mean(ssn.spot,int(np.rint(30.42*runmean_months+1)))
ssn_std_13=hs.running_mean(ssn.stand,int(np.rint(30.42*runmean_months+1)))
ssn.insert(1,'spot_mean_13',ssn_mean_13)
ssn.insert(2,'spot_std_13',ssn_std_13)
print('SIDC sunspots done')
# In[3]:
if load_data > 0:
##----------------------------- spacecraft
#fileomni="omni_1963_now.p"
#if get_new_omni==1: hd.save_omni_data(data_path,fileomni)
#[omni,homni]=pickle.load(open(data_path+fileomni, "rb" ) )
#print('load Ulysses RTN') #made with heliocats.data.save_ulysses_data
#fileuly='ulysses_1990_2009_rtn.p'
#[uly,huly]=pickle.load(open(data_path+fileuly, "rb" ) )
#print('load VEX data (Venus magnetosphere removed) SCEQ') #legacy from HELCATS project in SCEQ, removed magnetosphere
#filevex='vex_2007_2014_sceq_removed.p'
#[vex,hvex]=pickle.load(open(data_path+filevex, 'rb' ) )
#print('load MESSENGER data (Mercury magnetosphere removed) SCEQ') #legacy from HELCATS project in SCEQ, removed magnetosphere
#filemes='messenger_2007_2015_sceq_removed.p'
#[mes,hmes]=pickle.load(open(data_path+filemes, 'rb' ) )
print('load STEREO-B data RTN') #yearly magplasma files from stereo science center, conversion to SCEQ
filestb='stereob_2007_2014_rtn.p'
[stb,hstb]=pickle.load(open(data_path+filestb, "rb" ) )
########### CURRENT ACTIVE SPACECRAFT
############### convert MAVEN from Cyril's MAT file to pickle
#from heliocats import data as hd
#importlib.reload(hd) #reload again while debugging
#file_input=data_path+'input/Data-MAVEN-MAG_SolarWind_102014-012021.mat'
#filename=data_path+'maven_2014_2021_removed_no_plasma.p'
#hd.convert_MAVEN_mat_removed(file_input,filename)
#filemav=data_path+'maven_2014_2021_removed_no_plasma.p'
#filename=data_path+'maven_2014_2021_removed_smoothed_no_plasma.p'
#hd.MAVEN_smooth_orbit(filemav,filename)
#print('load MAVEN data MSO')
#filemav='maven_2014_2018.p'
#[mav,hmav]=pickle.load(open(filemav, 'rb' ) )
#combined plasma and mag
#filemav='maven_2014_2018_removed.p'
#[mavr,hmavr]=pickle.load(open(data_path+filemav, 'rb' ) )
#filemav='maven_2014_2018_removed_smoothed.p'
#[mav,hmav]=pickle.load(open(data_path+filemav, 'rb' ) )
#only mag
#filemav='maven_2014_2021_removed_no_plasma.p'
#[mav2,hmav2]=pickle.load(open(data_path+filemav, 'rb' ) )
#filemav='maven_2014_2021_removed_smoothed_no_plasma.p'
#[mavr2,hmavr2]=pickle.load(open(data_path+filemav, 'rb' ) )
#removed magnetosphere by C. Simon Wedlund, 1 data point per orbit, MSO
#filemav='maven_2014_2021_removed_smoothed.p'
#[mav,hmav]=pickle.load(open(data_path+filemav, 'rb' ) )
#use hd.save_msl_rad() first to convert data doseE_sol_filter_2019.dat to pickle file
#print('load MSL RAD')
#MSL RAD
#rad=hd.load_msl_rad(data_path)#, rad.time,rad.dose_sol
################################ Bepi Colombo
#print('load Bepi Colombo RTN')
#filebepi='bepi_ib_2019_now_rtn.p'
#[bepi,bepiheader]=pickle.load(open(data_path+filebepi, "rb" ) )
########### STA
print('load and merge STEREO-A data') #yearly magplasma files from stereo science center, conversion to SCEQ
filesta1='stereoa_2007_now_rtn.p'
[sta1,hsta1]=pickle.load(open(data_path+filesta1, "rb" ) )
#beacon data
filesta2='stereoa_beacon_rtn_last_300days_now.p'
[sta2,hsta2]=pickle.load(open(data_path+filesta2, "rb" ) )
#cutoff with end of science data
