def MAD(a, c=0.6745, axis=None):
"""
Median Absolute Deviation along given axis of an array:
median(abs(a - median(a))) / c
c = 0.6745 is the constant to convert from MAD to std; it is used by
default
"""
a = ma.masked_where(a!=a, a)
if a.ndim == 1:
d = ma.median(a)
m = ma.median(ma.fabs(a - d) / c)
else:
d = ma.median(a, axis=axis)
# I don't want the array to change so I have to copy it?
if axis > 0:
aswp = ma.swapaxes(a,0,axis)
else:
aswp = a
m = ma.median(ma.fabs(aswp - d) / c, axis=0)
return m
def MAD(a, c=0.6745, axis=None):
"""
Median Absolute Deviation along given axis of an array:
median(abs(a - median(a))) / c
c = 0.6745 is the constant to convert from MAD to std; it is used by
default
"""
a = ma.masked_where(a != a, a)
if a.ndim == 1:
d = ma.median(a)
m = ma.median(ma.fabs(a - d) / c)
else:
d = ma.median(a, axis=axis)
# I don't want the array to change so I have to copy it?
if axis > 0:
aswp = ma.swapaxes(a, 0, axis)
else:
aswp = a
m = ma.median(ma.fabs(aswp - d) / c, axis=0)
return m
def MAD(a, c=0.6745, axis=None):
a = ma.masked_where(a!=a, a)
if a.ndim == 1:
d = ma.median(a)
m = ma.median(ma.fabs(a - d) / c)
else:
d = ma.median(a, axis=axis)
# I don't want the array to change so I have to copy it?
if axis > 0:
aswp = ma.swapaxes(a,0,axis)
else:
aswp = a
m = ma.median(ma.fabs(aswp - d) / c, axis=0)
return m
def MAD(a, c=0.6745, axis=None):
a = ma.masked_where(a!=a, a)
if a.ndim == 1:
d = ma.median(a)
m = ma.median(ma.fabs(a - d) / c)
else:
d = ma.median(a, axis=axis)
# I don't want the array to change so I have to copy it?
if axis > 0:
aswp = ma.swapaxes(a,0,axis)
else:
aswp = a
m = ma.median(ma.fabs(aswp - d) / c, axis=0)
return m
def MAD(a, c=0.6745, axis=None):
"""
Median Absolute Deviation along given axis of an array:
median(abs(a - median(a))) / c
c = 0.6745 is the constant to convert from MAD to std
"""
a = ma.masked_where(a!=a, a)
if a.ndim == 1:
d = ma.median(a)
m = ma.median(ma.fabs(a - d) / c)
else:
d = ma.median(a, axis=axis)
if axis > 0:
aswp = ma.swapaxes(a,0,axis)
else:
aswp = a
m = ma.median(ma.fabs(aswp - d) / c, axis=0)
return m
def carbchem_revelle(op_swtch,mdi,T_cube,S_cube,TCO2_cube,TALK_cube,Pr=0.0,TB=0.0,Ni=100.0,Tl=1.0e-5):
# This function calculates the inorganic carbon chemistry balance
# according to the method of Peng et al 1987
# The parameters are set in the first few lines
#salinity needs to be converted into psu
#TCO2 and TALK must be in mol/kg
#the ones below here are not needed
# This procedure calculates the inorganic carbon chemistry balance
# according to the method of Peng et al 1987
# The parameters are set in the first few lines
#
# ops= 0 ; output is iteration count
# 1 ; pCO2
# 2 ; pH
# 3 ; [H2CO3]
# 4 ; [HCO3]
# 5 ; [CO3]
# 6 ; satn [co3] : calcite
# 7 ; saturation state: calcite
# 8 ; satn [CO3] : aragonite
# 9 ; saturation state: aragonite
# 10; Ravelle factor (DIC) calculated from Egleston et al. 2010
# 11; Alkalinity buffer factor calculated from Egleston et al. 2010
#make sure grids are same size
#make sure rthey years are the same
#extarct the data from the cubes
# from iris import *
# from iris.analysis import *
# import iris.analysis
# from numpy import *
# from matplotlib.pyplot import *
# from scipy.stats.mstats import *
# import iris.plot as iplt
# import seawater
# import numpy
# import iris.quickplot as quickplot
# import iris.analysis.stats as istats
# temp = iris.load_cube('/home/ph290/tmp/hadgem2es_potential_temperature_historical_regridded.nc').extract(Constraint(depth = 0))
# sal = iris.load_cube('/home/ph290/tmp/hadgem2es_salinity_historical_regridded.nc').extract(Constraint(depth = 0))
# carb = iris.load_cube('/home/ph290/tmp/hadgem2es_dissolved_inorganic_carbon_historical_regridded.nc').extract(Constraint(depth = 0))
# alk = iris.load_cube('/home/ph290/tmp/hadgem2es_total_alkalinity_historical_regridded.nc').extract(Constraint(depth = 0))
# import carbchem
# co2 = carbchem.carbchem(1,temp.data.fill_value,temp,sal,carb,alk)
# T_cube = temp
# S_cube = sal
# TCO2_cube = carb
# TALK_cube = alk
# mdi = temp.data.fill_value
t_lat = np.size(T_cube.coord('latitude').points)
s_lat = np.size(S_cube.coord('latitude').points)
c_lat = np.size(TCO2_cube.coord('latitude').points)
