Revert "Vector In and Vector Out to multilevel recursive (#3673)" (#3695

)

This reverts commit a95871e.

docs/nodes/generator/random_vector_mk3.rst

@@ -31,7 +31,7 @@ In the N-Panel (and on the right-click menu) you can find:
 
 **Match List**: Define how list with different lengths should be matched ('Short', 'Repeat Last' or 'Cycle')
 
-**Output NumPy**: Get NumPy arrays in stead of regular lists (makes the node faster).
+**Output NumPy**: Get NumPy arrays in stead of regular lists (makes the node faster). Available for Vertices, Edges and Pols
 
 Outputs
 -------

docs/nodes/vector/vector_in.rst

@@ -25,12 +25,6 @@ In the N-Panel (and on the right-click menu) you can find:
 
 **Implementation**: Python or NumPy. Python is the default and is usually faster if you input regular lists and want to get regular list. The NumPy implementation will be faster if you are using/getting lists of NumPy arrays.
 
-**Match List**: Define how list with different lengths should be matched ('Short', 'Repeat Last' or 'Cycle')
-
-**Simplify Output**: Method to keep output data suitable for most of the rest of the Sverchok nodes
-  - None: Do not perform any change on the data. Only for advanced users
-  - Flat: It will flat the output to keep vectors list in Level 3 (regular vector list)
-
 **Output NumPy**: Get NumPy arrays in stead of regular lists.
 
 Outputs

docs/nodes/vector/vector_out.rst

@@ -21,10 +21,6 @@ Advanced Parameters
 
 In the N-Panel (and on the right-click menu) you can find:
 
-**Simplify Output**: Method to keep output data suitable for most of the rest of the Sverchok nodes
-  - None: Do not perform any change on the data. Only for advanced users
-  - Flat: It will flat the output to keep vectors list in Level 3 (regular vector list)
-
 **Output NumPy**: Get NumPy arrays in stead of regular lists (makes the node faster if you input NumPy arrays).
 
 Outputs

nodes/vector/vector_in.py

@@ -19,8 +19,7 @@
 import bpy
 from bpy.props import FloatProperty, BoolProperty, StringProperty, EnumProperty
 from sverchok.node_tree import SverchCustomTreeNode
-from sverchok.data_structure import updateNode, list_match_func, numpy_list_match_modes, numpy_list_match_func
-from sverchok.utils.sv_itertools import recurse_f_level_control
+from sverchok.data_structure import updateNode, fullList, fullList_deep_copy, numpy_match_long_repeat
 from sverchok.utils.sv_itertools import sv_zip_longest
 from numpy import array
 
@@ -42,33 +41,27 @@ def execute(self, context):
 
         return{'FINISHED'}
 
-def python_pack_vecs(params, constant, matching_f):
-    X_, Y_, Z_ = params
-    output_numpy, _ = constant
+def python_pack_vecs(X_, Y_, Z_, output_numpy):
     series_vec = []
-    for vs in zip(X_, Y_, Z_):
-        x, y, z = matching_f(vs)
+    for x, y, z in zip(X_, Y_, Z_):
+        max_v = max(map(len, (x, y, z)))
+        fullList(x, max_v)
+        fullList(y, max_v)
+        fullList(z, max_v)
         series_vec.append(array([x,y,z]).T if output_numpy else list(zip(x, y, z)))
     return series_vec
 
-def numpy_pack_vecs(params, constant, matching_f):
-    X_, Y_, Z_ = params
-    output_numpy, match_mode = constant
-    np_match = numpy_list_match_func[match_mode]
+def numpy_pack_vecs(X_, Y_, Z_, output_numpy):
     series_vec = []
     for obj in zip(X_, Y_, Z_):
         np_obj = [array(p) for p in obj]
-        x, y, z = np_match(np_obj)
+        x, y, z = numpy_match_long_repeat(np_obj)
         vecs = array([x,y,z]).T
         series_vec.append(vecs if output_numpy else vecs.tolist())
     return series_vec
 
 class GenVectorsNode(bpy.types.Node, SverchCustomTreeNode):
-    """
-    Triggers: Combine XYZ
-    Tooltip:  Generate vectors from numbers list.
-    """
-
+    ''' Generator vectors '''
     bl_idname = 'GenVectorsNode'
     bl_label = 'Vector in'
     sv_icon = 'SV_VECTOR_IN'
@@ -77,7 +70,7 @@ class GenVectorsNode(bpy.types.Node, SverchCustomTreeNode):
     y_: FloatProperty(name='Y', description='Y', default=0.0, precision=3, update=updateNode)
     z_: FloatProperty(name='Z', description='Z', default=0.0, precision=3, update=updateNode)
 