sta2=sta2[np.where(sta2.time >= parse_time('2023-May-01 00:00').datetime)[0]]
#make array
sta=np.zeros(np.size(sta1.time)+np.size(sta2.time),dtype=[('time',object),('bx', float),('by', float),\
('bz', float),('bt', float),('vt', float),('np', float),('tp', float),\
('x', float),('y', float),('z', float),\
('r', float),('lat', float),('lon', float)])
#convert to recarray
sta = sta.view(np.recarray)
sta.time=np.hstack((sta1.time,sta2.time))
sta.bx=np.hstack((sta1.bx,sta2.bx))
sta.by=np.hstack((sta1.by,sta2.by))
sta.bz=np.hstack((sta1.bz,sta2.bz))
sta.bt=np.hstack((sta1.bt,sta2.bt))
sta.vt=np.hstack((sta1.vt,sta2.vt))
sta.np=np.hstack((sta1.np,sta2.np))
sta.tp=np.hstack((sta1.tp,sta2.tp))
sta.x=np.hstack((sta1.x,sta2.x))
sta.y=np.hstack((sta1.y,sta2.y))
sta.z=np.hstack((sta1.z,sta2.z))
sta.r=np.hstack((sta1.r,sta2.r))
sta.lon=np.hstack((sta1.lon,sta2.lon))
sta.lat=np.hstack((sta1.lat,sta2.lat))
del sta1
del sta2
print('STA Merging done')
##################### Wind
filewin="wind_1995_now_rtn.p"
[win,hwin]=pickle.load(open(data_path+filewin, "rb" ) )
################### SolO
print('load Solar Orbiter RTN')
filesolo='solo_2020_now_rtn.p'
[solo,hsolo]=pickle.load(open(data_path+filesolo, "rb" ) )
################### PSP
#print('load PSP data RTN')
filepsp='psp_2018_now_rtn.p'
#[psp,hpsp]=pickle.load(open(data_path+filepsp, "rb" ) )
print('load all data done')
print()
print()
print('time ranges of the in situ data: ')
print()
print('active spacecraft:')
print('Solar Orbiter ',str(solo.time[0])[0:10],str(solo.time[-1])[0:10])
#print('Bepi Colombo ',str(bepi.time[0])[0:10],str(bepi.time[-1])[0:10])
#print('Parker Solar Probe ',str(psp.time[0])[0:10],str(psp.time[-1])[0:10])
print('Wind ',str(win.time[0])[0:10],str(win.time[-1])[0:10])
print('STEREO-A ',str(sta.time[0])[0:10],str(sta.time[-1])[0:10])
#print('MAVEN ',str(mav.time[0])[0:10],str(mav.time[-1])[0:10])
#print('MSL/RAD ',str(rad.time[0])[0:10],str(rad.time[-1])[0:10])
#print()
#print('missions finished:')
#print('VEX ',str(vex.time[0])[0:10],str(vex.time[-1])[0:10])
#print('MESSENGER ',str(mes.time[0])[0:10],str(mes.time[-1])[0:10])
print('STEREO-B ',str(stb.time[0])[0:10],str(stb.time[-1])[0:10])
#print('Ulysses ',str(uly.time[0])[0:10],str(uly.time[-1])[0:10])
#print('Juno cruise phase ',str(uly.time[0])[0:10],str(uly.time[-1])[0:10])
print('done')
t1 = time.time()
print()
print('loading all data takes', np.round(t1-t0,2), 'seconds')
# In[4]:
########### load ICMECAT made with icmecat.py or ipynb
file='icmecat/HELIO4CAST_ICMECAT_v22_pandas.p'
print()
print('loaded ', file)
print()
print('Keys (parameters) in this pandas data frame are:')
[ic,h,p]=pickle.load(open(file, "rb" ) )
print(ic.keys())
print()
################### get indices of events for each spacecraft
#mercury_orbit_insertion_time= parse_time('2011-03-18').datetime
#spacecraft near the 4 terrestrial planets
#get indices for Mercury after orbit insertion in March 2011
#merci=np.where(np.logical_and(ic.sc_insitu =='MESSENGER', ic.icme_start_time > mercury_orbit_insertion_time))[0]
#vexi=np.where(ic.sc_insitu == 'VEX')[:][0]
wini=np.where(ic.sc_insitu == 'Wind')[:][0]
#mavi=np.where(ic.sc_insitu == 'MAVEN')[:][0]
#other spacecraft
#all MESSENGER events including cruise phase
#mesi=np.where(ic.sc_insitu == 'MESSENGER')[:][0]
