def Ar_S_model_pyneb(Line_dict, diags, Ar3_atom, Ar4_atom, S3_atom, S4_atom):
TSIII, NSII = diags.getCrossTemDen(diag_tem = '[SIII] 6312/9200+', diag_den = '[SII] 6731/6716',
value_tem = Line_dict['6312A'] / (Line_dict['9068.62A'] + Line_dict['9532A']),
value_den = Line_dict['6731A']/Line_dict['6716A'])
TOIII, NSII_2 = diags.getCrossTemDen(diag_tem = '[OIII] 4363/5007+', diag_den = '[SII] 6731/6716',
value_tem = Line_dict['4363A']/(Line_dict['5007A'] + Line_dict['4959A']),
value_den = Line_dict['6731A']/Line_dict['6716A'])
Ar3 = Ar3_atom.getIonAbundance(int_ratio = Line_dict['7751.11A'] + Line_dict['7135A'], tem=TSIII, den=NSII, to_eval = 'L(7751) + L(7136)', Hbeta = Line_dict['4861.36A'])
Ar4 = Ar4_atom.getIonAbundance(int_ratio = Line_dict['4740.12A'] + Line_dict['4711.26A'], tem=TOIII, den=NSII, to_eval = 'L(4740) + L(4711)', Hbeta = Line_dict['4861.36A'])
S3 = S3_atom.getIonAbundance(int_ratio = (Line_dict['9068.62A'] + Line_dict['9532A']), tem=TSIII, den=NSII, to_eval = 'L(9069)+L(9531)', Hbeta = Line_dict['4861.36A'])
S4 = S4_atom.getIonAbundance(int_ratio = (Line_dict['10.51m']), tem=TOIII, den=NSII, wave = 105000., Hbeta = Line_dict['4861.36A'])
x_axis = nplog10(Ar3) - nplog10(Ar4)
y_axis = nplog10(S3) - nplog10(S4)
indexes = x_axis>0.0
x_values, y_values = x_axis[indexes][0], y_axis[indexes][0]
if (isinf(x_values) == False) & (isinf(y_values) == False):
if (x_values < 3) and (y_values < 3):
return x_values, y_values
else:
return None, None
else:
return None, None
def Ar_S_abundances_model(Line_dict, diags, Ar3_atom, Ar4_atom, S3_atom, S4_atom, threshold = 3):
#Calculate sulfur properties
TSIII, NSII = diags.getCrossTemDen(diag_tem = '[SIII] 6312/9200+', diag_den = '[SII] 6731/6716',
value_tem = Line_dict['6312A'] / (Line_dict['9068.62A'] + Line_dict['9532A']),
value_den = Line_dict['6731A']/Line_dict['6716A'])
#Calculate Oxygen properties
TOIII, NSII_2 = diags.getCrossTemDen(diag_tem = '[OIII] 4363/5007+', diag_den = '[SII] 6731/6716',
value_tem = Line_dict['4363A']/(Line_dict['5007A'] + Line_dict['4959A']),
value_den = Line_dict['6731A']/Line_dict['6716A'])
#Determine ionic abundances
Ar3 = Ar3_atom.getIonAbundance(int_ratio = Line_dict['7751.11A'] + Line_dict['7135A'], tem=TSIII, den=NSII, to_eval = 'L(7751) + L(7136)', Hbeta = Line_dict['4861.36A'])
Ar4 = Ar4_atom.getIonAbundance(int_ratio = Line_dict['4740.12A'] + Line_dict['4711.26A'], tem=TOIII, den=NSII_2, to_eval = 'L(4740) + L(4711)', Hbeta = Line_dict['4861.36A'])
S3 = S3_atom.getIonAbundance(int_ratio = (Line_dict['9068.62A'] + Line_dict['9532A']), tem=TSIII, den=NSII, to_eval = 'L(9069)+L(9531)', Hbeta = Line_dict['4861.36A'])
S4 = S4_atom.getIonAbundance(int_ratio = (Line_dict['10.51m']), tem=TOIII, den=NSII_2, wave = 105000., Hbeta = Line_dict['4861.36A'])
#Calculate the logaritmic axis for the plot
x_axis = nplog10(S3) - nplog10(S4)
y_axis = nplog10(Ar3) - nplog10(Ar4)
if (isinf(x_axis) == False) & (isinf(y_axis) == False):
if (x_axis < threshold) and (y_axis < threshold):
return x_axis, y_axis
else:
return None, None
else:
return None, None
def import_popstar_data(Model_dict):
FilesFolder = '/home/vital/Dropbox/Astrophysics/Seminars/Cloudy School 2017/teporingos/'
if Model_dict['den'] == 1:
TableAddressn = 'mnras0403-2012-SD1_clusterTable1_10cm3.txt'
elif Model_dict['den'] == 2:
TableAddressn = 'mnras0403-2012-SD2_clusterTable2_100cm3.txt'
