本文整理汇总了Python中scipy.optimize.fsolve方法的典型用法代码示例。如果您正苦于以下问题：Python optimize.fsolve方法的具体用法？Python optimize.fsolve怎么用？Python optimize.fsolve使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块scipy.optimize的用法示例。

在下文中一共展示了optimize.fsolve方法的26个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: _fitstart

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def _fitstart(self, data):

g1 = _skew(data)

g2 = _kurtosis(data)

def func(x):

a, b = x

sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)

ku = a**3 - a**2*(2*b-1) + b**2*(b+1) - 2*a*b*(b+2)

ku /= a*b*(a+b+2)*(a+b+3)

ku *= 6

return [sk-g1, ku-g2]

a, b = optimize.fsolve(func, (1.0, 1.0))

return super(beta_gen, self)._fitstart(data, args=(a, b))

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:15，

示例2: _xinf_ND

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def _xinf_ND(xdot,x0,args=(),xddot=None,xtol=1.49012e-8):

"""Private function for wrapping the fsolving for x_infinity

for a variable x in N dimensions"""

try:

result = fsolve(xdot,x0,args,fprime=xddot,xtol=xtol,full_output=1)

except (ValueError, TypeError, OverflowError):

xinf_val = NaN

except:

print "Error in fsolve:", sys.exc_info()[0], sys.exc_info()[1]

xinf_val = NaN

else:

if result[2] in (1,2,3): #,4,5):

# 4,5 means "not making good progress" (see fsolve docstring)

xinf_val = float(result[0])

else:

xinf_val = NaN

return xinf_val

开发者ID:robclewley，项目名称:compneuro，代码行数:19，

示例3: computeShiftedGears

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def computeShiftedGears(m, alpha, t1, t2, x1, x2):

"""Summary

Args:

m (float): common module of both gears [length]

alpha (float): pressure-angle [rad]

t1 (int): number of teeth of gear1

t2 (int): number of teeth of gear2

x1 (float): relative profile-shift of gear1

x2 (float): relative profile-shift of gear2

Returns:

(float, float): distance between gears [length], pressure angle of the assembly [rad]

"""

def inv(x): return np.tan(x) - x

inv_alpha_w = inv(alpha) + 2 * np.tan(alpha) * (x1 + x2) / (t1 + t2)

def root_inv(x): return inv(x) - inv_alpha_w

alpha_w = opt.fsolve(root_inv, 0.)

dist = m * (t1 + t2) / 2 * np.cos(alpha) / np.cos(alpha_w)

return dist, alpha_w

开发者ID:looooo，项目名称:freecad.gears，代码行数:23，

示例4: calculate_psi_goal

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def calculate_psi_goal(psi_baseline, Au_baseline, Au_goal, deltaz,

c_baseline, c_goal):

"""Find the value for psi that has the same location w on the upper

surface of the goal as psi_baseline on the upper surface of the

baseline"""

def integrand(psi_baseline, Au, deltaz, c):

return c*np.sqrt(1 + dxi_u(psi_baseline, Au, deltaz/c)**2)

def equation(psi_goal, L_baseline, Au_goal, deltaz, c):

y, err = quad(integrand, 0, psi_goal, args=(Au_goal, deltaz, c))

return y - L_baseline

L_baseline, err = quad(integrand, 0, psi_baseline,

args=(Au_baseline, deltaz, c_baseline))

with warnings.catch_warnings():

warnings.simplefilter("ignore")

y = fsolve(equation, psi_baseline, args=(L_baseline, Au_goal, deltaz,

c_goal))

return y[0]

开发者ID:leal26，项目名称:AeroPy，代码行数:22，

示例5: interp_isentrope

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def interp_isentrope(interp, pressures, entropy, T_guess):

def _deltaS(args, S, P):

T = args[0]

return interp(P, T)[0] - S

sol = [T_guess]

temperatures = np.empty_like(pressures)

for i, P in enumerate(pressures):

sol = fsolve(_deltaS, sol, args=(entropy, P))

temperatures[i] = sol[0]

return temperatures

# Define function to self consistently calculate depth and gravity profiles

# from pressure and density profiles.

开发者ID:geodynamics，项目名称:burnman，代码行数:17，

示例6: equilibrium_pressure

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def equilibrium_pressure(minerals, stoichiometry, temperature, pressure_initial_guess=1.e5):

"""

Given a list of minerals, their reaction stoichiometries

and a temperature of interest, compute the

equilibrium pressure of the reaction.