stbi=np.where(ic.sc_insitu == 'STEREO-B')[:][0]
#ulyi=np.where(ic.sc_insitu == 'ULYSSES')[:][0]
#pspi=np.where(ic.sc_insitu == 'PSP')[:][0]
soli=np.where(ic.sc_insitu == 'SolarOrbiter')[:][0]
#beci=np.where(ic.sc_insitu == 'BepiColombo')[:][0]
stai=np.where(ic.sc_insitu == 'STEREO-A')[:][0]
###################### load ARRCAT and HIGEOCAT
#HIGEOCAT
higeocat=hc.load_higeocat_vot('data/HCME_WP3_V06.vot')
higeocat_time=parse_time(higeocat['Date']).datetime
#load arrcat as pandas dataframe
file='arrcat/HELCATS_ARRCAT_v20_pandas.p'
[ac_pandas,h]=pickle.load( open(file, 'rb'))
print()
print('catalogs:')
print()
print('HELCATS HIGeoCAT ',str(higeocat_time[0])[0:10],str(higeocat_time[-1])[0:10])
print('HELCATS ARRCAT ',np.sort(ac_pandas.sse_launch_time)[0][0:10],np.sort(ac_pandas.sse_launch_time)[-1][0:10])
# In[5]:
############### set limits of solar minimum, rising/declining phase and solar maximum
# minimim maximum times as given by
#http://www.sidc.be/silso/cyclesmm
#24 2008 12 2.2 2014 04 116.4
solarmin=parse_time('2008-12-01').datetime
minstart=solarmin-datetime.timedelta(days=366*1.5)
minend=solarmin+datetime.timedelta(days=365)
minstart_num=parse_time(minstart).plot_date
minend_num=parse_time(minend).plot_date
solarmax=parse_time('2014-04-01').datetime
maxstart=solarmax-datetime.timedelta(days=365*3)
maxend=solarmax+datetime.timedelta(days=365/2)
maxstart_num=parse_time(maxstart).plot_date
maxend_num=parse_time(maxend).plot_date
#rising phase not used
# risestart=parse_time('2010-01-01').datetime
# riseend=parse_time('2011-06-30').datetime
# risestart_num=parse_time('2010-01-01').plot_date
# riseend_num=parse_time('2011-06-30').plot_date
# declstart=parse_time('2015-01-01').datetime
# declend=parse_time('2018-12-31').datetime
# declstart_num=parse_time('2015-01-01').plot_date
# declend_num=parse_time('2018-12-31').plot_date
############### extract events by limits of solar minimum and maximum
iall_min=np.where(np.logical_and(ic.icme_start_time > minstart,ic.icme_start_time < minend))[0]
#iall_rise=np.where(np.logical_and(ic.icme_start_time > risestart,ic.icme_start_time < riseend))[0]
iall_max=np.where(np.logical_and(ic.icme_start_time > maxstart,ic.icme_start_time < maxend))[0]
wini_min=iall_min[np.where(ic.sc_insitu[iall_min]=='Wind')]
#wini_rise=iall_rise[np.where(ic.sc_insitu[iall_rise]=='Wind')]
wini_max=iall_max[np.where(ic.sc_insitu[iall_max]=='Wind')]
pspi_min=iall_min[np.where(ic.sc_insitu[iall_min]=='PSP')]
#wini_rise=iall_rise[np.where(ic.sc_insitu[iall_rise]=='Wind')]
pspi_max=iall_max[np.where(ic.sc_insitu[iall_max]=='PSP')]
vexi_min=iall_min[np.where(ic.sc_insitu[iall_min]=='VEX')]
#vexi_rise=iall_rise[np.where(ic.sc_insitu[iall_rise]=='VEX')]
vexi_max=iall_max[np.where(ic.sc_insitu[iall_max]=='VEX')]
mesi_min=iall_min[np.where(ic.sc_insitu[iall_min]=='MESSENGER')]
#mesi_rise=iall_rise[np.where(ic.sc_insitu[iall_rise]=='MESSENGER')]
mesi_max=iall_max[np.where(ic.sc_insitu[iall_max]=='MESSENGER')]
stai_min=iall_min[np.where(ic.sc_insitu[iall_min]=='STEREO-A')]
#stai_rise=iall_rise[np.where(ic.sc_insitu[iall_rise]=='STEREO-A')]
stai_max=iall_max[np.where(ic.sc_insitu[iall_max]=='STEREO-A')]
stbi_min=iall_min[np.where(ic.sc_insitu[iall_min]=='STEREO-B')]
#stbi_rise=iall_rise[np.where(ic.sc_insitu[iall_rise]=='STEREO-B')]