print 'Popstar table is', TableAddressn
Frame_MetalsEmission = pd.read_csv(FilesFolder + TableAddressn,
delim_whitespace=True)
nH = Model_dict['den']**2 #cm^-3
c = 29979245800.0 #cm/s
pc_to_cm = 3.0856776e18 #cm/pc
Frame_MetalsEmission['logR_cm'] = nplog10(Frame_MetalsEmission['logR'] *
pc_to_cm)
Frame_MetalsEmission['Q'] = nplog10(
power(10, Frame_MetalsEmission['logU']) * 4 * pi * c * nH *
power(Frame_MetalsEmission['logR'] * pc_to_cm, 2))
return Frame_MetalsEmission
def Ar_S_model(Line_dict, threshold = 0.0):
# Hbeta = Line_dict['4861.36A']
# x_axis = Line_dict['10.51m'] / Hbeta
# y_axis = Line_dict['4740.12A'] / Hbeta
# return x_axis, y_axis
# S2_S3_ratio = (Line_dict['6716A'] + Line_dict['6731A']) / (Line_dict['9068.62A'] + Line_dict['9532A'])
#
# x_axis = nplog10(S2_S3_ratio)
#
# return x_axis, None
S2_S3_ratio = (Line_dict['9068.62A'] + Line_dict['9532A']) / Line_dict['10.51m']
Ar2_Ar3_ratio = (Line_dict['7135A']) / (Line_dict['4740.12A'])
# S2_S3_ratio = (Line_dict['6716A'] + Line_dict['6731A']) / (Line_dict['9068.62A'] + Line_dict['9532A'])
# Ar2_Ar3_ratio = (Line_dict['3727A']) / (Line_dict['4959A'] + Line_dict['5007A'] )
x_axis = nplog10(S2_S3_ratio)
y_axis = nplog10(Ar2_Ar3_ratio)
if (x_axis < threshold):
print 'values',x_axis, y_axis
return x_axis, y_axis
else:
return None, None
def Ar_S_model(Line_dict, threshold=0.0):
# Hbeta = Line_dict['4861.36A']
# x_axis = Line_dict['10.51m'] / Hbeta
# y_axis = Line_dict['4740.12A'] / Hbeta
# return x_axis, y_axis
# S2_S3_ratio = (Line_dict['6716A'] + Line_dict['6731A']) / (Line_dict['9068.62A'] + Line_dict['9532A'])
#
# x_axis = nplog10(S2_S3_ratio)
#
# return x_axis, None
S2_S3_ratio = (Line_dict['9068.62A'] +
Line_dict['9532A']) / Line_dict['10.51m']
Ar2_Ar3_ratio = (Line_dict['7135A']) / (Line_dict['4740.12A'])
# S2_S3_ratio = (Line_dict['6716A'] + Line_dict['6731A']) / (Line_dict['9068.62A'] + Line_dict['9532A'])
# Ar2_Ar3_ratio = (Line_dict['3727A']) / (Line_dict['4959A'] + Line_dict['5007A'] )
x_axis = nplog10(S2_S3_ratio)
y_axis = nplog10(Ar2_Ar3_ratio)
if (x_axis < threshold):
print 'values', x_axis, y_axis
return x_axis, y_axis
else:
return None, None
def Ar_S_abundances_model(Line_dict,
diags,
Ar3_atom,
Ar4_atom,
S3_atom,
S4_atom,
threshold=3):
#Calculate sulfur properties
TSIII, NSII = diags.getCrossTemDen(
diag_tem='[SIII] 6312/9200+',
diag_den='[SII] 6731/6716',
value_tem=Line_dict['6312A'] /
(Line_dict['9068.62A'] + Line_dict['9532A']),
value_den=Line_dict['6731A'] / Line_dict['6716A'])
#Calculate Oxygen properties
TOIII, NSII_2 = diags.getCrossTemDen(
diag_tem='[OIII] 4363/5007+',
diag_den='[SII] 6731/6716',
value_tem=Line_dict['4363A'] /
(Line_dict['5007A'] + Line_dict['4959A']),
value_den=Line_dict['6731A'] / Line_dict['6716A'])
#Determine ionic abundances
Ar3 = Ar3_atom.getIonAbundance(int_ratio=Line_dict['7751.11A'] +
Line_dict['7135A'],
tem=TSIII,
den=NSII,
to_eval='L(7751) + L(7136)',
Hbeta=Line_dict['4861.36A'])
Ar4 = Ar4_atom.getIonAbundance(int_ratio=Line_dict['4740.12A'] +
Line_dict['4711.26A'],
tem=TOIII,
den=NSII_2,
to_eval='L(4740) + L(4711)',
Hbeta=Line_dict['4861.36A'])
S3 = S3_atom.getIonAbundance(int_ratio=(Line_dict['9068.62A'] +
Line_dict['9532A']),
tem=TSIII,
den=NSII,
to_eval='L(9069)+L(9531)',
Hbeta=Line_dict['4861.36A'])
S4 = S4_atom.getIonAbundance(int_ratio=(Line_dict['10.51m']),
tem=TOIII,
den=NSII_2,
wave=105000.,
Hbeta=Line_dict['4861.36A'])
#Calculate the logaritmic axis for the plot
x_axis = nplog10(S3) - nplog10(S4)
y_axis = nplog10(Ar3) - nplog10(Ar4)
if (isinf(x_axis) == False) & (isinf(y_axis) == False):
if (x_axis < threshold) and (y_axis < threshold):
return x_axis, y_axis
else:
return None, None
else:
return None, None
def Ar_S_model(Line_dict, threshold=0.0):
R3 = (Line_dict['9068.62A'] + Line_dict['9532A']) / Line_dict['6312A']
TSIII_4 = (0.5147 + 0.0003187 * R3 + (23.6404 / R3))
TOIII_4 = ((TSIII_4 + 0.0846) / 1.0807)
TSIII = TSIII_4 * 10000
TOIII = TOIII_4 * 10000
logS3HI = nplog10(
Line_dict['10.51m'] / Line_dict['4861.36A']
) + 6.3956 + 0.0416 / TOIII_4 - 0.4216 * nplog10(TOIII_4) - 12
logS2HI = nplog10(
(Line_dict['9068.62A'] + Line_dict['9532A']) / Line_dict['4861.36A']
) + 6.012 + 0.6309 / TSIII_4 - 0.5722 * nplog10(TSIII_4) - 12
logAr3HI = nplog10(
Line_dict['4740.12A'] / Line_dict['4861.36A']
) + 5.705 + 1.246 / TOIII_4 - 0.156 * nplog10(TOIII_4) - 12
logAr2HI = nplog10(
Line_dict['7135A'] / Line_dict['4861.36A']
) + 6.157 + 0.808 / TSIII_4 - 0.508 * nplog10(TSIII_4) - 12
x_axis = logS2HI - logS3HI
y_axis = logAr2HI - logAr3HI
indexes = x_axis > 0.0
return x_axis[indexes], y_axis[indexes], TSIII[indexes], TOIII[indexes]
def import_popstar_data():
FilesFolder = '/home/vital/Dropbox/Astrophysics/Lore/PopStar/'
TableAddressn100 = 'mnras0403-2012-SD2_clusterTable2.txt'
Frame_MetalsEmission = pd.read_csv(FilesFolder + TableAddressn100, delim_whitespace = True)
nH = 10.0 #cm^-3
c = 29979245800.0 #cm/s
pc_to_cm = 3.0856776e18 #cm/pc
Frame_MetalsEmission['logR_cm'] = nplog10(Frame_MetalsEmission['logR'] * pc_to_cm)
Frame_MetalsEmission['Q'] = nplog10(power(10, Frame_MetalsEmission['logU']) * 4 * pi * c * nH * power(Frame_MetalsEmission['logR'] * pc_to_cm, 2))
return Frame_MetalsEmission
def import_popstar_data():
FilesFolder = '/home/vital/Dropbox/Astrophysics/Lore/PopStar/'
TableAddressn100 = 'mnras0403-2012-SD2_clusterTable2.txt'
Frame_MetalsEmission = pd.read_csv(FilesFolder + TableAddressn100, delim_whitespace = True)
nH = 10.0 #cm^-3
c = 29979245800.0 #cm/s
pc_to_cm = 3.0856776e18 #cm/pc
Frame_MetalsEmission['logR_cm'] = nplog10(Frame_MetalsEmission['logR'] * pc_to_cm)
Frame_MetalsEmission['Q'] = nplog10(power(10, Frame_MetalsEmission['logU']) * 4 * pi * c * nH * power(Frame_MetalsEmission['logR'] * pc_to_cm, 2))
return Frame_MetalsEmission
def Ar_S_model_pyneb(Line_dict, diags, Ar3_atom, Ar4_atom, S3_atom, S4_atom):
TSIII, NSII = diags.getCrossTemDen(
diag_tem="[SIII] 6312/9200+",
diag_den="[SII] 6731/6716",
value_tem=Line_dict["6312.06A"] / (Line_dict["9068.62A"] + Line_dict["9532A"]),
value_den=Line_dict["S2_6731A"] / Line_dict["S2_6716A"],
)
TOIII, NSII_2 = diags.getCrossTemDen(
diag_tem="[OIII] 4363/5007+",
diag_den="[SII] 6731/6716",
value_tem=Line_dict["O3_4363A"] / (Line_dict["O3_5007A"] + Line_dict["O3_4959A"]),
value_den=Line_dict["S2_6731A"] / Line_dict["S2_6716A"],
)
Ar3 = Ar3_atom.getIonAbundance(
int_ratio=Line_dict["Ar3_7751A"] + Line_dict["Ar3_7135A"],
tem=TSIII,
den=NSII,
to_eval="L(7751) + L(7136)",
Hbeta=Line_dict["H1_4861A"],
)
Ar4 = Ar4_atom.getIonAbundance(
int_ratio=Line_dict["Ar4_4740A"] + Line_dict["Ar4_4711A"],
tem=TOIII,
den=NSII,
to_eval="L(4740) + L(4711)",
Hbeta=Line_dict["H1_4861A"],
)
S3 = S3_atom.getIonAbundance(
