def writeTiff(im_data, im_geotrans, im_proj, path): if 'int8' in im_data.dtype.name: datatype = gdal.GDT_Byte elif 'int16' in im_data.dtype.name: datatype = gdal.GDT_UInt16 else: datatype = gdal.GDT_Float32 if len(im_data.shape) == 3: im_bands, im_height, im_width = im_data.shape elif len(im_data.shape) == 2: im_data = np.array([im_data]) im_bands, im_height, im_width = im_data.shape # 创建文件 driver = gdal.GetDriverByName("GTiff") dataset = driver.Create(path, int(im_width), int(im_height), int(im_bands), datatype) if (dataset != None): dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数 dataset.SetProjection(im_proj) # 写入投影 for i in range(im_bands): dataset.GetRasterBand(i + 1).WriteArray(im_data[i]) del dataset解释一下

proj.rar_mstar_ven_apptbl.h

总之，proj.rar_mstar_ven_apptbl.h及相关文档是MSTAR芯片固件开发的关键组成部分，它们涉及到了固件的组织、加载和运行机制，以及开发和调试工具的使用。理解和掌握这些知识对于进行MSTAR平台的软件开发至关重要...

proj.rar_java飞机大战_site:www.pudn.com_炫酷飞机大战

【标题】"proj.rar_java飞机大战_site:www.pudn.com_炫酷飞机大战" 提供的信息表明，这是一个关于Java编程的项目，具体是一个飞机大战的游戏。"site:www.pudn.com" 暗示这个资源可能在PUDN（编程开发网络）网站上...

rom osgeo import gdal import copy import numpy as np from PIL import Image from tqdm import tqdm # read image def readTif(fileName): dataset = gdal.Open(fileName) if dataset == None: print(fileName + "文件无法打开") width = dataset.RasterXSize # 栅格矩阵的列数 height = dataset.RasterYSize # 栅格矩阵的行数 data = dataset.ReadAsArray(0, 0, width, height) return data, dataset # 保存tif文件函数 def writeTiff(im_data, im_geotrans, im_proj, path): if 'int8' in im_data.dtype.name: datatype = gdal.GDT_Byte elif 'int16' in im_data.dtype.name: datatype = gdal.GDT_UInt16 else: datatype = gdal.GDT_Float32 if len(im_data.shape) == 3: im_bands, im_height, im_width = im_data.shape elif len(im_data.shape) == 2: im_data = np.array([im_data]) im_bands, im_height, im_width = im_data.shape # 创建文件 driver = gdal.GetDriverByName("GTiff") dataset = driver.Create(path, int(im_width), int(im_height), int(im_bands), datatype) if (dataset != None): dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数 dataset.SetProjection(im_proj) # 写入投影 for i in range(im_bands): dataset.GetRasterBand(i + 1).WriteArray(im_data[i]) del dataset

你可以使用 Lua 的 io 库来读取和写入文件。以下是一个简单的 Lua 脚本，可以将 lvmvm 文件转换为 txt 文件： lua -- 打开 lvmvm 文件 local f = io.open("input.lvmvm", "rb") -- 读取文件内容 ...

File "1.py", line 64, in <module> in_proj = pyproj.Proj(int='epsg:4326') File "/home/admin1/anaconda3/envs/CFANet/lib/python3.6/site-packages/pyproj/init.py", line 358, in new return _proj.Proj.new(self, projstring) File "_proj.pyx", line 84, in _proj.Proj.cinit RuntimeError: b'projection not named'

这个错误是由于在创建 pyproj.Proj 对象时传入的投影字符串（projstring）不正确导致的。根据你提供的代码，问题出现在第 64 行，其中你正在尝试使用 'epsg:4326' 这个投影字符串创建一个 pyproj.Proj 对象。可能...

def init(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): super().init() self.positional_embedding = nn.Parameter(torch.randn(spacial_dim 2 + 1, embed_dim) / embed_dim 0.5) self.k_proj = nn.Linear(embed_dim, embed_dim) self.q_proj = nn.Linear(embed_dim, embed_dim) self.v_proj = nn.Linear(embed_dim, embed_dim) self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) self.num_heads = num_heads

在__init__方法中，模型定义了四个线性变换（k_proj、q_proj、v_proj和c_proj），用于将输入向量映射到键、查询、值和输出空间中。此外，模型还定义了一个位置嵌入矩阵，用于将序列中每个向量的位置信息编码到向量...