stbi_max=iall_max[np.where(ic.sc_insitu[iall_max]=='STEREO-B')]
# select the events at Mercury extra after orbit insertion, note that no events available for solar minimum
merci_min=iall_min[np.where(np.logical_and(ic.sc_insitu[iall_min] =='MESSENGER',ic.icme_start_time[iall_min] > parse_time('2011-03-18').datetime))]
#merci_rise=iall_rise[np.where(np.logical_and(ic.sc_insitu[iall_rise] =='MESSENGER',ic.icme_start_time[iall_rise] > parse_time('2011-03-18').datetime))]
merci_max=iall_max[np.where(np.logical_and(ic.sc_insitu[iall_max] =='MESSENGER',ic.icme_start_time[iall_max] > parse_time('2011-03-18').datetime))]
print(len(ic))
print('done')
# In[6]:
ic
# ## 2 ICME rate for solar cycles 23/24 from the Heliophysics System Observatory (ICMECAT and Richardson and Cane)
# ### Check data days available each year for each planet or spacecraft
# In[7]:
######################## make bin for each year for yearly histograms
#define dates of January 1 from 2007 to end year
years_jan_1_str=[str(i)+'-01-01' for i in np.arange(2007,last_year) ]
yearly_start_times=parse_time(years_jan_1_str).datetime
yearly_start_times_num=parse_time(years_jan_1_str).plot_date
#same for July 1 as middle of the year
years_jul_1_str=[str(i)+'-07-01' for i in np.arange(2007,last_year) ]
yearly_mid_times=parse_time(years_jul_1_str).datetime
yearly_mid_times_num=parse_time(years_jul_1_str).plot_date
#same for december 31
years_dec_31_str=[str(i)+'-12-31' for i in np.arange(2007,last_year) ]
yearly_end_times=parse_time(years_dec_31_str).datetime
yearly_end_times_num=parse_time(years_dec_31_str).plot_date
########### define arrays for total data days and fill with nan
total_data_days_yearly_win=np.zeros(np.size(yearly_mid_times))
total_data_days_yearly_win.fill(np.nan)
total_data_days_yearly_sta=np.zeros(np.size(yearly_mid_times))
total_data_days_yearly_sta.fill(np.nan)
total_data_days_yearly_stb=np.zeros(np.size(yearly_mid_times))
total_data_days_yearly_stb.fill(np.nan)
total_data_days_yearly_solo=np.zeros(np.size(yearly_mid_times))
total_data_days_yearly_solo.fill(np.nan)
#coverage not good enough
#total_data_days_yearly_vex=np.zeros(np.size(yearly_mid_times))
#total_data_days_yearly_vex.fill(np.nan)
#total_data_days_yearly_psp=np.zeros(np.size(yearly_mid_times))
#total_data_days_yearly_psp.fill(np.nan)
#total_data_days_yearly_bepi=np.zeros(np.size(yearly_mid_times))
#total_data_days_yearly_bepi.fill(np.nan)
#total_data_days_yearly_mes=np.zeros(np.size(yearly_mid_times))
#total_data_days_yearly_mes.fill(np.nan)
#total_data_days_yearly_mav=np.zeros(np.size(yearly_mid_times))
#total_data_days_yearly_mav.fill(np.nan)
######################## go through each year and search for available data
#time is available for all dates, so there are no NaNs in time, thus need to search for all not NaNs in Btotal variable
for i in range(np.size(yearly_mid_times)):
print(yearly_start_times[i])
#get indices of Wind time for the current year
thisyear=np.where(np.logical_and((win.time > yearly_start_times[i]),(win.time < yearly_end_times[i])))[0]
#get np.size of available data for each year
datas=np.size(np.where(np.isnan(win.bt[thisyear])==False))
#wind is 2 minute resolution
min_in_days=2/(60*24)
if datas > 0: total_data_days_yearly_win[i]=datas*min_in_days
