diff --git a/mysite/plots/avl_tree.py b/mysite/plots/avl_tree.py
index 6ff57d8c03bd9fdc8328c16757393673d7bd42fb..2ba973bdf9600d3ad154ab404b7f7c9c93ca58f3 100644
--- a/mysite/plots/avl_tree.py
+++ b/mysite/plots/avl_tree.py
@@ -164,6 +164,7 @@ class AVLTree(object):
self.preOrder(root.right)
def preOrder_array(self, root, array, key):
+ # array is an accumulator
if not root:
return
# print("{0} ".format(root.key), end="")
diff --git a/mysite/plots/dataset_creator.py b/mysite/plots/dataset_creator.py
index 5ccbd19a3f7b9137e3fec0ca0f7e17af1db2d1ae..0f66237495651d5667f639795daf8e4bc6a6b50d 100644
--- a/mysite/plots/dataset_creator.py
+++ b/mysite/plots/dataset_creator.py
@@ -1,83 +1,97 @@
-# import pandas as pd
-# from .packing_algo import ConvexPolygon, pack_polygons, truncate, RectangularContainer, plot_containers, ConvexContainer
-# from .polygon_creator import voronoi_polygons_wrapper, rectangle_cutter
-#
-#
-# # def build_aprox_factors():
-#
-#
-# def build_dataset_with_rectangle_cutter(rect_width: float, rect_height, repetition, angle_steps=90, cut_min=1,
-# cut_max=1, cut_steps=1, intervals=[0, 0.01, 0.05, 1], weights=[0, 0, 0.5, 1]):
-# if cut_min > cut_max:
-# cut_max = cut_min
-# data_dict_list = []
-# for cut_count in range(cut_min, cut_max + cut_steps, cut_steps):
-# cutted_polygons_lists = []
-# for n in range(0, repetition):
-# cutted_polygons = rectangle_cutter(rect_width, rect_height, cut_count, intervals=intervals, weights=weights)
-# cutted_polygons_lists.append(cutted_polygons)
-# dict_data = collect_rect_containers_data(rect_width, rect_height, cutted_polygons_lists, angle_steps)
-# data_dict_list.append(dict_data)
-# return data_dict_list
-#
-#
-# def build_dataset_with_voronoi(rect_width, rect_height, repetition, cut_min=5, cut_max=5, cut_steps=1, angle_steps=90):
-# if cut_min > cut_max:
-# cut_max = cut_min
-# data_dict_list = []
-# for cut_count in range(cut_min, cut_max + cut_steps, cut_steps):
-# cutted_polygons_lists = []
-# for n in range(0, repetition):
-# cutted_polygons = voronoi_polygons_wrapper(rect_width, rect_height, cut_count)
-# cutted_polygons_lists.append(cutted_polygons)
-# dict_data = collect_rect_containers_data(rect_width, rect_height, cutted_polygons_lists, angle_steps)
-# data_dict_list.append(dict_data)
-# return data_dict_list
-#
-#
-# def collect_rect_containers_data(rect_width: float, rect_height: float, cutted_polygons_lists: [[ConvexPolygon]],
-# angle_steps=90) -> {str, list}:
-# opt_area = rect_width * rect_height
-# opt_area_list = []
-# area_list = []
-# area_div_list = []
-# angle_0_area_div_list = []
-# angle_0_not_clipped_area_div_list = []
-# not_clipped_area_list = []
-# not_clipped_area_div_list = []
-# polygon_count_list = []
-# end_container_list = []
-# mini_container_count_list = []
-# for polygons in cutted_polygons_lists:
-# cc = ConvexContainer(polygons, steps=angle_steps)
-# end_container = cc.smallest_end_container
-# end_container_angle_0 = cc.angle_0_end_container
-#
-# c_area = end_container.container_area
-# c_not_opt_area = end_container.container_not_clipped_area
