def table8_X2Mg():
species = [
'He/Mg', 'C/Mg', 'N/Mg', 'O/Mg', 'Si/Mg', 'S/Mg', 'Fe/Mg', 'Ne/Mg'
]
ISSpecies = [
he2mgmedO3, c2mgmedO3, n2mgmedO3, o2mgmedO3, si2mgmedO3, s2mgmedO3,
fe2mgmedO3, ne2mgmedO3
]
CHSpecies = [
he2mgmedO2, c2mgmedO2, n2mgmedO2, o2mgmedO2, si2mgmedO2, s2mgmedO2,
fe2mgmedO2, ne2mgmedO2
]
ISSpeed = [
hespeedmg3, cspeedmg3, nspeedmg3, ospeedmg3, sispeedmg3, sspeedmg3,
fespeedmg3, nespeedmg3
]
CHSpeed = [
hespeedmg2, cspeedmg2, nspeedmg2, ospeedmg2, sispeedmg2, sspeedmg2,
fespeedmg2, nespeedmg2
]
print np.dtype(hespeedmg3)
for spec, IS, CH, speedis, speedch in zip(species, ISSpecies, CHSpecies,
ISSpeed, CHSpeed):
print np.dtype(speedis)
print speedch
interstream = (IS[np.where((speedis >= 425.0) & (speedis <= 575.0))])
coronalHole = (CH[np.where((speedch >= 425.0) & (speedch <= 575.0))])
print IS
print CH
ISMean = pR.geoMean(interstream)
CHMean = pR.geoMean(coronalHole)
isErrorHigh, N1 = pR.ErrorHigh(interstream)
isErrorLow = pR.ErrorLow(interstream)
chErrorHigh, N2 = pR.ErrorHigh(coronalHole)
chErrorLow = pR.ErrorLow(coronalHole)
N = (N1 + N2) / 2
highFracUncert = pR.fracUncert(isErrorHigh, chErrorHigh, ISMean,
CHMean, N)
lowFracUncert = pR.fracUncert(isErrorLow, chErrorLow, ISMean, CHMean,
N)
highuncert = highFracUncert
lowuncert = lowFracUncert
IS2CH = pR.geoMean(interstream) / pR.geoMean(coronalHole)
print spec + '\t' + str(np.round(IS2CH, 3)) + '\t' + '+' + str(
np.round(highuncert, 5)) + '/' + '-' + str(np.round(lowuncert, 5))
def table7_X2H():
species = ['He/H', 'C/H', 'N/H', 'Mg/H', 'Si/H', 'S/H', 'Fe/H', 'Ne/H']
ISSpecies = [
he2hmed3, c2hmed3, n2hmed3, mg2hmed3, si2hmed3, s2hmed3, fe2hmed3,
ne2hmed3
]
CHSpecies = [
he2hmed2, c2hmed2, n2hmed2, mg2hmed2, si2hmed2, s2hmed2, fe2hmed2,
ne2hmed2
]
CMESpecies = [
he2hmed1, c2hmed1, n2hmed1, mg2hmed1, si2hmed1, s2hmed1, fe2hmed1,
ne2hmed1
]
BulkSpecies = [
he2hmed0, c2hmed0, n2hmed0, mg2hmed0, si2hmed0, s2hmed0, fe2hmed0,
ne2hmed0
]
#photoSpecAbund = [0.0851,0.0002692,0.00006761,0.0000398,0.0000324,0.00001318,0.000031623,0.0000851]
photoSpecUncrt = [
0.00196, 0.0000311, 0.0000078, 0.00000367, 0.0000224, 0.000000911,
0.00000292, 0.0000198
]
for name, IS, CH, CME, Bulk, PhotAbund, PhotUncrt in zip(
species, ISSpecies, CHSpecies, CMESpecies, BulkSpecies,
PhotoAbundH.values(), photoSpecUncrt):
ISMean = pR.geoMean(IS)
ISErrHigh, N1 = pR.ErrorHigh(IS)
CHMean = pR.geoMean(CH)
CHErrHigh, N2 = pR.ErrorHigh(CH)
ISErrLow = pR.ErrorLow(IS)
CHErrLow = pR.ErrorLow(CH)
N = (N1 + N2) / 2
CMEMean = pR.geoMean(CME)
CMEstdD, _ = pR.Error(CME)
BulkMean = pR.geoMean(Bulk)
BulkstdD, _ = pR.Error(Bulk)
highFracUncert = pR.fracUncert(ISErrHigh, CHErrHigh, ISMean, CHMean, N)