Parameters

----------

minerals : list of minerals

List of minerals involved in the reaction.

stoichiometry : list of floats

Reaction stoichiometry for the minerals provided.

Reactants and products should have the opposite signs [mol]

temperature : float

Temperature of interest [K]

pressure_initial_guess : optional float

Initial pressure guess [Pa]

Returns

-------

pressure : float

The equilibrium pressure of the reaction [Pa]

"""

def eqm(P, T):

gibbs = 0.

for i, mineral in enumerate(minerals):

mineral.set_state(P[0], T)

gibbs = gibbs + mineral.gibbs * stoichiometry[i]

return gibbs

pressure = fsolve(eqm, [pressure_initial_guess], args=(temperature))[0]

return pressure

开发者ID:geodynamics，项目名称:burnman，代码行数:38，

示例7: equilibrium_temperature

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def equilibrium_temperature(minerals, stoichiometry, pressure, temperature_initial_guess=1000.):

"""

Given a list of minerals, their reaction stoichiometries

and a pressure of interest, compute the

equilibrium temperature of the reaction.

Parameters

----------

minerals : list of minerals

List of minerals involved in the reaction.

stoichiometry : list of floats

Reaction stoichiometry for the minerals provided.

Reactants and products should have the opposite signs [mol]

pressure : float

Pressure of interest [Pa]

temperature_initial_guess : optional float

Initial temperature guess [K]

Returns

-------

temperature : float

The equilibrium temperature of the reaction [K]

"""

def eqm(T, P):

gibbs = 0.

for i, mineral in enumerate(minerals):

mineral.set_state(P, T[0])

gibbs = gibbs + mineral.gibbs * stoichiometry[i]

return gibbs

temperature = fsolve(eqm, [temperature_initial_guess], args=(pressure))[0]

return temperature

开发者ID:geodynamics，项目名称:burnman，代码行数:38，

示例8: _digammainv

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def _digammainv(y):

# Inverse of the digamma function (real positive arguments only).

# This function is used in the `fit` method of `gamma_gen`.

# The function uses either optimize.fsolve or optimize.newton

# to solve `sc.digamma(x) - y = 0`. There is probably room for

# improvement, but currently it works over a wide range of y:

# >>> y = 64*np.random.randn(1000000)

# >>> y.min(), y.max()

# (-311.43592651416662, 351.77388222276869)

# x = [_digammainv(t) for t in y]

# np.abs(sc.digamma(x) - y).max()

# 1.1368683772161603e-13

#

_em = 0.5772156649015328606065120

func = lambda x: sc.digamma(x) - y

if y > -0.125:

x0 = np.exp(y) + 0.5

if y < 10:

# Some experimentation shows that newton reliably converges

# must faster than fsolve in this y range. For larger y,

# newton sometimes fails to converge.

value = optimize.newton(func, x0, tol=1e-10)

return value

elif y > -3:

x0 = np.exp(y/2.332) + 0.08661

else:

x0 = 1.0 / (-y - _em)

value, info, ier, mesg = optimize.fsolve(func, x0, xtol=1e-11,

full_output=True)

if ier != 1:

raise RuntimeError("_digammainv: fsolve failed, y = %r" % y)

return value[0]

## Gamma (Use MATLAB and MATHEMATICA (b=theta=scale, a=alpha=shape) definition)

## gamma(a, loc, scale) with a an integer is the Erlang distribution

## gamma(1, loc, scale) is the Exponential distribution

## gamma(df/2, 0, 2) is the chi2 distribution with df degrees of freedom.

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:43，

示例9: create_test_fn

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def create_test_fn(gen, tmax, dist_pts):

"""Create integration test function using the supplied generator.

The test function will return a dictionary of the two closest

points (keys 1 and 2) mapping to their respective distances and

pointset index positions.