#manual override because Wind data for 2018 and 2019 are heavily despiked
#total_data_days_yearly_win[-4]=360
#total_data_days_yearly_win[-3]=360
#total_data_days_yearly_win[-2]=360
#total_data_days_yearly_win[-1]=180
#all other data is in 1 min resolution
min_in_days=1/(60*24)
#for PSP
#thisyear=np.where(np.logical_and((psp.time > yearly_start_times[i]),(psp.time < yearly_end_times[i])))[0]
#datas=np.size(np.where(np.isnan(psp.bt[thisyear])==False))
#if datas >0: total_data_days_yearly_psp[i]=datas*min_in_days
#for Bepi
#thisyear=np.where(np.logical_and((bepi.time > yearly_start_times[i]),(bepi.time < yearly_end_times[i])))[0]
#datas=np.size(np.where(np.isnan(bepi.bt[thisyear])==False))
#if datas >0: total_data_days_yearly_bepi[i]=datas*min_in_days
#for solo
thisyear=np.where(np.logical_and((solo.time > yearly_start_times[i]),(solo.time < yearly_end_times[i])))[0]
datas=np.size(np.where(np.isnan(solo.bt[thisyear])==False))
if datas >0: total_data_days_yearly_solo[i]=datas*min_in_days
#same for STEREO-A
thisyear=np.where(np.logical_and((sta.time > yearly_start_times[i]),(sta.time < yearly_end_times[i])))[0]
datas=np.size(np.where(np.isnan(sta.bt[thisyear])==False))
if datas >0: total_data_days_yearly_sta[i]=datas*min_in_days
#same for STEREO-B
thisyear=np.where(np.logical_and((stb.time > yearly_start_times[i]),(stb.time < yearly_end_times[i])))[0]
datas=np.size(np.where(np.isnan(stb.bt[thisyear])==False))
if datas >0: total_data_days_yearly_stb[i]=datas*min_in_days
#same for MESSENGER
#thisyear=np.where(np.logical_and((mes.time > yearly_start_times[i]),(mes.time < yearly_end_times[i])))
#datas=np.size(np.where(np.isnan(mes.bt[thisyear])==False))
#if datas >0: total_data_days_yearly_mes[i]=datas*min_in_days
#same for Mercury alone with non-removed dataset
#start with 2011
# if i == 4:
# thisyear=np.where(np.logical_and((mesnon.time > mercury_orbit_insertion_time),(mesnon.time < yearly_end_times[i])))[0]
# datas=np.size(np.where(np.isnan(mesnon.bt[thisyear])==False))
# if datas >0: total_data_days_yearly_merc[i]=datas*min_in_days
# #2012 onwards
# if i > 4:
# thisyear=np.where(np.logical_and((mesnon.time > yearly_start_times[i]),(mesnon.time < yearly_end_times[i])))
# datas=np.size(np.where(np.isnan(mesnon.bt[thisyear])==False))
# if datas >0: total_data_days_yearly_merc[i]=datas*min_in_days
#same for VEX
#thisyear=np.where(np.logical_and((vex.time > yearly_start_times[i]),(vex.time < yearly_end_times[i])))[0]
#datas=np.size(np.where(np.isnan(vex.bt[thisyear])==False))
#if datas >0: total_data_days_yearly_vex[i]=datas*min_in_days
#for MAVEN different time resolution
#thisyear=np.where(np.logical_and((mav.time > yearly_start_times[i]),(mav.time < yearly_end_times[i])))[0]
#datas=np.size(np.where(np.isnan(mav.bt[thisyear])==False))
#datas_ind=np.where(np.isnan(mav.bt[thisyear])==False)
#sum all time intervals for existing data points, but avoid counting gaps where diff is > 1 orbit (0.25 days)
#alldiff=np.diff(parse_time(mav.time[datas_ind]).plot_date)
#smalldiff_ind=np.where(alldiff <0.25)
#if datas >0: total_data_days_yearly_mav[i]=np.sum(alldiff[smalldiff_ind])
print('Data days each year:')
print()
print('STB')
print(np.round(total_data_days_yearly_stb,1))
print('Wind')
print(np.round(total_data_days_yearly_win,1))
print('STA')
print(np.round(total_data_days_yearly_sta,1))
