class Simulation:
#loads fundemental information for simulation
def __init__(self,seed_type=0, base=0, steps=0,width=0,height=0, kernel=0):
self.seed_type = seed_type
self.base = base
self.steps = steps
self.width = width
self.height = height
self.kernel = kernel
self.shift = 0
self.step_count = 0
self.save_frames = False
def Set_Seed(self,seed):
self.seed = seed
self.frames = []
self.grid = Grid(self.width, self.height, self.base, self.seed, self.seed_type, self.shift, self.kernel)
self.grid.Zero_Grid()
self.frames.append(self.grid.data)
#new
# loads start conditions for each simulation
def Set_Initial_Condition(self, start_type, start_sub_type, gap = 1, groups = 1, duplicate_start_rows = True):
self.start_type = start_type
self.start_sub_type = start_sub_type
self.gap = gap
self.start_groups = groups
self.row_creator = Init_Row(self.width, self.base)
start_row = self.row_creator.Create_Row(start_type, start_sub_type, gap, groups)
for i in range(self.kernel.start_rows_needed):
self.grid.Replace_Row(i, start_row)
# replaces rows at the top of a simulation
# used for transitioning between simulations
def Insert_Rows_Top(self, rows):
for i in range(len(rows)):
self.grid.Replace_Row(self.step_count, rows[i])
# returns rows from either the front end or back end of a simulation
# used for transitioning between simulations
def Get_Rows(self, front_or_back, count):
rows = []
for i in range(count):
if front_or_back == 1:
rows.append(self.grid.data[i])
elif front_or_back == 2:
rows.append(self.grid.data[self.height - i])
return rows
#loads cmaps
def Set_Cmaps(self,cmaps):
self.cmaps = cmaps
#creates another row for the given simulation
def Next_Step(self, custom_row = []):
if len(custom_row ) != 0:
row = custom_row
else:
row = self.grid.Generate_Row_All_Kernels()
self.grid.Push_Row( row)
self.frames.append(self.grid.data)
self.step_count += 1
#displays current image
#only used when single images are created
def Display_Current_Figure(self):
print('Displaying Current Figure...')
max_image_size = 1024 * 1024 * 40 # 10485760bytes 10mb pictures
resize_factor = max_image_size / (self.width * self.height)
formatted_data = np.flip(np.array(self.frames[self.step_count]),0)
formatted_data *= 255 / formatted_data.max()
cmap_list = self.cmaps['Favorites']
cmap = cm.get_cmap(cmap_list[random.randint(0,len(cmap_list)) - 1])
im = Image.fromarray(np.uint8(cmap(formatted_data) * 255))
#scales image so file size is constant
scaled_img_x = int(math.sqrt(max_image_size * self.width / self.height))# - self.width)
scaled_img_y = int(math.sqrt(max_image_size * self.height / self.width))# - self.height)
im = im.resize((scaled_img_x, scaled_img_y), Image.NEAREST)
im.show()
#saves current image, also picks cmap based on selection
def Save_Current_Figure(self,path, mb, save_duplicates):#,index):#,cmap_str):
mbI = int(mb)
max_image_size = 1024 * 1024 * mbI / 4 # 10485760bytes 10mb pictures
resize_factor = max_image_size / (self.width * self.height)
cmap_index = random.randint(0,len(self.cmaps) - 1)
cmap_list = self.cmaps[self.cmaps['Selection']]
if save_duplicates:
saves = len(self.cmaps['Austere'])
else:
saves = 1
for i in range(saves):
if save_duplicates:
cmap_index = i#@ % len(self.cmaps['Favorites'])
else:
cmap_index = random.randint(0,len(cmap_list) - 1)
cmap = cm.get_cmap(cmap_list[cmap_index])
formatted_data = np.flip(np.array(self.frames[self.step_count]),0)
formatted_data = formatted_data * (255 / formatted_data.max())
im = Image.fromarray(np.uint8(cmap(formatted_data) * 255))
#scales image so file size is constant
scaled_img_x = int(math.sqrt(max_image_size * self.width / self.height))# - self.width)
scaled_img_y = int(math.sqrt(max_image_size * self.height / self.width))# - self.height)
im = im.resize((scaled_img_x, scaled_img_y), Image.NEAREST)
im.save(path + '_' + cmap.name + '.bmp',"BMP",quality=100)
