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everything_and_traning_series.py
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everything_and_traning_series.py
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import random
import numpy as np
from typing import List
from row_of_notes import RowOfNotes
from matrix_convert import gen_simple_note_string,keyvalue,matrix_merge
def create_matrix_blocks(timestamps, objects_list, interval=100):
# 构建一个空列表用于保存矩阵块
matrix_blocks = []
start_blocks = []
P_blocks = []
# 用于记录连续时间戳的起始和结束索引
start_index = 0
end_index = 0
start_blocks.append(timestamps[start_index])
# 遍历时间戳列表
for i in range(1, len(timestamps)):
if timestamps[i] - timestamps[start_index] <= interval-10: # 当连续时间戳在间隔范围内时
end_index = i # 更新结束索引
else: # 当间隔范围超出时
if end_index < start_index:
end_index = start_index# 若结束索引为0，说明此时还在间隔内，不生成矩阵块
interval_objects = objects_list[start_index:end_index+1] # 提取对应的物件状态
temp_block = np.array(interval_objects)
matrix_blocks.append(temp_block) # 创建矩阵块并添加到列表
# 计算矩阵块中所有1出现的概率
total_elements = temp_block.size # 获取矩阵中的总元素数量
count_ones = np.count_nonzero(temp_block == 1) # 计算矩阵中1的出现次数
P_blocks.append(count_ones / total_elements) # 计算概率并插入
start_index = i # 更新起始索引
start_blocks.append(timestamps[start_index]) # 更新起始时间戳
if i == len(timestamps) - 1: # 若遍历到最后一个时间戳，则生成最后一个矩阵块
end_index = i
interval_objects = objects_list[start_index:end_index+1] # 提取对应的物件状态
temp_block = np.array(interval_objects)
matrix_blocks.append(temp_block) # 创建矩阵块并添加到列表
# 计算矩阵块中所有1出现的概率
total_elements = temp_block.size # 获取矩阵中的总元素数量
count_ones = np.count_nonzero(temp_block == 1) # 计算矩阵中1的出现次数
P_blocks.append(count_ones / total_elements) # 计算概率并插入
return start_blocks, P_blocks, matrix_blocks
def make_disjoint(list1, list2):
zero_indices = [i for i in range(len(list1)) if list1[i] == 0 and list2[i] == 0]
for i in range(len(list1)):
if list1[i] == 1 and list2[i] == 1:
if zero_indices:
random_index = random.choice(zero_indices)
zero_indices.remove(random_index)
list2[random_index] = 1
list2[i] = 0
def everthing_to_jacks(keys, matrix_merged: List[RowOfNotes], to_keys, BPM_org_limitation=100.0,flag=1):
object_matrix = [[note[0] for note in obj.row] for obj in matrix_merged]
start_time_list = [obj.start_time for obj in matrix_merged]
start_time_list1,probability_list1,matrix_blocks = create_matrix_blocks(start_time_list, object_matrix, BPM_org_limitation-10.0)
new_matrix = []
for val in probability_list1:
row = [0] * to_keys # 生成长度为8，所有元素为0的列表
num_ones = int(val * to_keys) # 根据概率计算1的个数
adjustment = 0
if flag == 1:
adjustment = random.randint(0, 2) # 随机增加或减少±to_keys/4
elif flag == 0:
adjustment = random.randint(-2, 0) # 随机增加或减少±to_keys/4
num_ones += adjustment
num_ones = max(1, min(num_ones, to_keys)) # 限制最小为1，最大为8
for _ in range(num_ones):
index = random.randint(0, to_keys - 1) # 随机选择下标
row[index] = 1
new_matrix.append(row)
return start_time_list1, new_matrix
def adjust_rows(row1, row2):
for i in range(len(row1)):
if row1[i] == 1 and row2[i] == 1: # 如果相同下标同时为1
if i%2 == 0:
row1[i] = 0
else:#
row2[i] = 0 # 则将其中一个改为0
return row1, row2
def everthing_to_stream(keys, matrix_merged: List[RowOfNotes], to_keys, BPM_org_limitation=100.0):
start_time_list2, matrix2 = everthing_to_jacks(keys, matrix_merged, to_keys, (BPM_org_limitation/2+15.0),flag=0)