a_lat = np.size(TALK_cube.coord('latitude').points)
lat_test = t_lat == s_lat == c_lat == a_lat
t_lon = np.size(T_cube.coord('longitude').points)
s_lon = np.size(S_cube.coord('longitude').points)
c_lon = np.size(TCO2_cube.coord('longitude').points)
a_lon = np.size(TALK_cube.coord('longitude').points)
lon_test = t_lon == s_lon == c_lon == a_lon
if lat_test and lon_test:
output_cube = T_cube.copy()
T_cube = T_cube-273.15
T = T_cube.data.copy()
S = S_cube.data.copy()
TCO2_cube = TCO2_cube/1026.0
TCO2 = np.roll(ma.swapaxes(TCO2_cube.data.copy(),0,1),180)
#NOTE - this is only required here 'cos glodap and WOA are differently ordered - not necessary for other stuff
TALK_cube = TALK_cube/1026.0
TALK = np.roll(ma.swapaxes(TALK_cube.data.copy(),0,1),180)
print np.mean(T)
print np.mean(S)
print np.mean(TCO2)
print np.mean(TALK)
msk1=ma.masked_greater_equal(T,mdi-1.0,copy=True)
msk2=ma.masked_greater_equal(S,mdi-1.0,copy=True)
msk3=ma.masked_greater_equal(TCO2,mdi-1.0,copy=True)
msk4=ma.masked_greater_equal(TALK,mdi-1.0,copy=True)
msk=msk1.mask | msk2.mask | msk3.mask | msk4.mask
T[msk]=np.nan
S[msk]=np.nan
TALK[msk]=np.nan
TCO2[msk]=np.nan
# plt.contourf(T)
# plt.show()
# plt.contourf(TCO2)
# plt.show()
# T = np.array([13.74232016,25.0])
# S = np.array([33.74096661,35.0])
# TCO2 = np.array([0.0019863,2.0e-3])
# TALK = np.array([0.00226763,2.2e-3])
# msk = ma.masked_greater_equal(T,mdi-1.0,copy=True)
#create land-sea mask used by sea_msk.mask
salmin = 1.0
S2=np.copy(S)
S2[np.abs(S) < salmin]=salmin
tol = Tl
mxiter = Ni
op_fld = np.empty(T.shape)
op_fld.fill(np.NAN)
# TB = np.ones(T.shape)
# TB = 4.106e-4*S2/35.0
TB = np.empty_like(T)
TB = np.multiply(S2,4.106e-4/35.0, TB)
# this boron is from Peng
#convert to Kelvin
TK=np.copy(T[:])
TK += +273.15
alpha_s = np.ones(T.shape)
alpha_s = np.exp( ( -60.2409 + 9345.17/TK + 23.3585*np.log(TK/100.0) ) + ( 0.023517 - 0.023656*(TK/100.0) + 0.0047036*np.power((TK/100.0),2.0) )*S )
K1 = np.ones(T.shape)
K1 = np.exp( ( -2307.1266/TK + 2.83655 - 1.5529413*np.log(TK) ) - ( 4.0484/TK + 0.20760841 )*np.sqrt(S) + 0.08468345*S - 0.00654208*np.power(S,1.5) + np.log( 1.0 - 0.001005*S ) )
a = np.array([-25.50,-15.82,-29.48,-25.60,-48.76,-46.0])
b = np.array([0.1271,0.0219,0.2324,0.5304,0.5304])
c = np.array([0.0,0.0,0.0026080,0.0036246,0.0,0.0])
d = np.array([-3.08,1.13,(-2.84e-3)/(1.0e-3),-5.13,-11.76,-11.76])
e = np.array([0.0877,0.1475,0.0,0.0794,0.3692,0.3692])
if keyword.iskeyword(Pr):
instance = 0
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K1 = K1*pf
K2 = np.ones(T.shape)
K2 = np.exp( ( -3351.6106/TK - 9.226508 - 0.2005743*np.log(TK) ) - ( 23.9722/TK + 0.106901773 )*np.power(S,0.5) + 0.1130822*S - 0.00846934*np.power(S,1.5) + np.log( 1.0 - 0.001005*S ) )
if keyword.iskeyword(Pr):
instance = 1
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K2 = K2*pf
KB = np.ones(T.shape)
KB = np.exp( ( -8966.90 - 2890.53*np.power(S,0.5) - 77.942*S + 1.728*np.power(S,1.5)- 0.0996*np.power(S,2.0) )/TK + ( 148.0248 + 137.1942*np.power(S,0.5) + 1.62142*S ) - ( 24.4344 + 25.085*np.power(S,0.5) + 0.2474*S )*np.log(TK) + 0.053105*(np.power(S,0.5))*TK )
if keyword.iskeyword(Pr):
instance = 2
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
KB = KB*pf
KW = np.ones(T.shape)
KW = np.exp( ( -13847.26/TK + 148.96502 - 23.6521*np.log(TK) ) + ( 118.67/TK - 5.977 + 1.0495*np.log(TK) )*np.power(S,0.5) - 0.01615*S )
if keyword.iskeyword(Pr):
instance = 3
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
KW = KW*pf
if ( op_swtch >= 6 or op_swtch <= 9 ):
ca_conc = np.ones(T.shape)
ca_conc = 0.01028*S2/35.0
if ( op_swtch == 6 or op_swtch == 7 ):
K_SP_C = np.ones(T.shape)
K_SP_C = np.power(10.0,( ( -171.9065 - 0.077993*TK + 2839.319/TK + 71.595*np.log10(TK) ) + ( -0.77712 + 0.0028426*TK + 178.34/TK )*np.power(S,0.5) - 0.07711*S+ 0.0041249*np.power(S,1.5) ))
if keyword.iskeyword(Pr):
instance = 4
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K_SP_C = K_SP_C*pf
if ( op_swtch == 8 or op_swtch == 9 ):
K_SP_A = np.ones(T.shape)
K_SP_A = np.power(10,( ( -171.945 - 0.077993*TK + 2903.293/TK + 71.595*np.log10(TK) ) + ( -0.068393 + 0.0017276*TK + 88.135/TK )*np.power(S,0.5) - 0.10018*S + 0.0059415*np.power(S,1.5) ))
if keyword.iskeyword(Pr):
