fix(tutorials): accept xp kwarg in inline model_data_from

notebooks/cookbooks/configs.ipynb

@@ -77,15 +77,15 @@
         "        self.normalization = normalization\n",
         "        self.sigma = sigma\n",
         "\n",
-        "    def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:\n",
+        "    def model_data_from(self, xvalues: np.ndarray, xp=np) -> np.ndarray:\n",
         "        \"\"\"\n",
         "        The usual method that returns the 1D data of the `Gaussian` profile.\n",
         "        \"\"\"\n",
         "        transformed_xvalues = xvalues - self.centre\n",
         "\n",
-        "        return np.multiply(\n",
-        "            np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)),\n",
-        "            np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),\n",
+        "        return xp.multiply(\n",
+        "            xp.divide(self.normalization, self.sigma * xp.sqrt(2.0 * xp.pi)),\n",
+        "            xp.exp(-0.5 * xp.square(xp.divide(transformed_xvalues, self.sigma))),\n",
         "        )\n"
       ],
       "outputs": [],

notebooks/features/graphical_models.ipynb

@@ -24,7 +24,8 @@
         "simultaneously fitting 3 noisy 1D Gaussians. However, graphical models are an extensive feature and at the end of\n",
         "this example we will discuss other options available in **PyAutoFit** for composing a fitting a graphical model.\n",
         "\n",
-        "The **HowToFit** tutorials contain a chapter dedicated to composing and fitting graphical models.\n",
+        "The **HowToFit** tutorials contain a chapter dedicated to composing and fitting graphical models, available\n",
+        "in the standalone repository at https://github.com/PyAutoLabs/HowToFit (see `scripts/chapter_3_graphical_models/`).\n",
         "\n",
         "__Contents__\n",
         "\n",

notebooks/overview/overview_1_the_basics.ipynb

@@ -204,7 +204,7 @@
         "        self.normalization = normalization\n",
         "        self.sigma = sigma\n",
         "\n",
-        "    def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:\n",
+        "    def model_data_from(self, xvalues: np.ndarray, xp=np) -> np.ndarray:\n",
         "        \"\"\"\n",
         "        Returns the 1D Gaussian profile on a line of Cartesian x coordinates.\n",
         "\n",
@@ -221,9 +221,9 @@
         "        \"\"\"\n",
         "        transformed_xvalues = xvalues - self.centre\n",
         "\n",
-        "        return np.multiply(\n",
-        "            np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)),\n",
-        "            np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),\n",
+        "        return xp.multiply(\n",
+        "            xp.divide(self.normalization, self.sigma * xp.sqrt(2.0 * xp.pi)),\n",
+        "            xp.exp(-0.5 * xp.square(xp.divide(transformed_xvalues, self.sigma))),\n",
         "        )\n",
         "\n",
         "    @property\n",
@@ -981,7 +981,7 @@
         "        self.normalization = normalization\n",
         "        self.rate = rate\n",
         "\n",
-        "    def model_data_from(self, xvalues: np.ndarray):\n",
+        "    def model_data_from(self, xvalues: np.ndarray, xp=np):\n",
         "        \"\"\"\n",
         "        Returns the symmetric 1D Exponential on an input list of Cartesian x coordinates.\n",
         "\n",
@@ -995,9 +995,9 @@
         "        xvalues\n",
         "            The x coordinates in the original reference frame of the data.\n",
         "        \"\"\"\n",
-        "        transformed_xvalues = np.subtract(xvalues, self.centre)\n",
-        "        return self.normalization * np.multiply(\n",
-        "            self.rate, np.exp(-1.0 * self.rate * abs(transformed_xvalues))\n",
+        "        transformed_xvalues = xp.subtract(xvalues, self.centre)\n",
+        "        return self.normalization * xp.multiply(\n",
+        "            self.rate, xp.exp(-1.0 * self.rate * abs(transformed_xvalues))\n",
         "        )\n"
       ],
       "outputs": [],

notebooks/overview/overview_3_statistical_methods.ipynb

@@ -31,7 +31,7 @@
         "\n",
         "A full description of using hierarchical models is given below:\n",
         "\n",
-        "https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/howtofit/chapter_graphical_models/tutorial_4_hierachical_models.ipynb\n",
+        "https://github.com/PyAutoLabs/HowToFit/blob/main/notebooks/chapter_3_graphical_models/tutorial_4_hierachical_models.ipynb\n",
         "\n",
         "Model Comparison\n",
         "----------------\n",

scripts/cookbooks/configs.py

@@ -21,7 +21,7 @@
  - Labels: Config files which specify the labels of model component parameters for visualization.
 """
 
-# from autoconf import setup_notebook; setup_notebook()
+# from autoconf import setup_notebook; setup_notebook()
 
 import numpy as np
 from os import path
@@ -56,15 +56,15 @@ def __init__(
         self.normalization = normalization
         self.sigma = sigma
 
-    def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
+    def model_data_from(self, xvalues: np.ndarray, xp=np) -> np.ndarray:
         """
         The usual method that returns the 1D data of the `Gaussian` profile.
         """
         transformed_xvalues = xvalues - self.centre
 
-        return np.multiply(
-            np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)),
-            np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),
+        return xp.multiply(
+            xp.divide(self.normalization, self.sigma * xp.sqrt(2.0 * xp.pi)),
+            xp.exp(-0.5 * xp.square(xp.divide(transformed_xvalues, self.sigma))),
         )
 
 

scripts/overview/overview_1_the_basics.py

@@ -43,7 +43,7 @@
 To begin, lets import ``autofit`` (and ``numpy``) using the convention below:
 """
 
-# from autoconf import setup_notebook; setup_notebook()
+# from autoconf import setup_notebook; setup_notebook()
 
 import autofit as af
 import autofit.plot as aplt
@@ -150,7 +150,7 @@ def __init__(
         self.normalization = normalization
         self.sigma = sigma
 
-    def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
+    def model_data_from(self, xvalues: np.ndarray, xp=np) -> np.ndarray:
         """
         Returns the 1D Gaussian profile on a line of Cartesian x coordinates.
 
@@ -167,9 +167,9 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
         """
         transformed_xvalues = xvalues - self.centre
 
-        return np.multiply(
-            np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)),
-            np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),
+        return xp.multiply(
+            xp.divide(self.normalization, self.sigma * xp.sqrt(2.0 * xp.pi)),
+            xp.exp(-0.5 * xp.square(xp.divide(transformed_xvalues, self.sigma))),
         )
 
     @property
@@ -665,7 +665,7 @@ def __init__(
         self.normalization = normalization
         self.rate = rate
 
-    def model_data_from(self, xvalues: np.ndarray):
+    def model_data_from(self, xvalues: np.ndarray, xp=np):
         """
         Returns the symmetric 1D Exponential on an input list of Cartesian x coordinates.
 
@@ -679,9 +679,9 @@ def model_data_from(self, xvalues: np.ndarray):
         xvalues
             The x coordinates in the original reference frame of the data.
         """
-        transformed_xvalues = np.subtract(xvalues, self.centre)
-        return self.normalization * np.multiply(
-            self.rate, np.exp(-1.0 * self.rate * abs(transformed_xvalues))
+        transformed_xvalues = xp.subtract(xvalues, self.centre)
+        return self.normalization * xp.multiply(
+            self.rate, xp.exp(-1.0 * self.rate * abs(transformed_xvalues))
         )