From 3721567248c8d1bb0fb43b79cf049bce4ea52de9 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 14:00:08 -0800
Subject: [PATCH 01/30] Extend the standard library to include many operators

---
 pyiron_workflow/node_library/standard.py | 199 ++++++++++++++++++++++-
 1 file changed, 196 insertions(+), 3 deletions(-)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 2783aa08d..037ccfd11 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -17,7 +17,7 @@ def UserInput(user_input):
 
 class If(SingleValue):
     """
-    Has two extra signal channels: true and false. Evaluates the input as a boolean and
+    Has two extra signal channels: true and false. Evaluates the input as obj otheroolean and
     fires the corresponding output signal after running.
     """
 
@@ -29,7 +29,7 @@ def __init__(self, **kwargs):
     @staticmethod
     def if_(condition):
         if isclass(condition) and issubclass(condition, NotData):
-            raise TypeError(f"Logic 'If' node expected data but got NotData as input.")
+            raise TypeError(f"Logic 'If' node expected data otherut got NotData as input.")
         return bool(condition)
 
     def process_run_result(self, function_output):
@@ -43,8 +43,201 @@ def process_run_result(self, function_output):
         else:
             self.signals.output.false()
 
+# A bunch of (but not all) standard operators
+# Return values based on dunder methods, where available
+
+@single_value_node("str")
+def String(obj):
+    return str(obj)
+
+
+@single_value_node("bytes")
+def Bytes(obj):
+    return bytes(obj)
+
+
+@single_value_node("lt")
+def LessThan(obj, other):
+    return obj < other
+
+
+@single_value_node("le")
+def LessThanEquals(obj, other):
+    return obj <= other
+
+
+@single_value_node("gt")
+def GreaterThan(obj, other):
+    return obj > other
+
+
+@single_value_node("ge")
+def GreaterThanEquals(obj, other):
+    return obj >= other
+
+
+@single_value_node("hash")
+def Hash(obj):
+    return hash(obj)
+
+
+@single_value_node("bool")
+def Bool(obj):
+    return bool(obj)
+
+
+@single_value_node("getattr")
+def GetAttr(obj, name):
+    return getattr(obj, name)
+
+# These are not idempotent and thus not encouraged
+# @single_value_node("none")
+# def SetAttr(obj, name, value):
+#     setattr(obj, name, value)
+#     return None
+#
+#
+# @single_value_node("none")
+# def DelAttr(obj, name):
+#     delattr(obj, name)
+#     return None
+
+@single_value_node("dir")
+def Dir(obj):
+    return dir(obj)
+
+
+@single_value_node("len")
+def Length(obj):
+    return len(obj)
+
+
+@single_value_node("add")
+def Add(obj, other):
+    return obj + other
+
+
+@single_value_node("in")
+def Contains(obj, other):
+    return other in obj
+
+
+@single_value_node("sub")
+def Subtract(obj, other):
+    return obj - other
+
+
+@single_value_node("mul")
+def Multiply(obj, other):
+    return obj * other
+
+
+@single_value_node("matmul")
+def MatrixMultiply(obj, other):
+    return obj @ other
+
+
+@single_value_node("truediv")
+def Divide(obj, other):
+    return obj / other
+
+
+@single_value_node("floordiv")
+def FloorDivide(obj, other):
+    return obj // other
+
+
+@single_value_node("mod")
+def Modulo(obj, other):
+    return obj % other
+
+
+@single_value_node("pow")
+def Power(obj, other):
+    return obj ** other
+
+
+@single_value_node("and")
+def And(obj, other):
+    return obj & other
+
+
+@single_value_node("xor")
+def XOr(obj, other):
+    return obj ^ other
+
+
+@single_value_node("or")
+def Or(obj, other):
+    return obj ^ other
+
+
+@single_value_node("neg")
+def Negative(obj):
+    return -obj
+
+
+@single_value_node("pos")
+def Positive(obj):
+    return +obj
+
+
+@single_value_node("abs")
+def Absolute(obj):
+    return abs(obj)
+
+
+@single_value_node("invert")
+def Invert(obj):
+    return ~obj
+
+
+@single_value_node("int")
+def Int(obj):
+    return int(obj)
+
+
+@single_value_node("float")
+def Float(obj):
+    return float(obj)
+
+
+@single_value_node("round")
+def Round(obj):
+    return round(obj)
+
 
 nodes = [
-    UserInput,
+    Absolute,
+    Add,
+    And,
+    Bool,
+    Bytes,
+    Contains,
+    Dir,
+    Divide,
+    Float,
+    FloorDivide,
+    GetAttr,
+    GreaterThan,
+    GreaterThanEquals,
+    Hash,
     If,
+    Int,
+    Invert,
+    Length,
+    LessThan,
+    LessThanEquals,
+    MatrixMultiply,
+    Modulo,
+    Multiply,
+    Negative,
+    Or,
+    Positive,
+    Power,
+    Round,
+    String,
+    Subtract,
+    UserInput,
+    XOr,
 ]

From ee4e789068fdcdb571486babbbe7098e3eb60926 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 14:11:14 -0800
Subject: [PATCH 02/30] Use standard nodes

---
 pyiron_workflow/meta.py            |  8 ++++----
 tests/integration/test_workflow.py | 29 +++++++++++++----------------
 2 files changed, 17 insertions(+), 20 deletions(-)

diff --git a/pyiron_workflow/meta.py b/pyiron_workflow/meta.py
index 570ac4dc8..1e221e7fd 100644
--- a/pyiron_workflow/meta.py
+++ b/pyiron_workflow/meta.py
@@ -216,17 +216,17 @@ def while_loop(
         >>>
         >>> AddWhile = Workflow.create.meta.while_loop(
         ...     loop_body_class=Add,
-        ...     condition_class=LessThanTen,
+        ...     condition_class=Workflow.create.standard.LessThan,
         ...     internal_connection_map=[
-        ...         ("Add", "a + b", "LessThanTen", "value"),
+        ...         ("Add", "a + b", "LessThan", "obj"),
         ...         ("Add", "a + b", "Add", "a")
         ...     ],
-        ...     inputs_map={"Add__a": "a", "Add__b": "b"},
+        ...     inputs_map={"Add__a": "a", "Add__b": "b", "LessThan__other": "cap"},
         ...     outputs_map={"Add__a + b": "total"}
         ... )
         >>>
         >>> wf = Workflow("do_while")
-        >>> wf.add_while = AddWhile()
+        >>> wf.add_while = AddWhile(cap=10)
         >>>
         >>> wf.inputs_map = {
         ...     "add_while__a": "a",
diff --git a/tests/integration/test_workflow.py b/tests/integration/test_workflow.py
index 464b0d560..128ca00d7 100644
--- a/tests/integration/test_workflow.py
+++ b/tests/integration/test_workflow.py
@@ -147,23 +147,19 @@ def GreaterThan(x: float, threshold: float):
 
         with self.subTest("Self-data-loop"):
 
-            @Workflow.wrap_as.single_value_node()
-            def Add(a, b):
-                return a + b
-
-            @Workflow.wrap_as.single_value_node()
-            def LessThanTen(value):
-                return value < 10
-
             AddWhile = Workflow.create.meta.while_loop(
-                loop_body_class=Add,
-                condition_class=LessThanTen,
+                loop_body_class=Workflow.create.standard.Add,
+                condition_class=Workflow.create.standard.LessThan,
                 internal_connection_map=[
-                    ("Add", "a + b", "LessThanTen", "value"),
-                    ("Add", "a + b", "Add", "a")
+                    ("Add", "add", "LessThan", "obj"),
+                    ("Add", "add", "Add", "obj")
                 ],
-                inputs_map={"Add__a": "a", "Add__b": "b"},
-                outputs_map={"Add__a + b": "total"}
+                inputs_map={
+                    "Add__obj": "a",
+                    "Add__other": "b",
+                    "LessThan__other": "cap",
+                },
+                outputs_map={"Add__add": "total"}
             )
 
             wf = Workflow("do_while")
@@ -171,11 +167,12 @@ def LessThanTen(value):
 
             wf.inputs_map = {
                 "add_while__a": "a",
-                "add_while__b": "b"
+                "add_while__b": "b",
+                "add_while__cap": "cap"
             }
             wf.outputs_map = {"add_while__total": "total"}
 
-            out = wf(a=1, b=2)
+            out = wf(a=1, b=2, cap=10)
             self.assertEqual(out.total, 11)
 
     def test_executor_and_creator_interaction(self):

From 1608779cbfd617a80351d3faa5cbb7bdf910244b Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 14:12:01 -0800
Subject: [PATCH 03/30] Add an item node

---
 pyiron_workflow/node_library/standard.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 037ccfd11..8d01edcc2 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -102,6 +102,11 @@ def GetAttr(obj, name):
 #     delattr(obj, name)
 #     return None
 
+@single_value_node("getitem")
+def GetItem(obj, item):
+    return obj[item]
+
+
 @single_value_node("dir")
 def Dir(obj):
     return dir(obj)

From 026a1e5d082c832c2d4fc80da34424ae09609d75 Mon Sep 17 00:00:00 2001
From: pyiron-runner <pyiron@mpie.de>
Date: Thu, 7 Dec 2023 22:19:41 +0000
Subject: [PATCH 04/30] Format black

---
 pyiron_workflow/node_library/standard.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 8d01edcc2..a523d012b 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -29,7 +29,9 @@ def __init__(self, **kwargs):
     @staticmethod
     def if_(condition):
         if isclass(condition) and issubclass(condition, NotData):
-            raise TypeError(f"Logic 'If' node expected data otherut got NotData as input.")
+            raise TypeError(
+                f"Logic 'If' node expected data otherut got NotData as input."
+            )
         return bool(condition)
 
     def process_run_result(self, function_output):
@@ -43,9 +45,11 @@ def process_run_result(self, function_output):
         else:
             self.signals.output.false()
 
+
 # A bunch of (but not all) standard operators
 # Return values based on dunder methods, where available
 
+
 @single_value_node("str")
 def String(obj):
     return str(obj)
@@ -90,6 +94,7 @@ def Bool(obj):
 def GetAttr(obj, name):
     return getattr(obj, name)
 
+
 # These are not idempotent and thus not encouraged
 # @single_value_node("none")
 # def SetAttr(obj, name, value):
@@ -102,6 +107,7 @@ def GetAttr(obj, name):
 #     delattr(obj, name)
 #     return None
 
+
 @single_value_node("getitem")
 def GetItem(obj, item):
     return obj[item]
@@ -159,7 +165,7 @@ def Modulo(obj, other):
 
 @single_value_node("pow")
 def Power(obj, other):
-    return obj ** other
+    return obj**other
 
 
 @single_value_node("and")

From ebfd9d7e41697fe5886716ccc4e2b0e39ca30290 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 14:58:46 -0800
Subject: [PATCH 05/30] Re-order nodes

---
 pyiron_workflow/node_library/standard.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index a523d012b..bde203914 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -123,16 +123,16 @@ def Length(obj):
     return len(obj)
 
 
-@single_value_node("add")
-def Add(obj, other):
-    return obj + other
-
-
 @single_value_node("in")
 def Contains(obj, other):
     return other in obj
 
 
+@single_value_node("add")
+def Add(obj, other):
+    return obj + other
+
+
 @single_value_node("sub")
 def Subtract(obj, other):
     return obj - other

From e1660f2a631f57485d5253447b50d433ea5030f9 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 15:07:23 -0800
Subject: [PATCH 06/30] Remove the __len__ shortcut for channels

So we can inject length checks on OutputData values
---
 pyiron_workflow/channels.py |  3 ---
 tests/unit/test_channels.py | 22 ++++------------------
 2 files changed, 4 insertions(+), 21 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 63f4561c3..da79890ed 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -183,9 +183,6 @@ def connected(self) -> bool:
     def __iter__(self):
         return self.connections.__iter__()
 
-    def __len__(self):
-        return len(self.connections)
-
     @property
     def channel(self) -> Channel:
         return self
diff --git a/tests/unit/test_channels.py b/tests/unit/test_channels.py
index 66ab99ec8..b501875b6 100644
--- a/tests/unit/test_channels.py
+++ b/tests/unit/test_channels.py
@@ -59,20 +59,6 @@ def test_connection_validity(self):
         self.inp.connect(self.out)
         # A conjugate pair should work fine
 
-    def test_length(self):
-        self.inp.connect(self.out)
-        self.out2.connect(self.inp)
-        self.assertEqual(
-            2,
-            len(self.inp),
-            msg="Promised that channel length was number of connections"
-        )
-        self.assertEqual(
-            1,
-            len(self.out),
-            msg="Promised that channel length was number of connections"
-        )
-
     def test_connection_reflexivity(self):
         self.inp.connect(self.out)
 
@@ -359,13 +345,13 @@ def test_connections(self):
 
         with self.subTest("Ignore repeated connection"):
             self.out.connect(self.inp)
-            self.assertEqual(len(self.inp), 1)
-            self.assertEqual(len(self.out), 1)
+            self.assertEqual(len(self.inp.connections), 1)
+            self.assertEqual(len(self.out.connections), 1)
 
         with self.subTest("Check disconnection"):
             self.out.disconnect_all()
-            self.assertEqual(len(self.inp), 0)
-            self.assertEqual(len(self.out), 0)
+            self.assertEqual(len(self.inp.connections), 0)
+            self.assertEqual(len(self.out.connections), 0)
 
         with self.subTest("No connections to non-SignalChannels"):
             bad = InputData(label="numeric", node=DummyNode(), default=1, type_hint=int)

From 2c3081024b574434921867300b03ea73431c1acd Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 15:26:55 -0800
Subject: [PATCH 07/30] Parse (many but not all) operations on output nodes

by injecting new nodes!
---
 pyiron_workflow/channels.py                | 151 +++++++++++++++++++++
 tests/integration/test_output_injection.py |  79 +++++++++++
 2 files changed, 230 insertions(+)
 create mode 100644 tests/integration/test_output_injection.py

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index da79890ed..f64814ae9 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -488,6 +488,157 @@ class OutputData(DataChannel):
     def connection_partner_type(self):
         return InputData
 