-# c_not_r_area = end_container_angle_0.container_area
-# c_not_r_not_opt_area = end_container_angle_0.container_not_clipped_area
-# area_list.append(truncate(c_area, 1))
-# area_div_list.append(truncate(c_area / opt_area, 1))
-# not_clipped_area_list.append(truncate(c_not_opt_area, 1))
-# not_clipped_area_div_list.append(truncate(c_not_opt_area / opt_area, 1))
-#
-# angle_0_area_div_list.append(truncate(c_not_r_area / opt_area, 1))
-# angle_0_not_clipped_area_div_list.append(truncate(c_not_r_not_opt_area / opt_area, 1))
-# opt_area_list.append(opt_area)
-# polygon_count_list.append(len(polygons))
-# mini_container_count_list.append(len(end_container.mini_containers))
-# end_container_list.append(cc)
-# rect_containers_data_dict = {'area': area_list, 'area/opt_area': area_div_list,
-# 'not_clipped_area': not_clipped_area_list,
-# 'not_clipped_area/opt_area': not_clipped_area_div_list,
-# 'angle_0_area/opt_area': angle_0_area_div_list,
-# 'angle_0_not_clipped_area/opt_area': angle_0_not_clipped_area_div_list,
-# 'opt-area': opt_area_list, 'polygon count': polygon_count_list,
-# 'mini-container count': mini_container_count_list, 'Rectangular-Container': end_container_list}
-# return rect_containers_data_dict
-#
-#
+import pandas as pd
+
+from plots.packing_algo import ConvexPolygon, pack_polygons, truncate, RectangularContainer, ConvexContainer
+from plots.polygon_creator import voronoi_polygons_wrapper, rectangle_cutter
+
+
+# def build_aprox_factors():
+
+
+def build_dataset_with_rectangle_cutter(rect_width: float, rect_height, repetition, cut_list=[], angle_steps=90, cut_min=1,
+ cut_max=1, cut_steps=1, intervals=[0, 0.01, 0.05, 1], weights=[0, 0, 0.5, 1],save_container=True):
+ cut_numbers=[]
+ data_dict_list = []
+ if cut_list:
+ cut_numbers= cut_list
+ else:
+ if cut_min > cut_max:
+ cut_max = cut_min
+ for cut_number in range(cut_min, cut_max + 1, cut_steps):
+ cut_numbers.append(cut_number)
+ for cut_count in cut_numbers:
+ cutted_polygons_lists = []
+ for n in range(0, repetition):
+ cutted_polygons = rectangle_cutter(rect_width, rect_height, cut_count, intervals=intervals, weights=weights)
+ cutted_polygons_lists.append(cutted_polygons)
+ dict_data = collect_rect_containers_data(rect_width, rect_height, cutted_polygons_lists, angle_steps,save_container)
+ data_dict_list.append(dict_data)
+ return data_dict_list
+
+
+def build_dataset_with_voronoi(rect_width, rect_height, repetition, cut_list=[], cut_min=5, cut_max=5, cut_steps=1, angle_steps=90, save_container=True):
+ cut_numbers=[]
+ data_dict_list = []
+ if cut_list:
+ cut_numbers = cut_list
+ else:
+ if cut_min > cut_max:
+ cut_max = cut_min
+ for cut_number in range(cut_min, cut_max + 1, cut_steps):
+ cut_numbers.append(cut_number)
+ for cut_count in cut_numbers:
+ cutted_polygons_lists = []
+ for n in range(0, repetition):
+ cutted_polygons = voronoi_polygons_wrapper(rect_width, rect_height, cut_count)
+ cutted_polygons_lists.append(cutted_polygons)
+ dict_data = collect_rect_containers_data(rect_width, rect_height, cutted_polygons_lists, angle_steps,save_container)
