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odata.py
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odata.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 4 18:01:18 2020
@author: amaya
"""
import pandas as pd
import numpy as np
omniheaders = ['year',
'day',
'hour',
'Bartels',
'IMF_spacecraft',
'plasma_spacecraft',
'IMF_av_npoints',
'plasma_av_npoints',
'av_|B|',
'|av_B|',
'lat_av_B_GSE',
'lon_av_B_GSE',
'Bx',
'By_GSE',
'Bz_GSE',
'By_GSM',
'Bz_GSM',
'sigma_|B|',
'sigma_B',
'sigma_Bx',
'sigma_By',
'sigma_Bz',
'Tp',
'Np',
'V_plasma',
'phi_V_angle',
'theta_V_angle',
'Na/Np',
'P_dyn',
'sigma_Tp',
'sigma_Np',
'sigma_V',
'sigma_phi_V',
'sigma_theta_V',
'sigma_Na/Np',
'E',
'beta',
'Ma',
'Kp',
'R',
'Dst',
'AE',
'p_flux_>1MeV',
'p_flux_>2MeV',
'p_flux_>4MeV',
'p_flux_>10MeV',
'p_flux_>30MeV',
'p_flux_>60MeV',
'flag',
'Ap',
'f10.7',
'PC',
'AL',
'AU',
'M_ms']
omninulls = [
None,
None,
None,
9999,
0,
0,
999,
999,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
999.9,
9999999.,
999.9,
9999.,
999.9,
999.9,
9.999,
99.99,
9999999.,
999.9,
9999.,
999.9,
999.9,
9.999,
999.99,
999.99,
999.9,
99,
999,
99999,
9999,
999999.99,
99999.99,
99999.99,
99999.99,
99999.99,
99999.99,
0,
999,
999.9,
999.9,
99999,
99999,
99.9]
def omnireaddata(fname, cols):
raw = pd.read_csv(fname,header=None,
names=omniheaders,
delim_whitespace=True,
index_col=False,
usecols=cols)
raw['Datetime'] = pd.to_datetime(raw['year'].astype(str)+' '
+ raw['day'].astype(str)+' '
+ raw['hour'].astype(str),
format='%Y %j %H')
raw = raw.set_index('Datetime')
cols.remove('year')
cols.remove('day')
cols.remove('hour')
return raw[cols]
def omnigetnulls(data):
nulls = pd.DataFrame(omninulls,
index=omniheaders,
columns=['Null values'],
dtype=object)
return nulls.loc[data.columns]
def omnidata(omnidir, cols, ybeg, yend):
cols_needed = ['year','day','hour','V_plasma','Ma','Tp','Np','Bx','By_GSM','Bz_GSM']
for elem in cols_needed:
if elem not in cols: cols.append(elem)
fname = omnidir+"/omni2_"+str(ybeg)+"-"+str(yend)+".dat"
data = omnireaddata(fname, cols)
return data
def omniorigin(raw, nulls):
data = raw
for c in raw.columns:
data = data[data[c]!=nulls.loc[c][0]]
k_b = 8.617333262145e-5
Va=data['V_plasma']/data['Ma']
Sp=data['Tp']*k_b/data['Np']**(2./3.)
