Switch to readthedocs (#4)

.readthedocs.yaml

@@ -0,0 +1,10 @@
+version: 2
+build:
+  os: "ubuntu-22.04"
+  commands:
+    - curl -fsSL https://pixi.sh/install.sh | bash
+    - chmod +x ~/.pixi/bin/pixi
+    - ~/.pixi/bin/pixi run -e docs postinstall
+    - ~/.pixi/bin/pixi run -e docs docs
+    - mkdir -p $READTHEDOCS_OUTPUT/html/
+    - cp -r docs/_build/html/** $READTHEDOCS_OUTPUT/html/

README.md

@@ -53,46 +53,45 @@ It has a couple of key features:
       xp = a.__array_namespace__()
       return xp.mean(a[(low < a) & (a < high)])
 
-  arr = [-12.12, 1.12, 2.12, 2.13, 123.,]
-
-  np_result = mean_drop_outliers(npx.asarray(arr))
-  jax_result = mean_drop_outliers(jxp.asarray(arr))
-  ndx_result = mean_drop_outliers(ndx.asarray(arr))
-  print(np_result)  # 1.79
-  print(jax_result)  # 1.79
-  print(ndx_result) # Array(1.79, dtype=ndx.Float64)
-  assert np_result == ndx_result.to_numpy()
+  np_result = mean_drop_outliers(npx.asarray([-10, 0.5, 1, 5]))
+  jax_result = mean_drop_outliers(jxp.asarray([-10, 0.5, 1, 5]))
+  onnx_result = mean_drop_outliers(ndx.asarray([-10, 0.5, 1, 5]))
+
+  assert np_result == onnx_result.to_numpy() == jax_result == 0.75
   ```
 
-- It supports ONNX export. This allows you persist your logic into an ONNX computation graph for convenient and performant inference.
+- It supports ONNX export. This allows you persist your logic into an ONNX computation graph.
 
   ```python
-  import onnx
   import ndonnx as ndx
+  import onnx
 
-  a = ndx.array(shape=("N",), dtype=ndx.float64)
-  b = ndx.array(shape=("N",), dtype=ndx.float64)
-  out = a[:2] + b[:2]
-  model_proto = ndx.build({"a": a, "b": b}, {"c": out})
-  onnx.save(model_proto, "model.onnx")
+  # Instantiate placeholder ndonnx array
+  x = ndx.array(shape=("N",), dtype=ndx.float32)
+  y = mean_drop_outliers(x)
 
-  # Having serialised your model to disk, perform
-  # inference using a runtime of your choosing.
+  # Build and save ONNX model to disk
+  model = ndx.build({"x": x}, {"y": y})
+  onnx.save(model, "mean_drop_outliers.onnx")
+  ```
+
+  You can then make predictions using a runtime of your choice.
+
+  ```python
   import onnxruntime as ort
   import numpy as np
-  inference_session = ort.InferenceSession("model.onnx")
+  inference_session = ort.InferenceSession("mean_drop_outliers.onnx")
   prediction, = inference_session.run(None, {
-      "a": np.array([1, 2, 3], dtype=np.float64),
-      "b": np.array([4, 5, 6], dtype=np.float64),
+      "x": np.array([-10, 0.5, 1, 5], dtype=np.float32),
   })
-  print(prediction)  # array([5., 7.])
+  assert prediction == 0.75
   ```
 
 In the future we will be enabling a stable API for an extensible data type system. This will allow users to define their own data types and operations on arrays with these data types.
 
 ## Array API coverage
 
-Array API compatibility is tracked in the array-api coverage test suite in `api-coverage-tests`. Missing coverage is tracked in the `skips.txt` file. Contributions are welcome!
+Array API compatibility is tracked in `api-coverage-tests`. Missing coverage is tracked in the `skips.txt` file. Contributions are welcome!
 
 Summary(1119 total):