+    @staticmethod
+    def _other_label(other):
+        return other.channel.scoped_label if isinstance(other, HasChannel) else str(other)
+
+    def get_injected_label(self, injection_class, other=None):
+        suffix = f"_{self._other_label(other)}" if other is not None else ""
+        return f"{self.scoped_label}_{injection_class.__name__}{suffix}"
+
+    def _binary_injection(self, injection_class, other):
+        """A template for injecting binary function nodes"""
+        label = self.get_injected_label(injection_class, other)
+        try:
+            # First check if the node already exists
+            return self.node.parent.nodes[label]
+        except (AttributeError, KeyError):
+            # Fall back on creating a new node in case parent is None or node nexists
+            return injection_class(self, other, parent=self.node.parent, label=label)
+
+    def _unary_injection(self, injection_class):
+        """A template for injecting unary function nodes"""
+        # We can't just do an `if other is None` and combine this implementation with
+        # the binary, because users might pass `None` other data on purpose
+        label = self.get_injected_label(injection_class)
+        try:
+            # First check if the node already exists
+            return self.node.parent.nodes[label]
+        except (AttributeError, KeyError):
+            # Fall back on creating a new node in case parent is None or node nexists
+            return injection_class(self, parent=self.node.parent, label=label)
+
+    # We don't wrap __all__ the operators, because you might really want the string or
+    # hash or whatever of the actual channel. But we do wrap all the dunder methods
+    # that should be unambiguously referring to an operation on values
+
+    def __lt__(self, other):
+        from pyiron_workflow.node_library.standard import LessThan
+        return self._binary_injection(LessThan, other)
+
+    def __le__(self, other):
+        from pyiron_workflow.node_library.standard import LessThanEquals
+        return self._binary_injection(LessThanEquals, other)
+
+    def __gt__(self, other):
+        from pyiron_workflow.node_library.standard import GreaterThan
+        return self._binary_injection(GreaterThan, other)
+
+    def __ge__(self, other):
+        from pyiron_workflow.node_library.standard import GreaterThanEquals
+        return self._binary_injection(GreaterThanEquals, other)
+
+    def __bool__(self):
+        from pyiron_workflow.node_library.standard import Bool
+        return self._unary_injection(Bool)
+
+    def __getattr__(self, name):
+        from pyiron_workflow.node_library.standard import GetAttr
+        return self._binary_injection(GetAttr, name)
+
+    def __getitem__(self, item):
+        from pyiron_workflow.node_library.standard import GetItem
+        return self._binary_injection(GetItem, item)
+
+    def __len__(self):
+        from pyiron_workflow.node_library.standard import Length
+        return self._unary_injection(Length)
+
+    def __contains__(self, other):
+        from pyiron_workflow.node_library.standard import Contains
+        return self._binary_injection(Contains, other)
+
+    def __add__(self, other):
+        from pyiron_workflow.node_library.standard import Add
+        return self._binary_injection(Add, other)
+
+    def __sub__(self, other):
+        from pyiron_workflow.node_library.standard import Subtract
+        return self._binary_injection(Subtract, other)
+
+    def __mul__(self, other):
+        from pyiron_workflow.node_library.standard import Multiply
+        return self._binary_injection(Multiply, other)
+
+    def __matmul__(self, other):
+        from pyiron_workflow.node_library.standard import MatrixMultiply
+        return self._binary_injection(MatrixMultiply, other)
+
+    def __truediv__(self, other):
+        from pyiron_workflow.node_library.standard import Divide
+        return self._binary_injection(Divide, other)
+
+    def __floordiv__(self, other):
+        from pyiron_workflow.node_library.standard import FloorDivide
+        return self._binary_injection(FloorDivide, other)
+
+    def __mod__(self, other):
+        from pyiron_workflow.node_library.standard import Modulo
+        return self._binary_injection(Modulo, other)
+
+    def __pow__(self, other):
+        from pyiron_workflow.node_library.standard import Power
+        return self._binary_injection(Power, other)
+
+    def __and__(self, other):
+        from pyiron_workflow.node_library.standard import And
+        return self._binary_injection(And, other)
+
+    def __xor__(self, other):
+        from pyiron_workflow.node_library.standard import XOr
+        return self._binary_injection(XOr, other)
+
+    def __or__(self, other):
+        from pyiron_workflow.node_library.standard import Or
+        return self._binary_injection(Or, other)
+
+    def __neg__(self):
+        from pyiron_workflow.node_library.standard import Negative
+        return self._unary_injection(Negative)
+
+    def __pos__(self):
+        from pyiron_workflow.node_library.standard import Positive
+        return self._unary_injection(Positive)
+
+    def __abs__(self):
+        from pyiron_workflow.node_library.standard import Absolute
+        return self._unary_injection(Absolute)
+
+    def __invert__(self):
+        from pyiron_workflow.node_library.standard import Invert
+        return self._unary_injection(Invert)
+
+    def __int__(self):
+        from pyiron_workflow.node_library.standard import Int
+        return self._unary_injection(Int)
+
+    def __float__(self):
+        from pyiron_workflow.node_library.standard import Float
+        return self._unary_injection(Float)
+
+    def __round__(self):
+        from pyiron_workflow.node_library.standard import Round
+        return self._unary_injection(Round)
+
+    # Because we override __getattr__ we need to get and set state for serialization
+    def __getstate__(self):
+        return self.__dict__
+
+    def __setstate__(self, state):
+        # Update instead of overriding in case some other attributes were added on the
+        # main process while a remote process was working away
+        self.__dict__.update(**state)
+
 
 class SignalChannel(Channel, ABC):
     """
diff --git a/tests/integration/test_output_injection.py b/tests/integration/test_output_injection.py
new file mode 100644
index 000000000..b8bc0ef79
--- /dev/null
+++ b/tests/integration/test_output_injection.py
@@ -0,0 +1,79 @@
+import unittest
+
+from pyiron_workflow import Workflow
+from pyiron_workflow.node import Node
+
+
+class TestOutputInjection(unittest.TestCase):
+    """
+    I.e. the process of inserting new nodes on-the-fly by modifying output channels"
+    """
+
+    def test_a_few_operations(self):
+        wf = Workflow("output_manipulation")
+
+        wf.a = Workflow.create.standard.Add(1, 2)
+        wf.b = Workflow.create.standard.Add(3, 4)
+        wf.c = Workflow.create.standard.UserInput(list(range(10)))
+        wf.d = Workflow.create.standard.UserInput({"foo": 42})
+
+        class Something:
+            myattr = 1
+
+        wf.e = Workflow.create.standard.UserInput(Something())
+
+        wf.a.outputs.add < wf.b.outputs.add
+        wf.c.outputs.user_input[:5]
+        wf.d.outputs.user_input["foo"]
+        wf.e.outputs.user_input.myattr
+        out = wf()
+        self.assertDictEqual(
+            out,
+            {
+                'a__add_LessThan_b__add__lt': True,
+                'c__user_input_GetItem_slice(None, 5, None)__getitem': [0, 1, 2, 3, 4],
+                'd__user_input_GetItem_foo__getitem': 42,
+                'e__user_input_GetAttr_myattr__getattr': 1
+            }
+        )
+
+    def test_repeated_access(self):
+        wf = Workflow("output_manipulation")
+        wf.n = Workflow.create.standard.UserInput(list(range(10)))
+
+        a = wf.n.outputs.user_input[:4]
+        b = wf.n.outputs.user_input[:4]
+        c = wf.n.outputs.user_input[1:]
+
+        self.assertIs(
+            a,
+            b,
+            msg="The same operation should re-access an existing node in the parent"
+        )
+        self.assertIsNot(
+            a,
+            c,
+            msg="Unique operations should yield unique nodes"
+        )
+
+    def test_without_parent(self):
+        n = Workflow.create.standard.UserInput(list(range(10)))
+        d1 = n.outputs.user_input[5]
+        d2 = n.outputs.user_input[5]
+
+        self.assertIsInstance(d1, Node)
+        self.assertIsNot(
+            d1,
+            d2,
+            msg="Outside the scope of a parent, we can't expect to re-access an "
+                "equivalent node"
+        )
+        self.assertEqual(
+            d1.label,
+            d2.label,
+            msg="Equivalent operations should nonetheless generate equal labels"
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()

From 402beede52dc87022905f861e94166f70cb5de7a Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 15:32:02 -0800
Subject: [PATCH 08/30] Refactor: slide

---
 pyiron_workflow/function.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index 42f8aafc6..82d55e589 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -616,8 +616,13 @@ def color(self) -> str:
         """For drawing the graph"""
         return SeabornColors.cyan
 
-    def __getitem__(self, item):
-        return self.single_value.__getitem__(item)
+    def __repr__(self):
+        return self.single_value.__repr__()
+
+    def __str__(self):
+        return f"{self.label} ({self.__class__.__name__}) output single-value: " + str(
+            self.single_value
+        )
 
     def __getattr__(self, item):
         try:
@@ -628,13 +633,8 @@ def __getattr__(self, item):
                 f"{self.single_value}"
             ) from e
 
-    def __repr__(self):
-        return self.single_value.__repr__()
-
-    def __str__(self):
-        return f"{self.label} ({self.__class__.__name__}) output single-value: " + str(
-            self.single_value
-        )
+    def __getitem__(self, item):
+        return self.single_value.__getitem__(item)
 
 
 def _wrapper_factory(

From f1e4b4785f45e341dbc7afe7717829b9543e4f06 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 22:59:28 -0800
Subject: [PATCH 09/30] Fail with grace when running at initialization but not
 ready

This is necessary for node injection when processing output: if you're injecting a node on top of an existing result you probably want the injection immediately available, but if you're injecting it at the end of something that hasn't run yet you don't want to see an error.
---
 pyiron_workflow/node.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/pyiron_workflow/node.py b/pyiron_workflow/node.py
index 2026c0afd..125c3717b 100644
--- a/pyiron_workflow/node.py
+++ b/pyiron_workflow/node.py
@@ -71,6 +71,10 @@ def wrapped_method(node: Node, *args, **kwargs):  # rather node:Node
     return wrapped_method
 
 
+class ReadinessError(ValueError):
+    pass
+
+
 class Node(HasToDict, ABC, metaclass=AbstractHasPost):
     """
     Nodes are elements of a computational graph.
@@ -120,6 +124,8 @@ class Node(HasToDict, ABC, metaclass=AbstractHasPost):
         held by the output channels
     - If an error is encountered _after_ reaching the state of actually computing the
         node's task, the status will get set to failure
+    - Nodes can be instructed to run at the end of their initialization, but will exit
+        cleanly if they get to checking their readiness and find they are not ready
     - Nodes have a label by which they are identified
     - Nodes may open a working directory related to their label, their parent(age) and
         the python process working directory
@@ -247,7 +253,10 @@ def __init__(
 
     def __post__(self, *args, run_after_init: bool = False, **kwargs):
         if run_after_init:
-            self.run()
+            try:
+                self.run()
+            except ReadinessError:
+                pass
 
     @property
     @abstractmethod
@@ -376,7 +385,7 @@ def run(
             self.inputs.fetch()
 
         if check_readiness and not self.ready:
-            raise ValueError(
+            raise ReadinessError(
                 f"{self.label} received a run command but is not ready. The node "
                 f"should be neither running nor failed, and all input values should"
                 f" conform to type hints.\n" + self.readiness_report

From 120246ac2ef83b6b9ee70c1b2315ec6d86763549 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 23:03:56 -0800
Subject: [PATCH 10/30] Try to run operator injections at the end of
 initialization

---
 pyiron_workflow/channels.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index f64814ae9..819f585b9 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -261,6 +261,14 @@ class DataChannel(Channel, ABC):
     which is to say it is data (not `NotData`) and that it conforms to the type hint
     (if one is provided and checking is active).
 
+    Output data facilitates most (but not all) python operators by injecting a new
+    node to perform that operation. These new nodes are instructed to run at the end of
+    instantiation, but this fails cleanly in case they are not ready. This is intended
+    to accommodate two likely scenarios: if you're injecting a node on top of an
+    existing result you probably want the injection result to also be immediately
+    available, but if you're injecting it at the end of something that hasn't run yet
+    you don't want to see an error.
+
     TODO:
         - Storage (including priority and history)
         - Ontological hinting
@@ -504,7 +512,9 @@ def _binary_injection(self, injection_class, other):
             return self.node.parent.nodes[label]
         except (AttributeError, KeyError):
             # Fall back on creating a new node in case parent is None or node nexists
-            return injection_class(self, other, parent=self.node.parent, label=label)
+            return injection_class(
+                self, other, parent=self.node.parent, label=label, run_after_init=True
+            )
 
     def _unary_injection(self, injection_class):
         """A template for injecting unary function nodes"""
@@ -516,7 +526,9 @@ def _unary_injection(self, injection_class):
             return self.node.parent.nodes[label]
         except (AttributeError, KeyError):
             # Fall back on creating a new node in case parent is None or node nexists
-            return injection_class(self, parent=self.node.parent, label=label)
+            return injection_class(
+                self, parent=self.node.parent, label=label, run_after_init=True
+            )
 
     # We don't wrap __all__ the operators, because you might really want the string or
     # hash or whatever of the actual channel. But we do wrap all the dunder methods

From b65ccc44460bd153e9cbcf4a16db088d0dff6f5c Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 23:04:18 -0800
Subject: [PATCH 11/30] Extend single value node to use operators like output

---
 pyiron_workflow/function.py | 99 ++++++++++++++++++++++++++++++-------
 tests/unit/test_function.py | 29 +++++++----
 2 files changed, 102 insertions(+), 26 deletions(-)

diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index 82d55e589..e772d0fb3 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -582,13 +582,15 @@ class SingleValue(Function, HasChannel):
     """
     A node that _must_ return only a single value.
 
-    Attribute and item access is modified to finally attempt access on the output value.
-    Note that this means any attributes/method available on the output value become
-    available directly at the node level (at least those which don't conflict with the
-    existing node namespace).
+    Attribute and item access is modified to finally attempt access on the output
+    channel, and other operations (those supported by the output channel) are also
+    passed there automatically.
+    This means that the node itself can be used in place of its output channel,
+    and that the `value` attribtue directly accesses the output value.
 
