docstr: test scale minimizers except newton-cg (needs jacobian) and
  anneal (doesn't work out of the box).
docstr_tok: ["test","scale","minimizers","except","newton-cg","(","needs","jacobian",")","and","anneal","(","does","n't","work","out","of","the","box",")","."]
code: def setUp(self):
      """
      test scale minimizers except newton-cg (needs jacobian) and
      anneal (doesn't work out of the box).
      """
      p_true = Parameters()
      p_true.add("amp", value=14.0)
      p_true.add("period", value=5.33)
      p_true.add("shift", value=0.123)
      p_true.add("decay", value=0.010)
      self.p_true = p_true
  
      n = 2500
      xmin = 0.0
      xmax = 250.0
      noise = np.random.normal(scale=0.7215, size=n)
      self.x = np.linspace(xmin, xmax, n)
      self.data = self.residual(p_true, self.x) + noise
  
      fit_params = Parameters()
      fit_params.add("amp", value=11.0, min=5, max=20)
      fit_params.add("period", value=5.0, min=1.0, max=7)
      fit_params.add("shift", value=0.10, min=0.0, max=0.2)
      fit_params.add("decay", value=6.0e-3, min=0, max=0.1)
      self.fit_params = fit_params
      self.mini = Minimizer(
          self.residual, fit_params, [self.x, self.data]
      )
  
imports: import sys
  import unittest
  import numpy
  from numpy import pi
  from numpy.testing import assert_allclose, assert_almost_equal, assert_equal
  import pytest
  from scipy.optimize import rosen_der
  from uncertainties import ufloat
  from lmfit import Minimizer, Parameters, minimize
  from lmfit.lineshapes import gaussian
  from lmfit.minimizer import HAS_EMCEE, SCALAR_METHODS, MinimizerResult, coerce_float64
  from lmfit.models import SineModel
label_desc: Use image node_id 1 for calling the CommonMinimizerTest obj's underlying member method code with example usage: obj.setUp() without return types