"""

gen.set(tdata=[0,tmax])

def Tn_integ(ic):

gen.set(ics=ic)

try:

test_traj = gen.compute('test')

except:

print "Problem integrating test trajectory at i.c. ", ic

raise

test_pts = test_traj.sample(coords=dist_pts.all_pts.coordnames)

# distance of endpoint to pointset

try:

d_info = dist_pts(test_pts[-1], use_norm=True, minmax=['min'])

except ValueError:

# This error happens when fsolve tries initial conditions that

# break the integrator

return (_num_inf,NaN)

pos = d_info['min'][1]['pos']

return (test_pts[-1]-dist_pts.all_pts[pos], pos)

return Tn_integ

开发者ID:robclewley，项目名称:compneuro，代码行数:30，

示例10: create_test_fn_with_events

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def create_test_fn_with_events(gen, tmax, dist_pts, iso_ev, other_evnames, pars_to_vars):

"""Create integration test function using the supplied generator,

assuming it contains isochron-related events.

The test function will return a dictionary of the two closest

points (keys 1 and 2) mapping to their respective distances and

pointset index positions.

"""

def Tn_integ(ic):

gen.set(ics=ic, tdata=[0,tmax])

try:

test_traj = gen.compute('test')

except:

print "Problem integrating test trajectory at i.c. ", ic

raise

test_pts = test_traj.sample(coords=dist_pts.all_pts.coordnames)

# distance of endpoint to pointset

try:

d_info = dist_pts(test_pts[-1], use_norm=True, minmax=['min'])

except ValueError:

# This error happens when fsolve tries initial conditions that

# break the integrator

return (_num_inf,NaN)

# refine min position using isochron-related events

q=test_pts[-1]

perp_ev, t_ev = _find_min_pt(gen, q,

d_info['min'][1]['pos'], dist_pts.all_pts,

pars_to_vars, iso_ev, other_evnames)

ev_pt = perp_ev[0][q.coordnames]

# different return format to version w/o events

return (test_pts[-1]-ev_pt, t_ev, ev_pt)

return Tn_integ

开发者ID:robclewley，项目名称:compneuro，代码行数:35，

示例11: _xinf_1D

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def _xinf_1D(xdot,x0,args=(),xddot=None,xtol=1.49012e-8):

"""Private function for wrapping the solving for x_infinity

for a variable x in 1 dimension"""

try:

if xddot is None:

xinf_val = float(fsolve(xdot,x0,args,xtol=xtol))

else:

xinf_val = float(newton_meth(xdot,x0,fprime=xddot,args=args))

except RuntimeError:

xinf_val = NaN

return xinf_val

开发者ID:robclewley，项目名称:compneuro，代码行数:13，

示例12: test_pressure_network_no_gradient

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_pressure_network_no_gradient(self):

"""fsolve without gradient, equal pipes -> equal flows"""

k = np.ones(4) * 0.5

Qtot = 4

initial_guess = array([2., 0., 2., 0.])

final_flows, info, ier, mesg = optimize.fsolve(

pressure_network, initial_guess, args=(Qtot, k),

full_output=True)

assert_array_almost_equal(final_flows, np.ones(4))

assert_(ier == 1, mesg)

开发者ID:ktraunmueller，项目名称:Computable，代码行数:12，

示例13: test_pressure_network_with_gradient

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_pressure_network_with_gradient(self):

"""fsolve with gradient, equal pipes -> equal flows"""

k = np.ones(4) * 0.5

Qtot = 4

initial_guess = array([2., 0., 2., 0.])

final_flows = optimize.fsolve(

pressure_network, initial_guess, args=(Qtot, k),

fprime=pressure_network_jacobian)

assert_array_almost_equal(final_flows, np.ones(4))

开发者ID:ktraunmueller，项目名称:Computable，代码行数:11，

示例14: test_wrong_shape_func_callable

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_wrong_shape_func_callable(self):

"""The callable 'func' has no '__name__' attribute."""

func = ReturnShape(1)

# x0 is a list of two elements, but func will return an array with

# length 1, so this should result in a TypeError.

x0 = [1.5, 2.0]

assert_raises(TypeError, optimize.fsolve, func, x0)

