matplotlib · tacaswell · Jun 8, 2023 · May 18, 2023 · May 24, 2023 · May 25, 2023
diff --git a/data_prototype/conversion_node.py b/data_prototype/conversion_node.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+from collections.abc import Iterable, Callable, Sequence
+from collections import Counter
+from dataclasses import dataclass
+import inspect
+from functools import cached_property
+
+from typing import Any
+
+
+def evaluate_pipeline(nodes: Sequence[ConversionNode], input: dict[str, Any]):
+    for node in nodes:
+        input = node.evaluate(input)
+    return input
+
+
+@dataclass
+class ConversionNode:
+    required_keys: tuple[str, ...]
+    output_keys: tuple[str, ...]
+    trim_keys: bool
+
+    def preview_keys(self, input_keys: Iterable[str]) -> tuple[str, ...]:
+        if missing_keys := set(self.required_keys) - set(input_keys):
+            raise ValueError(f"Missing keys: {missing_keys}")
+        if self.trim_keys:
+            return tuple(sorted(set(self.output_keys)))
+        return tuple(sorted(set(input_keys) | set(self.output_keys)))
+
+    def evaluate(self, input: dict[str, Any]) -> dict[str, Any]:
+        if self.trim_keys:
+            return {k: input[k] for k in self.output_keys}
+        else:
+            if missing_keys := set(self.output_keys) - set(input):
+                raise ValueError(f"Missing keys: {missing_keys}")
+            return input
+
+
+@dataclass
+class UnionConversionNode(ConversionNode):
+    nodes: tuple[ConversionNode, ...]
+
+    @classmethod
+    def from_nodes(cls, *nodes: ConversionNode, trim_keys=False):
+        required = tuple(set(k for n in nodes for k in n.required_keys))
+        output = Counter(k for n in nodes for k in n.output_keys)
+        if duplicate := {k for k, v in output.items() if v > 1}:
+            raise ValueError(f"Duplicate keys from multiple input nodes: {duplicate}")
+        return cls(required, tuple(output), trim_keys, nodes)
+
+    def evaluate(self, input: dict[str, Any]) -> dict[str, Any]:
+        return super().evaluate({k: v for n in self.nodes for k, v in n.evaluate(input).items()})
+
+
+@dataclass
+class RenameConversionNode(ConversionNode):
+    mapping: dict[str, str]
+
+    @classmethod
+    def from_mapping(cls, mapping: dict[str, str], trim_keys=False):
+        required = tuple(mapping)
+        output = Counter(mapping.values())
+        if duplicate := {k for k, v in output.items() if v > 1}:
+            raise ValueError(f"Duplicate output keys in mapping: {duplicate}")
+        return cls(required, tuple(output), trim_keys, mapping)
+
+    def evaluate(self, input: dict[str, Any]) -> dict[str, Any]:
+        return super().evaluate({**input, **{out: input[inp] for (inp, out) in self.mapping.items()}})
+
+
+@dataclass
+class FunctionConversionNode(ConversionNode):
+    funcs: dict[str, Callable]
+
+    @cached_property
+    def _sigs(self):
+        return {k: (f, inspect.signature(f)) for k, f in self.funcs.items()}
+
+    @classmethod
+    def from_funcs(cls, funcs: dict[str, Callable], trim_keys=False):
+        sigs = {k: inspect.signature(f) for k, f in funcs.items()}
+        output = tuple(sigs)
+        input = []
+        for v in sigs.values():
+            input.extend(v.parameters.keys())
+        input = tuple(set(input))
+        return cls(input, output, trim_keys, funcs)
+
+    def evaluate(self, input: dict[str, Any]) -> dict[str, Any]:
+        return super().evaluate(
+            {
+                **input,
+                **{k: func(**{p: input[p] for p in sig.parameters}) for (k, (func, sig)) in self._sigs.items()},
+            }
+        )
+
+
+@dataclass
+class LimitKeysConversionNode(ConversionNode):
+    keys: set[str]
+
+    @classmethod
+    def from_keys(cls, keys: Sequence[str]):
+        return cls((), tuple(keys), trim_keys=True, keys=set(keys))
+
+    def evaluate(self, input: dict[str, Any]) -> dict[str, Any]:
+        return {k: v for k, v in input.items() if k in self.keys}
diff --git a/data_prototype/patches.py b/data_prototype/patches.py
@@ -44,8 +44,8 @@ class PatchWrapper(ProxyWrapper):
         "joinstyle",
     }
 
-    def __init__(self, data: DataContainer, nus=None, /, **kwargs):
-        super().__init__(data, nus)
+    def __init__(self, data: DataContainer, converters=None, /, **kwargs):
+        super().__init__(data, converters)
         self._wrapped_instance = self._wrapped_class([0, 0], 0, 0, **kwargs)
 
