Rename all instances of PyJulia to juliacall in docs

docs/backend.md

@@ -2,7 +2,7 @@
 
 If you have explored the [options](options.md) and [PySRRegressor reference](api.md), and still haven't figured out how to specify a constraint or objective required for your problem, you might consider editing the backend.
 The backend of PySR is written as a pure Julia package under the name [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl).
-This package is accessed with [`PyJulia`](https://github.com/JuliaPy/pyjulia), which allows us to transfer objects back and forth between the Python and Julia runtimes.
+This package is accessed with [`juliacall`](https://github.com/JuliaPy/PythonCall.jl), which allows us to transfer objects back and forth between the Python and Julia runtimes.
 
 PySR gives you access to everything in SymbolicRegression.jl, but there are some specific use-cases which require modifications to the backend itself.
 Generally you can do this as follows:

docs/examples.md

@@ -235,7 +235,7 @@ If in-bounds, it rounds it to the nearest integer, compures the corresponding pr
 converts it to the same type as input.
 
 Next, let's generate a list of primes for our test dataset.
-Since we are using PyJulia, we can just call `p` directly to do this:
+Since we are using juliacall, we can just call `p` directly to do this:
 
 ```python
 primes = {i: jl.p(i*1.0) for i in range(1, 999)}

examples/pysr_demo.ipynb

@@ -858,7 +858,7 @@
     "(However, note that this version assumes 64-bit float input, rather than any input type `T`)\n",
     "\n",
     "Next, let's generate a list of primes for our test dataset.\n",
-    "Since we are using PyJulia, we can just call `p` directly to do this:\n"
+    "Since we are using juliacall, we can just call `p` directly to do this:\n"
    ]
   },
   {
@@ -1293,7 +1293,7 @@
     "\n",
     "> **Warning**\n",
     ">\n",
-    "> First, let's save the data, because sometimes PyTorch and PyJulia's C bindings interfere and cause the colab kernel to crash. If we need to restart, we can just load the data without having to retrain the network:"
+    "> First, let's save the data, because sometimes PyTorch and juliacall's C bindings interfere and cause the colab kernel to crash. If we need to restart, we can just load the data without having to retrain the network:"
    ]
   },
   {
@@ -1324,7 +1324,7 @@
     "id": "krhaNlwFG-KT"
    },
    "source": [
-    "We can now load the data, including after a crash (be sure to re-run the import cells at the top of this notebook, including the one that starts PyJulia)."
+    "We can now load the data, including after a crash (be sure to re-run the import cells at the top of this notebook, including the one that starts juliacall)."
    ]
   },
   {
@@ -1378,7 +1378,7 @@
     "id": "1a738a33"
    },
    "source": [
-    "If this segfaults, restart the notebook, and run the initial imports and PyJulia part, but skip the PyTorch training. This is because PyTorch's C binding tends to interefere with PyJulia. You can then re-run the `pkl.load` cell to import the data."
+    "If this segfaults, restart the notebook, and run the initial imports and juliacall part, but skip the PyTorch training. This is because PyTorch's C binding tends to interefere with juliacall. You can then re-run the `pkl.load` cell to import the data."
    ]
   },
   {