<commit_before># -*- coding: utf-8 -*-

import numpy as np
from scipy import interpolate,optimize,integrate,special
from tec import TECBase

class DimensionlessLangmuirPoissonSoln(dict):
  """
  Numerical solution of Langmuir's dimensionless Poisson's equation.

  The purpose of this class is to provide an API to the solution of Langmuir's dimensionless Poisson's equation :cite:`10.1103/PhysRev.21.419` to provide the appropriate level of simplicity to the user. Via the class methods, the user can access either the dimensionless motive vs. dimensionless position or the dimensionless position vs. dimensionless motive, both of which are necessary in the Langmuir model. This class uses an ode solver to approximate the solution to the ode, then interpolation to return values at arbitrary abscissae -- see the source for details of the ode solver and interpolation algorithm.
  """
  
  def __init__(self):
    
    # Here is the algorithm:
    # 1. Set up the default ode solver parameters.
    # 2. Check to see if either the rhs or lhs params were passed as arguments. If not, use the default params.
    # 3. Cat the additional default ode solver parameters to the lhs and rhs set of params.
    # 4. Solve both the lhs and rhs odes.
    # 5. Create the lhs and rhs interpolation objects.
    
    self["lhs"] = self.calc_branch(-2.5538)
    self["rhs"] = self.calc_branch(100)

    # data = np.loadtxt("tec/models/kleynen_langmuir.dat")
    # rhs = data[565:-1,:]<commit_msg>Wasn't importing physical_constants.<commit_after># -*- coding: utf-8 -*-

import numpy as np
from scipy import interpolate,optimize,integrate,special
from tec import TECBase
from tec import physical_constants

class DimensionlessLangmuirPoissonSoln(dict):
  """
  Numerical solution of Langmuir's dimensionless Poisson's equation.

  The purpose of this class is to provide an API to the solution of Langmuir's dimensionless Poisson's equation :cite:`10.1103/PhysRev.21.419` to provide the appropriate level of simplicity to the user. Via the class methods, the user can access either the dimensionless motive vs. dimensionless position or the dimensionless position vs. dimensionless motive, both of which are necessary in the Langmuir model. This class uses an ode solver to approximate the solution to the ode, then interpolation to return values at arbitrary abscissae -- see the source for details of the ode solver and interpolation algorithm.
  """
  
  def __init__(self):
    
    # Here is the algorithm:
    # 1. Set up the default ode solver parameters.
    # 2. Check to see if either the rhs or lhs params were passed as arguments. If not, use the default params.
    # 3. Cat the additional default ode solver parameters to the lhs and rhs set of params.
    # 4. Solve both the lhs and rhs odes.
    # 5. Create the lhs and rhs interpolation objects.
    
    self["lhs"] = self.calc_branch(-2.5538)
    self["rhs"] = self.calc_branch(100)

    # data = np.loadtxt("tec/models/kleynen_langmuir.dat")
    # rhs = data[565:-1,:]
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