Merge pull request #112 from pyiron/test_docs

Test docs

.binder/environment.yml

@@ -11,7 +11,10 @@ dependencies:
 - python-graphviz =0.20.1
 - toposort =1.10
 - typeguard =4.1.5
+- ase =3.22.1
+- atomistics =0.1.12
 - lammps
+- phonopy =2.21.0
 - pyiron_atomistics =0.3.5
 - pyiron-data =0.0.24
 - numpy =1.26.0

.ci_support/environment-notebooks.yml

@@ -1,7 +1,10 @@
 channels:
   - conda-forge
 dependencies:
+  - ase =3.22.1
+  - atomistics =0.1.12
   - lammps
+  - phonopy =2.21.0
   - pyiron_atomistics =0.3.5
   - pyiron-data =0.0.24
   - numpy =1.26.0

README.md

@@ -36,50 +36,52 @@ Nodes (including macros) can be stored in plain text, and registered by future w
 Nodes can be used by themselves and -- other than being "delayed" in that their computation needs to be requested after they're instantiated -- they feel an awful lot like the regular python functions they wrap:
 
 ```python
-from pyiron_workflow import Workflow
+>>> from pyiron_workflow import Workflow
+>>>
+>>> @Workflow.wrap_as.single_value_node()
+... def add_one(x):
+...     return x + 1
+>>>
+>>> add_one(add_one(add_one(x=0)))()
+3
 
-@Workflow.wrap_as.single_value_node()
-def add_one(x):
-    return x + 1
-
-add_one(add_one(add_one(x=0)))()
->>> 3
 ```
 
 But the intent is to collect them together into a workflow and leverage existing nodes:
 
 ```python
-from pyiron_workflow import Workflow
-
-@Workflow.wrap_as.single_value_node()
-def add_one(x):
-    return x + 1
-
-@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"
-)
-
-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
-)
-
-diagram = wf.draw()
-
-import numpy as np
-fig = wf(add_one__x=np.arange(5)).plot__fig
+>>> from pyiron_workflow import Workflow
+>>>
+>>> @Workflow.wrap_as.single_value_node()
+... def add_one(x):
+...     return x + 1
+>>>
+>>> @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"
+... )
+>>> 
+>>> 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
+... )
+>>> 
+>>> diagram = wf.draw()
+>>> 
+>>> import numpy as np
+>>> fig = wf(add_one__x=np.arange(5)).plot__fig
+
 ```
 
 Which gives the workflow `diagram`

pyiron_workflow/executors/cloudpickleprocesspool.py

@@ -48,7 +48,9 @@ class CloudpickleProcessPoolExecutor(ProcessPoolExecutor):
         normal.
         >>> from functools import partialmethod
         >>>
-        >>> from pyiron_contrib.executors import CloudpickleProcessPoolExecutor
+        >>> from pyiron_workflow.executors.cloudpickleprocesspool import (
+        ...     CloudpickleProcessPoolExecutor
+        ... )
         >>>
         >>> class Foo:
         ...     '''
@@ -87,7 +89,7 @@ class CloudpickleProcessPoolExecutor(ProcessPoolExecutor):
         ...     return as_dynamic_foo
         >>>
         >>> @dynamic_foo()
-        >>> def UnpicklableCallable(unpicklable_arg):
+        ... def UnpicklableCallable(unpicklable_arg):
         ...     unpicklable_arg.result = "This was an arg"
         ...     return unpicklable_arg
         >>>