lowFracUncert = pR.fracUncert(ISErrLow, CHErrLow, ISMean, CHMean, N)
highuncert = highFracUncert
lowuncert = lowFracUncert
photMean = PhotAbund
photstdD = PhotUncrt
print (name + '\t' + str(np.round(ISMean,3)) + '\t' + str(np.round(CHMean,3)) + '\t' + str(np.round(CMEMean,3)) + '\t'\
+ str(np.round(BulkMean,3)) + '\t' + str(np.round(ISMean/CHMean,3)) + '(' + '+' + str(np.round(highuncert,5)) + '/' + '-' + str(np.round(lowuncert,5)) + ')'\
+ '\t' + str(photMean) + '(' + str(photstdD) + ')')
def table8_X2O():
species = [
'He/O', 'C/O', 'N/O', 'Mg/O', 'Si/O', 'S/O', 'Fe/O', 'Ne/O', 'C6/C5',
'O7/O6', 'Frac'
]
ISSpecies = [
he2omedO3, c2omedO3, n2omedO3, mg2omedO3, si2omedO3, s2omedO3,
fe2omedO3, ne2omedO3, c56med3, o76med3, fracmed3
]
CHSpecies = [
he2omedO2, c2omedO2, n2omedO2, mg2omedO2, si2omedO2, s2omedO2,
fe2omedO2, ne2omedO2, c56med2, o76med2, fracmed2
]
ISSpeed = [
hespeed3, cspeed3, nspeed3, mgspeed3, sispeed3, sspeed3, fespeed3,
nespeed3, c56speed3, o76speed3, fracspeed3
]
CHSpeed = [
hespeed2, cspeed2, nspeed2, mgspeed2, sispeed2, sspeed2, fespeed2,
nespeed2, c56speed2, o76speed2, fracspeed2
]
for spec, IS, CH, speedis, speedch in zip(species, ISSpecies, CHSpecies,
ISSpeed, CHSpeed):
interstream = (IS[np.where((speedis >= 425.0) & (speedis <= 575.0))])
coronalHole = (CH[np.where((speedch >= 425.0) & (speedch <= 575.0))])
ISMean = pR.geoMean(interstream)
CHMean = pR.geoMean(coronalHole)
isErrorHigh, N1 = pR.ErrorHigh(interstream)
isErrorLow = pR.ErrorLow(interstream)
chErrorHigh, N2 = pR.ErrorHigh(coronalHole)
chErrorLow = pR.ErrorLow(coronalHole)
N = (N1 + N2) / 2
highFracUncert = pR.fracUncert(isErrorHigh, chErrorHigh, ISMean,
CHMean, N)
lowFracUncert = pR.fracUncert(isErrorLow, chErrorLow, ISMean, CHMean,
N)
highuncert = highFracUncert
lowuncert = lowFracUncert
IS2CH = pR.geoMean(interstream) / pR.geoMean(coronalHole)
print spec + '\t' + str(np.round(IS2CH, 3)) + '\t' + '+' + str(
np.round(highuncert, 5)) + '/' + '-' + str(np.round(lowuncert, 5))
def table7_X2Mg():
species = [
'He/Mg', 'C/Mg', 'N/Mg', 'O/Mg', 'Si/Mg', 'S/Mg', 'Fe/Mg', 'Ne/Mg'
]
ISSpecies = [
he2mgmedO3, c2mgmedO3, n2mgmedO3, o2mgmedO3, si2mgmedO3, s2mgmedO3,
fe2mgmedO3, ne2mgmedO3
]
CHSpecies = [
he2mgmedO2, c2mgmedO2, n2mgmedO2, o2mgmedO2, si2mgmedO2, s2mgmedO2,
fe2mgmedO2, ne2mgmedO2
]
CMESpecies = [
he2mgmedO1, c2mgmedO1, n2mgmedO1, o2mgmedO1, si2mgmedO1, s2mgmedO1,
fe2mgmedO1, ne2mgmedO1
]
BulkSpecies = [
he2mgmedO0, c2mgmedO0, n2mgmedO0, o2mgmedO0, si2mgmedO0, s2mgmedO0,
fe2mgmedO0, ne2mgmedO0
]
PhotoAbundMg = {
'he2mg': 2140.0,
'c2mg': 6.76,
'n2mg': 1.7,
'ne2mg': 2.14,
'mg2mg': 1.0,
'si2mg': 0.813,
's2mg': 0.331,
'fe2mg': 0.794
}
photoSpecUncrt = [49.2, 0.780, 0.196, 1.42, 0.0562, 0.0229, 0.0733, 0.497]