int_ratio=(Line_dict["9068.62A"] + Line_dict["9532A"]),
tem=TSIII,
den=NSII,
to_eval="L(9069)+L(9531)",
Hbeta=Line_dict["H1_4861A"],
)
S4 = S3_atom.getIonAbundance(
int_ratio=(Line_dict["10.51m"]), tem=TOIII, den=NSII, to_eval="L(10.51)", Hbeta=Line_dict["H1_4861A"]
)
x_axis = nplog10(Ar3) - nplog10(Ar4)
y_axis = nplog10(S3) - nplog10(S4)
indexes = x_axis > 0.0
return x_axis[indexes], y_axis[indexes], TSIII[indexes], TOIII[indexes]
def Ar_S_model_pyneb(Line_dict, diags, Ar3_atom, Ar4_atom, S3_atom, S4_atom):
TSIII, NSII = diags.getCrossTemDen(
diag_tem='[SIII] 6312/9200+',
diag_den='[SII] 6731/6716',
value_tem=Line_dict['6312.06A'] /
(Line_dict['9068.62A'] + Line_dict['9532A']),
value_den=Line_dict['S2_6731A'] / Line_dict['S2_6716A'])
TOIII, NSII_2 = diags.getCrossTemDen(
diag_tem='[OIII] 4363/5007+',
diag_den='[SII] 6731/6716',
value_tem=Line_dict['O3_4363A'] /
(Line_dict['O3_5007A'] + Line_dict['O3_4959A']),
value_den=Line_dict['S2_6731A'] / Line_dict['S2_6716A'])
Ar3 = Ar3_atom.getIonAbundance(int_ratio=Line_dict['Ar3_7751A'] +
Line_dict['Ar3_7135A'],
tem=TSIII,
den=NSII,
to_eval='L(7751) + L(7136)',
Hbeta=Line_dict['H1_4861A'])
Ar4 = Ar4_atom.getIonAbundance(int_ratio=Line_dict['Ar4_4740A'] +
Line_dict['Ar4_4711A'],
tem=TOIII,
den=NSII,
to_eval='L(4740) + L(4711)',
Hbeta=Line_dict['H1_4861A'])
S3 = S3_atom.getIonAbundance(int_ratio=(Line_dict['9068.62A'] +
Line_dict['9532A']),
tem=TSIII,
den=NSII,
to_eval='L(9069)+L(9531)',
Hbeta=Line_dict['H1_4861A'])
S4 = S3_atom.getIonAbundance(int_ratio=(Line_dict['10.51m']),
tem=TOIII,
den=NSII,
to_eval='L(10.51)',
Hbeta=Line_dict['H1_4861A'])
x_axis = nplog10(Ar3) - nplog10(Ar4)
y_axis = nplog10(S3) - nplog10(S4)
indexes = x_axis > 0.0
return x_axis[indexes], y_axis[indexes], TSIII[indexes], TOIII[indexes]
def ave_power(self, mode="lin"):
"""
事前にスライスされた帯域のスペクトルパワーの平均を算出
:param mode:
:return: 平均パワー[db]
"""
from numpy import mean as npmean
from numpy import log10 as nplog10
power = self.get_pow()
if mode == "lin":
average_power = npmean(power)
return 10*nplog10(average_power)
elif mode == "log":
average_power = npmean(10 * nplog10(power))
return average_power
def ave_power(self, mode="lin"):
"""
事前にスライスされた帯域のスペクトルパワーの平均を算出
:param mode:
:return: 平均パワー[db]
"""
from numpy import mean as npmean
from numpy import log10 as nplog10
power = self.get_pow()
if mode == "lin":
average_power = npmean(power)
return 10 * nplog10(average_power)
elif mode == "log":
average_power = npmean(10 * nplog10(power))
return average_power
def Ar_S_model(Line_dict):
R3 = (Line_dict['9068.62A'] + Line_dict['9532A']) / Line_dict['6312.06A']
TSIII_4 = (0.5147 + 0.0003187 * R3 + (23.6404 / R3))
TOIII_4 = ((TSIII_4 + 0.0846)/1.0807)
TSIII = TSIII_4 * 10000
TOIII = TOIII_4 * 10000
logS3HI = nplog10(Line_dict['10.51m']/Line_dict['4861.36A']) + 6.3956 + 0.0416/TOIII_4 - 0.4216 * nplog10(TOIII_4) - 12
logS2HI = nplog10((Line_dict['9068.62A'] + Line_dict['9532A']) / Line_dict['4861.36A']) + 6.012 + 0.6309 / TSIII_4 - 0.5722 * nplog10(TSIII_4) -12
logAr3HI = nplog10(Line_dict['4740.12A'] / Line_dict['4861.36A']) + 5.705 + 1.246/TOIII_4 - 0.156 * nplog10(TOIII_4) - 12
logAr2HI = nplog10(Line_dict['7135A'] / Line_dict['4861.36A']) + 6.157 + 0.808/TSIII_4 - 0.508 * nplog10(TSIII_4) - 12
x_axis = logS2HI - logS3HI
y_axis = logAr2HI - logAr3HI
return x_axis, y_axis, TSIII, TOIII
