Rename aesara to pytensor.

examples/case_studies/GEV.ipynb

@@ -45,12 +45,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pymc as pm\n",
     "import pymc_experimental.distributions as pmx\n",
+    "import pytensor.tensor as at\n",
     "\n",
     "from arviz.plots import plot_utils as azpu"
    ]
@@ -984,7 +984,7 @@
       "Python version       : 3.10.6\n",
       "IPython version      : 8.5.0\n",
       "\n",
-      "aesara: 2.8.6\n",
+      "pytensor: 2.8.6\n",
       "arviz : 0.12.1\n",
       "\n",
       "pymc_experimental: 0.0.1\n",
@@ -993,7 +993,7 @@
       "arviz            : 0.12.1\n",
       "numpy            : 1.23.3\n",
       "json             : 2.0.9\n",
-      "aesara           : 2.8.6\n",
+      "pytensor           : 2.8.6\n",
       "pymc             : 3.9.3+1493.g372d7c24\n",
       "\n",
       "Watermark: 2.3.1\n",
@@ -1003,7 +1003,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,arviz"
+    "%watermark -n -u -v -iv -w -p pytensor,arviz"
    ]
   },
   {

examples/case_studies/GEV.myst.md

@@ -40,12 +40,12 @@ Note that this parametrization of the shape parameter $\xi$ is opposite in sign
 We will use the example of the Port Pirie annual maximum sea-level data used in {cite:t}`coles2001gev`, and compare with the frequentist results presented there.
 
 ```{code-cell} ipython3
-import aesara.tensor as at
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pymc as pm
 import pymc_experimental.distributions as pmx
+import pytensor.tensor as at
 
 from arviz.plots import plot_utils as azpu
 ```
@@ -232,7 +232,7 @@ az.plot_pair(idata, var_names=["μ", "σ", "ξ"], kind="kde", marginals=True, di
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w -p aesara,arviz
+%watermark -n -u -v -iv -w -p pytensor,arviz
 ```
 
 ```{code-cell} ipython3

examples/case_studies/LKJ.ipynb

@@ -987,7 +987,7 @@
       "Python version       : 3.7.12\n",
       "IPython version      : 5.5.0\n",
       "\n",
-      "aesara: 2.4.0\n",
+      "pytensor: 2.4.0\n",
       "xarray: 0.18.2\n",
       "\n",
       "pymc      : 4.0.0b2\n",
@@ -1004,7 +1004,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,xarray"
    ]
   }
  ],

examples/case_studies/LKJ.myst.md

@@ -326,5 +326,5 @@ id: kJCfuzGtr2Pq
 outputId: da547b05-d812-4959-aff6-cf4a12faca15
 ---
 %load_ext watermark
-%watermark -n -u -v -iv -w -p aesara,xarray
+%watermark -n -u -v -iv -w -p pytensor,xarray
 ```

examples/case_studies/bayesian_ab_testing_introduction.ipynb

@@ -2163,7 +2163,7 @@
       "Python version       : 3.8.10\n",
       "IPython version      : 8.4.0\n",
       "\n",
-      "aesara: 2.7.3\n",
+      "pytensor: 2.7.3\n",
       "xarray: 2022.3.0\n",
       "\n",
       "matplotlib: 3.5.2\n",
@@ -2181,7 +2181,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,xarray"
    ]
   },
   {

examples/case_studies/bayesian_ab_testing_introduction.myst.md

@@ -687,7 +687,7 @@ We also plan to create more PyMC tutorials on these topics, so stay tuned!
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w -p aesara,xarray
+%watermark -n -u -v -iv -w -p pytensor,xarray
 ```
 
 :::{include} ../page_footer.md

examples/case_studies/binning.ipynb

@@ -72,7 +72,7 @@
     "We are now in a position to sketch out a generative PyMC model:\n",
     "\n",
     "```python\n",
-    "import aesara.tensor as at\n",
+    "import pytensor.tensor as at\n",
     "\n",
     "with pm.Model() as model:\n",
     "    # priors\n",
@@ -120,12 +120,12 @@
    "source": [
     "import warnings\n",
     "\n",
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc as pm\n",
+    "import pytensor.tensor as at\n",
     "import seaborn as sns\n",
     "\n",
     "warnings.filterwarnings(action=\"ignore\", category=UserWarning)"
@@ -3538,7 +3538,7 @@
       "pymc      : 4.0.0b6\n",
       "arviz     : 0.12.1\n",
       "numpy     : 1.22.4\n",
-      "aesara    : 2.5.1\n",
+      "pytensor    : 2.5.1\n",
       "\n",
       "Watermark: 2.3.1\n",
       "\n"

examples/case_studies/binning.myst.md

@@ -69,7 +69,7 @@ In ordinal regression, the cutpoints are treated as latent variables and the par
 We are now in a position to sketch out a generative PyMC model:
 
 ```python
-import aesara.tensor as at
+import pytensor.tensor as at
 
 with pm.Model() as model:
     # priors
@@ -110,12 +110,12 @@ The approach was illustrated with a Gaussian distribution, and below we show a n
 
 import warnings
 
-import aesara.tensor as at
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import pymc as pm
+import pytensor.tensor as at
 import seaborn as sns
 
 warnings.filterwarnings(action="ignore", category=UserWarning)