print('Solar Orbiter')
print(np.round(total_data_days_yearly_solo,1))
#print()
#print('MESSENGER')
#print(np.round(total_data_days_yearly_mes,1))
#print()
#print('VEX at Venus')
#print(np.round(total_data_days_yearly_vex,1))
#print()
#print()
#print('MAVEN')
#print(np.round(total_data_days_yearly_mav,1))
#print('PSP')
#print(np.round(total_data_days_yearly_psp,1))
#print('Bepi')
#print(np.round(total_data_days_yearly_bepi,1))
print()
print('done')
# ### get yearly ICME rates at each spacecraft
# In[8]:
#define dates of January 1 from 2007 to 2022
years_jan_1_str_plus1=[str(i)+'-01-01' for i in np.arange(2007,last_year+1) ]
yearly_bin_edges=parse_time(years_jan_1_str_plus1).plot_date
#bin width in days
binweite=365/4
#(histmes1, bin_edgesmes) = np.histogram(parse_time(ic.icme_start_time[mesi]).plot_date, yearly_bin_edges)
#(histmav1, bin_edgesmav) = np.histogram(parse_time(ic.icme_start_time[mavi]).plot_date, yearly_bin_edges)
#(histpsp1, bin_edgespsp) = np.histogram(parse_time(ic.icme_start_time[pspi]).plot_date, yearly_bin_edges)
#(histbepi1, bin_edgesbepi) = np.histogram(parse_time(ic.icme_start_time[beci]).plot_date, yearly_bin_edges)
#(histvex1, bin_edgesvex) = np.histogram(parse_time(ic.icme_start_time[vexi]).plot_date, yearly_bin_edges)
(histstb1, bin_edgesstb) = np.histogram(parse_time(ic.icme_start_time[stbi]).plot_date, yearly_bin_edges)
(histwin1, bin_edgeswin) = np.histogram(parse_time(ic.icme_start_time[wini]).plot_date, yearly_bin_edges)
(histsta1, bin_edgessta) = np.histogram(parse_time(ic.icme_start_time[stai]).plot_date, yearly_bin_edges)
(histsolo1, bin_edgessolo) = np.histogram(parse_time(ic.icme_start_time[soli]).plot_date, yearly_bin_edges)
binedges=bin_edgeswin
#normalize each dataset for data gaps, so correcting ICME rate for actual data availability
#note that for VEX and MESSENGER this was done with the non-removed datasets (vexnon, mesnon)
#histvex=np.round(histvex1/total_data_days_yearly_vex*365.24,1)
#histmes=np.round(histmes1/total_data_days_yearly_mes*365.24,1)
#ok for these spacecraft as continously in the solar wind and the MAVEN data set is made without orbit gaps
#STA beacon data used in 2019 - set manually
#histsta[-3]=13
#STA beacon data used in 2020 - set manually
#histsta[-2]=10*4/3
#histmav=np.round(histmav1/total_data_days_yearly_mav*365.24,1)
#histmav[7]=np.nan #not enough data for 2014
#histpsp=np.round(histpsp1/total_data_days_yearly_psp*365.24,1)
#histbepi=np.round(histbepi1/total_data_days_yearly_bepi*365.24,1)
histsta=np.round(histsta1/total_data_days_yearly_sta*365.24,1)
histstb=np.round(histstb1/total_data_days_yearly_stb*365.24,1)
histwin=np.round(histwin1/total_data_days_yearly_win*365.24,1)
histsolo=np.round(histsolo1/total_data_days_yearly_solo*365.24,1)
print('corrected ICME rates for years')
print(yearly_mid_times)
print('Wind',histwin)
print('STA',histsta)
print('STB',histstb)
print('SolO',histsolo)
#print('VEX',histvex)
#print('MESSENGER',histmes)
#print('PSP',histpsp)
#print('MAVEN',histmav)
################### calculate general parameters
print()
print()
print('calculate ICME rate matrix std, mean, max, min')
#arrange icmecat rate data so each row contains std, mean, max, min
icrate=pd.DataFrame(np.zeros([len(yearly_mid_times),7]), columns=['year','year_date', 'median1','mean1', 'std1', 'max1', 'min1'] )