+ data_dict_list.append(dict_data)
+ return data_dict_list
+
+
+def collect_rect_containers_data(rect_width: float, rect_height: float, cutted_polygons_lists: [[ConvexPolygon]],
+ angle_steps=90, save_container=True) -> {str, list}:
+ opt_area = rect_width * rect_height
+ opt_area_list = []
+ area_list = []
+ area_div_list = []
+ angle_0_area_div_list = []
+ angle_0_not_clipped_area_div_list = []
+ not_clipped_area_list = []
+ not_clipped_area_div_list = []
+ polygon_count_list = []
+ end_container_list_plots = []
+ for polygons in cutted_polygons_lists:
+ cc = ConvexContainer(polygons, steps=angle_steps)
+ end_container = cc.smallest_rectangular_container
+ end_container_angle_0 = cc.angle_0_rectangular_container
+
+ c_area = end_container.container_area
+ c_not_opt_area = end_container.container_not_clipped_area
+ c_not_r_area = end_container_angle_0.container_area
+ c_not_r_not_opt_area = end_container_angle_0.container_not_clipped_area
+ area_list.append(truncate(c_area, 1))
+ area_div_list.append(truncate(c_area / opt_area, 1))
+ not_clipped_area_list.append(truncate(c_not_opt_area, 1))
+ not_clipped_area_div_list.append(truncate(c_not_opt_area / opt_area, 1))
+
+ angle_0_area_div_list.append(truncate(c_not_r_area / opt_area, 1))
+ angle_0_not_clipped_area_div_list.append(truncate(c_not_r_not_opt_area / opt_area, 1))
+ opt_area_list.append(opt_area)
+ polygon_count_list.append(len(polygons))
+ if save_container:
+ end_container_list_plots.append(cc.plot_container(render=False))
+ rect_containers_data_dict = {'area': area_list, 'area/opt_area': area_div_list,
+ 'not_clipped_area': not_clipped_area_list,
+ 'not_clipped_area/opt_area': not_clipped_area_div_list,
+ 'angle_0_area/opt_area': angle_0_area_div_list,
+ 'angle_0_not_clipped_area/opt_area': angle_0_not_clipped_area_div_list,
+ 'opt-area': opt_area_list,
+ 'polygon count': polygon_count_list}
+ if end_container_list_plots:
+ rect_containers_data_dict['End-Container']=end_container_list_plots
+ return rect_containers_data_dict
+
+
# # need to update like rectangle_cutter with deviation
# def find_repition_factor_rectangle_voronoi(cut, rep_high, rep_low=1, rep_steps=1, display_flag=True,
# intervals=[0, 0.01, 0.05, 1], weights=[0, 0, 0.5, 1]):
@@ -128,46 +142,48 @@
# return average_list
# average_list.append((rep, average))
# return average_list
-#
-#
-# def dict_list_to_ordered_panda_list(dictionary_list: [dict], ordered_by_area=True) -> [pd.DataFrame]:
-# data_frame_list = []
-# for dic in dictionary_list:
-# df = pd.DataFrame(dic)
-# if ordered_by_area:
-# data_frame_list.append(df.sort_values(by="area", ascending=False))
-# else:
-# data_frame_list.append(df.sort_values(by="not_clipped_area", ascending=False))
-# return data_frame_list
-#
-#
+
+
+def dict_list_to_ordered_panda_list(dictionary_list: [dict], ordered_by_area=True) -> [pd.DataFrame]:
+ data_frame_list = []
+ for dic in dictionary_list:
+ df = pd.DataFrame(dic)
+ if ordered_by_area:
+ data_frame_list.append(df.sort_values(by="area", ascending=False))
+ else:
+ data_frame_list.append(df.sort_values(by="not_clipped_area", ascending=False))