def Ar_S_lines_model(Line_dict, threshold = 0.0, z = None):
#Argon - sufur lines
S2_S3_ratio = (Line_dict['9068.62A'] + Line_dict['9532A']) / Line_dict['10.51m']
Ar2_Ar3_ratio = (Line_dict['7135A']) / (Line_dict['4740.12A'])
x_axis = nplog10(S2_S3_ratio)
y_axis = nplog10(Ar2_Ar3_ratio)
# #Argon - oxygen lines
# S2_S3_ratio = (Line_dict['6716A'] + Line_dict['6731A']) / (Line_dict['9068.62A'] + Line_dict['9532A'])
# O2_O3_ratio = (Line_dict['3727A']) / (Line_dict['4959A'] + Line_dict['5007A'])
# x_axis = nplog10(S2_S3_ratio)
# y_axis = nplog10(Ar2_Ar3_ratio)
#Special threshold for the missing lines
if (y_axis < threshold):
return x_axis, y_axis
else:
return None, None
def Ar_S_lines_model(Line_dict, threshold=0.0, z=None):
#Argon - sufur lines
S2_S3_ratio = (Line_dict['9068.62A'] +
Line_dict['9532A']) / Line_dict['10.51m']
Ar2_Ar3_ratio = (Line_dict['7135A']) / (Line_dict['4740.12A'])
x_axis = nplog10(S2_S3_ratio)
y_axis = nplog10(Ar2_Ar3_ratio)
# #Argon - oxygen lines
# S2_S3_ratio = (Line_dict['6716A'] + Line_dict['6731A']) / (Line_dict['9068.62A'] + Line_dict['9532A'])
# O2_O3_ratio = (Line_dict['3727A']) / (Line_dict['4959A'] + Line_dict['5007A'])
# x_axis = nplog10(S2_S3_ratio)
# y_axis = nplog10(Ar2_Ar3_ratio)
#Special threshold for the missing lines
if (y_axis < threshold):
return x_axis, y_axis
else:
return None, None
def Ar_S_model_pyneb(Line_dict, diags, Ar3_atom, Ar4_atom, S3_atom, S4_atom):
TSIII, NSII = diags.getCrossTemDen(diag_tem = '[SIII] 6312/9200+', diag_den = '[SII] 6731/6716',
value_tem = Line_dict['6312.06A'] / (Line_dict['9068.62A'] + Line_dict['9532A']),
value_den = Line_dict['S2_6731A']/Line_dict['S2_6716A'])
TOIII, NSII_2 = diags.getCrossTemDen(diag_tem = '[OIII] 4363/5007+', diag_den = '[SII] 6731/6716',
value_tem = Line_dict['O3_4363A']/(Line_dict['O3_5007A'] + Line_dict['O3_4959A']),
value_den = Line_dict['S2_6731A']/Line_dict['S2_6716A'])
Ar3 = Ar3_atom.getIonAbundance(int_ratio = Line_dict['Ar3_7751A'] + Line_dict['Ar3_7135A'], tem=TSIII, den=NSII, to_eval = 'L(7751) + L(7136)', Hbeta = Line_dict['H1_4861A'])
Ar4 = Ar4_atom.getIonAbundance(int_ratio = Line_dict['Ar4_4740A'] + Line_dict['Ar4_4711A'], tem=TOIII, den=NSII, to_eval = 'L(4740) + L(4711)', Hbeta = Line_dict['H1_4861A'])
S3 = S3_atom.getIonAbundance(int_ratio = (Line_dict['9068.62A'] + Line_dict['9532A']), tem=TSIII, den=NSII, to_eval = 'L(9069)+L(9531)', Hbeta = Line_dict['H1_4861A'])
S4 = S3_atom.getIonAbundance(int_ratio = (Line_dict['10.51m']), tem=TOIII, den=NSII, to_eval = 'L(10.51)', Hbeta = Line_dict['H1_4861A'])
x_axis = nplog10(Ar3) - nplog10(Ar4)
y_axis = nplog10(S3) - nplog10(S4)
indexes = x_axis>0.0
return x_axis[indexes], y_axis[indexes], TSIII[indexes], TOIII[indexes]
def Ar_S_model(Line_dict, threshold=0.0):
R3 = (Line_dict["9068.62A"] + Line_dict["9532A"]) / Line_dict["6312A"]
TSIII_4 = 0.5147 + 0.0003187 * R3 + (23.6404 / R3)
TOIII_4 = (TSIII_4 + 0.0846) / 1.0807
TSIII = TSIII_4 * 10000
TOIII = TOIII_4 * 10000
logS3HI = (
nplog10(Line_dict["10.51m"] / Line_dict["4861.36A"])
+ 6.3956
+ 0.0416 / TOIII_4
- 0.4216 * nplog10(TOIII_4)
- 12
)
logS2HI = (
nplog10((Line_dict["9068.62A"] + Line_dict["9532A"]) / Line_dict["4861.36A"])
+ 6.012
+ 0.6309 / TSIII_4
- 0.5722 * nplog10(TSIII_4)
- 12
)
logAr3HI = (
nplog10(Line_dict["4740.12A"] / Line_dict["4861.36A"]) + 5.705 + 1.246 / TOIII_4 - 0.156 * nplog10(TOIII_4) - 12