File "_proj.pyx", line 84, in _proj.Proj.cinit RuntimeError: b'projection not named'

具体地说，是在 _proj.Proj.__cinit__ 这个方法中出现了 RuntimeError: b'projection not named' 错误。这个错误的原因可能是在代码中没有正确命名投影。你需要确保在使用 _proj 模块中的 Proj 类之前，...

class Block(nn.Module): # 构建注意力Block模块 def init(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=GELU, norm_layer=nn.LayerNorm): super().init() self.norm1 = norm_layer(dim) self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) self.norm2 = norm_layer(dim) self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x 调用此结构时应该输入什么

调用这个 Block 类时，应该输入一个二维张量，尺寸为 [batch_size, dim]，代表一个 batch 中的一批输入特征。其中，dim 表示每个输入特征的维度。在使用这个 Block 类构建深度学习模型时，可以将这个 Block 类堆叠...

已知程序 import xarray as xr from collections import namedtuple import numpy as np from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter import matplotlib.ticker as mticker import cartopy.feature as cfeature import cartopy.crs as ccrs import matplotlib.pyplot as plt import matplotlib.cm as cm import matplotlib.colors as mcolors def region_mask(lon, lat, extents): lonmin, lonmax, latmin, latmax = extents return ( (lon >= lonmin) & (lon <= lonmax) & (lat >= latmin) & (lat <= latmax) ) Point = namedtuple('Point', ['x', 'y']) Pair = namedtuple('Pair', ['start', 'end']) time = '2023-05-04' filepath_DPR = r"C:\pythontest\zFactor\test1.nc4" extents = [110, 122, 25, 38] with xr.open_dataset(filepath_DPR) as f: lon_DPR = f['FS_Longitude'][:] lat_DPR = f['FS_Latitude'][:] zFactorFinalNearSurface = f['FS_SLV_zFactorFinalNearSurface'][:] nscan, nray = lon_DPR.shape midray = nray // 2 mask = region_mask(lon_DPR[:, midray], lat_DPR[:, midray], extents) index = np.s_[mask] lon_DPR = lon_DPR[index] lat_DPR = lat_DPR[index] zFactorFinalNearSurface = zFactorFinalNearSurface[index] for data in [ zFactorFinalNearSurface, ]: data.values[data <= -9999] = np.nan proj = ccrs.PlateCarree() fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection=proj) ax.coastlines(resolution='50m', lw=0.5) ax.add_feature(cfeature.OCEAN.with_scale('50m')) ax.add_feature(cfeature.LAND.with_scale('50m')) ax.set_xticks(np.arange(-180, 181, 5), crs=proj) ax.set_yticks(np.arange(-90, 91, 5), crs=proj) ax.xaxis.set_minor_locator(mticker.AutoMinorLocator(2)) ax.yaxis.set_minor_locator(mticker.AutoMinorLocator(2)) ax.xaxis.set_major_formatter(LongitudeFormatter()) ax.yaxis.set_major_formatter(LatitudeFormatter()) ax.set_extent(extents, crs=proj) ax.tick_params(labelsize='large') def make_zF_cmap(levels): '''制作雷达反射率的colormap.''' nbin = len(levels) - 1 cmap = cm.get_cmap('jet', nbin) norm = mcolors.BoundaryNorm(levels, nbin) return cmap, norm levels_zF = [0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45] cmap_zF, norm_zF = make_zF_cmap(levels_zF) im = ax.contourf( lon_DPR, lat_DPR, zFactorFinalNearSurface, levels_zF, # 三个物理量为 (500, 49)就是在500*49的格点上赋予这三个物理量 cmap=cmap_zF, norm=norm_zF, extend='both', transform=proj ) cbar = fig.colorbar(im, ax=ax, ticks=levels_zF) cbar.set_label('zFactor (dBZ)', fontsize='large') cbar.ax.tick_params(labelsize='large') ax.set_title(f'DPR zFactor on {time}', fontsize='x-large') plt.show()如何将其中的zFactorFinal变量变为二维

在这段代码中，zFactorFinalNearSurface的shape是(500, 49)，其中第一维是nscan(扫描线数)，第二维是nray(每个扫描线的射线数)。如果要将zFactorFinalNearSurface变为二维数组，可以将nscan和nray相乘得到总的格点数...

if isinstance(self.pretrained, str): self.apply(_init_weights) logger = get_root_logger() logger.info(f'load model from: {self.pretrained}') checkpoint = torch.load(self.pretrained, map_location='cpu') state_dict = checkpoint['model'] state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).repeat(1,1,self.patch_size[0],1,1) / self.patch_size[0] ## Duplicate weights for temporal attention and temporal norm # new_state_dict = state_dict.copy() # for key in state_dict: # if 'blocks' in key and 'attn' in key and 'relative' not in key and 'mask' not in key: # new_key = key.replace('attn','temporal_attn') # if not new_key in state_dict: # new_state_dict[new_key] = state_dict[key] # else: # new_state_dict[new_key] = state_dict[new_key] # if 'blocks' in key and 'norm1' in key and 'relative' not in key and 'mask' not in key: # new_key = key.replace('norm1','temporal_norm') # if not new_key in state_dict: # new_state_dict[new_key] = state_dict[key] # else: # new_state_dict[new_key] = state_dict[new_key] # state_dict = new_state_dict msg = self.load_state_dict(state_dict, strict=False) logger.info('Missing keys: {}'.format(msg.missing_keys)) logger.info('Unexpected keys: {}'.format(msg.unexpected_keys)) logger.info(f"=> loaded successfully '{self.pretrained}'") del checkpoint torch.cuda.empty_cache() elif self.pretrained is None: self.apply(_init_weights) else: raise TypeError('pretrained must be a str or None')