开发者ID:ktraunmueller，项目名称:Computable，代码行数:9，

示例15: test_wrong_shape_func_function

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_wrong_shape_func_function(self):

# x0 is a list of two elements, but func will return an array with

# length 1, so this should result in a TypeError.

x0 = [1.5, 2.0]

assert_raises(TypeError, optimize.fsolve, dummy_func, x0, args=((1,),))

开发者ID:ktraunmueller，项目名称:Computable，代码行数:7，

示例16: test_wrong_shape_fprime_callable

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_wrong_shape_fprime_callable(self):

"""The callables 'func' and 'deriv_func' have no '__name__' attribute."""

func = ReturnShape(1)

deriv_func = ReturnShape((2,2))

assert_raises(TypeError, optimize.fsolve, func, x0=[0,1], fprime=deriv_func)

开发者ID:ktraunmueller，项目名称:Computable，代码行数:7，

示例17: test_float32

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_float32(self):

func = lambda x: np.array([x[0] - 100, x[1] - 1000], dtype=np.float32)**2

p = optimize.fsolve(func, np.array([1, 1], np.float32))

assert_allclose(func(p), [0, 0], atol=1e-3)

开发者ID:ktraunmueller，项目名称:Computable，代码行数:6，

示例18: find_branch_cuts

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def find_branch_cuts(m, V):

'''Find all the solutions for the eigenvalue function.

Parameters

----------

m : int

The azimuthal order

V : scalar

The normalized frequency parameter of the fiber.

Returns

-------

Tuple

A tuple containing the solutions of the eigenvalue function. If no solutions were found returns None.

'''

# Make an initial rough grid

num_steps = 501

theta = np.linspace(np.pi * 0.499, 0, num_steps, endpoint=False)

u = V * np.cos(theta)

# Find the position where the eigenvalue equation goes through zero

diff = eigenvalue_equation(u, m, V)

fu = np.diff(np.sign(diff)) < 0

ind = np.where(abs(fu - 1) <= 0.01)[0]

if len(ind) > 0:

# Refine the zero with a rootfinding algorithm

u0 = fsolve(eigenvalue_equation, u[ind], args=(m, V))

w0 = np.sqrt(V**2 - u0**2)

return u0, w0

else:

return None

开发者ID:ehpor，项目名称:hcipy，代码行数:34，

示例19: calculate_max_camber

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def calculate_max_camber(Au, Al, delta_xi):

"""Calculate maximum camber and where it is. Returns (\psi, max_camber)"""

def dcamber(psi, Au, Al, delta_xi):

return 0.5*(dxi_u(psi, Au, delta_xi) + dxi_l(psi, Al, delta_xi))

solution = fsolve(dcamber, 0.5, args=(Au, Al, delta_xi))

# Outputs floats with psi and xi coordinates

return solution[0], calculate_camber(solution, Au, Al, delta_xi)[0]

开发者ID:leal26，项目名称:AeroPy，代码行数:11，

示例20: test_pressure_network_no_gradient

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_pressure_network_no_gradient(self):

# fsolve without gradient, equal pipes -> equal flows.

k = np.ones(4) * 0.5

Qtot = 4

initial_guess = array([2., 0., 2., 0.])

final_flows, info, ier, mesg = optimize.fsolve(

pressure_network, initial_guess, args=(Qtot, k),

full_output=True)

assert_array_almost_equal(final_flows, np.ones(4))

assert_(ier == 1, mesg)

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:12，

示例21: test_pressure_network_with_gradient

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_pressure_network_with_gradient(self):

# fsolve with gradient, equal pipes -> equal flows

k = np.ones(4) * 0.5

Qtot = 4

initial_guess = array([2., 0., 2., 0.])

final_flows = optimize.fsolve(

pressure_network, initial_guess, args=(Qtot, k),

fprime=pressure_network_jacobian)

assert_array_almost_equal(final_flows, np.ones(4))

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:11，

示例22: test_wrong_shape_func_callable

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_wrong_shape_func_callable(self):

func = ReturnShape(1)