)
logAr2HI = (
nplog10(Line_dict["7135A"] / Line_dict["4861.36A"]) + 6.157 + 0.808 / TSIII_4 - 0.508 * nplog10(TSIII_4) - 12
)
x_axis = logS2HI - logS3HI
y_axis = logAr2HI - logAr3HI
indexes = x_axis > 0.0
return x_axis[indexes], y_axis[indexes], TSIII[indexes], TOIII[indexes]
#Generate the scripts with the lines we want to print the flux
ct.lines_to_extract(ScriptFolder)
#Fore the each grid point run a cloudy simulation
Model_dict = OrderedDict()
for age in Grid_Values['age']:
for zStar in Grid_Values['zStars']:
for zGas in Grid_Values['zGas']:
for ionization_parameter in Grid_Values['u']:
Model_dict['Name'] = 'S_Ar_Test_' + 'age'+ age + '_zStar' + zStar + '_zGas' + zGas + '_u' + ionization_parameter
Model_dict['age'] = age
Model_dict['zStars'] = zStar
Model_dict['zGas'] = zGas
Model_dict['u'] = ionization_parameter
Model_dict['abundances_command'] = 'abundances H=0.0 He=-1.0'
for key in Solar_abundances:
Model_dict['abundances_command'] = '{previous_line} {new_element}={abundance}'.format(previous_line = Model_dict['abundances_command'], new_element=key, abundance=round(Solar_abundances[key] + nplog10(float(zGas)), 3))
ScriptName = Model_dict['Name'] + '.in'
#Generate the script
ct.popstar_S_Ar_Grid(ScriptFolder, Model_dict)
# ct.popstar_S_Ar_Grid_absAbundances(ScriptFolder, Model_dict)
#Run the cloudy script
ct.run_script(ScriptName, ScriptFolder)
print 'Data treated'
for zStar in Grid_Values['zStars']:
for zGas in Grid_Values['zGas']:
for ionization_parameter in Grid_Values['u']:
Model_dict[
'Name'] = 'S_Ar_Test_' + 'age' + age + '_zStar' + zStar + '_zGas' + zGas + '_u' + ionization_parameter
Model_dict['age'] = age
Model_dict['zStars'] = zStar
Model_dict['zGas'] = zGas
Model_dict['u'] = ionization_parameter
Model_dict['abundances_command'] = 'abundances H=0.0 He=-1.0'
for key in Solar_abundances:
Model_dict[
'abundances_command'] = '{previous_line} {new_element}={abundance}'.format(
previous_line=Model_dict['abundances_command'],
new_element=key,
abundance=round(
Solar_abundances[key] + nplog10(float(zGas)),
3))
ScriptName = Model_dict['Name'] + '.in'
#Generate the script
ct.popstar_S_Ar_Grid(ScriptFolder, Model_dict)
# ct.popstar_S_Ar_Grid_absAbundances(ScriptFolder, Model_dict)
#Run the cloudy script
ct.run_script(ScriptName, ScriptFolder)
print 'Data treated'
va[i] = temp[0]
# Insert value in Bellman Equation
xBellman = xBellman.xreplace({sa[i, 0]: va[i]})
# The polynomial starts from the steady state
sctay = sctay.subs({skss: vkss})
# Insert coefficient values in the polynomial
for i in range(n + 1):
sctay = sctay.xreplace({sa[i, 0]: va[i]})
## Make symbolic residual function:
# Copy Bellman equation into residual function
sResid = sBellman
# Replace all symbolic coefficients with their value
for i in range(n + 1):
sResid = sResid.xreplace({sa[i, 0]: va[i]})
# Scale residual function by steady-state consumption times rho
sResid = sResid / (vrho * sctay.subs({sk: vkss}))
# Convert symbolic residual function to numeric residual function
fResid = lambdify(sk, sResid.subs({skss: vkss}), "numpy")
# Compute residuals
fResidData = fResid(linspace(.1, 2.5, 100))
# Plot residuals in logaritmic scale
fig1 = plt.figure()
ax1a = fig1.add_subplot(111)
ax1a.plot(nplog10(fResidData))
def lightcurve_flux_measures(ftimes, fmags, ferrs, magsarefluxes=False):
'''
This calculates percentiles of the flux.
