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需要和重新设计音量的配置方式。开始做实验

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金羿ELS
2026-07-20 07:14:22 +08:00
parent b01fccbf4b
commit 813ffab782
10 changed files with 91 additions and 44 deletions
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resources/experiments/enfasted_list_merging_test.py
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import random
import time
from itertools import chain
from multiprocessing import Pool, Process, freeze_support
from rich.console import Console
from rich.progress import Progress
from rich.table import Table
console = Console()
# gening_stst = {"NOWIDX": 0, "DATA": {}}
# 生成单个字典的函数（用于多进程）
def generate_single_dict(args):
dict_id, dict_size = args
# if dict_id:
# console.print(
# f"字典 {dict_id + 1} 大小 {dict_size} 生成中...",
# )
# else:
# console.print(
# f"\n字典 {dict_id + 1} 大小 {dict_size} 生成中...",
# )
# final_d = {}
# gening_stst["DATA"][dict_id] = 0
# for i in range(dict_size):
# final_d[i] = [random.randint(0, 1000) for _ in range(random.randint(10000, 99999))]
# gening_stst["DATA"][dict_id] += 1
return dict_id, {
i: [random.randint(0, 1000) for _ in range(random.randint(10000, 90000))]
for i in range(dict_size)
}
# return dict_id, final_d
# 合并函数定义
def chain_merging(dict_info: dict):
return sorted(chain(*dict_info.values()))
def seq_merging(dict_info: dict):
return sorted([i for sub in dict_info.values() for i in sub])
def summing(*_):
k = []
for i in _:
k += i
return k
def plus_merging(dict_info: dict):
return sorted(summing(*dict_info.values()))
if __name__ == "__main__":
freeze_support() # Windows系统需要这个调用
# 测试配置
dict_size = 50 # 每个字典的键值对数量
num_tests = 50 # 测试次数
function_list = [chain_merging, seq_merging, plus_merging]
# dict_list = []
results = {func.__name__: [] for func in function_list}
# 多进程生成多个字典
with Progress() as progress:
task = progress.add_task("[green]进行速度测试...", total=num_tests)
# gen_task = progress.add_task("[cyan] - 生成测试数据...", total=num_tests)
with Pool() as pool:
args_list = [
(
i,
dict_size,
)
for i in range(num_tests)
]
# def disp_work():
# while gening_stst["NOWIDX"] < num_tests:
# progress.update(
# gen_task,
# advance=1,
# description=f"[cyan]正在生成 {gening_stst['DATA']['NOWIDX']}/{dict_size -1}",
# # description="正在生成..."+console._render_buffer(
# # console.render(table,),
# # ),
# )
# Process(target=disp_work).start()
for result in pool.imap_unordered(generate_single_dict, args_list):
# dict_list.append(result)
progress.update(
task,
advance=1,
description=f"[cyan]正在测试 {result[0] + 1}/{num_tests}",
# description="正在生成..."+console._render_buffer(
# console.render(table,),
# ),
# refresh=True,
)
# gening_stst["NOWIDX"] += 1
# for _ in range(num_tests):
# 随机选择字典和打乱函数顺序
# current_dict = generate_single_dict((_, dict_size))
# progress.update(
# test_task,
# advance=1,
# # description=f"[cyan]正在测试 {_}/{num_tests -1}",
# # description="正在测试..."+console._render_buffer(
# # console.render(table,progress.console.options),
# # ),
# # refresh=True,
# )
# rangen_task = progress.add_task(
# "[green]正在生成测试数据...",
# total=dict_size,
# )
# current_dict = {}
# desc = "正在生成序列 {}/{}".format("{}",dict_size-1)
# for i in range(dict_size):
# # print("正在生成第", i, "个序列",end="\r",flush=True)
# progress.update(rangen_task, advance=1, description=desc.format(i))
# current_dict[i] = [random.randint(0, 1000) for _ in range(random.randint(10000, 99999))]
shuffled_funcs = random.sample(function_list, len(function_list))
# table.rows
# table.columns = fine_column
# progress.live
# progress.console._buffer.extend(progress.console.render(table))
# for j in progress.console.render(table,progress.console.options):
# progress.console._buffer.insert(0,j)
for i, func in enumerate(shuffled_funcs):