     Promises (in addition parent class promises):
-    - Attribute and item access will finally attempt to access the output value
+    - Attribute and item access will finally attempt to access the output
+    - Other operators supported by the output channel operate there immediately.
     - The entire node can be used in place of its output value for connections, e.g.
         `some_node.input.some_channel = my_svn_instance`.
     """
@@ -603,8 +605,8 @@ def _get_output_labels(self, output_labels: str | list[str] | tuple[str] | None)
         return output_labels
 
     @property
-    def single_value(self):
-        return self.outputs[self.outputs.labels[0]].value
+    def output(self) -> OutputData:
+        return self.outputs[self.outputs.labels[0]]
 
     @property
     def channel(self) -> OutputData:
@@ -617,24 +619,87 @@ def color(self) -> str:
         return SeabornColors.cyan
 
     def __repr__(self):
-        return self.single_value.__repr__()
+        return self.output.value.__repr__()
 
     def __str__(self):
         return f"{self.label} ({self.__class__.__name__}) output single-value: " + str(
-            self.single_value
+            self.output.value
         )
 
     def __getattr__(self, item):
-        try:
-            return getattr(self.single_value, item)
-        except Exception as e:
-            raise AttributeError(
-                f"Could not find {item} as an attribute of the single value "
-                f"{self.single_value}"
-            ) from e
+        return getattr(self.output, item)
 
     def __getitem__(self, item):
-        return self.single_value.__getitem__(item)
+        return self.output.__getitem__(item)
+
+    def __lt__(self, other):
+        return self.output.__lt__(other)
+
+    def __le__(self, other):
+        return self.output.__le__(other)
+
+    def __gt__(self, other):
+        return self.output.__gt__(other)
+
+    def __ge__(self, other):
+        return self.output.__ge__(other)
+
+    def __bool__(self):
+        return self.output.__bool__()
+
+    def __len__(self):
+        return self.output.__len__()
+
+    def __contains__(self, other):
+        return self.output.__contains__(other)
+
+    def __add__(self, other):
+        return self.output.__add__(other)
+
+    def __sub__(self, other):
+        return self.output.__sub__(other)
+
+    def __matmul__(self, other):
+        return self.output.__matmul__(other)
+
+    def __truediv__(self, other):
+        return self.output.__truediv__(other)
+
+    def __floordiv__(self, other):
+        return self.output.__floordiv__(other)
+
+    def __mod__(self, other):
+        return self.output.__mod__(other)
+
+    def __pow__(self, other):
+        return self.output.__pow__(other)
+
+    def __and__(self, other):
+        return self.output.__and__(other)
+
+    def __xor__(self, other):
+        return self.output.__xor__(other)
+
+    def __or__(self, other):
+        return self.output.__or__(other)
+
+    def __neg__(self):
+        return self.output.__neg__()
+
+    def __pos__(self):
+        return self.output.__pos__()
+
+    def __abs__(self):
+        return self.output.__abs__()
+
+    def __int__(self):
+        return self.output.__int__()
+
+    def __float__(self):
+        return self.output.__float__()
+
+    def __round__(self):
+        return self.output.__round__()
 
 
 def _wrapper_factory(
diff --git a/tests/unit/test_function.py b/tests/unit/test_function.py
index 5a993c18d..285aa586c 100644
--- a/tests/unit/test_function.py
+++ b/tests/unit/test_function.py
@@ -480,31 +480,42 @@ def returns_foo() -> Foo:
             return Foo()
 
         svn = SingleValue(returns_foo, output_labels="foo")
+
+        self.assertEqual(
+            svn.connected,
+            False,
+            msg="Should return the _node_ attribute, not acting on the output channel"
+        )
+
+        injection = svn[0]  # Should pass cleanly, even though it tries to run
         svn.run()
 
         self.assertEqual(
-            svn.some_attribute,
+            svn.some_attribute.value,  # The call runs the dynamic node
             "exists",
-            msg="Should fall back to looking on the single value"
+            msg="Should fall back to acting on the output channel and creating a node"
         )
 
         self.assertEqual(
             svn.connected,
-            False,
-            msg="Should return the _node_ attribute, not the single value attribute"
+            True,
+            msg="Should now be connected to the dynamically created nodes"
         )
 
-        with self.assertRaises(AttributeError):
+        with self.assertRaises(
+            AttributeError,
+            msg="Aggressive running hits the problem that no such attribute exists"
+        ):
             svn.doesnt_exists_anywhere
 
         self.assertEqual(
-            svn[0],
+            injection(),
             True,
-            msg="Should fall back to looking on the single value"
+            msg="Should be able to query injection later"
         )
 
         self.assertEqual(
-            svn["some other key"],
+            svn["some other key"].value,
             False,
             msg="Should fall back to looking on the single value"
         )
@@ -531,7 +542,7 @@ def test_str(self):
         svn = SingleValue(plus_one)
         svn.run()
         self.assertTrue(
-            str(svn).endswith(str(svn.single_value)),
+            str(svn).endswith(str(svn.value)),
             msg="SingleValueNodes should have their output as a string in their string "
                 "representation (e.g., perhaps with a reminder note that this is "
                 "actually still a Function and not just the value you're seeing.)"

From 93ad2ffd620b972361ca73476b862051f7cb59a2 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Thu, 7 Dec 2023 23:23:22 -0800
Subject: [PATCH 12/30] :bug: include GetItem in the standard package

---
 pyiron_workflow/node_library/standard.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index bde203914..88d7d732a 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -230,6 +230,7 @@ def Round(obj):
     Float,
     FloorDivide,
     GetAttr,
+    GetItem,
     GreaterThan,
     GreaterThanEquals,
     Hash,

From b627a6938854876c1d99f2f2ae7f0bd8b7694704 Mon Sep 17 00:00:00 2001
From: pyiron-runner <pyiron@mpie.de>
Date: Fri, 8 Dec 2023 07:28:28 +0000
Subject: [PATCH 13/30] Format black

---
 pyiron_workflow/channels.py | 31 ++++++++++++++++++++++++++++++-
 1 file changed, 30 insertions(+), 1 deletion(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 819f585b9..889360590 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -498,7 +498,9 @@ def connection_partner_type(self):
 
     @staticmethod
     def _other_label(other):
-        return other.channel.scoped_label if isinstance(other, HasChannel) else str(other)
+        return (
+            other.channel.scoped_label if isinstance(other, HasChannel) else str(other)
+        )
 
     def get_injected_label(self, injection_class, other=None):
         suffix = f"_{self._other_label(other)}" if other is not None else ""
@@ -536,110 +538,137 @@ def _unary_injection(self, injection_class):
 
     def __lt__(self, other):
         from pyiron_workflow.node_library.standard import LessThan
+
         return self._binary_injection(LessThan, other)
 
     def __le__(self, other):
         from pyiron_workflow.node_library.standard import LessThanEquals
+
         return self._binary_injection(LessThanEquals, other)
 
     def __gt__(self, other):
         from pyiron_workflow.node_library.standard import GreaterThan
+
         return self._binary_injection(GreaterThan, other)
 
     def __ge__(self, other):
         from pyiron_workflow.node_library.standard import GreaterThanEquals
+
         return self._binary_injection(GreaterThanEquals, other)
 
     def __bool__(self):
         from pyiron_workflow.node_library.standard import Bool
+
         return self._unary_injection(Bool)
 
     def __getattr__(self, name):
         from pyiron_workflow.node_library.standard import GetAttr
+
         return self._binary_injection(GetAttr, name)
 
     def __getitem__(self, item):
         from pyiron_workflow.node_library.standard import GetItem
+
         return self._binary_injection(GetItem, item)
 
     def __len__(self):
         from pyiron_workflow.node_library.standard import Length
+
         return self._unary_injection(Length)
 
     def __contains__(self, other):
         from pyiron_workflow.node_library.standard import Contains
+
         return self._binary_injection(Contains, other)
 
     def __add__(self, other):
         from pyiron_workflow.node_library.standard import Add
+
         return self._binary_injection(Add, other)
 
     def __sub__(self, other):
         from pyiron_workflow.node_library.standard import Subtract
+
         return self._binary_injection(Subtract, other)
 
     def __mul__(self, other):
         from pyiron_workflow.node_library.standard import Multiply
+
         return self._binary_injection(Multiply, other)
 
     def __matmul__(self, other):
         from pyiron_workflow.node_library.standard import MatrixMultiply
+
         return self._binary_injection(MatrixMultiply, other)
 
     def __truediv__(self, other):
         from pyiron_workflow.node_library.standard import Divide
+
         return self._binary_injection(Divide, other)
 
     def __floordiv__(self, other):
         from pyiron_workflow.node_library.standard import FloorDivide
+
         return self._binary_injection(FloorDivide, other)
 
     def __mod__(self, other):
         from pyiron_workflow.node_library.standard import Modulo
+
         return self._binary_injection(Modulo, other)
 
     def __pow__(self, other):
         from pyiron_workflow.node_library.standard import Power
+
         return self._binary_injection(Power, other)
 
     def __and__(self, other):
         from pyiron_workflow.node_library.standard import And
+
         return self._binary_injection(And, other)
 
     def __xor__(self, other):
         from pyiron_workflow.node_library.standard import XOr
+
         return self._binary_injection(XOr, other)
 
     def __or__(self, other):
         from pyiron_workflow.node_library.standard import Or
+
         return self._binary_injection(Or, other)
 
     def __neg__(self):
         from pyiron_workflow.node_library.standard import Negative
+
         return self._unary_injection(Negative)
 
     def __pos__(self):
         from pyiron_workflow.node_library.standard import Positive
+
         return self._unary_injection(Positive)
 
     def __abs__(self):
         from pyiron_workflow.node_library.standard import Absolute
+
         return self._unary_injection(Absolute)
 
     def __invert__(self):
         from pyiron_workflow.node_library.standard import Invert
+
         return self._unary_injection(Invert)
 
     def __int__(self):
         from pyiron_workflow.node_library.standard import Int
+
         return self._unary_injection(Int)
 
     def __float__(self):
         from pyiron_workflow.node_library.standard import Float
+
         return self._unary_injection(Float)
 
     def __round__(self):
         from pyiron_workflow.node_library.standard import Round
+
         return self._unary_injection(Round)
 
     # Because we override __getattr__ we need to get and set state for serialization

From ae81c6d92421d7ca6622ead43a1430cb1d19500b Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 10:35:10 -0800
Subject: [PATCH 14/30] Don't define access to the output twice

I used this class to try out ChatGPT3.5. It was almost entirely drivel, but I'll be damned if it didn't notice that I defined this property twice.
---
 pyiron_workflow/function.py | 62 +++++++++++++++++--------------------
 1 file changed, 29 insertions(+), 33 deletions(-)

diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index e772d0fb3..4192ae108 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -602,16 +602,12 @@ def _get_output_labels(self, output_labels: str | list[str] | tuple[str] | None)
                 f"{self.__class__.__name__} must only have a single return value, but "
                 f"got multiple output labels: {output_labels}"
             )
-        return output_labels
-
-    @property
-    def output(self) -> OutputData:
-        return self.outputs[self.outputs.labels[0]]
+        return output_labels 
 
     @property
     def channel(self) -> OutputData:
         """The channel for the single output"""
-        return list(self.outputs.channel_dict.values())[0]
+        return self.outputs[self.outputs.labels[0]]
 
     @property
     def color(self) -> str:
@@ -619,87 +615,87 @@ def color(self) -> str:
         return SeabornColors.cyan
 
     def __repr__(self):
-        return self.output.value.__repr__()
+        return self.channel.value.__repr__()
 
     def __str__(self):
         return f"{self.label} ({self.__class__.__name__}) output single-value: " + str(
-            self.output.value
+            self.channel.value
         )
 
     def __getattr__(self, item):
-        return getattr(self.output, item)
+        return getattr(self.channel, item)
 
     def __getitem__(self, item):
-        return self.output.__getitem__(item)
+        return self.channel.__getitem__(item)
 
     def __lt__(self, other):
-        return self.output.__lt__(other)
+        return self.channel.__lt__(other)
 
     def __le__(self, other):
-        return self.output.__le__(other)
+        return self.channel.__le__(other)
 
     def __gt__(self, other):
-        return self.output.__gt__(other)
+        return self.channel.__gt__(other)
 
     def __ge__(self, other):
-        return self.output.__ge__(other)
+        return self.channel.__ge__(other)
 
     def __bool__(self):
-        return self.output.__bool__()
+        return self.channel.__bool__()
 
     def __len__(self):
-        return self.output.__len__()
+        return self.channel.__len__()
 
     def __contains__(self, other):
-        return self.output.__contains__(other)
+        return self.channel.__contains__(other)
 
     def __add__(self, other):
-        return self.output.__add__(other)
+        return self.channel.__add__(other)
 
     def __sub__(self, other):
-        return self.output.__sub__(other)
+        return self.channel.__sub__(other)
 
     def __matmul__(self, other):
-        return self.output.__matmul__(other)
+        return self.channel.__matmul__(other)
 
     def __truediv__(self, other):
-        return self.output.__truediv__(other)
+        return self.channel.__truediv__(other)
 
     def __floordiv__(self, other):
-        return self.output.__floordiv__(other)
+        return self.channel.__floordiv__(other)
 
     def __mod__(self, other):
-        return self.output.__mod__(other)
+        return self.channel.__mod__(other)
 
     def __pow__(self, other):
-        return self.output.__pow__(other)
+        return self.channel.__pow__(other)
 
     def __and__(self, other):
-        return self.output.__and__(other)
+        return self.channel.__and__(other)
 
     def __xor__(self, other):
-        return self.output.__xor__(other)
+        return self.channel.__xor__(other)
 
     def __or__(self, other):
-        return self.output.__or__(other)
+        return self.channel.__or__(other)
 
     def __neg__(self):
-        return self.output.__neg__()
+        return self.channel.__neg__()
 
     def __pos__(self):
-        return self.output.__pos__()
+        return self.channel.__pos__()
 
     def __abs__(self):
-        return self.output.__abs__()
+        return self.channel.__abs__()
 
     def __int__(self):
-        return self.output.__int__()
+        return self.channel.__int__()
 
     def __float__(self):
-        return self.output.__float__()
+        return self.channel.__float__()
 
     def __round__(self):
-        return self.output.__round__()
+        return self.channel.__round__()
 
 
 def _wrapper_factory(

From 2646953942b30ee75bd32a2bb634b70377263e96 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 10:48:09 -0800
Subject: [PATCH 15/30] Introduce a slice node

---
 pyiron_workflow/node_library/standard.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 88d7d732a..7181b5f90 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -46,6 +46,24 @@ def process_run_result(self, function_output):
             self.signals.output.false()
 
 
+@single_value_node("slice")
+def Slice(start=None, stop=None, step=None):
+    if start is None:
+        if stop is None:
+            raise ValueError(
+                "Slice must define at least start or stop, but both are None"
+            )
+        elif step is not None:
+            raise ValueError("If step is provided, start _must_ be provided")
+        else:
+            s = slice(stop)
+    elif stop is None:
+        raise ValueError("If start is provided, stop _must_ be provided")
+    else:
+        s = slice(start, stop, step)
+    return s
+
+
 # A bunch of (but not all) standard operators
 # Return values based on dunder methods, where available
 
@@ -248,6 +266,7 @@ def Round(obj):
     Positive,
     Power,
     Round,
+    Slice,
     String,
     Subtract,
     UserInput,

From 7ee71448f58466e304d49b9685375eb2846d8cd7 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 11:09:50 -0800
Subject: [PATCH 16/30] Replace the binary and unary injectors with one for
 arbitrary nargs

---
 pyiron_workflow/channels.py | 91 +++++++++++++++++++------------------
 1 file changed, 46 insertions(+), 45 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 889360590..bf0a57ba1 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -506,30 +506,32 @@ def get_injected_label(self, injection_class, other=None):
         suffix = f"_{self._other_label(other)}" if other is not None else ""
         return f"{self.scoped_label}_{injection_class.__name__}{suffix}"
 
-    def _binary_injection(self, injection_class, other):
-        """A template for injecting binary function nodes"""
-        label = self.get_injected_label(injection_class, other)
-        try:
-            # First check if the node already exists
-            return self.node.parent.nodes[label]
-        except (AttributeError, KeyError):
-            # Fall back on creating a new node in case parent is None or node nexists
-            return injection_class(
-                self, other, parent=self.node.parent, label=label, run_after_init=True
-            )
+    def _get_injection_label(self, injection_class, *args):
+        other_labels = "_".join(self._other_label(other) for other in args)
+        suffix = f"_{other_labels}" if len(args) > 0 else ""
+        return f"{self.scoped_label}_{injection_class.__name__}{suffix}"
 
-    def _unary_injection(self, injection_class):
-        """A template for injecting unary function nodes"""
-        # We can't just do an `if other is None` and combine this implementation with
-        # the binary, because users might pass `None` other data on purpose
-        label = self.get_injected_label(injection_class)
+    def _node_injection(self, injection_class, *args):
+        """
+        Create a new node with the same parent as this channel's node, and feed it
+        arguments, or load such a node if it already exists on the parent (based on a
+        name dynamically generated from the injected node class and arguments).
+
+        Args:
+            injection_class (type[Node]): The new node class to instantiate
+            *args: Any arguments for that function node
+
+        Returns:
+            (Node): The instantiated or loaded node.
+        """
+        label = self._get_injection_label(injection_class, *args)
         try:
             # First check if the node already exists
             return self.node.parent.nodes[label]
         except (AttributeError, KeyError):
             # Fall back on creating a new node in case parent is None or node nexists
             return injection_class(
-                self, parent=self.node.parent, label=label, run_after_init=True
+                self, *args, parent=self.node.parent, label=label, run_after_init=True
             )
 
     # We don't wrap __all__ the operators, because you might really want the string or
@@ -539,137 +541,136 @@ def _unary_injection(self, injection_class):
     def __lt__(self, other):
         from pyiron_workflow.node_library.standard import LessThan
 
-        return self._binary_injection(LessThan, other)
+        return self._node_injection(LessThan, other)
 
     def __le__(self, other):
         from pyiron_workflow.node_library.standard import LessThanEquals
 
-        return self._binary_injection(LessThanEquals, other)
+        return self._node_injection(LessThanEquals, other)
 
     def __gt__(self, other):
         from pyiron_workflow.node_library.standard import GreaterThan
 
-        return self._binary_injection(GreaterThan, other)
+        return self._node_injection(GreaterThan, other)
 
     def __ge__(self, other):
         from pyiron_workflow.node_library.standard import GreaterThanEquals
 
-        return self._binary_injection(GreaterThanEquals, other)
+        return self._node_injection(GreaterThanEquals, other)
 
     def __bool__(self):
         from pyiron_workflow.node_library.standard import Bool
 
-        return self._unary_injection(Bool)
+        return self._node_injection(Bool)
 
     def __getattr__(self, name):
         from pyiron_workflow.node_library.standard import GetAttr
 
-        return self._binary_injection(GetAttr, name)
+        return self._node_injection(GetAttr, name)
 
     def __getitem__(self, item):
         from pyiron_workflow.node_library.standard import GetItem
-
-        return self._binary_injection(GetItem, item)
+        return self._node_injection(GetItem, item)
 
     def __len__(self):
         from pyiron_workflow.node_library.standard import Length
 
-        return self._unary_injection(Length)
+        return self._node_injection(Length)
 
     def __contains__(self, other):
         from pyiron_workflow.node_library.standard import Contains
 
-        return self._binary_injection(Contains, other)
+        return self._node_injection(Contains, other)
 
     def __add__(self, other):
         from pyiron_workflow.node_library.standard import Add
 
-        return self._binary_injection(Add, other)
+        return self._node_injection(Add, other)
 
     def __sub__(self, other):
         from pyiron_workflow.node_library.standard import Subtract
 
-        return self._binary_injection(Subtract, other)
+        return self._node_injection(Subtract, other)
 
     def __mul__(self, other):
         from pyiron_workflow.node_library.standard import Multiply
 
-        return self._binary_injection(Multiply, other)
+        return self._node_injection(Multiply, other)
 
     def __matmul__(self, other):
         from pyiron_workflow.node_library.standard import MatrixMultiply
 
-        return self._binary_injection(MatrixMultiply, other)
+        return self._node_injection(MatrixMultiply, other)
 
     def __truediv__(self, other):
         from pyiron_workflow.node_library.standard import Divide
 
-        return self._binary_injection(Divide, other)
+        return self._node_injection(Divide, other)
 
     def __floordiv__(self, other):
         from pyiron_workflow.node_library.standard import FloorDivide
 
-        return self._binary_injection(FloorDivide, other)
+        return self._node_injection(FloorDivide, other)
 
     def __mod__(self, other):
         from pyiron_workflow.node_library.standard import Modulo
 
-        return self._binary_injection(Modulo, other)
+        return self._node_injection(Modulo, other)
 
     def __pow__(self, other):
         from pyiron_workflow.node_library.standard import Power
 
-        return self._binary_injection(Power, other)
+        return self._node_injection(Power, other)
 
     def __and__(self, other):
         from pyiron_workflow.node_library.standard import And
 
-        return self._binary_injection(And, other)
+        return self._node_injection(And, other)
 
     def __xor__(self, other):
         from pyiron_workflow.node_library.standard import XOr
 
-        return self._binary_injection(XOr, other)
+        return self._node_injection(XOr, other)
 
     def __or__(self, other):
         from pyiron_workflow.node_library.standard import Or
 
-        return self._binary_injection(Or, other)
+        return self._node_injection(Or, other)
 
     def __neg__(self):
         from pyiron_workflow.node_library.standard import Negative
 
-        return self._unary_injection(Negative)
+        return self._node_injection(Negative)
 
     def __pos__(self):
         from pyiron_workflow.node_library.standard import Positive
 
-        return self._unary_injection(Positive)
+        return self._node_injection(Positive)
 
     def __abs__(self):
         from pyiron_workflow.node_library.standard import Absolute
 
-        return self._unary_injection(Absolute)
+        return self._node_injection(Absolute)
 
     def __invert__(self):
         from pyiron_workflow.node_library.standard import Invert
 
-        return self._unary_injection(Invert)
+        return self._node_injection(Invert)
 
     def __int__(self):
         from pyiron_workflow.node_library.standard import Int
 
-        return self._unary_injection(Int)
+        return self._node_injection(Int)
 
     def __float__(self):
         from pyiron_workflow.node_library.standard import Float
 
-        return self._unary_injection(Float)
+        return self._node_injection(Float)
 
     def __round__(self):
         from pyiron_workflow.node_library.standard import Round
 
-        return self._unary_injection(Round)
+        return self._node_injection(Round)
 
     # Because we override __getattr__ we need to get and set state for serialization
     def __getstate__(self):

From 89e9356a2d680f41e3b7293b537c7739612ed328 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 12:16:43 -0800
Subject: [PATCH 17/30] Make it optional to inject the base channel as the 0th
 arg

---
 pyiron_workflow/channels.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index bf0a57ba1..fe4a1a4a6 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -511,7 +511,7 @@ def _get_injection_label(self, injection_class, *args):
         suffix = f"_{other_labels}" if len(args) > 0 else ""
         return f"{self.scoped_label}_{injection_class.__name__}{suffix}"
 
-    def _node_injection(self, injection_class, *args):
+    def _node_injection(self, injection_class, *args, inject_self=True):
         """
         Create a new node with the same parent as this channel's node, and feed it
         arguments, or load such a node if it already exists on the parent (based on a
@@ -520,6 +520,8 @@ def _node_injection(self, injection_class, *args):
         Args:
             injection_class (type[Node]): The new node class to instantiate
             *args: Any arguments for that function node
+            inject_self (bool): Whether to pre-pend the args with self. (Default is
+                True.)
 
         Returns:
             (Node): The instantiated or loaded node.
@@ -530,8 +532,9 @@ def _node_injection(self, injection_class, *args):
             return self.node.parent.nodes[label]
         except (AttributeError, KeyError):
             # Fall back on creating a new node in case parent is None or node nexists
+            node_args = (self, *args) if inject_self else args
             return injection_class(
-                self, *args, parent=self.node.parent, label=label, run_after_init=True
+                *node_args, parent=self.node.parent, label=label
             )
 
     # We don't wrap __all__ the operators, because you might really want the string or

From be84f146b07e8dd7c27c42238912b1131b218af8 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 12:16:54 -0800
Subject: [PATCH 18/30] Extend getitem to handle slices

---
 pyiron_workflow/channels.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index fe4a1a4a6..474724bd8 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -572,6 +572,16 @@ def __getattr__(self, name):
         return self._node_injection(GetAttr, name)
 
     def __getitem__(self, item):
+        # Break slices into deeper injections, if any slice arguments are channel-like
+        if isinstance(item, slice) and any(
+            isinstance(slice_input, HasChannel)
+            for slice_input in [item.start, item.stop, item.step]
+        ):
+            from pyiron_workflow.node_library.standard import Slice
+            item = self._node_injection(
+                Slice, item.start, item.stop, item.step, inject_self=False
+            )
+
         from pyiron_workflow.node_library.standard import GetItem
         return self._node_injection(GetItem, item)
 

From 12a4259db47eed1af45f5ab6e76547712bc7f927 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 13:34:54 -0800
Subject: [PATCH 19/30] Add equality and finish multiply

---
 pyiron_workflow/channels.py              | 15 +++++++++++++++
 pyiron_workflow/function.py              | 12 ++++++++++++
 pyiron_workflow/node_library/standard.py | 18 ++++++++++++++++++
 3 files changed, 45 insertions(+)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 474724bd8..9a31524f7 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -551,6 +551,16 @@ def __le__(self, other):
 
         return self._node_injection(LessThanEquals, other)
 
+    def eq(self, other):
+        from pyiron_workflow.node_library.standard import Equals
+
+        return self._node_injection(Equals, other)
+
+    def __ne__(self, other):
+        from pyiron_workflow.node_library.standard import NotEquals
+
+        return self._node_injection(NotEquals, other)
+
     def __gt__(self, other):
         from pyiron_workflow.node_library.standard import GreaterThan
 
@@ -610,6 +620,11 @@ def __mul__(self, other):
 
         return self._node_injection(Multiply, other)
 
+    def __rmul__(self, other):
+        from pyiron_workflow.node_library.standard import RightMultiply
+
+        return self._node_injection(RightMultiply, other)
+
     def __matmul__(self, other):
         from pyiron_workflow.node_library.standard import MatrixMultiply
 
diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index 4192ae108..b1af65753 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -634,6 +634,12 @@ def __lt__(self, other):
     def __le__(self, other):
         return self.channel.__le__(other)
 
+    def eq(self, other):
+        return self.channel.eq(other)
+
+    def __ne__(self, other):
+        return self.channel.__ne__(other)
+
     def __gt__(self, other):
         return self.channel.__gt__(other)
 
@@ -655,6 +661,12 @@ def __add__(self, other):
     def __sub__(self, other):
         return self.channel.__sub__(other)
 
+    def __mul__(self, other):
+        return self.channel.__mul__(other)
+
+    def __rmul__(self, other):
+        return self.channel.__rmul__(other)
+
     def __matmul__(self, other):
         return self.channel.__matmul__(other)
 
diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 7181b5f90..3e3b1c69e 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -88,6 +88,16 @@ def LessThanEquals(obj, other):
     return obj <= other
 
 
+@single_value_node("eq")
+def Equals(obj, other):
+    return obj == other
+
+
+@single_value_node("neq")
+def NotEquals(obj, other):
+    return obj != other
+
+
 @single_value_node("gt")
 def GreaterThan(obj, other):
     return obj > other
@@ -161,6 +171,11 @@ def Multiply(obj, other):
     return obj * other
 
 
+@single_value_node("rmul")
+def RightMultiply(obj, other):
+    return other * obj
+
+
 @single_value_node("matmul")
 def MatrixMultiply(obj, other):
     return obj @ other
@@ -245,6 +260,7 @@ def Round(obj):
     Contains,
     Dir,
     Divide,
+    Equals,
     Float,
     FloorDivide,
     GetAttr,
@@ -262,9 +278,11 @@ def Round(obj):
     Modulo,
     Multiply,
     Negative,
+    NotEquals,
     Or,
     Positive,
     Power,
+    RightMultiply,
     Round,
     Slice,
     String,

From c895cc24aa2e9bfac4fe3eade2960ce9d584d908 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 14:18:55 -0800
Subject: [PATCH 20/30] Give Slice's stop input a show-stopping default

So it doesn't execute prematurely with all those defaults and crash hard
---
 pyiron_workflow/node_library/standard.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 3e3b1c69e..1e7820df3 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -47,7 +47,7 @@ def process_run_result(self, function_output):
 
 
 @single_value_node("slice")
-def Slice(start=None, stop=None, step=None):
+def Slice(start=None, stop=NotData, step=None):
     if start is None:
         if stop is None:
             raise ValueError(

From 90a18102c8422e2d72d781f9cdaf8d03e15c79ea Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 14:20:11 -0800
Subject: [PATCH 21/30] Start modifying bad operators

Some operators like `len(obj)` get really pissy if `obj.__len__()` doesn't return the type they're expecting. Since we want to be returning, e.g. here, channels instead of ints, we'd better not mess with these operators. I'm following the practice of using their dunder names as the basis for a method.
---
 pyiron_workflow/channels.py | 25 ++++++++++++++-----------
 pyiron_workflow/function.py | 12 ++++++------
 2 files changed, 20 insertions(+), 17 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 9a31524f7..b6d9cc566 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -261,13 +261,16 @@ class DataChannel(Channel, ABC):
     which is to say it is data (not `NotData`) and that it conforms to the type hint
     (if one is provided and checking is active).
 
-    Output data facilitates most (but not all) python operators by injecting a new
-    node to perform that operation. These new nodes are instructed to run at the end of
-    instantiation, but this fails cleanly in case they are not ready. This is intended
-    to accommodate two likely scenarios: if you're injecting a node on top of an
-    existing result you probably want the injection result to also be immediately
-    available, but if you're injecting it at the end of something that hasn't run yet
-    you don't want to see an error.
+    Output data facilitates many (but not all) python operators by injecting a new
+    node to perform that operation. Where the operator is not supported, we try to
+    support using the operator's dunder name as a method, e.g. `==` gives us trouble
+    with hashing, but this exploits the dunder method `.__eq__(other)`, so you can call
+    `.eq(other)` on output data.
+    These new nodes are instructed to run at the end of instantiation, but this fails
+    cleanly in case they are not ready. This is intended to accommodate two likely
+    scenarios: if you're injecting a node on top of an existing result you probably
+    want the injection result to also be immediately available, but if you're injecting
+    it at the end of something that hasn't run yet you don't want to see an error.
 
     TODO:
         - Storage (including priority and history)
@@ -534,7 +537,7 @@ def _node_injection(self, injection_class, *args, inject_self=True):
             # Fall back on creating a new node in case parent is None or node nexists
             node_args = (self, *args) if inject_self else args
             return injection_class(
-                *node_args, parent=self.node.parent, label=label
+                *node_args, parent=self.node.parent, label=label, run_after_init=True
             )
 
     # We don't wrap __all__ the operators, because you might really want the string or
@@ -571,7 +574,7 @@ def __ge__(self, other):
 
         return self._node_injection(GreaterThanEquals, other)
 
-    def __bool__(self):
+    def bool(self):
         from pyiron_workflow.node_library.standard import Bool
 
         return self._node_injection(Bool)
@@ -595,12 +598,12 @@ def __getitem__(self, item):
         from pyiron_workflow.node_library.standard import GetItem
         return self._node_injection(GetItem, item)
 
-    def __len__(self):
+    def len(self):
         from pyiron_workflow.node_library.standard import Length
 
         return self._node_injection(Length)
 
-    def __contains__(self, other):
+    def contains(self, other):
         from pyiron_workflow.node_library.standard import Contains
 
         return self._node_injection(Contains, other)
diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index b1af65753..ecba3eef0 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -646,14 +646,14 @@ def __gt__(self, other):
     def __ge__(self, other):
         return self.channel.__ge__(other)
 
-    def __bool__(self):
-        return self.channel.__bool__()
+    def bool(self):
+        return self.channel.bool()
 
-    def __len__(self):
-        return self.channel.__len__()
+    def len(self):
+        return self.channel.len()
 
-    def __contains__(self, other):
-        return self.channel.__contains__(other)
+    def contains(self, other):
+        return self.channel.contains(other)
 
     def __add__(self, other):
         return self.channel.__add__(other)

From bcfdd7e1210e595ddd3d30589abb8394acaa1c74 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 20:01:35 -0800
Subject: [PATCH 22/30] Refactor: slide

---
 pyiron_workflow/channels.py | 38 ++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index b6d9cc566..17e87393b 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -544,6 +544,25 @@ def _node_injection(self, injection_class, *args, inject_self=True):
     # hash or whatever of the actual channel. But we do wrap all the dunder methods
     # that should be unambiguously referring to an operation on values
 
+    def __getattr__(self, name):
+        from pyiron_workflow.node_library.standard import GetAttr
+
+        return self._node_injection(GetAttr, name)
+
+    def __getitem__(self, item):
+        # Break slices into deeper injections, if any slice arguments are channel-like
+        if isinstance(item, slice) and any(
+            isinstance(slice_input, HasChannel)
+            for slice_input in [item.start, item.stop, item.step]
+        ):
+            from pyiron_workflow.node_library.standard import Slice
+            item = self._node_injection(
+                Slice, item.start, item.stop, item.step, inject_self=False
+            )
+
+        from pyiron_workflow.node_library.standard import GetItem
+        return self._node_injection(GetItem, item)
+
     def __lt__(self, other):
         from pyiron_workflow.node_library.standard import LessThan
 
@@ -579,25 +598,6 @@ def bool(self):
 
         return self._node_injection(Bool)
 
-    def __getattr__(self, name):
-        from pyiron_workflow.node_library.standard import GetAttr
-
-        return self._node_injection(GetAttr, name)
-
-    def __getitem__(self, item):
-        # Break slices into deeper injections, if any slice arguments are channel-like
-        if isinstance(item, slice) and any(
-            isinstance(slice_input, HasChannel)
-            for slice_input in [item.start, item.stop, item.step]
-        ):
-            from pyiron_workflow.node_library.standard import Slice
-            item = self._node_injection(
-                Slice, item.start, item.stop, item.step, inject_self=False
-            )
-
-        from pyiron_workflow.node_library.standard import GetItem
-        return self._node_injection(GetItem, item)
-
     def len(self):
         from pyiron_workflow.node_library.standard import Length
 

From fe5ad802f0fb704c7994f1ece4a7c76384eedbb0 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 20:34:58 -0800
Subject: [PATCH 23/30] Add invert on the SingleValue

---
 pyiron_workflow/function.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index ecba3eef0..12372f941 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -700,8 +700,8 @@ def __pos__(self):
     def __abs__(self):
         return self.channel.__abs__()
 
-    def __int__(self):
-        return self.channel.__int__()
+    def __invert__(self):
+        return self.channel.__invert__()
 
     def __float__(self):
         return self.channel.__float__()

From 563e0e73085065cabe95efb044af97cdfb7f0280 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 20:35:22 -0800
Subject: [PATCH 24/30] Redo casts as methods

The builtin functions do a stupid type check
---
 pyiron_workflow/channels.py | 4 ++--
 pyiron_workflow/function.py | 7 +++++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 17e87393b..dd85d689e 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -688,12 +688,12 @@ def __invert__(self):
 
         return self._node_injection(Invert)
 
-    def __int__(self):
+    def int(self):
         from pyiron_workflow.node_library.standard import Int
 
         return self._node_injection(Int)
 
-    def __float__(self):
+    def float(self):
         from pyiron_workflow.node_library.standard import Float
 
         return self._node_injection(Float)
diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index 12372f941..0eb4a5b34 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -703,8 +703,11 @@ def __abs__(self):
     def __invert__(self):
         return self.channel.__invert__()
 
-    def __float__(self):
-        return self.channel.__float__()
+    def int(self):
+        return self.channel.int()
+
+    def float(self):
+        return self.channel.float()
 
     def __round__(self):
         return self.channel.__round__()

From 860250eda681281151a730daa8373a2df2c37f3c Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Fri, 8 Dec 2023 20:35:42 -0800
Subject: [PATCH 25/30] Add tests for most of the new operators

And the way they propagate all the way to SingleValue
---
 tests/integration/test_output_injection.py | 184 +++++++++++++++++----
 1 file changed, 155 insertions(+), 29 deletions(-)

diff --git a/tests/integration/test_output_injection.py b/tests/integration/test_output_injection.py
index b8bc0ef79..3fc550434 100644
--- a/tests/integration/test_output_injection.py
+++ b/tests/integration/test_output_injection.py
@@ -8,42 +8,169 @@ class TestOutputInjection(unittest.TestCase):
     """
     I.e. the process of inserting new nodes on-the-fly by modifying output channels"
     """
+    def setUp(self) -> None:
+        self.wf = Workflow("injection")
+        self.int = Workflow.create.standard.UserInput(42, run_after_init=True)
+        self.list = Workflow.create.standard.UserInput(
+            list(range(10)), run_after_init=True
+        )
 
-    def test_a_few_operations(self):
-        wf = Workflow("output_manipulation")
+    def test_equality(self):
+        with self.subTest("True expressions"):
+            for expression in [
+                self.int < 100,
+                0 < self.int,
+                self.int <= 100,
+                self.int <= 42,
+                0 <= self.int,
+                self.int.eq(42),
+                self.int != 43,
+                self.int > 0,
+                100 > self.int,
+                self.int >= 0,
+                100 >= self.int,
+                self.int >= 42,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertTrue(expression.value)
+
+        with self.subTest("False expressions"):
+            for expression in [
+                self.int > 100,
+                0 > self.int,
+                self.int >= 100,
+                0 >= self.int,
+                self.int != 42,
+                self.int.eq(43),
+                self.int < 0,
+                100 < self.int,
+                self.int <= 0,
+                100 <= self.int,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertFalse(expression.value)
+
+    def test_bool(self):
+        b = self.int.bool()
+        self.assertTrue(b.value)
+        self.int.inputs.user_input = False
+        self.assertFalse(b())
+
+    def test_len(self):
+        self.assertEqual(10, self.list.len().value)
+
+    def test_contains(self):
+        self.assertTrue(self.list.contains(1).value)
+        self.assertFalse(self.list.contains(-1).value)
+
+    def test_algebra(self):
+        x = self.int  # 42
+        for lhs, rhs in [
+            (x + x, 2 * x),
+            (2 * x, x * 2),
+            (x * x, x**2),
+            (x - x, 0 * x),
+            (x + x - x, x),
+            (x / 42, x / x),
+            (x // 2, x / 2),
+            (x // 43, 0 * x),
+            ((x + 1) % x, x + 1 - x),
+            (-x, -1 * x),
+            (+x, (-x)**2 / x),
+            (x, abs(-x)),
+        ]:
+            with self.subTest(f"{lhs.label} == {rhs.label}"):
+                self.assertEqual(lhs.value, rhs.value)
+
+    # This passes fine, but requires numpy so don't include it
+    # def test_matmul(self):
+    #     import numpy as np
+    #
+    #     a = np.random.rand(2, 2)
+    #     b = np.random.rand(2, 2)
+    #     self.wf.a = Workflow.create.standard.UserInput(a, run_after_init=True)
+    #     self.wf.b = Workflow.create.standard.UserInput(b, run_after_init=True)
+    #     self.assertListEqual(
+    #         (self.wf.a @ self.wf.b).value.tolist(),
+    #         (a @ b).tolist()
+    #     )
+
+    def test_logic(self):
+        # Note: We can't invert with not etc. because overloading __bool__ does not work
+        self.true = Workflow.create.standard.UserInput(True, run_after_init=True)
+        self.false = Workflow.create.standard.UserInput(False, run_after_init=True)
 
-        wf.a = Workflow.create.standard.Add(1, 2)
-        wf.b = Workflow.create.standard.Add(3, 4)
-        wf.c = Workflow.create.standard.UserInput(list(range(10)))
-        wf.d = Workflow.create.standard.UserInput({"foo": 42})
+        with self.subTest("True expressions"):
+            for expression in [
+                self.true & True,
+                # True & self.true,  # There's no __land__ etc.
+                self.true & self.true,
+                self.true ^ False,
+                # False ^ self.true,
+                self.true ^ self.false,
+                self.false ^ self.true,
+                self.true | False,
+                self.true | self.false,
+                self.false | self.true,
+                self.false | False | self.true,
+                # False | self.true,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertTrue(expression.value)
+
+        with self.subTest("False expressions"):
+            for expression in [
+                self.true & False,
+                self.false & self.false,
+                self.false & self.true,
+                self.true & self.false,
+                self.true ^ self.true,
+                self.false ^ self.false,
+                self.false | self.false,
+                self.false | False,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertFalse(expression.value)
+
+    def test_casts(self):
+        self.float = Workflow.create.standard.UserInput(42.2, run_after_init=True)
+
+        self.assertIsInstance(self.int.float().value, float)
+        self.assertIsInstance(self.float.int().value, int)
+        self.assertEqual(self.int.value, round(self.float).value)
+
+    def test_access(self):
+
+        self.dict = Workflow.create.standard.UserInput(
+            {"foo": 42}, run_after_init=True
+        )
 
         class Something:
             myattr = 1
 
-        wf.e = Workflow.create.standard.UserInput(Something())
-
-        wf.a.outputs.add < wf.b.outputs.add
-        wf.c.outputs.user_input[:5]
-        wf.d.outputs.user_input["foo"]
-        wf.e.outputs.user_input.myattr
-        out = wf()
-        self.assertDictEqual(
-            out,
-            {
-                'a__add_LessThan_b__add__lt': True,
-                'c__user_input_GetItem_slice(None, 5, None)__getitem': [0, 1, 2, 3, 4],
-                'd__user_input_GetItem_foo__getitem': 42,
-                'e__user_input_GetAttr_myattr__getattr': 1
-            }
+        self.obj = Workflow.create.standard.UserInput(
+            Something(), run_after_init=True
         )
 
-    def test_repeated_access(self):
+        self.assertIsInstance(self.list[0].value, int)
+        self.assertEqual(5, self.list[:5].len().value)
+        self.assertEqual(4, self.list[1:5].len().value)
+        self.assertEqual(3, self.list[-3:].len().value)
+        self.assertEqual(2, self.list[1:5:2].len().value)
+        
+        self.assertEqual(42, self.dict["foo"].value)
+        self.assertEqual(1, self.obj.myattr.value)
+
+    def test_chaining(self):
+        self.assertFalse((self.list[:self.int//42][0] != 0).value)
+
+    def test_repeated_access_in_parent_scope(self):
         wf = Workflow("output_manipulation")
-        wf.n = Workflow.create.standard.UserInput(list(range(10)))
+        wf.list = Workflow.create.standard.UserInput(list(range(10)))
 
-        a = wf.n.outputs.user_input[:4]
-        b = wf.n.outputs.user_input[:4]
-        c = wf.n.outputs.user_input[1:]
+        a = wf.list[:4]
+        b = wf.list[:4]
+        c = wf.list[1:]
 
         self.assertIs(
             a,
@@ -57,9 +184,8 @@ def test_repeated_access(self):
         )
 
     def test_without_parent(self):
-        n = Workflow.create.standard.UserInput(list(range(10)))
-        d1 = n.outputs.user_input[5]
-        d2 = n.outputs.user_input[5]
+        d1 = self.list[5]
+        d2 = self.list[5]
 
         self.assertIsInstance(d1, Node)
         self.assertIsNot(

From d9caaedd35119d7de710ab3acf333c983fbddf8e Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Sat, 9 Dec 2023 13:28:37 -0800
Subject: [PATCH 26/30] Update atomistics notebook

---
 notebooks/atomistics_nodes.ipynb | 141 +++++++++++++++++--------------
 1 file changed, 76 insertions(+), 65 deletions(-)

diff --git a/notebooks/atomistics_nodes.ipynb b/notebooks/atomistics_nodes.ipynb
index 1ecd1c5a1..cfd988d1d 100644
--- a/notebooks/atomistics_nodes.ipynb
+++ b/notebooks/atomistics_nodes.ipynb
@@ -29,7 +29,8 @@
    "source": [
     "Workflow.register(\"calculator\", \"pyiron_workflow.node_library.atomistics.calculator\")\n",
     "Workflow.register(\"macro\", \"pyiron_workflow.node_library.atomistics.macro\")\n",
-    "Workflow.register(\"task\", \"pyiron_workflow.node_library.atomistics.task\")"
+    "Workflow.register(\"task\", \"pyiron_workflow.node_library.atomistics.task\")\n",
+    "Workflow.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")"
    ]
   },
   {
@@ -37,24 +38,49 @@
    "execution_count": 3,
    "id": "bc0e6187-236d-4cba-8674-de14a0520257",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "wf = Workflow(\"ev_curve\")\n",
     "\n",
     "wf.structure = wf.create.task.Bulk(\"Al\")\n",
     "wf.calculator = wf.create.calculator.Emt()\n",
+    "\n",
     "wf.ev = wf.create.macro.EnergyVolumeCurve(\n",
     "    structure=wf.structure, \n",
     "    calculator=wf.calculator,\n",
     ")\n",
+    "wf.ev_plot = wf.create.plotting.Scatter(\n",
+    "    wf.ev.outputs.result_dict['volume'],\n",
+    "    wf.ev.outputs.result_dict['energy']\n",
+    ")\n",
+    "\n",
     "wf.elastic = wf.create.macro.ElasticMatrix(\n",
     "    structure=wf.structure, \n",
     "    calculator=wf.calculator,\n",
     ")\n",
+    "wf.C = wf.elastic.outputs.result_dict[\"C\"]\n",
+    "\n",
     "wf.phonons = wf.create.macro.Phonons(\n",
     "    structure=wf.structure, \n",
     "    calculator=wf.calculator,\n",
-    ")"
+    ")\n",
+    "wf.dos_plot = wf.create.plotting.Scatter(\n",
+    "    wf.phonons.outputs.result_dict[1][\"frequency_points\"],\n",
+    "    wf.phonons.outputs.result_dict[1][\"total_dos\"],\n",
+    ")\n",
+    "\n",
+    "out = wf()"
    ]
   },
   {
@@ -62,36 +88,51 @@
    "execution_count": 4,
    "id": "585b69dc-7140-4891-be1d-9250827f96ac",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "out = wf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a523bcd8-c7f0-4b55-9592-5a3c37c6f34e",
-   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel accumulate_and_run was not connected to ran, andthus could not disconnect from it.\n",
+      "  warn(\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n",
+      "  warn(\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n",
+      "  warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "39.544084907317895"
+       "<matplotlib.collections.PathCollection at 0x149cb2590>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "out.ev__result_dict[\"bulkmodul_eq\"]"
+    "wf.ev_plot()  \n",
+    "# We should be able to look at .value.figure\n",
+    "# but we need to get matplotlib to clear the previous double plot\n",
+    "# This is something to fix in the plotting package."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "65c4cd50-85dd-405f-9d2f-a0f89cb39c7f",
+   "execution_count": 5,
+   "id": "56ec091c-c05a-4d98-8ec3-a98099a26857",
    "metadata": {},
    "outputs": [
     {
@@ -111,74 +152,44 @@
        "        32.8950073 ]])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "out.elastic__result_dict[\"C\"]"
+    "wf.C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "f36b7134-fdaf-439c-a8ed-8e0d12dea6ac",
+   "execution_count": 6,
+   "id": "b738be10-20f0-4be1-abf7-b2fe8053d170",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.00694078, 0.00988315, 0.01328576,\n",
-       "       0.01714862, 0.02147171, 0.02625505, 0.02985557, 0.037727  ,\n",
-       "       0.0434232 , 0.04946553, 0.05566541, 0.06215958, 0.07115592,\n",
-       "       0.08064991, 0.09079663, 0.09922499, 0.107314  , 0.11598167,\n",
-       "       0.12677452, 0.13892215, 0.15277843, 0.16509068, 0.17714585,\n",
-       "       0.18908449, 0.20207234, 0.21663715, 0.23193372, 0.24787345,\n",
-       "       0.26531108, 0.28151195, 0.29720939, 0.31555894, 0.33441182,\n",
-       "       0.35381457, 0.37348888, 0.39467179, 0.41600753, 0.43856869,\n",
-       "       0.46236269, 0.48653458, 0.51129518, 0.53659094, 0.56254718,\n",
-       "       0.59054813, 0.62101398, 0.65275413, 0.68514049, 0.71793811,\n",
-       "       0.75144708, 0.78583379, 0.82253802, 0.86542963, 0.90976761,\n",
-       "       0.95550071, 1.00263393, 1.05112517, 1.10148731, 1.33991857,\n",
-       "       1.36193113, 1.34430559, 1.32697925, 1.30233052, 1.2507499 ,\n",
-       "       1.49092299, 1.63962028, 3.26506443, 2.06872102, 2.14988263,\n",
-       "       2.25562045, 2.26389212, 2.24957095, 2.23068922, 2.19906969,\n",
-       "       2.0990768 , 1.92248659, 2.05381061, 2.22262812, 2.26534629,\n",
-       "       2.39290591, 2.44805769, 2.51126138, 2.56481713, 2.44897559,\n",
-       "       2.39584353, 2.4114828 , 2.49193391, 2.57081989, 2.57019593,\n",
-       "       2.41341987, 2.25573881, 2.29896005, 2.57661295, 2.6898915 ,\n",
-       "       2.52674647, 2.40732058, 2.39797664, 2.6860019 , 2.77461177,\n",
-       "       2.74267508, 2.68147413, 2.65588543, 2.70193114, 2.76928622,\n",
-       "       2.6439207 , 2.65435265, 2.90982222, 2.73490294, 1.98457979,\n",
-       "       1.72106217, 1.58627925, 1.56213379, 1.62154002, 1.73544337,\n",
-       "       1.70256902, 1.65407997, 1.66713389, 1.67643965, 1.63648852,\n",
-       "       1.60695654, 1.56655864, 1.61633361, 1.61150893, 1.55133986,\n",
-       "       1.53831926, 1.50303936, 1.46605859, 1.56752315, 1.43714305,\n",
-       "       1.24968842, 1.45944483, 1.66145587, 1.75923322, 1.73118812,\n",
-       "       1.5623509 , 1.49846885, 1.41120813, 1.37756017, 1.49546786,\n",
-       "       1.17761685, 1.07980165, 0.98605362, 1.08530243, 1.21699949,\n",
-       "       1.93599882, 2.10774636, 2.30948133, 2.46933119, 2.71827848,\n",
-       "       2.83882417, 2.97242386, 3.17702959, 3.24129876, 3.4949328 ,\n",
-       "       3.60987129, 3.83610362, 2.90427366, 2.52017744, 2.24833264,\n",
-       "       2.03412564, 1.82270575, 1.63369566, 1.45666495, 1.27615313,\n",
-       "       1.07133396, 0.8484739 , 0.60012783, 0.15450282, 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        ])"
+       "<matplotlib.collections.PathCollection at 0x149c72590>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "out.phonons__result_dict[1][\"total_dos\"]"
+    "wf.dos_plot()"
    ]
   }
  ],

From 77adf425be439365a3ca7664e4d0f103996285a9 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Sat, 9 Dec 2023 13:42:46 -0800
Subject: [PATCH 27/30] Update quickstart

---
 notebooks/quickstart.ipynb | 674 +++++++++++++++++++++++++++++--------
 1 file changed, 530 insertions(+), 144 deletions(-)

diff --git a/notebooks/quickstart.ipynb b/notebooks/quickstart.ipynb
index 38eda8dfb..344a454ee 100644
--- a/notebooks/quickstart.ipynb
+++ b/notebooks/quickstart.ipynb
@@ -189,7 +189,9 @@
     "\n",
     "We can work with nodes all by themselves, but since the whole point is to connect them together to make a computation graph, we can get extra tools by intentionally making these children of a `Workflow` node.\n",
     "\n",
-    "The `Workflow` class not only gives us access to the decorators for defining new nodes, but also lets us register modules of existing nodes and use them. Let's put together a workflow that uses both an existing node from a package, and a `Function` node that is more general than we used above in that it allows us to have multiple return values. This function node will also exploit our ability to name outputs and give type hints:"
+    "The `Workflow` class not only gives us access to the decorators for defining new nodes, but also lets us register modules of existing nodes and use them. Let's put together a workflow that uses both an existing node from a package, and a `Function` node that is more general than we used above in that it allows us to have multiple return values. This function node will also exploit our ability to name outputs and give type hints. \n",
+    "\n",
+    "We can also take output channels (or single value nodes) and perform many (but not all...) python operations on them to dynamically create new output nodes! Below see how we do math and indexing right on the output channels:"
    ]
   },
   {
@@ -207,238 +209,611 @@
        "<!-- Generated by graphviz version 8.0.5 (0)\n",
        " -->\n",
        "<!-- Title: clustermy_workflow Pages: 1 -->\n",
-       "<svg width=\"863pt\" height=\"406pt\"\n",
-       " viewBox=\"0.00 0.00 862.92 406.25\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 402.25)\">\n",
+       "<svg width=\"3668pt\" height=\"569pt\"\n",
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+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 565.25)\">\n",
        "<title>clustermy_workflow</title>\n",
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-       "<text text-anchor=\"middle\" x=\"427.46\" y=\"-4.95\" font-family=\"Times,serif\" font-size=\"14.00\">my_workflow: Workflow</text>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-565.25 3663.64,-565.25 3663.64,4 -4,4\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1829.82\" y=\"-4.95\" font-family=\"Times,serif\" font-size=\"14.00\">my_workflow: Workflow</text>\n",
        "<g id=\"clust1\" class=\"cluster\">\n",
        "<title>clustermy_workflowInputs</title>\n",
        "<defs>\n",
-       "<linearGradient id=\"clust1_l_0\" gradientUnits=\"userSpaceOnUse\" x1=\"8\" y1=\"-121.25\" x2=\"153.25\" y2=\"-121.25\" >\n",
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        "<g id=\"clust2\" class=\"cluster\">\n",
        "<title>clustermy_workflowOutputs</title>\n",
        "<defs>\n",
-       "<linearGradient id=\"clust2_l_1\" gradientUnits=\"userSpaceOnUse\" x1=\"846.92\" y1=\"-121.25\" x2=\"748.55\" y2=\"-121.25\" >\n",
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        "</g>\n",
        "<g id=\"clust3\" class=\"cluster\">\n",
-       "<title>clustermy_workflowarrays</title>\n",
+       "<title>clustermy_workflowarange</title>\n",
        "<defs>\n",
-       "<linearGradient id=\"clust3_l_2\" gradientUnits=\"userSpaceOnUse\" x1=\"321.28\" y1=\"-116.25\" x2=\"321.28\" y2=\"-336.25\" >\n",
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        "</g>\n",
        "<g id=\"clust4\" class=\"cluster\">\n",
-       "<title>clustermy_workflowarraysInputs</title>\n",
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        "<g id=\"clust6\" class=\"cluster\">\n",
-       "<title>clustermy_workflowplot</title>\n",
+       "<title>clustermy_workflowarange__len_Subtract_1</title>\n",
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-       "<text text-anchor=\"middle\" x=\"610.93\" y=\"-372.95\" font-family=\"Times,serif\" font-size=\"14.00\">plot: Scatter</text>\n",
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-       "<title>clustermy_workflowplotInputs</title>\n",
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+       "<title>clustermy_workflowarange__len_Subtract_1Outputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust9\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_Slice_None_arange__len_Subtract_1__sub_None</title>\n",
+       "<defs>\n",
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+       "</g>\n",
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+       "</g>\n",
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+       "<title>clustermy_workflowarange__arange_Slice_None_arange__len_Subtract_1__sub_NoneOutputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust12\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_GetItem_arange__arange_Slice_None_arange__len_Subtract_1__sub_None__slice</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust13\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_GetItem_arange__arange_Slice_None_arange__len_Subtract_1__sub_None__sliceInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust14\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_GetItem_arange__arange_Slice_None_arange__len_Subtract_1__sub_None__sliceOutputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust15\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_GetItem_arange__arange_Slice_None_arange__len_Subtract_1__sub_None__slice__getitem_Power_2</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust16\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_GetItem_arange__arange_Slice_None_arange__len_Subtract_1__sub_None__slice__getitem_Power_2Inputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust17\" class=\"cluster\">\n",
+       "<title>clustermy_workflowarange__arange_GetItem_arange__arange_Slice_None_arange__len_Subtract_1__sub_None__slice__getitem_Power_2Outputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust18\" class=\"cluster\">\n",
+       "<title>clustermy_workflowplot</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"3415.65\" y=\"-426.95\" font-family=\"Times,serif\" font-size=\"14.00\">plot: Scatter</text>\n",
+       "</g>\n",
+       "<g id=\"clust19\" class=\"cluster\">\n",
+       "<title>clustermy_workflowplotInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
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-       "<!-- clustermy_workflowInputsarrays__x -->\n",
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       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x1175b8490>"
+       "<graphviz.graphs.Digraph at 0x10f79ba50>"
       ]
      },
      "execution_count": 7,
@@ -451,17 +826,19 @@
     "\n",
     "wf = Workflow(\"my_workflow\")\n",
     "\n",
-    "@Workflow.wrap_as.function_node(\"x\", \"x_sq\")\n",
-    "def SquareRange(x: int) -> tuple[np.ndarray, np.ndarray]:\n",
-    "    x = np.arange(x)\n",
-    "    return x, (x**2)\n",
+    "@Workflow.wrap_as.function_node(\"arange\", \"len\")\n",
+    "def Arange(n: int) -> tuple[np.ndarray, int]:\n",
+    "    \"\"\"\n",
+    "    Two outputs is silly overkill, but just to demonstrate how Function nodes work\n",
+    "    \"\"\"\n",
+    "    return np.arange(n), n\n",
     "\n",
     "wf.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")\n",
     "\n",
-    "wf.arrays = SquareRange()\n",
+    "wf.arange = Arange(10)\n",
     "wf.plot = wf.create.plotting.Scatter(\n",
-    "    x=wf.arrays.outputs.x,\n",
-    "    y=wf.arrays.outputs.x_sq\n",
+    "    x=wf.arange.outputs.arange[:wf.arange.outputs.len -1],\n",
+    "    y=wf.arange.outputs.arange[:wf.arange.outputs.len -1]**2\n",
     ")\n",
     "\n",
     "wf.draw()"
@@ -472,7 +849,7 @@
    "id": "ffc897e4-0f12-4231-8ebe-82862c890de5",
    "metadata": {},
    "source": [
-    "We can see that the workflow automatically exposes unconnected IO of its children and gives them a name based on the child node's name and that node's IO name.\n",
+    "We can see that the workflow automatically exposes unconnected IO of its children and gives them a name based on the child node's name and that node's IO name. Further, the math and indexing we do automatically injects new nodes after the output. Note that the slicing nodes get re-used in both occurrences for computational efficiency.\n",
     "\n",
     "Let's run our workflow and look at the result:"
    ]
@@ -483,10 +860,18 @@
    "id": "c499c0ed-7af5-491a-b340-2d2f4f48529c",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/node.py:613: UserWarning: The keyword 'arrays__x' was not found among input labels. If you are trying to update a node keyword, please use attribute assignment directly instead of calling\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1179f9c10>"
+       "<matplotlib.collections.PathCollection at 0x10ff903d0>"
       ]
      },
      "execution_count": 8,
@@ -495,7 +880,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -527,13 +912,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The channel x cannot take the value `5.5` because it is not compliant with the type hint <class 'int'>\n"
+      "Can only set Channel object or connect to existing channels, but the attribute x got assigned 5.5 of type <class 'float'>\n"
      ]
     }
    ],
    "source": [
     "try:\n",
-    "    wf.arrays.inputs.x = 5.5\n",
+    "    wf.arange.inputs.x = 5.5\n",
     "except TypeError as e:\n",
     "    message = e.args[0]\n",
     "    print(message)"
@@ -560,15 +945,15 @@
    "source": [
     "@Workflow.wrap_as.macro_node()\n",
     "def MySquarePlot(macro):\n",
-    "    macro.arrays = SquareRange()\n",
+    "    macro.arange = Arange()\n",
     "    macro.plot = macro.create.plotting.Scatter(\n",
-    "        x=macro.arrays.outputs.x,\n",
-    "        y=macro.arrays.outputs.x_sq\n",
+    "        x=macro.arange.outputs.arange,\n",
+    "        y=macro.arange.outputs.arange**2\n",
     "    )\n",
-    "    macro.inputs_map = {\"arrays__x\": \"n\"}\n",
+    "    macro.inputs_map = {\"arange__n\": \"n\"}\n",
     "    macro.outputs_map = {\n",
-    "        \"arrays__x\": \"x\",\n",
-    "        \"arrays__x_sq\": \"y\",\n",
+    "        \"arange__arange\": \"x\",\n",
+    "        \"arange__len\": \"n\",\n",
     "        \"plot__fig\": \"fig\"\n",
     "    }\n",
     "    # Note that we also forced regularly hidden IO to be exposed!\n",
@@ -584,8 +969,9 @@
     {
      "data": {
       "text/plain": [
-       "{'square_plot__fig': <matplotlib.collections.PathCollection at 0x117de73d0>,\n",
-       " 'shifted_square_plot__fig': <matplotlib.collections.PathCollection at 0x117de1490>}"
+       "{'square_plot__n': 10,\n",
+       " 'square_plot__fig': <matplotlib.collections.PathCollection at 0x1100aab10>,\n",
+       " 'plus_one_square_plot__fig': <matplotlib.collections.PathCollection at 0x10fe77710>}"
       ]
      },
      "execution_count": 11,
@@ -594,7 +980,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -607,10 +993,10 @@
     "wf2 = Workflow(\"my_composed_workflow\")\n",
     "\n",
     "wf2.square_plot = MySquarePlot(n=10)\n",
-    "wf2.shift = AddOne(wf2.square_plot.outputs.x)\n",
-    "wf2.shifted_square_plot = wf2.create.plotting.Scatter(\n",
-    "    x=wf2.shift,\n",
-    "    y=wf2.square_plot.outputs.y,\n",
+    "wf2.plus_one = wf2.square_plot.outputs.x + 1\n",
+    "wf2.plus_one_square_plot = wf2.create.plotting.Scatter(\n",
+    "    x=wf2.square_plot.outputs.x,\n",
+    "    y=wf2.plus_one**2,\n",
     ")\n",
     "wf2()"
    ]

From 99bd93c34236d922df3506928239b15f9adc6ad2 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Sat, 9 Dec 2023 14:20:31 -0800
Subject: [PATCH 28/30] Update deepdive

---
 notebooks/deepdive.ipynb | 2170 +++++++++++++++++++++++++-------------
 1 file changed, 1438 insertions(+), 732 deletions(-)

diff --git a/notebooks/deepdive.ipynb b/notebooks/deepdive.ipynb
index 1c4d22eea..781dd5369 100644
--- a/notebooks/deepdive.ipynb
+++ b/notebooks/deepdive.ipynb
@@ -524,8 +524,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to run, andthus could not disconnect from it.\n",
-      "  warn(\n",
       "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n",
       "  warn(\n"
      ]
@@ -636,6 +634,210 @@
     "This let's us set up nodes which only start running after _all_ of their up-data-stream nodes have fired their `ran` signal. This is the default behaviour when `Composite` `Workflow` or `Macro` nodes automate their execution flow for DAG data graphs. We'll look at it again near the end of the notebook when we talk about remote execution."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6d464066-4271-41be-a34f-20c78d75867c",
+   "metadata": {},
+   "source": [
+    "# Output manipulation\n",
+    "\n",
+    "Most (but not all) python operations can be performed _directly on output channels_. This works by injecting new nodes after the output channels to perform the requested operation.\n",
+    "\n",
+    "Let's look at how we can repeat some of the above examples much more succinctly using this feature:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "98312fbb-0e87-417c-9780-d22903cdb3f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subtract and square\n",
+    "\n",
+    "x = Linear(x=4)\n",
+    "y = Linear(x=1)\n",
+    "((x.outputs.x - y.outputs.x)**2).pull()  # It's just a node so we can pull it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b0e8fc87-fba1-4501-882b-f162c4eadf97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Times two\n",
+    "l = Linear(x=10, run_after_init=True)\n",
+    "(2*l.outputs.x).value  # These nodes will try to run right away if everything upstream is ready"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3052c26c-3559-4d61-8c93-08708293b88c",
+   "metadata": {},
+   "source": [
+    "This also works with more sophisticated features like attribute and item access, including slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8a195c41-233e-4076-ad77-008c93297f9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "foo = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6805b0c3-9103-49f4-bc29-569b0b4d6ed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function list.reverse()>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo.reverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7c4cbe66-9b0a-428b-835f-31959a7f75bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_list = Linear(x=[1,2,3,4], run_after_init=True)\n",
+    "a_list.outputs.x[:2].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "16c2d0de-de6f-4b33-84e4-aefbe5db4177",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_dict = Linear(x={\"a\": 1, \"b\": 2}, run_after_init=True)\n",
+    "a_dict.outputs.x[\"a\"].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "30b4ed75-bb73-44bb-b6d9-fe525b924652",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Foo:\n",
+    "    bar = 42\n",
+    "    \n",
+    "an_object = Linear(x=Foo(), run_after_init=True)\n",
+    "an_object.outputs.x.bar.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eab9c6b6-c954-471a-8613-792590e0464f",
+   "metadata": {},
+   "source": [
+    "Some features don't work this way, e.g. overriding the `==` operator has other deterious effects so we don't do that. Most of these operators are available as a method on output channels, based on their dunder name. E.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "786b1402-b595-4337-8872-fd58687c2725",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(True, False)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = Linear(x=42)\n",
+    "b = Linear(x=42)\n",
+    "c = Linear(x=0)\n",
+    "\n",
+    "a_eq_b = a.outputs.x.eq(b.outputs.x)\n",
+    "a_eq_c = a.outputs.x.eq(c.outputs.x)\n",
+    "\n",
+    "(a_eq_b | a_eq_c).pull(), (a_eq_b & a_eq_c).pull()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e5c531a3-77e4-48ad-a189-fed619e79baa",
@@ -645,14 +847,14 @@
     "\n",
     "Many functions return just a single value. In this case, we can take advantage of the `SingleValue` node class which employs a bunch of syntactic tricks to make our lives easier.\n",
     "\n",
-    "The main difference between this and it's parent the `Function` class is that attribute and item access fall back to looking for attributes and items of this single output value.\n",
+    "The main difference between this and it's parent the `Function` class is that attribute and item access fall back to looking for attributes and items of this single output channel. I.e. you can use a single value node in many places you'd use an output channel, including in connection formation and output manipulation\n",
     "\n",
-    "Let's look at a use case:"
+    "Let's look at a use case for output manipulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "id": "1a4e9693-0980-4435-aecc-3331d8b608dd",
    "metadata": {},
    "outputs": [],
@@ -664,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "id": "7c4d314b-33bb-4a67-bfb9-ed77fba3949c",
    "metadata": {},
    "outputs": [
@@ -689,8 +891,8 @@
     "lin()\n",
     "\n",
     "print(type(lin.outputs.linspace.value))  # Output is just what we expect\n",
-    "print(lin[1:4])  # Gets items from the output\n",
-    "print(lin.mean())  # Finds the method on the output -- a special feature of SingleValueNode"
+    "print(lin[1:4].value)  # Gets items from the output\n",
+    "print(lin.mean.value())  # Outputs the method on the output, which we can then call"
    ]
   },
   {
@@ -698,12 +900,12 @@
    "id": "eef23cb0-6192-4fe6-b9cc-007e261e347a",
    "metadata": {},
    "source": [
-    "The other advantage is that single value nodes can also be connected directly to input, since there is only one possible data connection. Of course it has a construction decorator just like `Function`, so let's replace `@function_node` with `@single_value_node` in one of our examples above to see how it tightens up the syntax a bit:"
+    "Our examples above also become more compact:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "id": "61ae572f-197b-4a60-8d3e-e19c1b9cc6e2",
    "metadata": {},
    "outputs": [
@@ -713,7 +915,7 @@
        "4"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -734,6 +936,35 @@
     "t2.pull()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "01780601-f2fd-4730-acb8-95a7359d4b3c",
+   "metadata": {},
+   "source": [
+    "Or even just"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "5dd7ebc2-b45f-4759-bfc4-d4dd29afe216",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2*l).value"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b2e56a64-d053-4127-bb8c-069777c1c6b5",
@@ -744,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "id": "6569014a-815b-46dd-8b47-4e1cd4584b3b",
    "metadata": {},
    "outputs": [
@@ -758,7 +989,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -813,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "id": "1cd000bd-9b24-4c39-9cac-70a3291d0660",
    "metadata": {},
    "outputs": [],
@@ -840,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "id": "7964df3c-55af-4c25-afc5-9e07accb606a",
    "metadata": {},
    "outputs": [
@@ -866,7 +1097,7 @@
     "for i, (label, node) in enumerate(wf.nodes.items()):\n",
     "    x = i / len(wf)\n",
     "    node(x=x)\n",
-    "    print(f\"{label} == {node.label}) {x} > 0.5 {node.single_value}\")"
+    "    print(f\"{label} == {node.label}) {x} > 0.5 {node.value}\")"
    ]
   },
   {
@@ -887,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 38,
    "id": "809178a5-2e6b-471d-89ef-0797db47c5ad",
    "metadata": {},
    "outputs": [
@@ -941,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 39,
    "id": "52c48d19-10a2-4c48-ae81-eceea4129a60",
    "metadata": {},
    "outputs": [
@@ -951,7 +1182,7 @@
        "{'ay': 3, 'a + b + 2': 7}"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -979,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 40,
    "id": "bb35ba3e-602d-4c9c-b046-32da9401dd1c",
    "metadata": {},
    "outputs": [
@@ -989,7 +1220,7 @@
        "(7, 3)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1008,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 41,
    "id": "2b0d2c85-9049-417b-8739-8a8432a1efbe",
    "metadata": {},
    "outputs": [
@@ -1350,10 +1581,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x10c35b450>"
+       "<graphviz.graphs.Digraph at 0x142cbe050>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1380,14 +1611,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 42,
    "id": "ae500d5e-e55b-432c-8b5f-d5892193cdf5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a4dc82761024407e909840aab64b54de",
+       "model_id": "f77a418c673c4579b9a3a6cbd090ef8e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1406,10 +1637,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x14e7c5d50>"
+       "<matplotlib.collections.PathCollection at 0x1590fb090>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1452,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 43,
    "id": "2114d0c3-cdad-43c7-9ffa-50c36d56d18f",
    "metadata": {},
    "outputs": [
@@ -1666,10 +1897,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x14e8bfb90>"
+       "<graphviz.graphs.Digraph at 0x142519950>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1706,7 +1937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 44,
    "id": "c71a8308-f8a1-4041-bea0-1c841e072a6d",
    "metadata": {},
    "outputs": [],
@@ -1716,7 +1947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 45,
    "id": "2b9bb21a-73cd-444e-84a9-100e202aa422",
    "metadata": {},
    "outputs": [
@@ -1734,7 +1965,7 @@
        "13"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1781,7 +2012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 46,
    "id": "3668f9a9-adca-48a4-84ea-13add965897c",
    "metadata": {},
    "outputs": [
@@ -1791,7 +2022,7 @@
        "{'intermediate': 102, 'plus_three': 103}"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1829,7 +2060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 47,
    "id": "9aaeeec0-5f88-4c94-a6cc-45b56d2f0111",
    "metadata": {},
    "outputs": [],
@@ -1848,18 +2079,12 @@
     "    macro.outputs_map = {\n",
     "        \"calc__energy_pot\": \"energy\",\n",
     "        \"structure__structure\": \"structure\",\n",
-    "    }\n",
-    "\n",
-    "@Workflow.wrap_as.single_value_node()\n",
-    "def PerAtomEnergyDifference(structure1, energy1, structure2, energy2):\n",
-    "    # The unrelaxed structure is fine, we're just using it to get n_atoms\n",
-    "    de = (energy2[-1]/len(structure2)) - (energy1[-1]/len(structure1))\n",
-    "    return de"
+    "    }"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 48,
    "id": "a832e552-b3cc-411a-a258-ef21574fc439",
    "metadata": {},
    "outputs": [],
@@ -1868,12 +2093,28 @@
     "wf.element = wf.create.standard.UserInput()\n",
     "wf.min_phase1 = LammpsMinimize(element=wf.element)\n",
     "wf.min_phase2 = LammpsMinimize(element=wf.element)\n",
-    "wf.compare = PerAtomEnergyDifference(\n",
-    "    wf.min_phase1.outputs.structure,\n",
-    "    wf.min_phase1.outputs.energy,\n",
-    "    wf.min_phase2.outputs.structure,\n",
-    "    wf.min_phase2.outputs.energy,\n",
-    ")\n",
+    "\n",
+    "wf.e1 = wf.min_phase1.outputs.energy[-1]\n",
+    "wf.n1 = wf.min_phase1.outputs.structure.len()\n",
+    "wf.e2 = wf.min_phase2.outputs.energy[-1]\n",
+    "wf.n2 = wf.min_phase2.outputs.structure.len()\n",
+    "wf.compare = (wf.e2 / wf.n2) - (wf.e1 / wf.n1)\n",
+    "\n",
+    "\n",
+    "# Or we could write a single node to do that:\n",
+    "\n",
+    "# @Workflow.wrap_as.single_value_node()\n",
+    "# def PerAtomEnergyDifference(structure1, energy1, structure2, energy2):\n",
+    "#     # The unrelaxed structure is fine, we're just using it to get n_atoms\n",
+    "#     sub = (energy2[-1]/len(structure2)) - (energy1[-1]/len(structure1))\n",
+    "#     return sub\n",
+    "\n",
+    "# wf.compare = PerAtomEnergyDifference(\n",
+    "#     wf.min_phase1.outputs.structure,\n",
+    "#     wf.min_phase1.outputs.energy,\n",
+    "#     wf.min_phase2.outputs.structure,\n",
+    "#     wf.min_phase2.outputs.energy,\n",
+    "# )\n",
     "\n",
     "wf.inputs_map = {\n",
     "    \"element__user_input\": \"element\",\n",
@@ -1886,7 +2127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 49,
    "id": "b764a447-236f-4cb7-952a-7cba4855087d",
    "metadata": {},
    "outputs": [
@@ -1899,1251 +2140,1715 @@
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+       "</g>\n",
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+       "<title>clusterphase_preferencee1Outputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-2198.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust15\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen1</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1246.97\" y=\"-2456.95\" font-family=\"Times,serif\" font-size=\"14.00\">n1: Length</text>\n",
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+       "<title>clusterphase_preferencen1Inputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
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+       "<title>clusterphase_preferencen1Outputs</title>\n",
+       "<defs>\n",
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+       "</defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-2399.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust18\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2</title>\n",
+       "<defs>\n",
+       "<linearGradient id=\"clust18_l_17\" gradientUnits=\"userSpaceOnUse\" x1=\"1251.68\" y1=\"-30.25\" x2=\"1251.68\" y2=\"-304.25\" >\n",
+       "<stop offset=\"0\" style=\"stop-color:#17becf;stop-opacity:1.;\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1251.68\" y=\"-286.95\" font-family=\"Times,serif\" font-size=\"14.00\">e2: GetItem</text>\n",
+       "</g>\n",
+       "<g id=\"clust19\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2Inputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1151.27\" y=\"-256.95\" font-family=\"Times,serif\" font-size=\"14.00\">Inputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust20\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2Outputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-256.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust21\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen2</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1246.97\" y=\"-514.95\" font-family=\"Times,serif\" font-size=\"14.00\">n2: Length</text>\n",
+       "</g>\n",
+       "<g id=\"clust22\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen2Inputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1151.27\" y=\"-484.95\" font-family=\"Times,serif\" font-size=\"14.00\">Inputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust23\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen2Outputs</title>\n",
+       "<defs>\n",
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+       "</linearGradient>\n",
+       "</defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-484.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust24\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2__getitem_Divide_n2__len</title>\n",
+       "<defs>\n",
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+       "<stop offset=\"0\" style=\"stop-color:#17becf;stop-opacity:1.;\"/>\n",
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+       "</linearGradient>\n",
+       "</defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1573.85\" y=\"-832.95\" font-family=\"Times,serif\" font-size=\"14.00\">e2__getitem_Divide_n2__len: Divide</text>\n",
+       "</g>\n",
+       "<g id=\"clust25\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2__getitem_Divide_n2__lenInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust26\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2__getitem_Divide_n2__lenOutputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1656.47\" y=\"-802.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust27\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__len</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1573.85\" y=\"-2336.95\" font-family=\"Times,serif\" font-size=\"14.00\">e1__getitem_Divide_n1__len: Divide</text>\n",
+       "</g>\n",
+       "<g id=\"clust28\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__lenInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust29\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__lenOutputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1656.47\" y=\"-2252.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust30\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencecompare</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1840.6\" y=\"-940.95\" font-family=\"Times,serif\" font-size=\"14.00\">compare: Subtract</text>\n",
+       "</g>\n",
+       "<g id=\"clust31\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencecompareInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust32\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencecompareOutputs</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Inputsstructure__cubic -->\n",
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        "<title>clusterphase_preferencemin_phase1Inputsstructure__cubic</title>\n",
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        "<!-- clusterphase_preferenceInputsmin_phase1__structure__cubic&#45;&gt;clusterphase_preferencemin_phase1Inputsstructure__cubic -->\n",
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        "<title>clusterphase_preferenceInputsmin_phase1__structure__cubic&#45;&gt;clusterphase_preferencemin_phase1Inputsstructure__cubic</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Inputscalc__n_ionic_steps -->\n",
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        "<title>clusterphase_preferencemin_phase1Inputscalc__n_ionic_steps</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__energy_tot -->\n",
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        "<title>clusterphase_preferencemin_phase1Outputscalc__energy_tot</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__forces -->\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__indices -->\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__pressures -->\n",
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        "<!-- clusterphase_preferencemin_phase2Outputscalc__pressures -->\n",
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+       "<!-- clusterphase_preferencee1__getitem_Divide_n1__lenOutputstruediv -->\n",
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+       "</g>\n",
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+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__lenOutputstruediv&#45;&gt;clusterphase_preferencecompareInputsother</title>\n",
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        "<!-- clusterphase_preferencecompareInputsrun -->\n",
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        "</g>\n",
        "<!-- clusterphase_preferencecompareOutputsran -->\n",
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        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x14e987250>"
+       "<graphviz.graphs.Digraph at 0x1591ab990>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3154,7 +3859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 50,
    "id": "b51bef25-86c5-4d57-80c1-ab733e703caf",
    "metadata": {},
    "outputs": [
@@ -3170,12 +3875,12 @@
    ],
    "source": [
     "out = wf(element=\"Al\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=4, lattice_guess2=4)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 51,
    "id": "091e2386-0081-436c-a736-23d019bd9b91",
    "metadata": {},
    "outputs": [
@@ -3199,7 +3904,7 @@
    ],
    "source": [
     "out = wf(element=\"Mg\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=3, lattice_guess2=3)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
@@ -3216,7 +3921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 52,
    "id": "4cdffdca-48d3-4486-9045-48102c7e5f31",
    "metadata": {},
    "outputs": [
@@ -3230,9 +3935,9 @@
       "  warn(\n",
       "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel element was not connected to user_input, andthus could not disconnect from it.\n",
       "  warn(\n",
-      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel structure was not connected to structure1, andthus could not disconnect from it.\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel structure was not connected to obj, andthus could not disconnect from it.\n",
       "  warn(\n",
-      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel energy was not connected to energy1, andthus could not disconnect from it.\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel energy was not connected to obj, andthus could not disconnect from it.\n",
       "  warn(\n"
      ]
     }
@@ -3254,7 +3959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 53,
    "id": "ed4a3a22-fc3a-44c9-9d4f-c65bc1288889",
    "metadata": {},
    "outputs": [
@@ -3279,12 +3984,12 @@
    "source": [
     "# Bad guess\n",
     "out = wf(element=\"Al\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=3, lattice_guess2=3.1)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 54,
    "id": "5a985cbf-c308-4369-9223-b8a37edb8ab1",
    "metadata": {},
    "outputs": [
@@ -3309,7 +4014,7 @@
    "source": [
     "# Good guess\n",
     "out = wf(element=\"Al\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=4.05, lattice_guess2=3.2)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
@@ -3352,7 +4057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 55,
    "id": "aa575249-b209-4e0c-9ea6-a82bc69dc833",
    "metadata": {},
    "outputs": [
@@ -3361,25 +4066,21 @@
      "output_type": "stream",
      "text": [
       "None 1\n",
-      "<Future at 0x1512f00d0 state=pending> <class 'pyiron_workflow.channels.NotData'>\n"
+      "<Future at 0x15872c9d0 state=pending> <class 'pyiron_workflow.channels.NotData'>\n"
      ]
     }
    ],
    "source": [
-    "@Workflow.wrap_as.single_value_node(\"sum\")\n",
-    "def Add(x, y):\n",
-    "    return x + y\n",
-    "\n",
     "wf = Workflow(\"with_executor\")\n",
-    "wf.a1 = Add(0, 1)\n",
-    "wf.a2 = Add(2, 3)\n",
-    "wf.b = Add(wf.a1, wf.a2)\n",
+    "wf.a1 = wf.create.standard.Add(0, 1)\n",
+    "wf.a2 = wf.create.standard.Add(2, 3)\n",
+    "wf.b = wf.a1 + wf.a2\n",
     "\n",
     "wf.a2.executor = wf.create.Executor()\n",
     "wf()\n",
     "\n",
-    "print(wf.a1.future, wf.a1.outputs.sum.value)\n",
-    "print(wf.a2.future, wf.a2.outputs.sum.value)"
+    "print(wf.a1.future, wf.a1.outputs.add.value)\n",
+    "print(wf.a2.future, wf.a2.outputs.add.value)"
    ]
   },
   {
@@ -3392,7 +4093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 56,
    "id": "c1b7b4e9-1c76-470c-ba6e-a58ea3f611f6",
    "metadata": {},
    "outputs": [
@@ -3420,7 +4121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 57,
    "id": "7e98058b-a791-4cb1-ae2c-864ad7e56cee",
    "metadata": {},
    "outputs": [],
@@ -3438,7 +4139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 58,
    "id": "0d1b4005-488e-492f-adcb-8ad7235e4fe3",
    "metadata": {},
    "outputs": [
@@ -3447,7 +4148,7 @@
      "output_type": "stream",
      "text": [
       "None 1\n",
-      "<Future at 0x151389a90 state=pending> <class 'pyiron_workflow.channels.NotData'>\n",
+      "<Future at 0x15bb49bd0 state=pending> <class 'pyiron_workflow.channels.NotData'>\n",
       "Finally 5\n",
       "b (Add) output single-value: 6\n"
      ]
@@ -3456,15 +4157,15 @@
    "source": [
     "with Workflow.create.Executor() as executor:\n",
     "    wf = Workflow(\"with_executor\")\n",
-    "    wf.a1 = Add(0, 1)\n",
-    "    wf.a2 = Add(2, 3)\n",
-    "    wf.b = Add(wf.a1, wf.a2)\n",
+    "    wf.a1 = wf.create.standard.Add(0, 1)\n",
+    "    wf.a2 = wf.create.standard.Add(2, 3)\n",
+    "    wf.b = wf.a1 + wf.a2\n",
     "\n",
     "    wf.a2.executor = executor\n",
     "    wf()\n",
     "    \n",
-    "    print(wf.a1.future, wf.a1.outputs.sum.value)\n",
-    "    print(wf.a2.future, wf.a2.outputs.sum.value)\n",
+    "    print(wf.a1.future, wf.a1.outputs.add.value)\n",
+    "    print(wf.a2.future, wf.a2.outputs.add.value)\n",
     "    \n",
     "    print(\"Finally\", wf.a2.future.result())\n",
     "    print(wf.b)"
@@ -3482,7 +4183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 59,
    "id": "d03ca074-35a0-4e0d-9377-d4eaa5521f85",
    "metadata": {},
    "outputs": [],
@@ -3501,7 +4202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 60,
    "id": "a7c07aa0-84fc-4f43-aa4f-6498c0837d76",
    "metadata": {},
    "outputs": [
@@ -3509,7 +4210,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.029127317946404\n"
+      "6.018927805998828\n"
      ]
     }
    ],
@@ -3533,7 +4234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 61,
    "id": "b062ab5f-9b98-4843-8925-b93bf4c173f8",
    "metadata": {},
    "outputs": [
@@ -3541,7 +4242,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.678937633987516\n"
+      "3.0853979130042717\n"
      ]
     }
    ],
@@ -3615,7 +4316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 62,
    "id": "0b373764-b389-4c24-8086-f3d33a4f7fd7",
    "metadata": {},
    "outputs": [
@@ -3629,7 +4330,7 @@
        " 17.230249999999995]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3666,7 +4367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 63,
    "id": "0dd04b4c-e3e7-4072-ad34-58f2c1e4f596",
    "metadata": {},
    "outputs": [
@@ -3725,7 +4426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 64,
    "id": "2dfb967b-41ac-4463-b606-3e315e617f2a",
    "metadata": {},
    "outputs": [
@@ -3749,7 +4450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 65,
    "id": "2e87f858-b327-4f6b-9237-c8a557f29aeb",
    "metadata": {},
    "outputs": [
@@ -3757,8 +4458,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.189 <= 0.2\n",
-      "Finally 0.189\n"
+      "0.676 > 0.2\n",
+      "0.208 > 0.2\n",
+      "0.919 > 0.2\n",
+      "0.834 > 0.2\n",
+      "0.952 > 0.2\n",
+      "0.031 <= 0.2\n",
+      "Finally 0.031\n"
      ]
     }
    ],

From 24de8b38e94b85bb0f45e968f828f9bdacca7c66 Mon Sep 17 00:00:00 2001
From: liamhuber <liamhuber@greyhavensolutions.com>
Date: Sat, 9 Dec 2023 14:37:57 -0800
Subject: [PATCH 29/30] Update README

---
 README.md                       |  51 +++++++++++++++++---------------
 docs/_static/readme_diagram.png | Bin 66041 -> 153751 bytes
 docs/_static/readme_shifted.png | Bin 10735 -> 0 bytes
 3 files changed, 27 insertions(+), 24 deletions(-)
 delete mode 100644 docs/_static/readme_shifted.png

diff --git a/README.md b/README.md
index de9ec21db..5ee2c292b 100644
--- a/README.md
+++ b/README.md
@@ -47,40 +47,43 @@ Nodes can be used by themselves and -- other than being "delayed" in that their
 
 ```
 
-But the intent is to collect them together into a workflow and leverage existing nodes:
+But the intent is to collect them together into a workflow and leverage existing nodes. We can directly perform (many but not quite all) python actions natively on output channels, can build up data graph topology by simply assigning values (to attributes or at instantiation), and can package things together into reusable macros with customizable IO interfaces:
 
 ```python
 >>> from pyiron_workflow import Workflow
+>>> Workflow.register("plotting", "pyiron_workflow.node_library.plotting")
 >>>
 >>> @Workflow.wrap_as.single_value_node()
-... def add_one(x):
-...     return x + 1
+... def Arange(n: int):
+...     import numpy as np
+...     return np.arange(n)
 >>>
 >>> @Workflow.wrap_as.macro_node()
-... def add_three_macro(macro):
-...     macro.start = add_one()
-...     macro.middle = add_one(x=macro.start)
-...     macro.end = add_one(x=macro.middle)
-...     macro.inputs_map = {"start__x": "x"}
-...     macro.outputs_map = {"end__x + 1": "y"}
->>> 
->>> Workflow.register(
-...     "plotting", 
-...     "pyiron_workflow.node_library.plotting"
-... )
+... def PlotShiftedSquare(macro):
+...     macro.shift = macro.create.standard.UserInput(0)
+...     macro.arange = Arange()
+...     macro.plot = macro.create.plotting.Scatter(
+...         x=macro.arange + macro.shift,
+...         y=macro.arange**2
+...     )
+...     macro.inputs_map = {
+...         "shift__user_input": "shift",
+...         "arange__n": "n",
+...     }
+...     macro.outputs_map = {"plot__fig": "fig"}
 >>> 
->>> wf = Workflow("add_5_and_plot")
->>> wf.add_one = add_one()
->>> wf.add_three = add_three_macro(x=wf.add_one)
->>> wf.plot = wf.create.plotting.Scatter(
-...     x=wf.add_one,
-...     y=wf.add_three.outputs.y
-... )
+>>> wf = Workflow("plot_with_and_without_shift")
+>>> wf.n = wf.create.standard.UserInput()
+>>> wf.no_shift = PlotShiftedSquare(shift=0, n=10)
+>>> wf.shift = PlotShiftedSquare(shift=2, n=10)
+>>> wf.inputs_map = {
+...     "n__user_input": "n",
+...     "shift__shift": "shift"
+... }
 >>> 
 >>> diagram = wf.draw()
 >>> 
->>> import numpy as np
->>> fig = wf(add_one__x=np.arange(5)).plot__fig
+>>> out = wf(shift=3, n=10)
 
 ```
 
@@ -90,7 +93,7 @@ Which gives the workflow `diagram`
 
 And the resulting `fig`
 
-![](docs/_static/readme_shifted.png)
+![](docs/_static/readme_fig.png)
 
 ## Installation
 
diff --git a/docs/_static/readme_diagram.png b/docs/_static/readme_diagram.png
index b5e242731422842d3b0cf63e4739b65449c0fe18..45e495153535ef0a350322ce3326083170c73db3 100644
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From 2c33750c4ca8b57fbb63f027185037eb2aef3a2b Mon Sep 17 00:00:00 2001
From: pyiron-runner <pyiron@mpie.de>
Date: Sat, 9 Dec 2023 22:39:01 +0000
Subject: [PATCH 30/30] Format black

---
 pyiron_workflow/channels.py | 2 ++
 pyiron_workflow/function.py | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index dd85d689e..6065691db 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -556,11 +556,13 @@ def __getitem__(self, item):
             for slice_input in [item.start, item.stop, item.step]
         ):
             from pyiron_workflow.node_library.standard import Slice
+
             item = self._node_injection(
                 Slice, item.start, item.stop, item.step, inject_self=False
             )
 
         from pyiron_workflow.node_library.standard import GetItem
+
         return self._node_injection(GetItem, item)
 
     def __lt__(self, other):
diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index 0eb4a5b34..82e6498c4 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -602,7 +602,7 @@ def _get_output_labels(self, output_labels: str | list[str] | tuple[str] | None)
                 f"{self.__class__.__name__} must only have a single return value, but "
                 f"got multiple output labels: {output_labels}"
             )
-        return output_labels 
+        return output_labels
 
     @property
     def channel(self) -> OutputData: