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Added comments. CURA-3239

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Jack Ha 2017-04-04 09:59:42 +02:00
parent 1df9066340
commit 3d16c4120e
1 changed files with 20 additions and 6 deletions
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26
cura/Arrange.py
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@ -11,7 +11,7 @@ import copy
## Return object for bestSpot
LocationSuggestion = namedtuple("LocationSuggestion", ["x", "y", "penalty_points", "priority"])
## The Arrange classed is used together with ShapeArray. The class tries to find
## The Arrange classed is used together with ShapeArray. Use it to find
# good locations for objects that you try to put on a build place.
# Different priority schemes can be defined so it alters the behavior while using
# the same logic.
@ -29,8 +29,8 @@ class Arrange:
#
# Either fill in scene_root and create will find all sliceable nodes by itself,
# or use fixed_nodes to provide the nodes yourself.
# \param scene_root
# \param fixed_nodes
# \param scene_root Root for finding all scene nodes
# \param fixed_nodes Scene nodes to be placed
@classmethod
def create(cls, scene_root = None, fixed_nodes = None, scale = 0.5):
arranger = Arrange(220, 220, 110, 110, scale = scale)
@ -52,6 +52,10 @@ class Arrange:
## Find placement for a node (using offset shape) and place it (using hull shape)
# return the nodes that should be placed
# \param node
# \param offset_shape_arr ShapeArray with offset, used to find location
# \param hull_shape_arr ShapeArray without offset, for placing the shape
# \param count Number of objects
def findNodePlacements(self, node, offset_shape_arr, hull_shape_arr, count = 1, step = 1):
nodes = []
start_prio = 0
@ -75,7 +79,7 @@ class Arrange:
nodes.append(new_node)
return nodes
## Fill priority, take offset as center. lower is better
## Fill priority, center is best. lower value is better
def centerFirst(self):
# Distance x + distance y: creates diamond shape
#self._priority = numpy.fromfunction(
@ -86,7 +90,7 @@ class Arrange:
self._priority_unique_values = numpy.unique(self._priority)
self._priority_unique_values.sort()
##
## Fill priority, back is best. lower value is better
def backFirst(self):
self._priority = numpy.fromfunction(
lambda i, j: 10 * j + abs(self._offset_x - i), self.shape, dtype=numpy.int32)
@ -95,6 +99,9 @@ class Arrange:
## Return the amount of "penalty points" for polygon, which is the sum of priority
# 999999 if occupied
# \param x x-coordinate to check shape
# \param y y-coordinate
# \param shape_arr the ShapeArray object to place
def checkShape(self, x, y, shape_arr):
x = int(self._scale * x)
y = int(self._scale * y)
@ -115,6 +122,9 @@ class Arrange:
## Find "best" spot for ShapeArray
# Return namedtuple with properties x, y, penalty_points, priority
# \param shape_arr ShapeArray
# \param start_prio Start with this priority value (and skip the ones before)
# \param step Slicing value, higher = more skips = faster but less accurate
def bestSpot(self, shape_arr, start_prio = 0, step = 1):
start_idx_list = numpy.where(self._priority_unique_values == start_prio)
if start_idx_list:
@ -135,7 +145,11 @@ class Arrange:
return LocationSuggestion(x = projected_x, y = projected_y, penalty_points = penalty_points, priority = prio)
return LocationSuggestion(x = None, y = None, penalty_points = None, priority = prio) # No suitable location found :-(
## Place the object
## Place the object.
# Marks the locations in self._occupied and self._priority
# \param x x-coordinate
# \param y y-coordinate
# \param shape_arr ShapeArray object
def place(self, x, y, shape_arr):
x = int(self._scale * x)
y = int(self._scale * y)