python气象自动绘图函数设计思想与使用方法

map
教程1
库包
我的论文
相关论文
Auto_paint_self.py
Python机器学习原理及在气象中的应用.pptx
Python气象自动绘图函数引导.txt
python气象自动绘图说明.txt
python入门.pptx


下面是详细目录文件清单：
名称
+D:\ \python气象自动绘图函数设计思想与使用方法\
+map
| bou2_4l.dbf
| bou2_4l.shp
| bou2_4l.shx
| bou2_4p.dbf
| bou2_4p.shp
| bou2_4p.shx
| country1.dbf
| country1.shp
| country1.shx
| MeteoInfo_csharp_1.1.3.5R1.zip
| province.CPG
| province.dbf
| province.prj
| province.sbn
| province.sbx
| province.shp
| province.shx
| province.xml.xml
| stations_lat_lon.xlsx
| tibet_new.dbf
| tibet_new.prj
| tibet_new.sbn
| tibet_new.sbx
| tibet_new.shp
| tibet_new.shx
| tibet_new.xml.xml
| world_adm0_Project.dbf
| world_adm0_Project.prj
| world_adm0_Project.sbn
| world_adm0_Project.sbx
| world_adm0_Project.shp
| world_adm0_Project.shp.xml
| world_adm0_Project.shx
+教程1
|+.ipynb_checkpoints
| | Auto_adaboost_Bayesian_Optimization-checkpoint.ipynb
| | Auto_adaboost_chose-checkpoint.ipynb
| | Auto_adaboost-checkpoint.ipynb
| | Auto_ANN_chose-checkpoint.ipynb
| | Auto_ANN_pytorch-checkpoint.ipynb
| | Auto_ANN-checkpoint.ipynb
| | Auto_any_vertical-checkpoint.ipynb
| | Auto_blocking_high-checkpoint.ipynb
| | Auto_catboost_Bayesian_Optimization-checkpoint.ipynb
| | Auto_catboost_chose-checkpoint.ipynb
| | Auto_catboost-checkpoint.ipynb
| | Auto_cdf_matching-checkpoint.ipynb
| | Auto_chose_data-checkpoint.ipynb
| | Auto_CNN_chose-checkpoint.ipynb
| | Auto_CNN_LSTMorGRUorRNN_chose-checkpoint.ipynb
| | Auto_CNN_LSTMorGRUorRNN_more_output-checkpoint.ipynb
| | Auto_CNN_LSTMorGRUorRNN_pytorch-checkpoint.ipynb
| | Auto_CNN_LSTMorGRUorRNN-checkpoint.ipynb
| | Auto_CNN_pytorch-checkpoint.ipynb
| | Auto_CNN_TCN_chose-checkpoint.ipynb
| | Auto_CNN_TCN_more_output-checkpoint.ipynb
| | Auto_CNN_TCN_pytorch-checkpoint.ipynb
| | Auto_CNN_TCN-checkpoint.ipynb
| | Auto_CNN_Transformer_chose-checkpoint.ipynb
| | Auto_CNN_Transformer_more_output-checkpoint.ipynb
| | Auto_CNN_Transformer_pytorch-checkpoint.ipynb
| | Auto_CNN_Transformer-checkpoint.ipynb
| | Auto_CNN-checkpoint.ipynb
| | Auto_create_gif-checkpoint.ipynb
| | Auto_draw_taylor-checkpoint.ipynb
| | Auto_EfficientTemp_ESR_GAN_pytorch-checkpoint.ipynb
| | Auto_EfficientTemp_ESR_GAN-checkpoint.ipynb
| | Auto_EfficientTemp_MSG_SE_Densenet_GAN-checkpoint.ipynb
| | Auto_EfficientTempNet_pytorch-checkpoint.ipynb
| | Auto_EfficientTempNet-checkpoint.ipynb
| | Auto_EfficientTempt_MSG_SE_Densenet_GAN_pytorch-checkpoint.ipynb
| | Auto_EMD-checkpoint.ipynb
| | Auto_eof-checkpoint.ipynb
| | Auto_ESR_EfficientTemp_GAN_pytorch-checkpoint.ipynb
| | Auto_ESR_EfficientTemp_GAN-checkpoint.ipynb
| | Auto_ESRGAN_pytorch-checkpoint.ipynb
| | Auto_ESRGAN-checkpoint.ipynb
| | Auto_GCN-checkpoint.ipynb
| | Auto_heatwave-checkpoint.ipynb
| | Auto_IBTrACS_read-checkpoint.ipynb
| | Auto_imageline_to_data-checkpoint.ipynb