Texp=np.power(data['V_plasma']/258.0, 3.113)
Tratio=Texp/(data['Tp']*k_b)
data['Va']=Va
data['Sp']=Sp
data['Texp']=Texp
data['Tratio']=Tratio
e_plane=np.array([0.277 * np.log10(data['Sp'])+
0.055 * np.log10(data['Tratio'])+
1.83 < np.log10(data['Va'])]).T
c_plane=np.array([-0.525 * np.log10(data['Tratio'])-
0.676 * np.log10(data['Va'])+
1.74 < np.log10(data['Sp'])]).T
s_plane=np.array([-0.125 * np.log10(data['Tratio'])-
0.658 * np.log10(data['Va'])+
1.04 > np.log10(data['Sp'])]).T
ejecta= pd.Series((v[0] for v in e_plane), name='bools').values
chole = pd.Series((v[0] for v in c_plane), name='bools').values
srev = pd.Series((v[0] for v in s_plane), name='bools').values
data.loc[ejecta, 'SW_type'] = 2
data.loc[~ejecta&chole, 'SW_type'] = 1
data.loc[~ejecta&srev, 'SW_type'] = 3
data.loc[~ejecta&~chole&~srev, 'SW_type'] = 0
raw = raw.merge(pd.DataFrame(data[['Va','Sp','Texp','Tratio','SW_type']]),
how='right',left_index=True, right_index=True)
nulls.loc['Va']=nulls.loc['V_plasma'].values
nulls.loc['Sp']=nulls.loc['V_plasma'].values
nulls.loc['Texp']=nulls.loc['V_plasma'].values
nulls.loc['Tratio']=nulls.loc['V_plasma'].values
nulls.loc['SW_type']=-999
raw['SW_type'].fillna(4, inplace=True)
return raw
def omniderived(raw, nulls):
data = raw
xcols = ['BI','Viscous_function','Newell']
cols_needed = ['V_plasma','Np','Bx','By_GSM','Bz_GSM']
for c in cols_needed:
data = data[data[c]!=nulls.loc[c][0]]
B_GSM = np.sqrt(data['Bx']*data['Bx'] +
data['By_GSM']*data['By_GSM'] +
data['Bz_GSM']*data['Bz_GSM'])
data = data[B_GSM>0]
B_GSM = B_GSM[B_GSM>0]
theta = np.arcsin(np.abs(data['By_GSM'])/B_GSM)
BI=1e-4*data['V_plasma']**2 + 11.7*B_GSM*np.sin(theta/2.0)**3
Vf=np.sqrt(data['Np'])*data['V_plasma']**2
Nf=data['V_plasma']**(4./3.) * B_GSM**(2./3.) * np.sin(theta/2.0)**(8./3.)
data['BI']=BI
data['Viscous_function']=Vf
data['Newell']=Nf
raw = raw.merge(pd.DataFrame(data[xcols]),
how='right', left_index=True, right_index=True)
for i in xcols:
nulls.loc[i] = nulls.loc['V_plasma'].values
return data
def omniaddshift(raw, nulls, ftime, ftdelta):
data = raw
onulls = nulls.copy()
for i in range(-ftime,-ftime-ftdelta,-1):
data = data.join(raw.shift(i).add_suffix("_"+str(abs(i))))
for c in onulls.index:
nulls.loc[c+"_"+str(abs(i))] = onulls.loc[c]
data = data.dropna(axis=0)
return data
def omniaddextra(data, nulls, xcols, window=5, center=False):
for end in ['min','max','mean','std','var']:
func = getattr(pd.core.window.Rolling, end)
for c in xcols:
if c.endswith(end):
var = c[:-len(end)-1]
varfunc = func(data[var].rolling(window, center=center))
data[c] = varfunc
for end in ['delta']:
fmax = pd.core.window.Rolling.max
fmin = pd.core.window.Rolling.min
for c in xcols:
if c.endswith(end):
var = c[:-len(end)-1]
vmax = fmax(data[var].rolling(window, center=center))
vmin = fmin(data[var].rolling(window, center=center))
data[c] = vmax - vmin
for end in ['acor']:
func = lambda x: pd.Series(x).autocorr()
for c in xcols:
if c.endswith(end):
var = c[:-len(end)-1]
print('Calculating rolling autocorrelation for '+var+'...')
data[c] = data[var].rolling(window, center=center).apply(func, raw=False)
data = data.dropna(axis=0)
#Appending new nulls using the mutable argument reference
for i in xcols:
nulls.loc[i] = -9999.9
return data
if __name__ == "__main__":
omnidir = "/home/amaya/Workdir/MachineLearning/Data/OMNI"
cols = ['av_|B|',
'|av_B|',
'By_GSE',
'Bz_GSE',
'By_GSM',
'Bz_GSM',
'sigma_|B|',
'sigma_B',
'Tp',
'Np',
'V_plasma',
'phi_V_angle',
'theta_V_angle',
'Na/Np',
'P_dyn',
'E',
'beta',
'Ma',
'Dst',
'AE',
'f10.7']
data = omnidata(omnidir, cols, 2000, 2011)
nulls = omnigetnulls(data)
data = omniorigin(data, nulls)
data = omniderived(data, nulls)
xcols = ['Bz_GSE_delta','Bz_GSE_acor']
data = omniaddextra(data, nulls, xcols)