首先判断是否需要加载预训练模型，如果需要，则从指定的路径加载预训练模型的参数，并将模型的 patch_embed.proj.weight 层的权重进行重新计算，以适应输入的 patch 大小。然后将加载的参数应用到当前模型中。如果...

geodetic_to_gauss_trans(double lon, double lat, int zone_mode, double custom_longitude) { if ((lon >= -180 && lon <= 180) && (lat >= -90 && lat <= 90) && (zone_mode == -1 || zone_mode == 0 || zone_mode == 1) && (custom_longitude >= -180 && custom_longitude <= 180)) { switch (zone_mode) { case 1: if (lon >= 1.5) { zone_ = int((lon + 1.5) / 3); central_meridian_ = zone_ * 3; } if (lon < 1.5) { zone_ = int((lon + 1.5) / 3) + 120; central_meridian_ = zone_ * 3 - 360; } break; case -1: if (lon >= 0) { zone_ = int(lon / 6) + 1; central_meridian_ = zone_ * 6 - 3; } if (lon < 0) { zone_ = int(lon / 6) + 60; central_meridian_ = (zone_ * 6 - 3) - 360; } break; case 0: central_meridian_ = custom_longitude; break; } } else { x_ = 0; y_ = 0; return false; } std::string proj_string = "+proj=tmerc +lat_0=0 +lon_0=central_meridian +k=1 +x_0=500000 +y_0=0 +ellps=GRS80 +units=m +no_defs +type=crs"; std::string to_replace = "central_meridian"; std::string replace_with = std::to_string(central_meridian_); size_t pos = proj_string.find(to_replace); proj_string.replace(pos, to_replace.length(), replace_with); PJ_CONTEXT C = proj_context_create(); PJ P = proj_create(C, proj_string.c_str()); PJ G = proj_crs_get_geodetic_crs(C, P); PJ_AREA A = nullptr; const char const options = nullptr; PJ *G2P = proj_create_crs_to_crs_from_pj(C, G, P, A, options); PJ_COORD c_in{}; c_in.lpzt.z = 0.0; c_in.lpzt.t = HUGE_VAL; c_in.lp.lam = lon; c_in.lp.phi = lat; PJ_COORD c_out = proj_trans(G2P, PJ_FWD, c_in); x_ = c_out.enu.n; y_ = c_out.enu.e; // PJ_COORD c_inv = proj_trans(G2P, PJ_DIRECTION::PJ_INV, c_out); std::cout.precision(20); std::cout << std::fixed; std::cout << x_ << "," << y_ << std::endl; std::cout << std::fixed << c_inv.lp.lam << "," << c_inv.lp.phi << std::endl; proj_destroy(P); proj_destroy(G); proj_destroy(G2P); proj_context_destroy(C); return true; }

接着，利用Proj库中的函数进行投影变换，将经纬度坐标转换为高斯投影坐标系下的坐标。最后，将计算得到的坐标存储在函数的成员变量x_和y_中，并返回true表示转换成功。需要注意的是，这段代码中使用了Proj库进行...

class CAM_Module(Module): """ Channel attention module""" def init(self, in_dim): super(CAM_Module, self).init() self.chanel_in = in_dim self.gamma = Parameter(torch.zeros(1)) self.softmax = Softmax(dim=-1) def forward(self,x): """ inputs : x : input feature maps( B X C X H X W) returns : out : attention value + input feature attention: B X C X C """ m_batchsize, C, height, width, channle = x.size() #print(x.size()) proj_query = x.view(m_batchsize, C, -1) proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1) #形状转换并交换维度 energy = torch.bmm(proj_query, proj_key) energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy)-energy attention = self.softmax(energy_new) proj_value = x.view(m_batchsize, C, -1) out = torch.bmm(attention, proj_value) out = out.view(m_batchsize, C, height, width, channle) # print('out', out.shape) # print('x', x.shape) out = self.gammaout + x #CH*W return out 将其改成keras代码

self.gamma = self.add_weight(name='gamma', shape=[1], initializer='zeros', trainable=True) self.softmax = Softmax(axis=-1) def call(self, x): """ inputs : x : input feature maps( B X H X W X C)...

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File "_proj.pyx", line 84, in _proj.Proj.cinit RuntimeError: b'projection not named'