净面积计算工具

abaqus-各个单元分开附材料属性,用于处理材料各个网格单元不同时的情况！(Each element is attached to material properties separately)

使用pyqt 实现windows下串口通信，包括串口选择配置，数据收发等。当然也可以加入pygraph实现波形显示，可做为上位机程序的参考(Using pyqt to achieve windows serial communication, including serial selection, configuration, data transceiver. Of course, you can also join the pygraph to achieve waveform display, can be used as a reference to the host computer program)

说明：  1997年landsat tm全波段遥感数据part02，部分参数： GROUP = L1_METADATA_FILE GROUP = METADATA_FILE_INFO ORIGIN = "Image courtesy of the U.S. Geological Survey" REQUEST_ID = "0601411252739_00046" LANDSAT_SCENE_ID = "LT51230331997191HAJ00" FILE_DATE = 2014-11-28T07:45:51Z STATION_ID = "HAJ" PROCESSING_SOFTWARE_VERSION = "LPGS_12.5.0" DATA_CATEGORY = "NOMINAL" END_GROUP = METADATA_FILE_INFO GROUP = PRODUCT_METADATA DATA_TYPE = "L1G" DATA_TYPE_L0RP = "TMR_L0RP" OUTPUT_FORMAT = "GEOTIFF" EPHEMERIS_TYPE = "PREDICTIVE" SPACECRAFT_ID = "LANDSAT_5" SENSOR_ID = "TM" SENSOR_MODE = "SAM" WRS_PATH = 123 WRS_ROW = 033(1997 landsat tm remote sensing data part02)

Excel数据导入到arcgis Layer(Excel To Layer)

Python implementation of 1D SPH schemes

说明：  一个很详细的GIS开发文档，很具有参考价值的，(GIS developed a very detailed document)

最近一直在用keras，说点个人感受。 1、keras根植于python及theano，人气比较旺。 2、提供较为上层的框架，搞个深度学习的原型非常方便。 3、更新很快，我记得几个月前还没有multi-task的能力，最近再查就提供了graph的对象。 4、最重要的，文档很全。这点超过其它类似的基于theano的框架（Lasagne, Opendeep, Blocks）(Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast )

获取本地图片和拍照并剪辑成头像，网上demo一般功能比较少，这里我运用到自己的项目，获取头像(To obtain the local pictures and photos and clips into head, online demo general function less, I applied to their own projects, obtain the Avatar)

单图像和多图像三维重建，基于theano包的一个重建代码，够20个子了吗(3d reconstruction from both single and muti image)

运用格子搜索法确定SVR中的参数c和gamma(Using Lattice Search Method to Determine Parameters C and Gamma in SVR)