for name, IS, CH, CME, Bulk, PhotAbund, PhotUncrt in zip(
species, ISSpecies, CHSpecies, CMESpecies, BulkSpecies,
PhotoAbundMg.values(), photoSpecUncrt):
ISMean = pR.geoMean(IS)
ISErrHigh, N1 = pR.ErrorHigh(IS)
CHMean = pR.geoMean(CH)
CHErrHigh, N2 = pR.ErrorHigh(CH)
ISErrLow = pR.ErrorLow(IS)
CHErrLow = pR.ErrorLow(CH)
N = (N1 + N2) / 2
CMEMean = pR.geoMean(CME)
CMEstdD, _ = pR.Error(CME)
BulkMean = pR.geoMean(Bulk)
BulkstdD, _ = pR.Error(Bulk)
highFracUncert = pR.fracUncert(ISErrHigh, CHErrHigh, ISMean, CHMean, N)
lowFracUncert = pR.fracUncert(ISErrLow, CHErrLow, ISMean, CHMean, N)
highuncert = highFracUncert
lowuncert = lowFracUncert
photMean = PhotAbund
photstdD = PhotUncrt
print (name + '\t' + str(np.round(ISMean,3)) + '\t' + str(np.round(CHMean,3)) + '\t' + str(np.round(CMEMean,3)) + '\t'\
+ str(np.round(BulkMean,3)) + '\t' + str(np.round(ISMean/CHMean,3)) + '(' + '+' + str(np.round(highuncert,5)) + '/' + '-' + str(np.round(lowuncert,5)) + ')'\
+ '\t' + str(photMean) + '(' + str(photstdD) + ')')
def table7_X2O():
species = [
'He/O', 'C/O', 'N/O', 'Mg/O', 'Si/O', 'S/O', 'Fe/O', 'Ne/O', 'C6/C5',
'O7/O6', 'Frac'
]
ISSpecies = [
he2omedO3, c2omedO3, n2omedO3, mg2omedO3, si2omedO3, s2omedO3,
fe2omedO3, ne2omedO3, c56med3, o76med3, fracmed3
]
CHSpecies = [
he2omedO2, c2omedO2, n2omedO2, mg2omedO2, si2omedO2, s2omedO2,
fe2omedO2, ne2omedO2, c56med2, o76med2, fracmed2
]
CMESpecies = [
he2omedO1, c2omedO1, n2omedO1, mg2omedO1, si2omedO1, s2omedO1,
fe2omedO1, ne2omedO1, c56med1, o76med1, fracmed1
]
BulkSpecies = [
he2omedO0, c2omedO0, n2omedO0, mg2omedO0, si2omedO0, s2omedO0,
fe2omedO0, ne2omedO0, c56med0, o76med0, fracmed0
]
PhotoAbundO = [
174.0, 0.550, 0.138, 0.174, 0.0813, 0.0661, 0.0269, 0.0646, '-', '-',
'-'
]
photoSpecUncrt = [
4.0, 0.0634, 0.0159, 0.0075, 0.00457, 0.00186, 0.00596, 0.0404, '--',
'--', '--'
]
for name, IS, CH, CME, Bulk, PhotAbund, PhotUncrt in zip(
species, ISSpecies, CHSpecies, CMESpecies, BulkSpecies,
PhotoAbundO, photoSpecUncrt):
ISMean = pR.geoMean(IS)
ISErrHigh, N1 = pR.ErrorHigh(IS)
CHMean = pR.geoMean(CH)
CHErrHigh, N2 = pR.ErrorHigh(CH)
ISErrLow = pR.ErrorLow(IS)
CHErrLow = pR.ErrorLow(CH)
N = (N1 + N2) / 2
CMEMean = pR.geoMean(CME)
#CMEstdD,_ = pR.stdDev(CME)
BulkMean = pR.geoMean(Bulk)
#BulkstdD,_ = pR.stdDev(Bulk)
highFracUncert = pR.fracUncert(ISErrHigh, CHErrHigh, ISMean, CHMean, N)
lowFracUncert = pR.fracUncert(ISErrLow, CHErrLow, ISMean, CHMean, N)
highuncert = highFracUncert
lowuncert = lowFracUncert
#errorInMean = uncert/np.sqrt(N)
photMean = PhotAbund
photstdD = PhotUncrt
print (name + '\t' + str(np.round(ISMean,3)) + '\t' + str(np.round(CHMean,3)) + '\t' + str(np.round(CMEMean,3)) + '\t'\