-
+    advanced_mode: BoolProperty(name='deep copy', update=updateNode, default=True)
     show_3d_cursor_button: BoolProperty(name='show button', update=updateNode, default=False)
     implementation_modes = [
         ("NumPy", "NumPy", "NumPy", 0),
@@ -87,21 +80,7 @@ class GenVectorsNode(bpy.types.Node, SverchCustomTreeNode):
         name='Implementation', items=implementation_modes,
         description='Choose calculation method',
         default="Python", update=updateNode)
-    list_match: EnumProperty(
-        name="List Match",
-        description="Behavior on different list lengths",
-        items=numpy_list_match_modes, default="REPEAT",
-        update=updateNode)
-
-    correct_output_modes = [
-        ('NONE', 'None', 'Leave at multi-object level (Advanced)', 0),
-        ('FLAT', 'Flat Output', 'Flat to object level', 2),
-    ]
-    correct_output: EnumProperty(
-        name="Simplify Output",
-        description="Behavior on different list lengths, object level",
-        items=correct_output_modes, default="FLAT",
-        update=updateNode)
+
     output_numpy: BoolProperty(
         name='Output NumPy',
         description='Output NumPy arrays',
@@ -124,18 +103,15 @@ def draw_buttons(self, context, layout):
             get_cursor.treename = self.id_data.name
 
     def draw_buttons_ext(self, context, layout):
+        layout.row().prop(self, 'advanced_mode')
         layout.label(text="Implementation:")
         layout.prop(self, "implementation", expand=True)
-        layout.prop(self, 'list_match')
         layout.prop(self, "output_numpy", toggle=False)
-        layout.prop(self, 'correct_output')
-
 
     def rclick_menu(self, context, layout):
+        layout.prop(self, "advanced_mode", text="use deep copy")
         layout.prop_menu_enum(self, "implementation", text="Implementation")
-        layout.prop_menu_enum(self, 'list_match')
-        layout.prop(self, 'output_numpy', toggle=True)
-        layout.prop_menu_enum(self, 'correct_output')
+        layout.prop(self, "output_numpy", toggle=True)
         layout.prop(self, "show_3d_cursor_button", text="show 3d cursor button")
         if not any(s.is_linked for s in self.inputs):
             opname = 'node.sverchok_vector_from_cursor'
@@ -144,20 +120,23 @@ def rclick_menu(self, context, layout):
             get_cursor.treename = self.id_data.name
 
     def process(self):
-
         if not self.outputs['Vectors'].is_linked:
             return
-
-        params = [si.sv_get(default=[[]], deepcopy=False) for si in self.inputs]
-
-        matching_f = list_match_func[self.list_match]
-        desired_levels = [2, 2, 2]
-        ops = [self.output_numpy, self.list_match]
-        concatenate = 'APPEND' if self.correct_output == 'NONE' else "EXTEND"
+        inputs = self.inputs
+        X_ = inputs['X'].sv_get()
+        Y_ = inputs['Y'].sv_get()
+        Z_ = inputs['Z'].sv_get()
+        series_vec = []
+        max_obj = max(map(len, (X_, Y_, Z_)))
         pack_func = numpy_pack_vecs if self.implementation == 'NumPy' else python_pack_vecs
-        result = recurse_f_level_control(params, ops, pack_func, matching_f, desired_levels, concatenate=concatenate)
+        fullList_main = fullList_deep_copy if self.advanced_mode else fullList
+        fullList_main(X_, max_obj)
+        fullList_main(Y_, max_obj)
+        fullList_main(Z_, max_obj)
+        series_vec = pack_func(X_, Y_, Z_, self.output_numpy)
+
 
-        self.outputs[0].sv_set(result)
+        self.outputs['Vectors'].sv_set(series_vec)
 
 
 def register():

nodes/vector/vector_out.py

@@ -17,77 +17,29 @@
 # ##### END GPL LICENSE BLOCK #####
 
 import bpy
-from bpy.props import BoolProperty, EnumProperty
+from bpy.props import BoolProperty
 from sverchok.node_tree import SverchCustomTreeNode
-from sverchok.data_structure import dataCorrect_np, updateNode, levels_of_list_or_np
+from sverchok.data_structure import dataCorrect_np, updateNode
 from numpy import ndarray, array
 
 def unpack_np(obj):
     return (obj[:, 0], obj[:, 1], obj[:, 2])
 