     @_stale_wrapper

diff --git a/data_prototype/tests/test_containers.py b/data_prototype/tests/test_containers.py
@@ -14,7 +14,6 @@ def ac():
 
 
 def _verify_describe(container):
-
     data, cache_key = container.query(IdentityTransform(), [100, 100])
     desc = container.describe()
 

diff --git a/data_prototype/wrappers.py b/data_prototype/wrappers.py
@@ -16,6 +16,13 @@
 from matplotlib.artist import Artist as _Artist
 
 from data_prototype.containers import DataContainer, _MatplotlibTransform
+from data_prototype.conversion_node import (
+    ConversionNode,
+    RenameConversionNode,
+    evaluate_pipeline,
+    FunctionConversionNode,
+    LimitKeysConversionNode,
+)
 
 
 class _BBox(Protocol):
@@ -139,45 +146,26 @@ def _query_and_transform(self, renderer, *, xunits: List[str], yunits: List[str]
             return self._cache[cache_key]
         except KeyError:
             ...
-        # TODO decide if units go pre-nu or post-nu?
-        for x_like in xunits:
-            if x_like in data:
-                data[x_like] = ax.xaxis.convert_units(data[x_like])
-        for y_like in yunits:
-            if y_like in data:
-                data[y_like] = ax.xaxis.convert_units(data[y_like])
-
-        # doing the nu work here is nice because we can write it once, but we
-        # really want to push this computation down a layer
-        # TODO sort out how this interoperates with the transform stack
-        transformed_data = {}
-        for k, (nu, sig) in self._sigs.items():
-            to_pass = set(sig.parameters)
-            transformed_data[k] = nu(**{k: data[k] for k in to_pass})
+        # TODO units
+        transformed_data = evaluate_pipeline(self._converters, data)
 
         self._cache[cache_key] = transformed_data
         return transformed_data
 
-    def __init__(self, data, nus, **kwargs):
+    def __init__(self, data, converters: ConversionNode | list[ConversionNode] | None, **kwargs):
         super().__init__(**kwargs)
         self.data = data
         self._cache = LFUCache(64)
         # TODO make sure mutating this will invalidate the cache!
-        self._nus = nus or {}
-        for k in self.required_keys:
-            self._nus.setdefault(k, _make_identity(k))
-        desc = data.describe()
-        for k in self.expected_keys:
-            if k in desc:
-                self._nus.setdefault(k, _make_identity(k))
-        self._sigs = {k: (nu, inspect.signature(nu)) for k, nu in self._nus.items()}
+        if isinstance(converters, ConversionNode):
+            converters = [converters]
+        self._converters: list[ConversionNode] = converters or []
+        setters = list(self.expected_keys | self.required_keys)
+        if hasattr(self, "_wrapped_class"):
+            setters += [f[4:] for f in dir(self._wrapped_class) if f.startswith("set_")]
+        self._converters.append(LimitKeysConversionNode.from_keys(setters))
         self.stale = True
 
-    # TODO add a setter
-    @property
-    def nus(self):
-        return dict(self._nus)
-
 
 class ProxyWrapper(ProxyWrapperBase):
     _privtized_methods: Tuple[str, ...] = ()
@@ -192,7 +180,7 @@ def __getattr__(self, key):
         return getattr(self._wrapped_instance, key)
 
     def __setattr__(self, key, value):
-        if key in ("_wrapped_instance", "data", "_cache", "_nus", "stale", "_sigs"):
+        if key in ("_wrapped_instance", "data", "_cache", "_converters", "stale", "_sigs"):
             super().__setattr__(key, value)
         elif hasattr(self, "_wrapped_instance") and hasattr(self._wrapped_instance, key):
             setattr(self._wrapped_instance, key, value)
@@ -205,9 +193,12 @@ class LineWrapper(ProxyWrapper):
     _privtized_methods = ("set_xdata", "set_ydata", "set_data", "get_xdata", "get_ydata", "get_data")
     required_keys = {"x", "y"}
 
-    def __init__(self, data: DataContainer, nus=None, /, **kwargs):
-        super().__init__(data, nus)
+    def __init__(self, data: DataContainer, converters=None, /, **kwargs):
+        super().__init__(data, converters)
         self._wrapped_instance = self._wrapped_class(np.array([]), np.array([]), **kwargs)
+        self._converters.insert(-1, RenameConversionNode.from_mapping({"x": "xdata", "y": "ydata"}))
+        setters = [f[4:] for f in dir(self._wrapped_class) if f.startswith("set_")]
+        self._converters[-1] = LimitKeysConversionNode.from_keys(setters)
 
     @_stale_wrapper
     def draw(self, renderer):
@@ -218,7 +209,6 @@ def draw(self, renderer):
 
     def _update_wrapped(self, data):
         for k, v in data.items():
-            k = {"x": "xdata", "y": "ydata"}.get(k, k)
             getattr(self._wrapped_instance, f"set_{k}")(v)
 
 
@@ -238,8 +228,8 @@ class PathCollectionWrapper(ProxyWrapper):
         "get_paths",
     )
 
-    def __init__(self, data: DataContainer, nus=None, /, **kwargs):
-        super().__init__(data, nus)
+    def __init__(self, data: DataContainer, converters=None, /, **kwargs):
+        super().__init__(data, converters)
         self._wrapped_instance = self._wrapped_class([], **kwargs)
         self._wrapped_instance.set_transform(mtransforms.IdentityTransform())
 