'''
ndet = len(fmags)
if ndet > 9:
# get the fluxes
if magsarefluxes:
series_fluxes = fmags
else:
series_fluxes = 10.0**(-0.4 * fmags)
series_flux_median = npmedian(series_fluxes)
# get the percent_amplitude for the fluxes
series_flux_percent_amplitude = (npmax(npabs(series_fluxes)) /
series_flux_median)
# get the flux percentiles
series_flux_percentiles = nppercentile(
series_fluxes,
[5.0, 10, 17.5, 25, 32.5, 40, 60, 67.5, 75, 82.5, 90, 95])
series_frat_595 = (series_flux_percentiles[-1] -
series_flux_percentiles[0])
series_frat_1090 = (series_flux_percentiles[-2] -
series_flux_percentiles[1])
series_frat_175825 = (series_flux_percentiles[-3] -
series_flux_percentiles[2])
series_frat_2575 = (series_flux_percentiles[-4] -
series_flux_percentiles[3])
series_frat_325675 = (series_flux_percentiles[-5] -
series_flux_percentiles[4])
series_frat_4060 = (series_flux_percentiles[-6] -
series_flux_percentiles[5])
# calculate the flux percentile ratios
series_flux_percentile_ratio_mid20 = series_frat_4060 / series_frat_595
series_flux_percentile_ratio_mid35 = series_frat_325675 / series_frat_595
series_flux_percentile_ratio_mid50 = series_frat_2575 / series_frat_595
series_flux_percentile_ratio_mid65 = series_frat_175825 / series_frat_595
series_flux_percentile_ratio_mid80 = series_frat_1090 / series_frat_595
# calculate the ratio of F595/median flux
series_percent_difference_flux_percentile = (series_frat_595 /
series_flux_median)
series_percentile_magdiff = -2.5 * nplog10(
series_percent_difference_flux_percentile)
return {
'flux_median': series_flux_median,
'flux_percent_amplitude': series_flux_percent_amplitude,
'flux_percentiles': series_flux_percentiles,
'flux_percentile_ratio_mid20': series_flux_percentile_ratio_mid20,
'flux_percentile_ratio_mid35': series_flux_percentile_ratio_mid35,
'flux_percentile_ratio_mid50': series_flux_percentile_ratio_mid50,
'flux_percentile_ratio_mid65': series_flux_percentile_ratio_mid65,
'flux_percentile_ratio_mid80': series_flux_percentile_ratio_mid80,
'percent_difference_flux_percentile': series_percentile_magdiff,
}
else:
LOGERROR('not enough detections in this magseries '
'to calculate flux measures')
return None
va[i] = temp[0]
# Insert value in Bellman Equation
xBellman = xBellman.xreplace({sa[i,0]: va[i]})
# The polynomial starts from the steady state
sctay = sctay.subs({skss: vkss})
# Insert coefficient values in the polynomial
for i in range(n+1):
sctay = sctay.xreplace({sa[i,0]: va[i]})
## Make symbolic residual function:
# Copy Bellman equation into residual function
sResid = sBellman
# Replace all symbolic coefficients with their value
for i in range(n+1):
sResid = sResid.xreplace({sa[i,0]: va[i]})
# Scale residual function by steady-state consumption times rho
sResid = sResid/(vrho*sctay.subs({sk: vkss}))
# Convert symbolic residual function to numeric residual function
fResid = lambdify(sk,sResid.subs({skss: vkss}),"numpy")
# Compute residuals
fResidData = fResid(linspace(.1,2.5,100))
# Plot residuals in logaritmic scale
fig1 = plt.figure()
ax1a = fig1.add_subplot(111)
ax1a.plot(nplog10(fResidData))
for j in range(n+1):
sResid = sResid.xreplace({sa[i,j]: va[i,j]})
# Scale residual function by steady-state consumption times rho
sResid = sResid/(vrho*su1.subs({sk: vkss, skss: vkss, ssigma: 0, sgamma: vgamma}).xreplace({sa[0,0]: va[0,0]}))
# Convert symbolic residual function to numeric residual function
fResid = lambdify([sk,ssigma],sResid.subs({skss: vkss, srho: vrho,\