+ str(np.round(BulkMean,3)) + '\t' + str(np.round(ISMean/CHMean,3)) + '(' + '+' + str(np.round(highuncert,5)) + '/' + '-' + str(np.round(lowuncert,5)) + ')'\
+ '\t' + str(photMean) + '(' + str(photstdD) + ')')
def histWRanges(flagnum, fluence, dataLim, speed=np.arange(200, 1825, 25)):
'''returns the minimum, average, and maximum variances of fluence ratios
per speed bin (200km/s - 800km/s)
INPUTS:
speed - wind speed array to select ranges of data against.
flagnum - regime number for species (1:CME, 2:CH, 3:IS, 0:bulk)
fluence - name of species
dataLim - scalar limit for purity of data ranges to be analyzed
'''
lower = []
upper = []
ratiomean = []
xspeed = []
if fluence == 'mg2o':
for i in range(1, len(speed)):
ind = np.where((regime['v_p'] >= speed[i - 1] - 12.5)
& (regime['v_p'] <= speed[i] - 12.5)
& (regime['flag'] == flagnum)
& (regime['v_p'] != np.inf))
var = np.array(sorted(abundn[ind]['mg2o']))
var = 1.0 / var
if (np.size(var) != 0) & (len(var) >= dataLim):
xspeed.append(np.average(regime['v_p'][ind]))
try:
upp = fracs([var[int(len(var) * 0.90)]], name=None)
low = fracs([var[int(len(var) * 0.10)]], name=None)
med = pR.geoMean(fracs(var, name=fluence))
lower.append(med - low)
upper.append(upp - med)
ratiomean.append(pR.geoMean(fracs(var, name=fluence)))
# xspeed.append(np.average(regime['v_p'][np.where()]))
except ValueError:
pass
elif fluence == 'o76':
for i in range(1, len(speed)):
ind = np.where((regime['v_p'] >= speed[i - 1] - 12.5)
& (regime['v_p'] <= speed[i] - 12.5)
& (regime['flag'] == flagnum))
var = sorted(abundn[ind][fluence])
#xspeed = sorted()
if (np.size(var) != 0) & (len(var) >= dataLim):
xspeed.append(np.average(regime['v_p'][ind]))
try:
upp = fracs([var[int(len(var) * 0.90)]], name=None)
low = fracs([var[int(len(var) * 0.10)]], name=None)
med = pR.geoMean(fracs(var, name=fluence))
lower.append(med - low)
upper.append(upp - med)
ratiomean.append(pR.geoMean(fracs(var, name=fluence)))
# xspeed.append(np.average(regime['v_p'][np.where()]))
except ValueError:
pass
else:
for i in range(1, len(speed)):
ind = np.where((regime['v_p'] >= speed[i - 1] - 12.5)
& (regime['v_p'] <= speed[i] - 12.5)
& (regime['flag'] == flagnum))
var = sorted(abundn[ind][fluence] / abundn[ind]['mg2o'])
#xspeed = sorted()
if (np.size(var) != 0) & (len(var) >= dataLim):
xspeed.append(np.average(regime['v_p'][ind]))
try:
upp = fracs([var[int(len(var) * 0.90)]], name=None)
low = fracs([var[int(len(var) * 0.10)]], name=None)
med = pR.geoMean(fracs(var, name=fluence))
lower.append(med - low)
upper.append(upp - med)
ratiomean.append(pR.geoMean(fracs(var, name=fluence)))
# xspeed.append(np.average(regime['v_p'][np.where()]))
except ValueError:
pass
return lower, upper, ratiomean, np.array(xspeed)