| | Auto_Liang_Kleeman_information_flow-checkpoint.ipynb
| | Auto_Liang_Kleeman_relative_flow-checkpoint.ipynb
| | Auto_lightgbm_Bayesian_Optimization-checkpoint.ipynb
| | Auto_lightgbm_chose-checkpoint.ipynb
| | Auto_lightgbm-checkpoint.ipynb
| | Auto_linregress-checkpoint.ipynb
| | Auto_LSTMorGRUorRNN_chose-checkpoint.ipynb
| | Auto_LSTMorGRUorRNN_more_output-checkpoint.ipynb
| | Auto_LSTMorGRUorRNN_pytorch-checkpoint.ipynb
| | Auto_LSTMorGRUorRNN-checkpoint.ipynb
| | Auto_more_gaussianprocessregressor-checkpoint.ipynb
| | Auto_more_lassoregress-checkpoint.ipynb
| | Auto_more_linregress-checkpoint.ipynb
| | Auto_more_ridgeregress-checkpoint.ipynb
| | Auto_MSG_SE_Densenet_EfficientTemp_GAN-checkpoint.ipynb
| | Auto_MSG_SE_Densenet_EfficientTempt_GAN_pytorch-checkpoint.ipynb
| | Auto_MSG_SE_Densenet_GAN_pytorch-checkpoint.ipynb
| | Auto_MSG_SE_Densenet_GAN-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_LSTMorGRUorRNN_more_output-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_LSTMorGRUorRNN_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_LSTMorGRUorRNN-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_TCN_more_output-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_TCN_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_TCN-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_Transformer_more_output-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_Transformer_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_CNN_Transformer-checkpoint.ipynb
| | Auto_Multi_Scale_CNN-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_LSTMorGRUorRNN_more_output-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_LSTMorGRUorRNN_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_LSTMorGRUorRNN-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_TCN_more_output-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_TCN_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_TCN-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_Transformer_more_output-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_Transformer_pytorch-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet_Transformer-checkpoint.ipynb
| | Auto_Multi_Scale_Resnet-checkpoint.ipynb
| | Auto_ngboost_Bayesian_Optimization-checkpoint.ipynb
| | Auto_ngboost_chose-checkpoint.ipynb
| | Auto_ngboost-checkpoint.ipynb
| | Auto_optical_flow-checkpoint.ipynb
| | Auto_partical_r-checkpoint.ipynb
| | Auto_Plumb-checkpoint.ipynb
。。。。。。。。。。。。。。。内容太很，贴子字数九 限制！＃
| | DBSCAN_eval-checkpoint.ipynb
| | deal_data_time-checkpoint.ipynb
| | K_means_eval-checkpoint.ipynb
| | open_data_grib-checkpoint.ipynb
| | open_data_nc-checkpoint.ipynb
| | open_data_txt_grid-checkpoint.ipynb
| | open_data_txt_station-checkpoint.ipynb
| | open_hdf-checkpoint.ipynb
| | selftime-checkpoint.ipynb
| | specx_anal-checkpoint.ipynb
| Auto_adaboost.ipynb
| Auto_adaboost_Bayesian_Optimization.ipynb
| Auto_adaboost_chose.ipynb
| Auto_ANN.ipynb
| Auto_ANN_chose.ipynb
| Auto_ANN_pytorch.ipynb
| Auto_any_vertical.ipynb
| Auto_blocking_high.ipynb
| Auto_catboost.ipynb
| Auto_catboost_Bayesian_Optimization.ipynb
| Auto_catboost_chose.ipynb
| Auto_cdf_matching.ipynb
| Auto_chose_data.ipynb
| Auto_CNN.ipynb
| Auto_CNN_chose.ipynb
| Auto_CNN_LSTMorGRUorRNN.ipynb
| Auto_CNN_LSTMorGRUorRNN_chose.ipynb
| Auto_CNN_LSTMorGRUorRNN_more_output.ipynb
| Auto_CNN_LSTMorGRUorRNN_pytorch.ipynb
| Auto_CNN_pytorch.ipynb
| Auto_CNN_TCN.ipynb
| Auto_CNN_TCN_chose.ipynb
| Auto_CNN_TCN_more_output.ipynb
| Auto_CNN_TCN_pytorch.ipynb
| Auto_CNN_Transformer.ipynb
| Auto_CNN_Transformer_chose.ipynb
| Auto_CNN_Transformer_more_output.ipynb
| Auto_CNN_Transformer_pytorch.ipynb
| Auto_create_gif.ipynb
| Auto_draw_taylor.ipynb
| Auto_EfficientTemp_ESR_GAN.ipynb
| Auto_EfficientTemp_ESR_GAN_pytorch.ipynb
| Auto_EfficientTemp_MSG_SE_Densenet_GAN.ipynb
| Auto_EfficientTempNet.ipynb
| Auto_EfficientTempNet_pytorch.ipynb
| Auto_EfficientTempt_MSG_SE_Densenet_GAN_pytorch.ipynb
| Auto_EMD.ipynb
| Auto_eof.ipynb
| Auto_ESR_EfficientTemp_GAN.ipynb
| Auto_ESR_EfficientTemp_GAN_pytorch.ipynb
| Auto_ESRGAN.ipynb
| Auto_ESRGAN_pytorch.ipynb
| Auto_GCN.ipynb
| Auto_heatwave.ipynb
| Auto_IBTrACS_read.ipynb
| Auto_imageline_to_data.ipynb
| Auto_Liang_Kleeman_information_flow.ipynb
| Auto_Liang_Kleeman_relative_flow.ipynb
| Auto_lightgbm.ipynb
| Auto_lightgbm_Bayesian_Optimization.ipynb
| Auto_lightgbm_chose.ipynb
| Auto_linregress.ipynb
| Auto_LSTMorGRUorRNN.ipynb
| Auto_LSTMorGRUorRNN_chose.ipynb
| Auto_LSTMorGRUorRNN_more_output.ipynb
| Auto_LSTMorGRUorRNN_pytorch.ipynb
| Auto_more_gaussianprocessregressor.ipynb
| Auto_more_lassoregress.ipynb
| Auto_more_linregress.ipynb
| Auto_more_ridgeregress.ipynb
| Auto_MSG_SE_Densenet_EfficientTemp_GAN.ipynb
| Auto_MSG_SE_Densenet_EfficientTempt_GAN_pytorch.ipynb
| Auto_MSG_SE_Densenet_GAN.ipynb
| Auto_MSG_SE_Densenet_GAN_pytorch.ipynb
| Auto_Multi_Scale_CNN.ipynb
| Auto_Multi_Scale_CNN_LSTMorGRUorRNN.ipynb
| Auto_Multi_Scale_CNN_LSTMorGRUorRNN_more_output.ipynb
| Auto_Multi_Scale_CNN_LSTMorGRUorRNN_pytorch.ipynb
| Auto_Multi_Scale_CNN_pytorch.ipynb
| Auto_Multi_Scale_CNN_TCN.ipynb
| Auto_Multi_Scale_CNN_TCN_more_output.ipynb
| Auto_Multi_Scale_CNN_TCN_pytorch.ipynb
| Auto_Multi_Scale_CNN_Transformer.ipynb
| Auto_Multi_Scale_CNN_Transformer_more_output.ipynb
| Auto_Multi_Scale_CNN_Transformer_pytorch.ipynb
| Auto_Multi_Scale_Resnet.ipynb
| Auto_Multi_Scale_Resnet_LSTMorGRUorRNN.ipynb
| Auto_Multi_Scale_Resnet_LSTMorGRUorRNN_more_output.ipynb
| Auto_Multi_Scale_Resnet_LSTMorGRUorRNN_pytorch.ipynb
| Auto_Multi_Scale_Resnet_pytorch.ipynb
| Auto_Multi_Scale_Resnet_TCN.ipynb
| Auto_Multi_Scale_Resnet_TCN_more_output.ipynb
| Auto_Multi_Scale_Resnet_TCN_pytorch.ipynb
| Auto_Multi_Scale_Resnet_Transformer.ipynb
| Auto_Multi_Scale_Resnet_Transformer_more_output.ipynb
| Auto_Multi_Scale_Resnet_Transformer_pytorch.ipynb
| Auto_ngboost.ipynb
| Auto_ngboost_Bayesian_Optimization.ipynb
| Auto_ngboost_chose.ipynb
| Auto_optical_flow.ipynb
| Auto_partical_r.ipynb
| Auto_Plumb.ipynb
| Auto_r.ipynb
| Auto_RandomForest.ipynb
| Auto_RandomForest_Bayesian_Optimization.ipynb
| Auto_RandomForest_chose.ipynb
| Auto_region_data_mask.ipynb
| Auto_Resnet.ipynb
| Auto_Resnet_chose.ipynb
| Auto_Resnet_LSTMorGRUorRNN.ipynb
| Auto_Resnet_LSTMorGRUorRNN_chose.ipynb
| Auto_Resnet_LSTMorGRUorRNN_more_output.ipynb
| Auto_Resnet_LSTMorGRUorRNN_pytorch.ipynb
| Auto_Resnet_pytorch.ipynb
| Auto_Resnet_TCN.ipynb
| Auto_Resnet_TCN_chose.ipynb
| Auto_Resnet_TCN_more_output.ipynb
| Auto_Resnet_TCN_pytorch.ipynb
| Auto_Resnet_Transformer.ipynb
| Auto_Resnet_Transformer_chose.ipynb
| Auto_Resnet_Transformer_more_output.ipynb
| Auto_Resnet_Transformer_pytorch.ipynb
| Auto_smooth.ipynb
| Auto_SOM.ipynb
| Auto_SRGAN.ipynb
| Auto_SRGAN_pytorch.ipynb
| Auto_stepwise_regression.ipynb
| Auto_sudden_drought.ipynb
| Auto_SVD.ipynb
| Auto_SVM.ipynb
| Auto_SVM_Bayesian_Optimization.ipynb
| Auto_SVM_chose.ipynb
| Auto_T_N.ipynb
| Auto_TC_compare.ipynb
| Auto_TCN.ipynb
| Auto_TCN_chose.ipynb
| Auto_TCN_more_output.ipynb
| Auto_TCN_pytorch.ipynb
| Auto_test_of_abrupt_change.ipynb
| Auto_Transformer.ipynb
| Auto_Transformer_chose.ipynb
| Auto_Transformer_more_output.ipynb
| Auto_Transformer_pytorch.ipynb
| Auto_triple_collocation.ipynb
| Auto_TSNE.ipynb
| Auto_Unet.ipynb
| Auto_Unet_chose.ipynb
| Auto_Unet_pytorch.ipynb
| Auto_UZ_detect.ipynb
| Auto_UZ_track.ipynb
| Auto_violin.ipynb
| Auto_wave.ipynb
| Auto_windrose.ipynb
| Auto_xgboost.ipynb
| Auto_xgboost_Bayesian_Optimization.ipynb
| Auto_xgboost_chose.ipynb
| Autobar_or_line.ipynb
| Autoshaded_quiver.ipynb
| create_nc.ipynb
| data_all.ipynb
| DBSCAN_eval.ipynb
| deal_data_time.ipynb
| K_means_eval.ipynb
| open_data_grib.ipynb
| open_data_nc.ipynb
| open_data_txt_grid.ipynb
| open_data_txt_station.ipynb
| open_hdf.ipynb
| selftime.ipynb
| specx_anal.ipynb
+库包
| Buishand_U.py
| lanczos.py
| Little_wave.py
| maskout_country.py
| maskout_province.py
| mktest.py
| Move_t_test.py
| Pettitt.py
| regression_mode.py
| SNHT.py
| Taylor.py
+我的论文
| 8-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
| 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
| 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
+相关论文
| Fischer2010热浪定义.pdf
| 两种不同的识别旱灾的方法。比较它们的优势和局限性.pdf
| 骤旱定义初稿.docx
Auto_paint_self.py
Python机器学习原理及在气象中的应用.pptx
Python气象自动绘图函数引导.txt
python气象自动绘图说明.txt
python入门.pptx 。。。。。。。。。。。。。。。内容太很，贴子字数九 限制！＃到时候详细的内容，请参考下面的详细目录文件清单！！