sgamma: vgamma}),"numpy")
# Compute residuals
kPoints = 100
sPoints = 100
kRange = linspace(.8,1.2,kPoints)
sRange = linspace(0,.001,sPoints)
fResidX = npzeros([kPoints,sPoints])
fResidY = npzeros([kPoints,sPoints])
fResidZ = npzeros([kPoints,sPoints])
for i in range(kPoints):
fResidX[i,:] = kRange[i]
fResidY[i,:] = sRange
fResidZ[i,:] = nplog10(abs(fResid(kRange[i],sRange)))
# Plot residuals in logaritmic scale
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(fResidX, fResidY, fResidZ, rstride=10, cstride=10)
plt.show()
def lightcurve_flux_measures(ftimes, fmags, ferrs, magsarefluxes=False):
'''This calculates percentiles and percentile ratios of the flux.
Parameters
----------
ftimes,fmags,ferrs : np.array
The input mag/flux time-series with all non-finite elements removed.
magsarefluxes : bool
If the `fmags` array actually contains fluxes, will not convert `mags`
to fluxes before calculating the percentiles.
Returns
-------
dict
A dict with all of the light curve flux percentiles and percentile
ratios calculated.
'''
ndet = len(fmags)
if ndet > 9:
# get the fluxes
if magsarefluxes:
series_fluxes = fmags
else:
series_fluxes = 10.0**(-0.4 * fmags)
series_flux_median = npmedian(series_fluxes)
# get the percent_amplitude for the fluxes
series_flux_percent_amplitude = (npmax(npabs(series_fluxes)) /
series_flux_median)
# get the flux percentiles
series_flux_percentiles = nppercentile(
series_fluxes,
[5.0, 10, 17.5, 25, 32.5, 40, 60, 67.5, 75, 82.5, 90, 95])
series_frat_595 = (series_flux_percentiles[-1] -
series_flux_percentiles[0])
series_frat_1090 = (series_flux_percentiles[-2] -
series_flux_percentiles[1])
series_frat_175825 = (series_flux_percentiles[-3] -
series_flux_percentiles[2])
series_frat_2575 = (series_flux_percentiles[-4] -
series_flux_percentiles[3])
series_frat_325675 = (series_flux_percentiles[-5] -
series_flux_percentiles[4])
series_frat_4060 = (series_flux_percentiles[-6] -
series_flux_percentiles[5])
# calculate the flux percentile ratios
series_flux_percentile_ratio_mid20 = series_frat_4060 / series_frat_595
series_flux_percentile_ratio_mid35 = series_frat_325675 / series_frat_595
series_flux_percentile_ratio_mid50 = series_frat_2575 / series_frat_595
series_flux_percentile_ratio_mid65 = series_frat_175825 / series_frat_595
series_flux_percentile_ratio_mid80 = series_frat_1090 / series_frat_595
# calculate the ratio of F595/median flux
series_percent_difference_flux_percentile = (series_frat_595 /
series_flux_median)
series_percentile_magdiff = -2.5 * nplog10(
series_percent_difference_flux_percentile)
return {
'flux_median': series_flux_median,
'flux_percent_amplitude': series_flux_percent_amplitude,
'flux_percentiles': series_flux_percentiles,
'flux_percentile_ratio_mid20': series_flux_percentile_ratio_mid20,
'flux_percentile_ratio_mid35': series_flux_percentile_ratio_mid35,
'flux_percentile_ratio_mid50': series_flux_percentile_ratio_mid50,
'flux_percentile_ratio_mid65': series_flux_percentile_ratio_mid65,
'flux_percentile_ratio_mid80': series_flux_percentile_ratio_mid80,
'percent_difference_flux_percentile': series_percentile_magdiff,
}
else:
LOGERROR('not enough detections in this magseries '
'to calculate flux measures')
return None
def transform_non_affine(self, a):
masked = ma.masked_where(a > 1 - 10**(-1 - self.nines), a)
if masked.mask.any():
return -ma.log10(1 - a)
else:
return -nplog10(1 - a)