# x0 is a list of two elements, but func will return an array with

# length 1, so this should result in a TypeError.

x0 = [1.5, 2.0]

assert_raises(TypeError, optimize.fsolve, func, x0)

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:8，

示例23: test_wrong_shape_fprime_callable

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def test_wrong_shape_fprime_callable(self):

func = ReturnShape(1)

deriv_func = ReturnShape((2,2))

assert_raises(TypeError, optimize.fsolve, func, x0=[0,1], fprime=deriv_func)

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:6，

示例24: getTemperatureAtDensity

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def getTemperatureAtDensity(self, targetDensity, temperatureGuessInC):

"""Get the temperature at which the perturbed density occurs."""

densFunc = (

lambda temp: self.density(Tc=temp) - targetDensity

) # 0 at tempertature of targetDensity

tAtTargetDensity = float(

fsolve(densFunc, temperatureGuessInC)

) # is a numpy array if fsolve is called

return tAtTargetDensity

开发者ID:terrapower，项目名称:armi，代码行数:11，

示例25: calculo

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def calculo(self):

self.areaCalculada = Area(self.kwargs["area"])

self.deltaP = Pressure(self.kwargs["deltaP"])

if self.kwargs["potencialCarga"]:

self.potencialCarga = PotencialElectric(

self.kwargs["potencialCarga"])

else:

self.potencialCarga = PotencialElectric(24000)

if self.kwargs["potencialDescarga"]:

self.potencialDescarga = PotencialElectric(

self.kwargs["potencialDescarga"])

else:

self.potencialDescarga = PotencialElectric(24000)

if self.kwargs["epsilon"]:

self.epsilon = Dimensionless(self.kwargs["epsilon"])

else:

self.epsilon = Dimensionless(4.)

if self.kwargs["metodo"] == 1:

def f(area):

eta_i = self.calcularRendimientos_parciales(area)

eta = self.calcularRendimiento(eta_i)

return eta-self.kwargs["rendimientoAdmisible"]

self.areaCalculada = Area(fsolve(f, 100)[0])

eta_i = self.calcularRendimientos_parciales(self.areaCalculada)

self.rendimiento_parcial = eta_i

self.rendimiento = self.calcularRendimiento(eta_i)

self.CalcularSalidas()

开发者ID:jjgomera，项目名称:pychemqt，代码行数:34，

示例26: calculo

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# 需要导入模块: from scipy import optimize [as 别名]

# 或者: from scipy.optimize import fsolve [as 别名]

def calculo(self):

entrada = self.kwargs["entrada"]

self.deltaP = unidades.DeltaP(self.kwargs["DeltaP"])

self.HeatCalc = unidades.Power(self.kwargs["Heat"])

if self.kwargs["Tout"]:

Tout = unidades.Temperature(self.kwargs["Tout"])

elif self.kwargs["DeltaT"]:

Tout = unidades.Temperature(entrada.T+self.kwargs["DeltaT"])

A = unidades.Area(self.kwargs["A"])

U = unidades.HeatTransfCoef(self.kwargs["U"])

Text = unidades.Temperature(self.kwargs["Text"])

if self.modo == 1:

self.salida = [entrada.clone(T=Tout, P=entrada.P-self.deltaP)]

self.HeatCalc = unidades.Power(self.salida[0].h-entrada.h)

else:

if self.modo == 2:

self.HeatCalc = unidades.Power(0)

else:

self.HeatCalc = unidades.Power(A*U*(Text-entrada.T))

def f():

output = entrada.clone(T=T, P=entrada.P-self.deltaP)

return output.h-entrada.h-self.HeatCalc

T = fsolve(f, entrada.T)[0]

if T > max(Text, entrada.T) or T < min(Text, entrada.T):

T = self.Text

self.salida = [entrada.clone(T=T, P=entrada.P-self.deltaP)]

self.Tin = entrada.T

self.ToutCalc = self.salida[0].T

self.deltaT = unidades.DeltaT(self.ToutCalc-entrada.T)

开发者ID:jjgomera，项目名称:pychemqt，代码行数:34，

注：本文中的scipy.optimize.fsolve方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。