@@ -262,17 +252,17 @@ class ImageWrapper(ProxyWrapper):
     _wrapped_class = _AxesImage
     required_keys = {"xextent", "yextent", "image"}
 
-    def __init__(self, data: DataContainer, nus=None, /, cmap=None, norm=None, **kwargs):
-        nus = dict(nus or {})
+    def __init__(self, data: DataContainer, converters=None, /, cmap=None, norm=None, **kwargs):
+        converters = converters or []
         if cmap is not None or norm is not None:
-            if nus is not None and "image" in nus:
+            if converters is not None and "image" in converters:
                 raise ValueError("Conflicting input")
             if cmap is None:
                 cmap = mpl.colormaps["viridis"]
             if norm is None:
                 raise ValueError("not sure how to do autoscaling yet")
-            nus["image"] = lambda image: cmap(norm(image))
-        super().__init__(data, nus)
+            converters.append(FunctionConversionNode.from_funcs({"image": lambda image: cmap(norm(image))}))
+        super().__init__(data, converters)
         kwargs.setdefault("origin", "lower")
         self._wrapped_instance = self._wrapped_class(None, **kwargs)
 
@@ -293,8 +283,8 @@ class StepWrapper(ProxyWrapper):
     _privtized_methods = ()  # ("set_data", "get_data")
     required_keys = {"edges", "density"}
 
-    def __init__(self, data: DataContainer, nus=None, /, **kwargs):
-        super().__init__(data, nus)
+    def __init__(self, data: DataContainer, converters=None, /, **kwargs):
+        super().__init__(data, converters)
         self._wrapped_instance = self._wrapped_class([], [1], **kwargs)
 
     @_stale_wrapper
@@ -312,8 +302,8 @@ class FormatedText(ProxyWrapper):
     _wrapped_class = _Text
     _privtized_methods = ("set_text",)
 
-    def __init__(self, data: DataContainer, nus=None, /, **kwargs):
-        super().__init__(data, nus)
+    def __init__(self, data: DataContainer, converters=None, /, **kwargs):
+        super().__init__(data, converters)
         self._wrapped_instance = self._wrapped_class(text="", **kwargs)
 
     @_stale_wrapper
@@ -368,8 +358,8 @@ class ErrorbarWrapper(MultiProxyWrapper):
     required_keys = {"x", "y"}
     expected_keys = {f"{axis}{dirc}" for axis in ["x", "y"] for dirc in ["upper", "lower"]}
 
-    def __init__(self, data: DataContainer, nus=None, /, **kwargs):
-        super().__init__(data, nus)
+    def __init__(self, data: DataContainer, converters=None, /, **kwargs):
+        super().__init__(data, converters)
         # TODO all of the kwarg teasing apart that is needed
         color = kwargs.pop("color", "k")
         lw = kwargs.pop("lw", 2)

diff --git a/examples/2Dfunc.py b/examples/2Dfunc.py
@@ -13,7 +13,6 @@
 from data_prototype.wrappers import ImageWrapper
 from data_prototype.containers import FuncContainer
 
-import matplotlib as mpl
 from matplotlib.colors import Normalize
 
 
@@ -25,9 +24,8 @@
         "image": (("N", "M"), lambda x, y: np.sin(x).reshape(1, -1) * np.cos(y).reshape(-1, 1)),
     },
 )
-cmap = mpl.colormaps["viridis"]
-norm = Normalize(-1, 1)
-im = ImageWrapper(fc, {"image": lambda image: cmap(norm(image))})
+norm = Normalize(vmin=-1, vmax=1)
+im = ImageWrapper(fc, norm=norm)
 
 fig, ax = plt.subplots()
 ax.add_artist(im)

diff --git a/examples/animation.py b/examples/animation.py
@@ -17,6 +17,7 @@
 from matplotlib.animation import FuncAnimation
 
 from data_prototype.containers import _MatplotlibTransform, Desc
+from data_prototype.conversion_node import FunctionConversionNode
 
 from data_prototype.wrappers import LineWrapper, FormatedText
 
@@ -63,9 +64,9 @@ def update(frame, art):
 lw = LineWrapper(sot_c, lw=5, color="green", label="sin(time)")
 fc = FormatedText(
     sot_c,
-    {"text": lambda phase: f"ϕ={phase:.2f}"},
-    x=2 * np.pi,
-    y=1,
+    FunctionConversionNode.from_funcs(
+        {"text": lambda phase: f"ϕ={phase:.2f}", "x": lambda: 2 * np.pi, "y": lambda: 1}
+    ),
     ha="right",
 )
 fig, ax = plt.subplots()
Original file line number	Diff line number	Diff line change
Expand Up		@@ -14,7 +14,6 @@ def ac():


		def _verify_describe(container):

		data, cache_key = container.query(IdentityTransform(), [100, 100])
		desc = container.describe()

Expand Down