def calc_R_Zh_relationship(self):
'''
calc_R_Zh_relationship calculates the power law fit for Zh based
upon scattered radar parameters. It returns the scale and exponential
parameter a and b in the first tuple, and the second returned argument
gives the covariance matrix of the fit.
'''
popt, pcov = expfit(np.power(10, 0.1 * self.Zh[self.rain_rate > 0]),
self.rain_rate[self.rain_rate > 0])
return popt, pcov
def calc_R_kdp_relationship(self):
'''
calc_R_kdp_relationship calculates a power fit for the rainfall-kdp
relationship based upon the calculated radar parameters(which should
have already been run). It returns the scale and exponential
parameter a and b in the first tuple, and the second returned argument
gives the covariance matrix of the fit.
'''
popt, pcov = expfit(self.Kdp[self.rain_rate > 0],
self.rain_rate[self.rain_rate > 0])
return popt, pcov
def calculate_R_Zh_relationship(self):
'''
calculate_R_Zh_relationship calculates the power law fit for Zh based
upon scattered radar parameters. It returns the scale and exponential
parameter a and b in the first tuple, and the second returned argument
gives the covariance matrix of the fit.
Returns:
--------
popt: tuple
a,b,c fits for relationship.
pcov: array
Covariance matrix of fits.
'''
popt, pcov = expfit(np.power(10, 0.1 * self.fields['Zh']['data'][self.rain_rate['data'] > 0]),
self.fields['rain_rate']['data'][self.fields['rain_rate']['data'] > 0])
return popt, pcov
def calculate_R_Kdp_relationship(self):
'''
calculate_R_kdp_relationship calculates a power fit for the rainfall-kdp
relationship based upon the calculated radar parameters(which should
have already been run). It returns the scale and exponential
parameter a and b in the first tuple, and the second returned argument
gives the covariance matrix of the fit.
'''
if 'rain_rate' in self.fields.keys():
filt = np.logical_and(self.fields['Kdp']['data'] > 0,
self.fields['rain_rate']['data'] > 0)
popt, pcov = expfit(self.fields['Kdp']['data'][filt],
self.fields['rain_rate']['data'][filt])
return popt, pcov
else:
print("Please run calculate_RR() function first.")
return None
def ajuste(fn, x, y):
""" Calcula os coeficientes de um ajuste usando o método dos mínimos
quadrados.
Entradas:
fn: uma string indicando a forma da função a ser ajustada.
x: o vetor contendo as abscissas.
y: o vetor contendo as ordenadas.
Saída: a função ajustada e uma legenda para ela. A legenda poderá ser
usada em um gráfico do matplotlib.
"""
if fn == 'n':
# Ajusta os coeficientes de um polinômio de grau 1
coefs = np.polyfit(x, y, 1)
f = np.poly1d(coefs)
legenda = legenda_poli(coefs)
elif fn == 'n^2':
# Ajusta os coeficientes de um polinômio de grau 2
coefs = np.polyfit(x, y, 2)
f = np.poly1d(coefs)
legenda = legenda_poli(coefs)
elif fn == 'nln(n)':
# Ajusta os coeficientes a e b da função f(n)= a*n*ln(n) + b
coefs = nlognfit(x, y)
a, b = coefs
f = lambda n: a * n * np.log(n) + b
legenda = legenda_nln(a, b)
elif fn == 'ln(n)':
# Ajusta os coeficientes a e b da função f(n)= a*ln(n) + b
coefs = logfit(x, y)
a, b = coefs
f = lambda n: a * np.log(n) + b
legenda = legenda_ln(a, b)
elif fn == 'e(n)':
# Ajusta os coeficientes a e b da função f(n)= ae^(bx)
coefs = expfit(x, y)
a, b = coefs
f = lambda n: a * np.exp(b * n)
legenda = legenda_en(a, b)
else:
print("A função 'ajuste' nao esta preparada para ajustar " + fn)
raise
return (f, '$' + legenda + '$')
def calculate_R_Kdp_relationship(self):
'''
calculate_R_kdp_relationship calculates a power fit for the rainfall-kdp
relationship based upon the calculated radar parameters(which should
have already been run). It returns the scale and exponential
parameter a and b in the first tuple, and the second returned argument
gives the covariance matrix of the fit.
'''
if 'rain_rate' in self.fields.keys():
filt = np.logical_and(
self.fields['Kdp']['data'] > 0, self.fields['rain_rate']['data'] > 0)
popt, pcov = expfit(self.fields['Kdp']['data'][filt],
self.fields['rain_rate']['data'][filt])
return popt, pcov
else:
print("Please run calculate_RR() function first.")
return None
def calculate_R_Zh_relationship(self):
'''
calculate_R_Zh_relationship calculates the power law fit for Zh based
upon scattered radar parameters. It returns the scale and exponential
parameter a and b in the first tuple, and the second returned argument
gives the covariance matrix of the fit.
Returns:
--------
popt: tuple
a,b,c fits for relationship.
pcov: array
Covariance matrix of fits.
'''
popt, pcov = expfit(
np.power(
10,
0.1 * self.fields['Zh']['data'][self.rain_rate['data'] > 0]),
self.fields['rain_rate']['data'][
self.fields['rain_rate']['data'] > 0])
return popt, pcov
RHS1 = [ r + 1/p for p in ps_]
print r - rr
# rl = [laplace_mechanism(compute_sum_ti_p(p, data) , len(ps)*1.0/EPSILON) for p in ps]
# rl = [laplace_mechanism(compute_sum_ti_p(p, data) , len(ps)*1.0/EPSILON) for p in ps]
# rl1 = [laplace_mechanism(compute_sum_ti_p_log(p, data), bb*len(ps)*1.0/EPSILON) for p in ps]
rl_ = [compute_sum_ti_p(p, data) for p in ps_]
rl1_ = [compute_sum_ti_p_log(p, data) for p in ps_]
etm = [
laplace_mechanism(compute_sum_ti_p_log(ps[i], data) , bb*1.0/(0.8*EPSILON/2.0*2.0*(i+1)/len(ps)/(len(ps)+1.0) ) ) /
laplace_mechanism(compute_sum_ti_p(ps[i], data) , 1.0/( 0.8*EPSILON/2.0*2.0*(i+1)/len(ps)/(len(ps)+1.0)))
for i in range(len(ps))
]
fit = expfit(ps, 10, 1)
a0 = np.ones(2)
alp,bet,lam,aopt = fit.getOptCoeffs(etm,a0)
y = fit.getCurrentState()
# fit = expfit(ps, 30, 1)
# a0 = np.ones(2)
# alp,bet,lam,aopt = fit.getOptCoeffs(rl,a0)
# y = fit.getCurrentState()
#
# fit = expfit(ps, 30, 1)
# a0 = np.ones(2)
# alp,bet,lam,aopt = fit.getOptCoeffs(rl1,a0)
# y1 = fit.getCurrentState()
# z = np.polyfit(ps, rl, 2)
