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Added first arranger tests, small refactors. CURA-3239

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Jack Ha 2017-04-03 16:36:48 +02:00
parent a83b1dd638
commit d1b9078657
3 changed files with 88 additions and 4 deletions
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13
cura/Arrange.py
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@ -2,10 +2,15 @@ from UM.Scene.Iterator.DepthFirstIterator import DepthFirstIterator
from UM.Logger import Logger
from cura.ShapeArray import ShapeArray
from collections import namedtuple
import numpy
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
# 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
@ -54,7 +59,7 @@ class Arrange:
for i in range(count):
new_node = copy.deepcopy(node)
x, y, penalty_points, start_prio = self.bestSpot(
x, y = self.bestSpot(
offset_shape_arr, start_prio = start_prio, step = step)
transformation = new_node._transformation
if x is not None: # We could find a place
@ -80,6 +85,7 @@ class Arrange:
self._priority_unique_values = numpy.unique(self._priority)
self._priority_unique_values.sort()
##
def backFirst(self):
self._priority = numpy.fromfunction(
lambda i, j: 10 * j + abs(self._offset_x - i), self.shape, dtype=numpy.int32)
@ -107,6 +113,7 @@ class Arrange:
return numpy.sum(prio_slice[numpy.where(shape_arr.arr == 1)])
## Find "best" spot for ShapeArray
# Return namedtuple with properties x, y, penalty_points, priority
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:
@ -124,8 +131,8 @@ class Arrange:
# array to "world" coordinates
penalty_points = self.checkShape(projected_x, projected_y, shape_arr)
if penalty_points != 999999:
return projected_x, projected_y, penalty_points, prio
return None, None, None, prio # No suitable location found :-(
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
def place(self, x, y, shape_arr):