+ return data_frame_list
+
+
# def display_panda_df_list(df_list: [pd.DataFrame]) -> None:
# for df in df_list:
# display(df)
-#
-#
-# def build_aprox_values(panda_data):
-# mean_opt = 0
-# mean_not_clipped = 0
-# mean_angle_0 = 0
-# mean_angle_0_not_clipped = 0
-# counter = 0
-# mean_best = 0
-# mean_worst = 0
-# for data in panda_data:
-# mean_opt += data["area/opt_area"].mean(axis=0)
-# mean_best += data["area/opt_area"].iloc[-1]
-# mean_worst += data["area/opt_area"].iloc[0]
-# mean_not_clipped += data["not_clipped_area/opt_area"].mean(axis=0)
-# mean_angle_0 += data["angle_0_area/opt_area"].mean(axis=0)
-# mean_angle_0_not_clipped += data["angle_0_not_clipped_area/opt_area"].mean(axis=0)
-# counter += 1
-# mean_opt = mean_opt / counter
-# mean_best = mean_best / counter
-# mean_worst = mean_worst / counter
-# mean_not_clipped = mean_not_clipped / counter
-# mean_angle_0 = mean_angle_0 / counter
-# mean_angle_0_not_clipped = mean_angle_0_not_clipped / counter
-# aprox_dict = dict(aprox=mean_opt, aprox_best=mean_best, aprox_worst=mean_worst, aprox_not_clipped=mean_not_clipped,
-# aprox_angle_0=mean_angle_0, aprox_angle_0_not_clipped=mean_angle_0_not_clipped)
-# return aprox_dict
\ No newline at end of file
+
+
+def build_aprox_values(panda_data):
+ mean_opt = 0
+ mean_not_clipped = 0
+ mean_angle_0 = 0
+ mean_angle_0_not_clipped = 0
+ counter = 0
+ mean_best = 0
+ mean_worst = 0
+ for data in panda_data:
+ mean_opt += data["area/opt_area"].mean(axis=0)
+ mean_best += data["area/opt_area"].iloc[-1]
+ mean_worst += data["area/opt_area"].iloc[0]
+ mean_not_clipped += data["not_clipped_area/opt_area"].mean(axis=0)
+ mean_angle_0 += data["angle_0_area/opt_area"].mean(axis=0)
+ mean_angle_0_not_clipped += data["angle_0_not_clipped_area/opt_area"].mean(axis=0)
+ counter += 1
+ mean_opt = mean_opt / counter
+ mean_best = mean_best / counter
+ mean_worst = mean_worst / counter
+ mean_not_clipped = mean_not_clipped / counter
+ mean_angle_0 = mean_angle_0 / counter
+ mean_angle_0_not_clipped = mean_angle_0_not_clipped / counter
+ aprox_dict = dict(aprox=mean_opt, aprox_best=mean_best, aprox_worst=mean_worst, aprox_not_clipped=mean_not_clipped,
+ aprox_angle_0=mean_angle_0, aprox_angle_0_not_clipped=mean_angle_0_not_clipped)
+ return aprox_dict
+
+
diff --git a/mysite/plots/packing_algo.py b/mysite/plots/packing_algo.py
index 4ae3f8703d31677c183fa0185f40d15ac447d100..33c5136cc7afc454998d5e81c8149246f551b4ef 100644
--- a/mysite/plots/packing_algo.py
+++ b/mysite/plots/packing_algo.py
@@ -199,7 +199,7 @@ class ConvexPolygon(object):
spine_bottom, spine_top (Point_xy, Point_xy): a tuple a the lowest and highest vertex or bottom left and
top left
"""
- y_sorted_p_p = sorted(self.__shell, key=lambda k: k[1]) # ordering the vertices to their y value
+ y_sorted_p_p = sorted(self.__shell[0:-1], key=lambda k: k[1]) # ordering the vertices to their y values
if y_sorted_p_p[0][1] == y_sorted_p_p[1][1]: # if there are two lowest vertices take the left one
if y_sorted_p_p[0][0] < y_sorted_p_p[1][0]:
spine_bottom = y_sorted_p_p[0]
diff --git a/mysite/plots/polygon.txt b/mysite/plots/polygon.txt
index 57a6ad0083f1a246c14d564b339e882314a519cc..688a4d0ded902598826d12a23ee236bc4b44c907 100644
--- a/mysite/plots/polygon.txt
+++ b/mysite/plots/polygon.txt
@@ -1,9 +1,11 @@
Polygon
[(686.0, 0.0), (0.0, 41.0), (289.0, 371.0), (686.0, 0.0)]
-[(686.0, 0.0), (1101.9999999999995, 905.0), (1291.9999999999995, 62.0), (686.0, 0.0)]
-[(1842.7583497053038, 0.0), (1286.7583497053042, 4.0), (1332.7583497053042, 513.0), (2047.7583497053038, 608.0), (2023.7583497053038, 423.0), (1842.7583497053038, 0.0)]
-[(316.9999999999998, 905.0), (0.0, 1490.0), (356.9999999999998, 1527.0), (741.9999999999998, 1428.0), (724.9999999999998, 1078.0), (316.9999999999998, 905.0)]
-[(586.0, 1527.0), (0.0, 1540.0), (300.0, 1762.0), (586.0, 1527.0)]
-[(27.26304579339723, 1762.0), (8.413205537806178, 1763.8987341772151), (0.0, 1788.26582278481), (1.490947816826413, 1799.6582278481012), (7.8807241746538885, 1800.0801687763712), (51.43769968051117, 1795.649789029536), (27.26304579339723, 1762.0)]
-[(4.1324156191531145, 1800.0801687763712), (0.0, 1810.483927967384), (13.756594100601824, 1811.809503462764), (13.267229093070533, 1804.739767487404), (4.1324156191531145, 1800.0801687763712)]
-[(0.05437388972569579, 1811.809503462764), (0.0, 1817.1519743986896), (9.406682922545912, 1816.3485953105805), (8.482326797209026, 1813.6171064110094), (0.05437388972569579, 1811.809503462764)]
\ No newline at end of file
+[(1237.7196765498647, 0.0), (681.7196765498652, 4.0), (727.7196765498652, 513.0), (1442.7196765498647, 608.0), (1418.7196765498647, 423.0), (1237.7196765498647, 0.0)]
+[(1756.7358927660807, 0.0), (1439.735892766081, 585.0), (1796.7358927660807, 622.0), (2181.7358927660807, 523.0), (2164.7358927660807, 173.0), (1756.7358927660807, 0.0)]
+[(586.0, 622.0), (0.0, 635.0), (300.0, 857.0), (586.0, 622.0)]
+[(10.22364217252396, 857.0), (0.8519701810436633, 891.493670886076), (0.0, 916.493670886076), (12.247071352502662, 907.9493670886076), (10.22364217252396, 857.0)]
+[(22.56508289290622, 857.0), (11.7024630845995, 894.2362869198312), (13.299907174056372, 910.1645569620254), (23.09756425605852, 907.9493670886076), (22.56508289290622, 857.0)]
+[(27.26304579339723, 916.493670886076), (8.413205537806178, 918.3924050632911), (0.0, 942.7594936708861), (1.490947816826413, 954.1518987341772), (7.8807241746538885, 954.5738396624472), (51.43769968051117, 950.1434599156119), (27.26304579339723, 916.493670886076)]
+[(50.25001012809531, 916.493670886076), (40.25849830881026, 926.7050419270416), (40.158081104596334, 934.4428510388293), (47.63916281853338, 934.3794263739785), (50.25001012809531, 916.493670886076)]
+[(4.1324156191531145, 954.5738396624472), (0.0, 964.97759885346), (13.756594100601824, 966.30317434884), (13.267229093070533, 959.2334383734799), (4.1324156191531145, 954.5738396624472)]
+[(0.05437388972569579, 966.30317434884), (0.0, 971.6456452847656), (9.406682922545912, 970.8422661966565), (8.482326797209026, 968.1107772970854), (0.05437388972569579, 966.30317434884)]
\ No newline at end of file