Moved ParameterMap to separate file.

Author: nwittler

c3/c3objs.py

@@ -1,6 +1,5 @@
 """Basic custom objects."""
 
-from typing import List, Dict, Tuple
 import numpy as np
 import tensorflow as tf
 from c3.utils.utils import num3str
@@ -188,252 +187,3 @@ def set_opt_value(self, val: float) -> None:
         self.value = tf.acos(tf.cos(
             (tf.reshape(val, self.shape) + 1) * np.pi / 2
         )) / np.pi * 2 - 1
-
-
-class ParameterMap:
-    """
-    Collects information about control and model parameters and provides different representations
-    depending on use.
-    """
-
-    def __init__(
-        self,
-        instructions: list,
-        generator=None,
-        model=None
-    ):
-        self.instructions = {}
-        self.opt_map = []
-        for instr in instructions:
-            self.instructions[instr.name] = instr
-
-        # Collecting model components
-        components = {}
-        if model:
-            components.update(model.couplings)
-            components.update(model.subsystems)
-            components.update(model.tasks)
-        if generator:
-            components.update(generator.devices)
-        self.__components = components
-
-        par_lens = {}
-        pars = {}
-        # Initializing model parameters
-        for comp in self.__components.values():
-            for par_name, par_value in comp.params.items():
-                par_id = (comp.name, par_name)
-                par_lens[par_id] = par_value.length
-                pars[par_id] = par_value
-
-        # Initializing control parameters
-        for gate in self.instructions:
-            instr = self.instructions[gate]
-            for chan in instr.comps.keys():
-                for comp in instr.comps[chan]:
-                    for par_name, par_value in instr.comps[chan][comp].params.items():
-                        par_id = (gate, chan, comp, par_name)
-                        par_lens[par_id] = par_value.length
-                        pars[par_id] = par_value
-
-        self.__par_lens = par_lens
-        self.__pars = pars
-
-        self.model = model
-        self.generator = generator
-
-    def get_full_params(self) -> Dict[str, Quantity]:
-        """
-        Returns the full parameter vector, including model and control parameters.
-        """
-        return self.__pars
-
-    def get_opt_units(self) -> List[str]:
-        """
-        Returns a list of the units of the optimized quantities.
-        """
-        units = []
-        for equiv_ids in self.opt_map:
-            units.append(self.__pars[equiv_ids[0]].unit)
-        return units
-
-    def get_parameter(self, par_id: Tuple[str]) -> Quantity:
-        """
-        Return one the current parameters.
-
-        Parameters
-        ----------
-        par_id: tuple
-            Hierarchical identifier for parameter.
-
-        Returns
-        -------
-        Quantity
-
-        """
-        try:
-            value = self.__pars[par_id]
-        except KeyError as ke:
-            for id in self.__pars:
-                if id[0] == par_id[0]:
-                    print(f"Found {id[0]}.")
-            raise Exception(f"C3:ERROR:Parameter {par_id} not defined.") from ke
-        return value
-
-    def get_parameters(self) -> List[Quantity]:
-        """
-        Return the current parameters.
-
-        Parameters
-        ----------
-        opt_map: list
-            Hierarchical identifier for parameters.
-
-        Returns
-        -------
-        list of Quantity
-
-        """
-        values = []
-        for equiv_ids in self.opt_map:
-            try:
-                values.append(self.__pars[equiv_ids[0]])
-            except KeyError as ke:
-                for par_id in self.__pars:
-                    if par_id[0] == equiv_ids[0][0]:
-                        print(f"Found {par_id[0]}.")
-                raise Exception(f"C3:ERROR:Parameter {equiv_ids[0]} not defined.") from ke
-        return values
-
-    def set_parameters(self, values: list, opt_map=None) -> None:
-        """Set the values in the original instruction class.
-
-        Parameters
-        ----------
-        values: list
-            List of parameter values. Can be nested, if a parameter is matrix valued.
-        opt_map: list
-            Corresponding identifiers for the parameter values.
-
-        """
-        val_indx = 0
-        if opt_map is None:
-            opt_map = self.opt_map
-        for equiv_ids in opt_map:
-            for id in equiv_ids:
-                try:
-                    par = self.__pars[id]
-                except ValueError as ve:
-                    raise Exception(f"C3:ERROR:{id} not defined.") from ve
-                try:
-                    par.set_value(values[val_indx])
-                except ValueError as ve:
-                    raise Exception(
-                        f"C3:ERROR:Trying to set {'-'.join(id)} to value {values[val_indx]} "
-                        f"but has to be within {par.offset:.3} .. {(par.offset + par.scale):.3}."
-                    ) from ve
-            val_indx += 1
-
-    def get_parameters_scaled(self) -> np.ndarray:
-        """
-        Return the current parameters. This fuction should only be called by an optimizer. Are you
-        an optimizer?
-
-        Parameters
-        ----------
-        opt_map: tuple
-            Hierarchical identifier for parameters.
-
-        Returns
-        -------
-        list of Quantity
-
-        """
-        values = []
-        for equiv_ids in self.opt_map:
-            par = self.__pars[equiv_ids[0]]
-            values.append(par.get_opt_value())
-        return np.array(values).flatten()
-
-    def set_parameters_scaled(self, values: list) -> None:
-        """
-        Set the values in the original instruction class. This fuction should only be called by
-        an optimizer. Are you an optimizer?
-
-        Parameters
-        ----------
-        values: list
-            List of parameter values. Matrix valued parameters need to be flattened.
-        opt_map: list
-            Corresponding identifiers for the parameter values.
-
-        """
-        val_indx = 0
-        for equiv_ids in self.opt_map:
-            par_len = self.__pars[equiv_ids[0]].length
-            for id in equiv_ids:
-                par = self.__pars[id]
-                par.set_opt_value(values[val_indx:val_indx + par_len])
-            val_indx += par_len
-
-    def set_opt_map(self, opt_map) -> None:
-        """
-        Set the opt_map, i.e. which parameters will be optimized.
-        """
-        for equiv_ids in opt_map:
-            for pid in equiv_ids:
-                if not pid in self.__pars:
-                    raise Exception(f"C3:ERROR:Parameter {pid} not defined.")
-        self.opt_map = opt_map
-
-    def __str__(self) -> str:
-        """
-        Return a multi-line human-readable string of all defined parameter names and
-        current values.
-
-        Returns
-        -------
-        str
-            Parameters and their values
-        """
-        ret = []
-
-        for par_id, par in self.__pars.items():
-            nice_id = "-".join(par_id)
-            ret.append(f"{nice_id:38}: {par}\n")
-
-        return "".join(ret)
-
-    def str_parameters(self, opt_map) -> str:
-        """
-        Return a multi-line human-readable string of the optmization parameter names and
-        current values.
-
-        Parameters
-        ----------
-        opt_map: list
-            Optionally use only the specified parameters.
-
-        Returns
-        -------
-        str
-            Parameters and their values
-        """
-        ret = []
-        for equiv_ids in opt_map:
-            par_id = equiv_ids[0]
-            par = self.__pars[equiv_ids[0]]
-            nice_id = "-".join(par_id)
-            ret.append(f"{nice_id:38}: {par}\n")
-            if len(equiv_ids) > 1:
-                for eid in equiv_ids[1:]:
-                    ret.append("-".join(eid))
-                    ret.append("\n")
-                ret.append("\n")
-        return "".join(ret)
-
-    def print_parameters(self):
-        """
-        Print current parameters to stdout.
-        """
-        print(self.str_parameters(self.opt_map))