-
 def unpack_list(obj):
     return (list(x) for x in zip(*obj))
 
-
 def unpack_list_to_np(obj):
     return (array(x) for x in zip(*obj))
 
-
-def unpack(data, output_numpy):
-    X, Y, Z = [], [], []
-    if output_numpy:
-        unpack_func = unpack_np if isinstance(data[0], ndarray) else unpack_list_to_np
-    else:
-        unpack_func = unpack_list
-    for obj in data:
-        print(obj)
-        x_, y_, z_ = unpack_func(obj)
-        X.append(x_)
-        Y.append(y_)
-        Z.append(z_)
-    return X, Y, Z
-
-
-def unpack_multilevel(data, level, output_numpy):
-    if level > 3:
-        X, Y, Z = [], [], []
-        for obj in data:
-
-            x,y,z = unpack_multilevel(obj, level-1, output_numpy)
-            X.append(x)
-            Y.append(y)
-            Z.append(z)
-    elif level < 3:
-        X, Y, Z = unpack_multilevel([data], level+1, output_numpy)
-    else:
-        X,Y,Z = unpack(data, output_numpy)
-
-    return X,Y,Z
-
-
 class VectorsOutNode(bpy.types.Node, SverchCustomTreeNode):
-    """
-    Triggers: Separate XYZ
-    Tooltip:  Split X, Y and Z components from vector.
-    """
+    ''' Vectors out '''
     bl_idname = 'VectorsOutNode'
     bl_label = 'Vector out'
     sv_icon = 'SV_VECTOR_OUT'
-
     output_numpy: BoolProperty(
         name='Output NumPy',
         description='Output NumPy arrays',
         default=False, update=updateNode)
-    correct_output_modes = [
-        ('NONE', 'None', 'Leave at multi-object level (Advanced)', 0),
-        ('FLAT', 'Flat Output', 'Flat to object level', 2),
-    ]
-    correct_output: EnumProperty(
-        name="Simplify Output",
-        description="Behavior on different list lengths, object level",
-        items=correct_output_modes, default="FLAT",
-        update=updateNode)
 
     def sv_init(self, context):
         self.inputs.new('SvVerticesSocket', "Vectors")
@@ -98,28 +50,28 @@ def sv_init(self, context):
 
     def draw_buttons_ext(self, ctx, layout):
         layout.prop(self, "output_numpy", toggle=False)
-        layout.prop(self, "correct_output")
 
     def rclick_menu(self, context, layout):
         layout.prop(self, "output_numpy", toggle=True)
-        layout.prop_menu_enum(self, 'correct_output')
 
     def process(self):
-        if not (self.inputs['Vectors'].is_linked and any(s.is_linked for s in self.outputs)):
-            return
-
-        vectors = self.inputs['Vectors'].sv_get(deepcopy=False)
-
-        if self.correct_output == 'FLAT':
-            data = dataCorrect_np(vectors)
-            xyz = unpack(data, self.output_numpy)
-        else:
-            input_level = levels_of_list_or_np(vectors)
-            xyz = unpack_multilevel(vectors, input_level, self.output_numpy)
-
-        for i, name in enumerate(['X', 'Y', 'Z']):
-            if self.outputs[name].is_linked:
-                self.outputs[name].sv_set(xyz[i])
+        if self.inputs['Vectors'].is_linked and any(s.is_linked for s in self.outputs):
+            xyz = self.inputs['Vectors'].sv_get(deepcopy=False)
+
+            data = dataCorrect_np(xyz)
+            X, Y, Z = [], [], []
+            if self.output_numpy:
+                unpack_func = unpack_np if isinstance(data[0], ndarray) else unpack_list_to_np
+            else:
+                unpack_func = unpack_list
+            for obj in data:
+                x_, y_, z_ = unpack_func(obj)
+                X.append(x_)
+                Y.append(y_)
+                Z.append(z_)
+            for i, name in enumerate(['X', 'Y', 'Z']):
+                if self.outputs[name].is_linked:
+                    self.outputs[name].sv_set([X, Y, Z][i])
 
 
 def register():