instance = 5
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K_SP_A = K_SP_A*pf
# Get first estimate for H+ concentration.
AC, AW, AB, aH, count = carbiter(T, TCO2, TALK, TB, msk, tol, mxiter, K1, K2, KB, KW)
# plt.contourf(aH)
# plt.show()
# plt.contourf(AC)
# plt.show()
# plt.contourf(AW)
# plt.show()
# plt.contourf(aH)
# plt.show()
# now we have aH we can calculate...
denom = np.zeros(T.shape)
H2CO3 = np.zeros(T.shape)
HCO3 = np.zeros(T.shape)
CO3 = np.zeros(T.shape)
pH = np.zeros(T.shape)
pCO2 = np.zeros(T.shape)
if ( op_swtch == 6 or op_swtch == 7 ):
sat_CO3_C = np.zeros(T.shape)
if ( op_swtch == 7 ):
sat_stat_C = np.zeros(T.shape)
if ( op_swtch == 8 or op_swtch == 9 ):
sat_CO3_A = np.zeros(T.shape)
if ( op_swtch == 9 ):
sat_stat_A = np.zeros(T.shape)
denom = np.power(aH,2.0) + K1*aH + K1*K2
H2CO3 = TCO2*np.power(aH,2.0)/denom
HCO3 = TCO2*K1*aH/denom
CO3 = TCO2*K1*K2/denom
# plt.contourf(K1)
# plt.show()
# plt.contourf(aH) -no
# plt.show()
# plt.contourf(denom) -no
# plt.show()
pH = -np.log10(aH)
pCO2 = H2CO3/alpha_s
if ( op_swtch == 6 or op_swtch == 7 ):
sat_CO3_C = K_SP_C/ca_conc
if ( op_swtch == 7 ):
sat_stat_C = CO3/sat_CO3_C
if ( op_swtch == 8 or op_swtch == 9 ):
sat_CO3_A = K_SP_A/ca_conc
if ( op_swtch == 9 ):
sat_stat_A = CO3/sat_CO3_A
TALKc=+HCO3+2*(CO3)
var1=HCO3
DIC_buffer=HCO3
ALK_buffer=HCO3
var1=HCO3+4*(CO3)+((aH*AB)/(KB+aH))-AW
DIC_buffer=TCO2-((TALKc*TALKc)/var1)
ALK_buffer=((TALKc*TALKc)-TCO2*var1)/TALKc
output_cube = output_cube*0.0+np.nan
if ( op_swtch == 0 ):
op_fld = np.zeros(T.shape)
op_fld = count
elif ( op_swtch == 1 ):
print np.mean(pCO2)
output_cube.data = pCO2*1.0e6
output_cube.standard_name = 'surface_partial_pressure_of_carbon_dioxide_in_sea_water'
output_cube.long_name = 'CO2 concentration'
output_cube.units = 'uatm'
elif ( op_swtch == 2 ):
output_cube.data = pH
output_cube.standard_name = 'sea_water_ph_reported_on_total_scale'
output_cube.long_name = 'pH'
output_cube.units = '1'
elif ( op_swtch == 3 ):
output_cube.data = H2CO3
elif ( op_swtch == 4 ):
output_cube.data = HCO3
elif ( op_swtch == 5 ):
output_cube.data = CO3
elif ( op_swtch == 6 ):
output_cube.data = sat_CO3_C
elif ( op_swtch == 7 ):
output_cube.data = sat_stat_C
elif ( op_swtch == 8 ):
output_cube.data = sat_CO3_A
elif ( op_swtch == 9 ):
output_cube.data = sat_stat_A
elif ( op_swtch == 10 ):
output_cube.data = TCO2/DIC_buffer
elif ( op_swtch == 11 ):
output_cube.data = ALK_buffer*1000.0
return output_cube