start = time.perf_counter()
func(result[1])
elapsed = time.perf_counter() - start
results[func.__name__].append(elapsed)
# gening_stst["NOWIDX"] = num_tests
# fine_column = table.columns.copy()
# for func in function_list:
# name = func.__name__
# table.add_row(
# name,
# f"-",
# f"-",
# f"-",
# f"-",
# )
# # proc_pool = []
# 测试执行部分（保持顺序执行）
# with Progress() as progress:
# # progress.live.update(table, refresh=True)
# # progress.live.process_renderables([table],)
# # print([console._render_buffer(
# # console.render(table,),
# # )])
# # progress.console._buffer.extend(progress.console.render(table))
# test_task = progress.add_task("[cyan]进行速度测试...", total=num_tests)
# for _ in range(num_tests):
# # 随机选择字典和打乱函数顺序
# # current_dict = generate_single_dict((_, dict_size))
# progress.update(
# test_task,
# advance=1,
# description=f"[cyan]正在测试 {_}/{num_tests -1}",
# # description="正在测试..."+console._render_buffer(
# # console.render(table,progress.console.options),
# # ),
# # refresh=True,
# )
# rangen_task = progress.add_task(
# "[green]正在生成测试数据...",
# total=dict_size,
# )
# current_dict = {}
# desc = "正在生成序列 {}/{}".format("{}",dict_size-1)
# for i in range(dict_size):
# # print("正在生成第", i, "个序列",end="\r",flush=True)
# progress.update(rangen_task, advance=1, description=desc.format(i))
# current_dict[i] = [random.randint(0, 1000) for _ in range(random.randint(10000, 99999))]
# shuffled_funcs = random.sample(function_list, len(function_list))
# # table.rows
# # table.columns = fine_column
# # progress.live
# # progress.console._buffer.extend(progress.console.render(table))
# # for j in progress.console.render(table,progress.console.options):
# # progress.console._buffer.insert(0,j)
# for i, func in enumerate(shuffled_funcs):
# start = time.perf_counter()
# func(current_dict)
# elapsed = time.perf_counter() - start
# results[func.__name__].append(elapsed)
# times = results[func.__name__]
# avg_time = sum(times) / len(times)
# min_time = min(times)
# max_time = max(times)
# table.columns[0]
# table.columns[0]._cells[i] = func.__name__
# table.columns[1]._cells[i] = f"{avg_time:.5f}"
# table.columns[2]._cells[i] = f"{min_time:.5f}"
# table.columns[3]._cells[i] = f"{max_time:.5f}"
# table.columns[4]._cells[i] = str(len(times))
# progress.update(test_task, advance=0.5)
# 结果展示部分
# 结果表格
table = Table(title="\n[cyan]性能测试结果", show_header=True, header_style="bold")
table.add_column("函数名称", style="dim", width=15)
table.add_column("平均耗时 (秒)", justify="right")
table.add_column("最小耗时 (秒)", justify="right")
table.add_column("最大耗时 (秒)", justify="right")
table.add_column("测试次数", justify="right")
for i, func in enumerate(function_list):
name = func.__name__
times = results[name]
avg_time = sum(times) / len(times)
min_time = min(times)
max_time = max(times)
table.add_row(
name,
f"{avg_time:.5f}",
f"{min_time:.5f}",
f"{max_time:.5f}",
str(len(times)),
)
# table.columns[0]._cells[i] = name
# table.columns[1]._cells[i] = f"{avg_time:.5f}"
# table.columns[2]._cells[i] = f"{min_time:.5f}"
# table.columns[3]._cells[i] = f"{max_time:.5f}"
# table.columns[4]._cells[i] = str(len(times))
console.print(table)
resources/experiments/genexpr_vs_yieldfrom.py
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# 模拟两种写法
def method_A(self, start, end):
yield from (f"{track}.get_range(start, end)" for track in self)
def method_B(self, start, end):
return (f"{track}.get_range(start, end)" for track in self)
tracks = ["A", "B"]
gen_a = method_A(tracks, 0, 10)
print(list(gen_a))
gen_b = method_B(tracks, 0, 10)
print(list(gen_b))
# they are the same output
resources/experiments/list_merging_text.py
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import random
import time
from itertools import chain
print("生成序列中")
fine_dict = {}
for i in range(50):
print("正在生成第", i, "个序列",end="\r",flush=True)
fine_dict[i] = [random.randint(0, 1000) for _ in range(random.randint(10000, 99999))]
print("序列生成完成")
def chain_merging(dict_info: dict):
return sorted(chain(*dict_info.values()))
def seq_merging(dict_info: dict):
return sorted([i for sub in dict_info.values() for i in sub])
def summing(*_):
k = []
for i in _:
k += i
return k
def plus_merging(dict_info: dict):
return sorted(summing(*dict_info.values()))
function_list = [chain_merging, seq_merging, plus_merging]
for func in function_list:
print("正在使用",func.__name__,"函数",)
start = time.time()
func(fine_dict)
print("耗时",time.time() - start)
print("结束")
resources/experiments/pgb-animate.json
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{
"rawtext": [
{
"translate": "%%4",
"with": {
"rawtext": [
{
"selector": "@e[name=某实体,scores={计分板=0..93}]"
},
{
"selector": "@e[name=某实体,scores={计分板=1..93}]"
},
{
"selector": "@e[name=某实体,scores={计分板=92..93}]"
},
{
"text": "显示第一段"
},
{
"text": "显示第二段"
},
{
"text": "显示第三段"
},
{
"text": "NaN"
}
]
}
}
]
}
resources/experiments/volumn_function_fit.py
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import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
def q_function1(x, a, a2, c1,):
return a * np.log( x + a2,)+ c1
def q_function2(x, b, b2, b3, b4, c2):
return b * ((x + b2) ** b3) + b4 * (x+b2) + c2
x_data = np.array([0, 16, 32, 48, 64, 80, 96, 112, 128])
y_data = np.array([16, 10, 6.75, 4, 2.5, 1.6, 0.8, 0.3, 0])
p_est1, err_est1 = curve_fit(q_function1, x_data[:5], y_data[:5], maxfev=1000000)
p_est2, err_est2 = curve_fit(q_function2, x_data[4:], y_data[4:], maxfev=1000000)
print(q_function1(x_data[:5], *p_est1))
print(q_function2(x_data[4:], *p_est2))
print("参数一：",*p_est1)
print("参数二：",*p_est2)
# 绘制图像
plt.plot(
np.arange(0, 64.1, 0.1), q_function1(np.arange(0, 64.1, 0.1), *p_est1), label=r"FIT1"
)
plt.plot(
np.arange(64, 128.1, 0.1), q_function2(np.arange(64, 128.1, 0.1), *p_est2), label=r"FIT2"
)
plt.scatter(x_data, y_data, color="red") # 标记给定的点
# plt.xlabel('x')
# plt.ylabel('y')
plt.title("Function Fit")
plt.legend()
# plt.grid(True)
plt.show()
resources/experiments/volumn_function_test.py
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import matplotlib.pyplot as plt
import numpy as np
# 定义对数函数
def q_function1(vol):
# return -23.65060754864053*((x+508.2130392724084)**0.8433764630986903) + 7.257078620637543 * (x+407.86870598508153) + 1585.6201108739122
# return -58.863374003875954 *((x+12.41481943150274 )**0.9973316187745871 ) +57.92341268595151 * (x+ 13.391132186222036) + -32.92986286030519
return -8.081720684086314 * np.log( vol + 14.579508825070013,)+ 37.65806375944386
def q_function2(vol):
return 0.2721359356095803 * ((vol + 2592.272889454798) ** 1.358571233418649) + -6.313841334963396 * (vol + 2592.272889454798) + 4558.496367823575
# 生成 x 值
x_values = np.linspace(0, 128, 1000)
x_data = np.array([0,16,32,48,64,80,96,112,128])
y_data = np.array([16, 10, 6.75, 4, 2.5, 1.6, 0.8, 0.3, 0])
print(q_function1(x_data))
print(q_function2(x_data))
# 绘制图像
plt.plot(x_values, q_function1(x_values,),label = "fit1")
plt.plot(x_values, q_function2(x_values,),label = "fit2")
plt.scatter(x_data, y_data, color='red') # 标记给定的点
# plt.scatter(x_data, y_data2, color='green') # 标记给定的点
# plt.scatter(x_data, y_data3, color='blue') # 标记给定的点
plt.xlabel('x')
plt.ylabel('y')
plt.title('Function')
plt.legend()
plt.grid(True)
plt.show()