for i in range(len(matrix2) - 1):
make_disjoint(matrix2[i-1], matrix2[i])
return start_time_list2, matrix2
def row_of_notes_matrix_generation(start_time_list2: List[int], matrix3: List[List[int]]):
for i in range(len(start_time_list2)):
for j in range(len(matrix3[i])):
if matrix3[i][j] == 1:
matrix3[i][j] = [1, gen_simple_note_string(start_time_list2[i])]
if matrix3[i][j] == 0:
matrix3[i][j] = [0, ""]
def jacks_in_stream(matrix_merged: List[RowOfNotes], interval=8, add_num=1,BPM_org_limitation=100.0, flag=1):
columns = []
object_matrix = [[note[0] for note in obj.row] for obj in matrix_merged]#NOTE矩阵
start_time_list = [obj.start_time for obj in matrix_merged]#时间戳列表
holding_time_list = [obj.holding_time for obj in matrix_merged]#面条持续时间列表
keys = len(object_matrix[0])
for i in range(keys):
columns.append([row[i] for row in object_matrix])
for j in range(len(columns[0])):
if j % interval == 0 and 0 < j < len(columns[0])-2:
previous_row_ones = []
previous_row_notes = object_matrix[j-1]
for i, note in enumerate(previous_row_notes):
if note == 1:
previous_row_ones.append(i)
if start_time_list[j] > holding_time_list[j] and start_time_list[j] - start_time_list[j-1]>BPM_org_limitation:
random_numbers = random.sample(previous_row_ones, min(add_num,len(previous_row_ones)))
for num in random_numbers:
columns[num][j] = 1
if flag == 1:
columns[num][j + 1] = 0
all_zero_indices = []
for n in range(keys):
if columns[n][j]==0 or columns[n][j+2]==0:
all_zero_indices.append(n)
if all_zero_indices:
if num<= keys/2:
all_zero_indices = [x for x in all_zero_indices if x >= keys/2]
else:
all_zero_indices = [x for x in all_zero_indices if x <= keys/2]
if all_zero_indices:
random_index = random.choice(all_zero_indices)
all_zero_indices.remove(random_index)
columns[random_index][j + 1] = 1
object_matrix = [list(row) for row in zip(*columns)]
return start_time_list, object_matrix
def write_notes_to_str(matrix_ok):
note_str = ""
to_keys = len(matrix_ok[0])
for row_notes in matrix_ok:
for i, note in enumerate(row_notes):
if note[0] == 1:
# column_value = key_dict[to_keys - 1][i]
column_value = keyvalue(i, to_keys)
note_str += str(column_value) + "," + note[1] + "\n"
return note_str
#e.p
# notes = """9,192,110,5,0,0:0:0:0:
# 448,192,110,1,0,0:0:0:0:
# 228,192,110,1,0,0:0:0:0:
# 155,192,110,1,0,0:0:0:0:
# 9,192,335,1,0,0:0:0:0:
# 301,192,335,1,0,0:0:0:0:
# 82,192,448,1,0,0:0:0:0:
# 374,192,448,1,0,0:0:0:0:
# 9,192,561,1,0,0:0:0:0:
# """
# lines_note_objs = notes.strip().splitlines()
# matrix = []
# for line in lines_note_objs:
# matrix.append(RowOfNotes.new_row_from_original_str_line(line,7))
# matrix = matrix_merge(matrix)
#
# start_time_list, matrix = jacks_in_stream(matrix)
# row_of_notes_matrix_generation(start_time_list, matrix)
# str = write_notes_to_str(matrix)
# print(str)
# # —————————————万物化系列e.p———————————————
# # # start_time_list, jack_matrix = everthing_to_jacks(4,matrix,8)
# # # # row_of_notes_matrix_generation(start_time_list, jack_matrix)
# # # jack_str = write_notes_to_str(jack_matrix)
# # # print(jack_str)
# # start_time_list1, stream_matrix = everthing_to_stream(4,matrix,8)
# # row_of_notes_matrix_generation(start_time_list1, stream_matrix)
# # stream_str = write_notes_to_str(stream_matrix)
# # print(stream_str)