Doc fix

Examples/base/plot_baseclass1.py

@@ -41,13 +41,13 @@ def from_pars(cls, A: float = 1, f: float = 1, p: float = 0):
         return cls(A, f, p)
 
     def __call__(self, t):
-        return self.A.raw_value * np.sin(2 * np.pi * self.f.raw_value * t + self.p.raw_value)
+        return self.A.value * np.sin(2 * np.pi * self.f.value * t + self.p.value)
 
     def plot(self, time, axis=None, **kwargs):
         if axis is None:
             axis = plt
         else:
-            axis.set_title(f'A={self.A.raw_value}, F={self.f.raw_value}, P={self.p.raw_value}')
+            axis.set_title(f'A={self.A.value}, F={self.f.value}, P={self.p.value}')
         p = axis.plot(time, self(time), **kwargs)
         return p
 

examples_old/example1.py

@@ -16,7 +16,7 @@
 
 def fit_fun(x):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return b.c.raw_value + b.m.raw_value * x
+    return b.c.value + b.m.value * x
 
 
 f = Fitter()

examples_old/example1_dream.py

@@ -14,7 +14,7 @@
 
 def fit_fun(x):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return b.c.raw_value + b.m.raw_value * x
+    return b.c.value + b.m.value * x
 
 
 f = Fitter()

examples_old/example2.py

@@ -27,11 +27,11 @@ def _defaults(self):
 
     @property
     def gradient(self):
-        return self.m.raw_value
+        return self.m.value
 
     @property
     def intercept(self):
-        return self.c.raw_value
+        return self.c.value
 
     def fit_func(self, x: np.ndarray) -> np.ndarray:
         return self.gradient * x + self.intercept

examples_old/example3.py

@@ -57,14 +57,14 @@ def gradient(self):
         if self.interface:
             return self.interface.get_value('m')
         else:
-            return self.m.raw_value
+            return self.m.value
 
     @property
     def intercept(self):
         if self.interface:
             return self.interface.get_value('c')
         else:
-            return self.c.raw_value
+            return self.c.value
 
     def fit_func(self, x: np.ndarray) -> np.ndarray:
         if self.interface:

examples_old/example4.py

@@ -407,14 +407,14 @@ def gradient(self):
         if self.interface:
             return self.interface().get_value("m")
         else:
-            return self.m.raw_value
+            return self.m.value
 
     @property
     def intercept(self):
         if self.interface:
             return self.interface().get_value("c")
         else:
-            return self.c.raw_value
+            return self.c.value
 
     def __repr__(self):
         return f"Line: m={self.m}, c={self.c}"

examples_old/example5_broken.py

@@ -325,14 +325,14 @@ def gradient(self):
         # if self.interface:
         #     return self.interface().get_value('m')
         # else:
-        return self.m.raw_value
+        return self.m.value
 
     @property
     def intercept(self):
         # if self.interface:
         #     return self.interface().get_value('c')
         # else:
-        return self.c.raw_value
+        return self.c.value
 
     def __repr__(self):
         return f"Line: m={self.m}, c={self.c}"

examples_old/example_dataset2.py

@@ -16,7 +16,7 @@
 
 def fit_fun(x, *args, **kwargs):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return b.c.raw_value + b.m.raw_value * x
+    return b.c.value + b.m.value * x
 
 
 f = Fitter()

examples_old/example_dataset2pt2_broken.py

@@ -22,7 +22,7 @@
 
 def fit_fun(x, *args, **kwargs):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return b.c.raw_value + b.m.raw_value * x
+    return b.c.value + b.m.value * x
 
 
 nx = 1E3

examples_old/example_dataset3.py

@@ -36,7 +36,7 @@
 
 def fit_fun(x, *args, **kwargs):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return np.sin(2*np.pi*(x[:, 0] + b.s_off.raw_value)) * np.cos(2*np.pi*(x[:, 1] + b.c_off.raw_value))
+    return np.sin(2*np.pi*(x[:, 0] + b.s_off.value)) * np.cos(2*np.pi*(x[:, 1] + b.c_off.value))
 
 
 f = Fitter()

examples_old/example_dataset3pt2.py

@@ -34,7 +34,7 @@
 
 def fit_fun(x, *args, **kwargs):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return np.sin(2*np.pi*(x[:, 0] + b.s_off.raw_value)) * np.cos(2*np.pi*(x[:, 1] + b.c_off.raw_value))
+    return np.sin(2*np.pi*(x[:, 0] + b.s_off.value)) * np.cos(2*np.pi*(x[:, 1] + b.c_off.value))
 
 
 fig, ax = plt.subplots(2, 3, sharey=True, sharex=True)
@@ -55,7 +55,7 @@ def fit_fun(x, *args, **kwargs):
     p1 = d[f'computed_{minimizer}'].plot(ax=ax[0, idx], cbar_kwargs={'cax': cbar_ax1})
     p2 = d[f'dz_{minimizer}'].plot(ax=ax[1, idx], cbar_kwargs={'cax': cbar_ax2})
     ax[0, idx].set_title(f'{minimizer}')
-    ax[1, idx].set_title('s_off - {:0.03f}\nc_off - {:0.03f}'.format(b.s_off.raw_value, b.c_off.raw_value))
+    ax[1, idx].set_title('s_off - {:0.03f}\nc_off - {:0.03f}'.format(b.s_off.value, b.c_off.value))
     ax[0, idx].set_aspect('equal', 'box')
     ax[1, idx].set_aspect('equal', 'box')
 fig.subplots_adjust(right=0.8)

examples_old/example_dataset4.py

@@ -18,7 +18,7 @@
 
 def fit_fun(x, *args, **kwargs):
     # In the real case we would gust call the evaluation fn without reference to the BaseObj
-    return b.c.raw_value + b.m.raw_value * x
+    return b.c.value + b.m.value * x
 
 
 f = Fitter()

examples_old/example_dataset4_2.py

@@ -33,7 +33,7 @@ def from_params(cls, amplitude: float = 1, phase: float = 0, period: float = 2*n
 
     def fit_fun(self, x, *args, **kwargs):
         # In the real case we would gust call the evaluation fn without reference to the BaseObj
-        return self.amplitude.raw_value * np.sin((x + self.phase.raw_value)/self.period.raw_value)
+        return self.amplitude.value * np.sin((x + self.phase.value)/self.period.value)
 
 b = Wavey.from_params()
 bb = Wavey.from_params(1.1, 0.1, 1.9*np.pi)