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| 文件名: python气象自动绘图函数设计思想与使用方法.part2.rar | |
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+map 57.5 MB
bou2_4l.dbf 148.0 KB bou2_4l.shp 1.3 MB bou2_4l.shx 14.0 KB bou2_4p.dbf 84.3 KB bou2_4p.shp 1.4 MB bou2_4p.shx 7.3 KB country1.dbf 510.0 KB country1.shp 3.6 MB country1.shx 2.0 KB MeteoInfo_csharp_1.1.3.5R1.zip 36.5 MB province.CPG 5 Byte province.dbf 3.9 KB province.prj 140 Byte province.sbn 468 Byte province.sbx 148 Byte province.shp 12.1 MB province.shx 380 Byte province.xml.xml 130.0 KB stations_lat_lon.xlsx 139.0 KB tibet_new.dbf 350 Byte tibet_new.prj 145 Byte tibet_new.sbn 132 Byte tibet_new.sbx 116 Byte tibet_new.shp 11.5 KB tibet_new.shx 108 Byte tibet_new.xml.xml 17.6 KB world_adm0_Project.dbf 14.0 KB world_adm0_Project.prj 145 Byte world_adm0_Project.sbn 2.1 KB world_adm0_Project.sbx 268 Byte world_adm0_Project.shp 1.5 MB world_adm0_Project.shp.xml 8.9 KB world_adm0_Project.shx 1.7 KB +机器学习 377.0 MB boost_1_70_0-msvc-14.1-64.exe 175.0 MB cmake-3.23.2-windows-x86_64.msi 27.9 MB LightGBM(已经编译).zip 89.1 MB mingw-w64-install.exe 938.0 KB vs_Community.exe 1.2 MB xgboost-1.2.0_SNAPSHOT+4729458a363c64291e84da28b408a0ac8d7851fa-py3-none-win_amd64.whl 83.1 MB +教程1 48.5 MB +.ipynb_checkpoints 24.2 MB Auto_adaboost.ipynb 36.4 KB Auto_adaboost_Bayesian_Optimization.ipynb 6.0 KB Auto_adaboost_chose.ipynb 37.3 KB Auto_ANN.ipynb 68.9 KB Auto_ANN_chose.ipynb 161.0 KB Auto_ANN_KAN_pytorch.ipynb 35.0 KB Auto_ANN_pytorch.ipynb 32.0 KB Auto_any_vertical.ipynb 16.8 KB Auto_blocking_high.ipynb 14.6 KB Auto_catboost.ipynb 38.1 KB Auto_catboost_Bayesian_Optimization.ipynb 6.1 KB Auto_catboost_chose.ipynb 38.8 KB Auto_cdf_matching.ipynb 4.2 KB Auto_check_data_time.ipynb 2.3 KB Auto_chose_data.ipynb 3.1 KB Auto_CNN.ipynb 128.0 KB Auto_CNN_chose.ipynb 338.0 KB Auto_CNN_KAN_pytorch.ipynb 44.9 KB Auto_CNN_LSTMorGRUorRNN.ipynb 159.0 KB Auto_CNN_LSTMorGRUorRNN_chose.ipynb 434.0 KB Auto_CNN_LSTMorGRUorRNN_more_output.ipynb 272.0 KB Auto_CNN_LSTMorGRUorRNN_pytorch.ipynb 53.0 KB Auto_CNN_pytorch.ipynb 42.2 KB Auto_CNN_TCN.ipynb 141.0 KB Auto_CNN_TCN_chose.ipynb 379.0 KB Auto_CNN_TCN_more_output.ipynb 261.0 KB Auto_CNN_TCN_pytorch.ipynb 48.2 KB Auto_CNN_Transformer.ipynb 156.0 KB Auto_CNN_Transformer_chose.ipynb 434.0 KB Auto_CNN_Transformer_more_output.ipynb 273.0 KB Auto_CNN_Transformer_pytorch.ipynb 51.7 KB Auto_create_gif.ipynb 2.3 KB Auto_DeepForest.ipynb 35.4 KB Auto_DeepForest_Bayesian_Optimization.ipynb 5.3 KB Auto_DeepForest_chose.ipynb 37.6 KB Auto_draw_taylor.ipynb 2.7 KB Auto_EfficientTemp_ESR_GAN.ipynb 93.8 KB Auto_EfficientTemp_ESR_GAN_pytorch.ipynb 45.5 KB Auto_EfficientTemp_MSG_SE_Densenet_GAN.ipynb 115.0 KB Auto_EfficientTempNet.ipynb 22.6 KB Auto_EfficientTempNet_pytorch.ipynb 23.0 KB Auto_EfficientTempt_MSG_SE_Densenet_GAN_pytorch.ipynb 49.2 KB Auto_EMD.ipynb 3.2 KB Auto_eof.ipynb 4.4 KB Auto_ERA5_download.ipynb 3.7 KB Auto_ESR_EfficientTemp_GAN.ipynb 94.1 KB Auto_ESR_EfficientTemp_GAN_pytorch.ipynb 45.5 KB Auto_ESRGAN.ipynb 81.7 KB Auto_ESRGAN_pytorch.ipynb 34.4 KB Auto_GCN.ipynb 114.0 KB Auto_heatwave.ipynb 2.9 KB Auto_IBTrACS_read.ipynb 4.3 KB Auto_imageline_to_data.ipynb 3.5 KB Auto_Latent_Diffusion_Model_pytorch.ipynb 83.6 KB Auto_Liang_Kleeman_information_flow.ipynb 4.1 KB Auto_Liang_Kleeman_relative_flow.ipynb 4.3 KB Auto_lightgbm.ipynb 41.9 KB Auto_lightgbm_Bayesian_Optimization.ipynb 7.4 KB Auto_lightgbm_chose.ipynb 51.6 KB Auto_linregress.ipynb 3.3 KB Auto_LSTMorGRUorRNN.ipynb 94.2 KB Auto_LSTMorGRUorRNN_chose.ipynb 238.0 KB Auto_LSTMorGRUorRNN_more_output.ipynb 106.0 KB Auto_LSTMorGRUorRNN_pytorch.ipynb 45.8 KB Auto_more_gaussianprocessregressor.ipynb 3.5 KB Auto_more_lassoregress.ipynb 3.3 KB Auto_more_linregress.ipynb 3.6 KB Auto_more_ridgeregress.ipynb 3.5 KB Auto_MSG_SE_Densenet_EfficientTemp_GAN.ipynb 115.0 KB Auto_MSG_SE_Densenet_EfficientTempt_GAN_pytorch.ipynb 49.1 KB Auto_MSG_SE_Densenet_GAN.ipynb 106.0 KB Auto_MSG_SE_Densenet_GAN_pytorch.ipynb 38.2 KB Auto_Multi_Scale_CNN.ipynb 129.0 KB Auto_Multi_Scale_CNN_LSTMorGRUorRNN.ipynb 159.0 KB Auto_Multi_Scale_CNN_LSTMorGRUorRNN_more_output.ipynb 274.0 KB Auto_Multi_Scale_CNN_LSTMorGRUorRNN_pytorch.ipynb 57.1 KB Auto_Multi_Scale_CNN_pytorch.ipynb 45.0 KB Auto_Multi_Scale_CNN_TCN.ipynb 142.0 KB Auto_Multi_Scale_CNN_TCN_more_output.ipynb 263.0 KB Auto_Multi_Scale_CNN_TCN_pytorch.ipynb 50.9 KB Auto_Multi_Scale_CNN_Transformer.ipynb 157.0 KB Auto_Multi_Scale_CNN_Transformer_more_output.ipynb 275.0 KB Auto_Multi_Scale_CNN_Transformer_pytorch.ipynb 54.2 KB Auto_Multi_Scale_Resnet.ipynb 480.0 KB Auto_Multi_Scale_Resnet_LSTMorGRUorRNN.ipynb 532.0 KB Auto_Multi_Scale_Resnet_LSTMorGRUorRNN_more_output.ipynb 645.0 KB Auto_Multi_Scale_Resnet_LSTMorGRUorRNN_pytorch.ipynb 75.8 KB Auto_Multi_Scale_Resnet_pytorch.ipynb 63.0 KB Auto_Multi_Scale_Resnet_TCN.ipynb 514.0 KB Auto_Multi_Scale_Resnet_TCN_more_output.ipynb 629.0 KB Auto_Multi_Scale_Resnet_TCN_pytorch.ipynb 69.5 KB Auto_Multi_Scale_Resnet_Transformer.ipynb 529.0 KB Auto_Multi_Scale_Resnet_Transformer_more_output.ipynb 642.0 KB Auto_Multi_Scale_Resnet_Transformer_pytorch.ipynb 72.9 KB Auto_ngboost.ipynb 36.3 KB Auto_ngboost_Bayesian_Optimization.ipynb 6.1 KB Auto_ngboost_chose.ipynb 39.4 KB Auto_optical_flow.ipynb 2.3 KB Auto_partical_r.ipynb 7.7 KB Auto_pcolormesh.ipynb 4.5 KB Auto_Plumb.ipynb 2.7 KB Auto_r.ipynb 3.8 KB Auto_RandomForest.ipynb 30.6 KB Auto_RandomForest_Bayesian_Optimization.ipynb 5.7 KB Auto_RandomForest_chose.ipynb 35.8 KB Auto_region_data_mask.ipynb 2.1 KB Auto_Resnet.ipynb 479.0 KB Auto_Resnet_chose.ipynb 1.2 MB Auto_Resnet_LSTMorGRUorRNN.ipynb 531.0 KB Auto_Resnet_LSTMorGRUorRNN_chose.ipynb 1.5 MB Auto_Resnet_LSTMorGRUorRNN_more_output.ipynb 643.0 KB Auto_Resnet_LSTMorGRUorRNN_pytorch.ipynb 72.0 KB Auto_Resnet_pytorch.ipynb 59.4 KB Auto_Resnet_TCN.ipynb 514.0 KB Auto_Resnet_TCN_chose.ipynb 1.5 MB Auto_Resnet_TCN_more_output.ipynb 627.0 KB Auto_Resnet_TCN_pytorch.ipynb 65.8 KB Auto_Resnet_Transformer.ipynb 528.0 KB Auto_Resnet_Transformer_chose.ipynb 1.5 MB Auto_Resnet_Transformer_more_output.ipynb 640.0 KB Auto_Resnet_Transformer_pytorch.ipynb 69.2 KB Auto_smooth.ipynb 7.9 KB Auto_SOM.ipynb 7.8 KB Auto_SRFlow_pytorch.ipynb 28.5 KB Auto_SRGAN.ipynb 72.9 KB Auto_SRGAN_pytorch.ipynb 32.7 KB Auto_stepwise_regression.ipynb 2.7 KB Auto_sudden_drought.ipynb 19.8 KB Auto_SVD.ipynb 5.4 KB Auto_SVM.ipynb 59.5 KB Auto_SVM_Bayesian_Optimization.ipynb 9.7 KB Auto_SVM_chose.ipynb 50.6 KB Auto_T_N.ipynb 3.1 KB Auto_TC_compare.ipynb 3.2 KB Auto_TCN.ipynb 78.8 KB Auto_TCN_chose.ipynb 187.0 KB Auto_TCN_more_output.ipynb 96.6 KB Auto_TCN_pytorch.ipynb 38.3 KB Auto_test_of_abrupt_change.ipynb 4.5 KB Auto_Transformer.ipynb 82.3 KB Auto_Transformer_chose.ipynb 203.0 KB Auto_Transformer_more_output.ipynb 111.0 KB Auto_Transformer_pytorch.ipynb 41.9 KB Auto_TransUnet.ipynb 177.0 KB Auto_TransUnet_pytorch.ipynb 44.3 KB Auto_triple_collocation.ipynb 3.1 KB Auto_TSNE.ipynb 3.3 KB Auto_Unet.ipynb 155.0 KB Auto_Unet_chose.ipynb 423.0 KB Auto_Unet_pytorch.ipynb 42.5 KB Auto_UZ_detect.ipynb 8.4 KB Auto_UZ_track.ipynb 9.6 KB Auto_VDVAE_SR_pytorch.ipynb 101.0 KB Auto_violin.ipynb 3.3 KB Auto_wasserstein_stability_analysis.ipynb 3.3 KB Auto_wave.ipynb 4.0 KB Auto_windrose.ipynb 4.2 KB Auto_xgboost.ipynb 41.3 KB Auto_xgboost_Bayesian_Optimization.ipynb 6.9 KB Auto_xgboost_chose.ipynb 47.5 KB Autobar_or_line.ipynb 69.1 KB Autoshaded_quiver.ipynb 373.0 KB create_nc.ipynb 8.7 KB data_all.ipynb 40.8 KB DBSCAN_eval.ipynb 3.5 KB deal_data_time.ipynb 246.0 KB K_means_eval.ipynb 4.5 KB open_data_grib.ipynb 33.4 KB open_data_nc.ipynb 50.1 KB open_data_txt_grid.ipynb 9.8 KB open_data_txt_station.ipynb 14.4 KB open_hdf.ipynb 10.6 KB selftime.ipynb 4.7 KB specx_anal.ipynb 3.5 KB +库包 24.9 KB Buishand_U.py 521 Byte lanczos.py 433 Byte Little_wave.py 5.1 KB maskout_country.py 3.1 KB maskout_province.py 3.1 KB mktest.py 1.3 KB Move_t_test.py 5.9 KB Pettitt.py 593 Byte regression_mode.py 1.6 KB SNHT.py 493 Byte Taylor.py 2.8 KB +我的论文 20.0 MB 8-叶宇辰-基于机器学习的长江流域夏季延伸期预报及土壤温湿度的可能贡献(答辩版本).pdf 4.5 MB 基于机器学习的中国夏季降水...期预报及土壤湿度的可能贡献_叶宇辰.pdf 3.8 MB 三作- Long-term hourly air quality data bridging of neighboring sites using automated machine learning A case study in the Greater Bay area of China.pdf 11.7 MB +相关论文 6.3 MB Fischer2010热浪定义.pdf 3.1 MB 两种不同的识别旱灾的方法。比较它们的优势和局限性.pdf 3.2 MB 骤旱定义初稿.docx 16.8 KB Auto_paint_self.py 22.3 MB Python机器学习原理及在气象中的应用.pptx 37.5 MB Python气象自动绘图函数引导.txt 130.0 KB python气象自动绘图说明.txt 974.0 KB python入门.pptx 106.0 MB |
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