fix: typos for 0.20 zfit

Author: jonas-eschle

_website/tutorials/components/20 - Composite Models.ipynb

@@ -143,8 +143,9 @@
    "source": [
     "## creating an extended PDF\n",
     "\n",
-    "An extended PDF can either be created with the `create_extended(yield_param)` method or with the simple Python\n",
-    "syntax of multiplying a PDF with the parameter."
+    "An extended PDF can be created using the `extended` argument in the initialization.\n",
+    " \n",
+    "Alternatively, an extended PDF from a non-extended PDF can be created with the `create_extended(yield_param)` method."
    ]
   },
   {
@@ -153,7 +154,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "yield1 = zfit.Parameter(\"yield_gauss1\", 100, 0, 1000)"
+    "yield1 = zfit.Parameter(\"yield_gauss1\", 100, 0, 1000)\n",
+    "gauss3 = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3, extended=yield1)"
    ]
   },
   {
@@ -170,9 +172,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "gauss3_ext = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3, extended=yield1)"
-   ]
+   "source": []
   }
  ],
  "metadata": {

_website/tutorials/components/33 - Binned fits.ipynb

@@ -571,7 +571,7 @@
    "source": [
     "modifier_constraints = zfit.constraint.GaussianConstraint(params=list(modifiers.values()), observation=np.ones(len(modifiers)),\n",
     "                                   uncertainty=0.1 * np.ones(len(modifiers)))\n",
-    "alpha_constraint = zfit.constraint.GaussianConstraint(alpha, 0, 1)"
+    "alpha_constraint = zfit.constraint.GaussianConstraint(alpha, 0, sigma=1)"
    ]
   },
   {

_website/tutorials/components/50 - Custom code and run mode.ipynb

@@ -410,7 +410,7 @@
    "outputs": [],
    "source": [
     "%%timeit -n1 -r1\n",
-    "zfit.run.set_mode(graph=True)  # running in graph mode\n",
+    "zfit.run.set_graph_mode(True)  # running in graph mode\n",
     "for i in range(100):\n",
     "    for _ in range(n_evals):\n",
     "        graph_func2(i)"
@@ -424,7 +424,7 @@
    "source": [
     "%%timeit -n1 -r1\n",
     "zfit.run.clear_graph_cache()\n",
-    "zfit.run.set_mode(graph=True)  # running in graph mode but clearing unused caches\n",
+    "zfit.run.set_graph_mode(graph=True)  # running in graph mode but clearing unused caches\n",
     "for i in range(100):\n",
     "    zfit.run.clear_graph_cache()\n",
     "    for _ in range(n_evals):\n",
@@ -486,7 +486,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zfit.run.set_mode(graph=False)  # test first in eager mode"
+    "zfit.run.set_graph_mode(False)  # test first in eager mode"
    ]
   },
   {
@@ -548,6 +548,13 @@
    "source": [
     "That's a significant speedup! It is clear that for a few evaluations, it does not matter _too much_. But this is about the scalability: imagine we have a large fit, where a minimizer needs hundreds or thousands of evaluations: that's when the initial Graph building becomes neglectible and the speedup matters."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

components/20 - Composite Models.ipynb

@@ -143,8 +143,9 @@
    "source": [
     "## creating an extended PDF\n",
     "\n",
-    "An extended PDF can either be created with the `create_extended(yield_param)` method or with the simple Python\n",
-    "syntax of multiplying a PDF with the parameter."
+    "An extended PDF can be created using the `extended` argument in the initialization.\n",
+    " \n",
+    "Alternatively, an extended PDF from a non-extended PDF can be created with the `create_extended(yield_param)` method."
    ]
   },
   {
@@ -153,7 +154,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "yield1 = zfit.Parameter(\"yield_gauss1\", 100, 0, 1000)"
+    "yield1 = zfit.Parameter(\"yield_gauss1\", 100, 0, 1000)\n",
+    "gauss3 = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3, extended=yield1)"
    ]
   },
   {
@@ -170,9 +172,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "gauss3_ext = zfit.pdf.Gauss(obs=obs2, mu=mu3, sigma=sigma3, extended=yield1)"
-   ]
+   "source": []
   }
  ],
  "metadata": {

components/33 - Binned fits.ipynb

@@ -571,7 +571,7 @@
    "source": [
     "modifier_constraints = zfit.constraint.GaussianConstraint(params=list(modifiers.values()), observation=np.ones(len(modifiers)),\n",
     "                                   uncertainty=0.1 * np.ones(len(modifiers)))\n",
-    "alpha_constraint = zfit.constraint.GaussianConstraint(alpha, 0, 1)"
+    "alpha_constraint = zfit.constraint.GaussianConstraint(alpha, 0, sigma=1)"
    ]
   },
   {

components/50 - Custom code and run mode.ipynb

@@ -410,7 +410,7 @@
    "outputs": [],
    "source": [
     "%%timeit -n1 -r1\n",
-    "zfit.run.set_mode(graph=True)  # running in graph mode\n",
+    "zfit.run.set_graph_mode(True)  # running in graph mode\n",
     "for i in range(100):\n",
     "    for _ in range(n_evals):\n",
     "        graph_func2(i)"
@@ -424,7 +424,7 @@
    "source": [
     "%%timeit -n1 -r1\n",
     "zfit.run.clear_graph_cache()\n",
-    "zfit.run.set_mode(graph=True)  # running in graph mode but clearing unused caches\n",
+    "zfit.run.set_graph_mode(graph=True)  # running in graph mode but clearing unused caches\n",
     "for i in range(100):\n",
     "    zfit.run.clear_graph_cache()\n",
     "    for _ in range(n_evals):\n",
@@ -486,7 +486,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zfit.run.set_mode(graph=False)  # test first in eager mode"
+    "zfit.run.set_graph_mode(False)  # test first in eager mode"
    ]
   },
   {
@@ -548,6 +548,13 @@
    "source": [
     "That's a significant speedup! It is clear that for a few evaluations, it does not matter _too much_. But this is about the scalability: imagine we have a large fit, where a minimizer needs hundreds or thousands of evaluations: that's when the initial Graph building becomes neglectible and the speedup matters."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {