class_docstr: Proximal operator of the weighted L1 norm (weighted
  soft-thresholding)
  
  Parameters
  ----------
  strength : `float`
      Level of L1 penalization
  
  weights : `numpy.ndarray`, shape=(n_coeffs,)
      The weights to be used in the L1 penalization
  
  range : `tuple` of two `int`, default=`None`
      Range on which the prox is applied. If `None` then the prox is
      applied on the whole vector
  
  positive : `bool`, default=`False`
      If True, apply L1 penalization together with a projection
      onto the set of vectors with non-negative entries
  
  Attributes
  ----------
  dtype : `{'float64', 'float32'}`
      Type of the arrays used.
class_docstr_tok: ["Proximal","operator","of","the","weighted","L1","norm","(","weighted","soft-thresholding",")","Parameters","--","--","--","--","--","strength",":","`","float","`","Level","of","L1","penalization","weights",":","`","numpy.ndarray","`",",","shape=","(","n_coeffs",",",")","The","weights","to","be","used","in","the","L1","penalization","range",":","`","tuple","`","of","two","`","int","`",",","default=","`","None","`","Range","on","which","the","prox","is","applied",".","If","`","None","`","then","the","prox","is","applied","on","the","whole","vector","positive",":","`","bool","`",",","default=","`","False","`","If","True",",","apply","L1","penalization","together","with","a","projection","onto","the","set","of","vectors","with","non-negative","entries","Attributes","--","--","--","--","--","dtype",":","`","{","'float64","'",",","'float32","'","}","`","Type","of","the","arrays","used","."]
code: def __init__(
      self,
      strength: float,
      weights: np.ndarray,
      range: tuple = None,
      positive: bool = False,
  ):
      Prox.__init__(self, range)
      self.positive = positive
      self.strength = strength
      self.weights = weights
      self._prox = self._build_cpp_prox("float64")
  
file_path: tick/tick/prox/prox_l1w.py
imports: import numpy
  from .base import Prox
  from .build.prox import ProxL1wDouble
  from .build.prox import ProxL1wFloat
label_desc: Use image node_id 1 to create a new ProxL1w object from inherited base classes: Prox with example: obj = ProxL1w(strength, weights, range, positive)