for i in np.arange(0,len(yearly_mid_times)):
icrate.at[i,'year']=yearly_mid_times_num[i]
icrate.at[i,'year_date']=yearly_mid_times[i].year
icrate.at[i,'median1']=np.round( np.nanmedian([histwin[i],histsta[i],histstb[i],histsolo[i]]) )
icrate.at[i,'mean1']=np.round(np.nanmean([histwin[i],histsta[i],histstb[i],histsolo[i]]))
icrate.at[i,'std1']=np.round(np.nanstd([histwin[i],histsta[i],histstb[i],histsolo[i]]))
icrate.at[i,'max1']=np.nanmax([histwin[i],histsta[i],histstb[i],histsolo[i]])
icrate.at[i,'min1']=np.nanmin([histwin[i],histsta[i],histstb[i],histsolo[i]])
#--------
sns.set_context("talk")
sns.set_style('darkgrid')
plt.figure(11,figsize=(12,6),dpi=80)
plt.ylabel('ICMEs per year, ICMECAT')
#plt.plot(yearly_mid_times,histmes,'-',label='MESSENGER')
#plt.plot(yearly_mid_times,histmav,'-',label='MAVEN')
#plt.plot(yearly_mid_times,histpsp,'-',label='PSP')
#plt.plot(yearly_mid_times,histbepi,'-',label='BepiColombo')
#plt.plot(yearly_mid_times,histvex,'-',label='VEX')
plt.plot(yearly_mid_times,histwin,'-',label='Wind', color='mediumseagreen')
plt.plot(yearly_mid_times,histsta,'-',label='STEREO-A', color='red',alpha=0.5)
plt.plot(yearly_mid_times,histstb,'-',label='STEREO-B', color='blue',alpha=0.5)
plt.plot(yearly_mid_times,histsolo,'-',label='Solar Orbiter', color='black',alpha=0.5)
plt.plot([icrate.year,icrate.year],[icrate.mean1-icrate.std1,icrate.mean1+icrate.std1],'-k',lw=0.5)
plt.plot(icrate.year,icrate.mean1,'ok',markerfacecolor='white', label='mean +/- std')
plt.legend(loc=2,fontsize=10)
# In[9]:
icrate
# ### get Richardson and Cane ICME rate for comparison
# In[10]:
#convert times in dataframe from richardson and cane list to numpy array
r1=np.array(rc_df['Disturbance Y/M/D (UT) (a)'])
#to get ICME rate, go through all rows
rc_year=np.zeros(len(r1))
#extract string and check whether its a viable float and non nan:
for p in np.arange(0,len(r1)):
rc_yearstr=str(r1[p,0])
if hs.is_float(rc_yearstr[0:4]):
if np.isfinite(float(rc_yearstr[0:4])):
rc_year[p]=float(rc_yearstr[0:4]) #rc_year contains all ICME
rc_year.sort()
rc_icme_per_year=np.trim_zeros(rc_year)
#print(rc_year)
#plot check whats in this array
#sns.set_style('darkgrid')
#fig=plt.figure(12,figsize=(12,5),dpi=80)
#ax11=sns.histplot(rc_icme_per_year,bins=24,kde=None)
#plt.ylabel('ICMEs per year, RC list')
#count all full years from 1996-2022
bins_years=2023-1996
#get yearly ICME rate (use range to get correct numbers)
rc_rate_values=np.histogram(rc_icme_per_year,bins=bins_years,range=(1996,2023))[0]
rc_rate_time=np.histogram(rc_icme_per_year,bins=bins_years,range=(1996,2023))[1][0:-1]
years_jul_1_str_rc=[str(i)+'-07-01' for i in np.arange(1996,2022) ]
yearly_mid_times_rc=parse_time(years_jul_1_str_rc).datetime
yearly_mid_times_num_rc=parse_time(years_jul_1_str_rc).plot_date
#print(yearly_mid_times_rc)
#plt.figure(2)
#plt.plot(yearly_mid_times_rc,rc_rate_values)
print(rc_rate_values)
print(rc_rate_time)
# ### **Figure 1** plot ICME frequency cycle 24
# In[11]:
sns.set_context("talk")
#sns.set_style('whitegrid',{'grid.linestyle': '--'})
sns.set_style("ticks",{'grid.linestyle': '--'})
fsize=15
fig=plt.figure(1,figsize=(13,6),dpi=100)
sns.set_style("ticks",{'grid.linestyle': '--'})
ax2 = plt.subplot(111)