From 3af5cb0301277da95bb0fe3cc4a5d4806e61b552 Mon Sep 17 00:00:00 2001
From: Dominik Demuth <dominik.demuth@physik.tu-darmstadt.de>
Date: Sat, 16 Sep 2023 14:16:45 +0200
Subject: [PATCH] add todos

---
 src/nmreval/fit/minimizer.py | 15 +++++++++++++--
 src/nmreval/fit/parameter.py | 11 +++++++++++
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/src/nmreval/fit/minimizer.py b/src/nmreval/fit/minimizer.py
index 50fa4e4..5594fd1 100644
--- a/src/nmreval/fit/minimizer.py
+++ b/src/nmreval/fit/minimizer.py
@@ -29,6 +29,7 @@ def _cost_scipy_glob(p: list[float], data: list[Data], varpars: list[str], used_
     for keys, values in zip(varpars, p):
         for data_i in data:
             if keys in data_i.parameter.keys():
+                # TODO move this to scaled_value setter
                 data_i.parameter[keys].scaled_value = values
                 data_i.parameter[keys].namespace[keys] = data_i.parameter[keys].value
     r = []
@@ -220,7 +221,7 @@ class FitRoutine(object):
         logger.info('Fit aborted by user')
         self._abort = True
 
-    def run(self, mode: str=None):
+    def run(self, mode: str = None):
         self._abort = False
 
         if mode is None:
@@ -262,6 +263,16 @@ class FitRoutine(object):
 
         return self.result
 
+    def make_preview(self, x: np.ndarray) -> list[np.ndarray]:
+        y_pred = []
+        fit_groups, linked_parameter = self.prepare_links()
+        for data_groups in fit_groups:
+            data = data_groups[0]
+            actual_parameters = [p.value for p in data.parameter.values()]
+            y_pred.append(data.func(actual_parameters, x))
+
+        return y_pred
+
     def _prep_data(self, data):
         if data.get_model() is None:
             data._model = self.fit_model
@@ -317,6 +328,7 @@ class FitRoutine(object):
             d._model = None
 
         self._no_own_model = []
+        Parameters.reset()
 
     def _least_squares_single(self, data, p0, lb, ub, var):
         self.step = 0
@@ -345,7 +357,6 @@ class FitRoutine(object):
         with np.errstate(all='ignore'):
             res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
 
-
         err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
         for v, var_pars_k in zip(data, data_pars):
             self.make_results(v, res.x, var, var_pars_k, res.jac.shape,
diff --git a/src/nmreval/fit/parameter.py b/src/nmreval/fit/parameter.py
index e9bf2c1..6d5457e 100644
--- a/src/nmreval/fit/parameter.py
+++ b/src/nmreval/fit/parameter.py
@@ -88,6 +88,15 @@ class Parameters(dict):
                     expression = re.sub(re.escape(n), k, expression)
                 p._expr = expression
 
+    def fix(self):
+        for v in self.keys():
+            v._value = v.value
+            v.namespace = {}
+
+    @staticmethod
+    def reset():
+        Parameters.namespace = {}
+
     def get_key(self, name: str) -> str | None:
         for k, v in self.items():
             if name == v.name:
@@ -104,6 +113,7 @@ class Parameter:
     Container for one parameter
     """
 
+    # TODO Parameter should know its own key
     def __init__(self, name: str, value: float | str, var: bool = True, lb: float = -np.inf, ub: float = np.inf):
         self._value: float | None = None
         self.var: bool = bool(var) if var is not None else True
@@ -181,6 +191,7 @@ class Parameter:
 
     @property
     def value(self) -> float:
+        # TODO first _value, then _expr
         if self._expr is not None and self.eval_allowed:
             return eval(self._expr, {}, self.namespace)
         elif self._value is not None: