CF - 359 - B. Permutation(构造)

本文提供了一个关于 CodeForces 359/B 题目的解决方案,通过构造特定的排列来解决给定问题,确保了排列的特性符合题目要求。

题意:输入n和k,求一个1到2*n的排列,使得|a1 - a2| + |a3 - a4| + ... + |a2n-1 - a2n| - |a1-a2 + a3-a4 + ... + a2n-1-a2n| = 2 * k(1 ≤ n ≤ 50000, 0 ≤ 2k ≤ n)。。。

题目链接:http://codeforces.com/problemset/problem/359/B

——>>构造,若能出现k对2,剩下的为0,就行了。。。

#include <cstdio>

using namespace std;

int main()
{
    int n, k;
    while(scanf("%d%d", &n, &k) == 2) {
        printf("1 2");
        if(k) {
            for(int i = 2; i <= k; i++) printf(" %d %d", (i<<1)-1, i<<1);
            for(int i = k+1; i <= n; i++) printf(" %d %d", i<<1, (i<<1)-1);
        }
        else {
            for(int i = 2; i <= n; i++) printf(" %d %d", (i<<1)-1, i<<1);
        }
        puts("");
    }
    return 0;
}


仿照这个代码对CMIP6中的pp数据进行降维:import xarray as xr import numpy as np # 1. 读取原始文件 file_path = r'E:\Except-Desktop\DC\CMIP6-download\historical-pp\pp_c2.nc' ds = xr.open_dataset(file_path) print(ds) # 2. 提取并展平 lat/lon lat_2d = ds['lat'].values lon_2d = ds['lon'].values lat_1d = lat_2d.flatten() lon_1d = lon_2d.flatten() # 3.构建新坐标 new_coords_flat = { 'grid_point': (['grid_point'], np.arange(len(lat_1d))), 'lat': (['grid_point'], lat_1d), 'lon': (['grid_point'], lon_1d), } if 'time' in ds.coords: new_coords_flat['time'] = ds['time'] if 'lev' in ds.coords: new_coords_flat['lev'] = ds['lev'] elif 'lev' in ds.dims: new_coords_flat['lev'] = (['lev'], np.arange(ds.dims['lev'])) # 4. 构建新的变量 data_vars_flat = {} for var_name in ds.data_vars: var = ds[var_name] dims = list(var.dims) new_dims = [] for d in dims: if d == 'nlat' or d == 'nlon': if 'grid_point' not in new_dims: new_dims.append('grid_point') else: new_dims.append(d) data = var.values if 'nlat' in var.dims and 'nlon' in var.dims: nlat_pos = var.dims.index('nlat') nlon_pos = var.dims.index('nlon') shape = list(data.shape) shape[nlat_pos:nlon_pos+1] = [shape[nlat_pos] * shape[nlon_pos]] data = data.reshape(shape) data_vars_flat[var_name] = (new_dims, data, var.attrs) # 5. 创建展平后的数据集 flat_ds = xr.Dataset(data_vars=data_vars_flat, coords=new_coords_flat) flat_ds.attrs = ds.attrs # 6. 进行重构:从 grid_point -> lat/lon 二维结构 lat_1d_flat = flat_ds['lat'].values lon_1d_flat = flat_ds['lon'].values lat_unique = np.unique(lat_1d_flat) lon_unique = np.unique(lon_1d_flat) # 建立 (lat, lon) 到 grid_point 的映射 lat_lon_to_point = {} for idx, (lat, lon) in enumerate(zip(lat_1d_flat, lon_1d_flat)): lat_lon_to_point[(lat, lon)] = idx # 准备新坐标 new_coords_restored = { 'time': (['time'], flat_ds['time'].values), 'lat': (['lat'], lat_unique), 'lon': (['lon'], lon_unique), } # 安全处理 lev 坐标 if 'lev' in flat_ds.coords: new_coords_restored['lev'] = (['lev'], flat_ds['lev'].values) elif 'lev' in flat_ds.dims: new_coords_restored['lev'] = (['lev'], np.arange(flat_ds.dims['lev'])) else: print("Warning: 'lev' not found in dataset.") # 重构数据 value = flat_ds['pp'].values shape = value.shape new_shape = (shape[0], shape[1], len(lat_unique), len(lon_unique)) reshaped = np.full(new_shape, np.nan) for i in range(len(lat_unique)): for j in range(len(lon_unique)): lat_val = lat_unique[i] lon_val = lon_unique[j] if (lat_val, lon_val) in lat_lon_to_point: idx = lat_lon_to_point[(lat_val, lon_val)] reshaped[:, :, i, j] = value[:, :, idx] # 构建数据变量 data_vars_restored = { 'pp': (['time', 'lev', 'lat', 'lon'], reshaped, flat_ds['pp'].attrs), } # 处理边界数据 if 'lat_bnds' in flat_ds: lat_bnds_1d = flat_ds['lat_bnds'].values lat_bnds = np.full((len(lat_unique), 2), np.nan) for i in range(len(lat_unique)): lat_val = lat_unique[i] for lon_val in lon_unique: if (lat_val, lon_val) in lat_lon_to_point: idx = lat_lon_to_point[(lat_val, lon_val)] lat_bnds[i, :] = lat_bnds_1d[idx, [0, -1]] break data_vars_restored['lat_bnds'] = (['lat', 'bnds'], lat_bnds) if 'lon_bnds' in flat_ds: lon_bnds_1d = flat_ds['lon_bnds'].values lon_bnds = np.full((len(lon_unique), 2), np.nan) for j in range(len(lon_unique)): lon_val = lon_unique[j] for lat_val in lat_unique: if (lat_val, lon_val) in lat_lon_to_point: idx = lat_lon_to_point[(lat_val, lon_val)] lon_bnds[j, :] = lon_bnds_1d[idx, [0, -1]] break data_vars_restored['lon_bnds'] = (['lon', 'bnds'], lon_bnds) # 创建最终数据集 final_ds = xr.Dataset(data_vars=data_vars_restored, coords=new_coords_restored) final_ds.attrs = flat_ds.attrs # 保存结果 output_path = r'E:\Except-Desktop\DC\CMIP6-download\historical-pp\pp_c2_restored.nc' final_ds.to_netcdf(output_path) print(f"转换后的文件已保存到: {output_path}") # 打印结构 print("\n转换后数据集结构:") print(xr.open_dataset(output_path)) pp的相关信息为:<xarray.Dataset> Size: 12GB Dimensions: (time: 1032, lev: 15, nlat: 384, nlon: 320, d2: 2, vertices: 4) Coordinates: lat (nlat, nlon) float64 983kB ... * lev (lev) float64 120B 500.0 1.5e+03 2.5e+03 ... 1.35e+04 1.45e+04 lon (nlat, nlon) float64 983kB ... * nlat (nlat) int32 2kB 1 2 3 4 5 6 7 8 ... 378 379 380 381 382 383 384 * nlon (nlon) int32 1kB 1 2 3 4 5 6 7 8 ... 314 315 316 317 318 319 320 * time (time) object 8kB 2015-01-15 13:00:00.000008 ... 2100-12-15 12... Dimensions without coordinates: d2, vertices Data variables: pp (time, lev, nlat, nlon) float32 8GB ... time_bnds (time, d2) object 17kB ... lat_bnds (time, nlat, nlon, vertices) float32 2GB ... lon_bnds (time, nlat, nlon, vertices) float32 2GB ... lev_bnds (time, lev, d2) float32 124kB ... Attributes: (12/45) Conventions: CF-1.7 CMIP-6.2 activity_id: ScenarioMIP branch_method: standard branch_time_in_child: 735110.0 branch_time_in_parent: 735110.0 case_id: 43 ... ... sub_experiment_id: none table_id: Omon tracking_id: hdl:21.14100/0208fb60-6f8c-40f4-a0bf-127e8e5e8052 variable_id: pp variant_info: CMIP6 CESM2 future scenario SSP5-8.5 between 2014... variant_label: r1i1p1f1
最新发布
10-08
with_progress({ + # 初始化进度条 + p <- progressor(along = 1:2) + + # 执行100次置换 + placebo_dist <- manual_placebo(data, n_permute = 2) + }) One outcome and one treatment time found. Running single_augsynth. One outcome and one treatment time found. Running single_augsynth. Warning message: In handle_progression(condition, debug = debug) : Received a progression ‘update’ request (amount=1; msg=‘character(0)) but is not listening to this progressor. This can happen when code signals more progress updates than it configured the progressor to do. When the progressor completes all steps, it shuts down resulting in the global progression handler to no longer listen to it. To troubleshoot this, try with progressr::handlers("debug") > # 清理内存碎片 > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 2983096 159.4 5026872 268.5 5026872 268.5 Vcells 10326783 78.8 36845423 281.2 112127404 855.5 > # 5. 结果查看 ------------------------------------------------------------------ > # 查看前10次置换结果 > print(head(placebo_dist, 10)) Time Estimate lower_bound upper_bound p_val permutation pseudo_unit 2016-04-01...1 2016-04-01 -0.18368508 NA NA NA 1 1 2016-04-02...2 2016-04-02 -0.31366935 NA NA NA 1 1 2016-04-03...3 2016-04-03 -0.49916072 NA NA NA 1 1 2016-04-04...4 2016-04-04 0.30554081 NA NA NA 1 1 2016-04-05...5 2016-04-05 -0.33573735 NA NA NA 1 1 2016-04-06...6 2016-04-06 0.07909620 NA NA NA 1 1 2016-04-07...7 2016-04-07 -0.21757939 NA NA NA 1 1 2016-04-08...8 2016-04-08 -0.45763920 NA NA NA 1 1 2016-04-09...9 2016-04-09 0.07273611 NA NA NA 1 1 2016-04-10...10 2016-04-10 0.17557989 NA NA NA 1 1 >
03-25
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值