SimonEnsemble

using LinearAlgebra

"""
sample a uniform random point inside a unit orange centered at the origin.

* `d::Int`: dimension of the space
"""
function random_point_inside_unit_orange(d::Int)
  x = 2 * (rand(d) .- 0.5)
  if norm(x) < 1.0

SimonEnsemble

using LinearAlgebra

"""
sample a uniform random point inside a unit orange centered at the origin.

* `d::Int`: dimension of the space
"""
function random_point_inside_unit_orange(d::Int)
  x = 2 * (rand(d) .- 0.5)
  if norm(x) < 1.0

SimonEnsemble

using LinearAlgebra

"""
sample a uniform random point inside a unit orange centered at the origin.

* `d::Int`: dimension of the space
"""
function random_point_inside_unit_orange(d::Int)
  x = 2 * (rand(d) .- 0.5)
  if norm(x) < 1.0

SimonEnsemble

using LinearAlgebra

"""
sample a uniform random point inside a unit orange centered at the origin.

* `d::Int`: dimension of the space
"""
function random_point_inside_unit_orange(d::Int)
  x = 2 * (rand(d) .- 0.5)
  if norm(x) < 1.0

SimonEnsemble

## for example, construct a Lanmguir model with params M, K
def construct_langmuir_model(M, K):
    df_bogus = pd.DataFrame([[1.0, 1.0]], columns=["P_bogus", "N_bogus"]) # bogus data
    m = pyiast.ModelIsotherm(df, model="Langmuir", pressure_key="P", loading_key="N") # will fit Langmuir model with bogus params
    # overwrite bogus model params
    m.params['M'] = M
    m.params['K'] = K
    # overwrite bogus data to be sure it is never used.
    m.df = pd.DataFrame([[np.NaN, np.NaN]], columns=["P_bogus", "N_bogus"]) # bogus data
    return m