Python虹膜NetCDF时间错误
昨天和今天之间似乎出现了问题。我想知道你是否可以提供帮助？这都是使用iris运行Python的。
基本上当我昨天运行我的代码时，我在这里得到了第一张图，但今天我得到了第二张图：
如果我自己只运行一个CORDEX模型（蓝线）和一个观察到的数据（黑色），你可以看到它们都运行但是cordex模型似乎有一些有趣的时间。
所以我打印了每个时间坐标我得到以下CORDEX模型
DimCoord([netcdftime._netcdftime.DatetimeNoLeap(1989, 1, 16, 12, 0, 0, 0, 4, 16),
netcdftime._netcdftime.DatetimeNoLeap(1989, 2, 15, 0, 0, 0, 0, 6, 46),
netcdftime._netcdftime.DatetimeNoLeap(1989, 3, 16, 12, 0, 0, 0, 0, 75),
netcdftime._netcdftime.DatetimeNoLeap(1989, 4, 16, 0, 0, 0, 0, 3, 106),
netcdftime._netcdftime.DatetimeNoLeap(1989, 5, 16, 12, 0, 0, 0, 5, 136),
netcdftime._netcdftime.DatetimeNoLeap(1989, 6, 16, 0, 0, 0, 0, 1, 167),
netcdftime._netcdftime.DatetimeNoLeap(1989, 7, 16, 12, 0, 0, 0, 3, 197),
netcdftime._netcdftime.DatetimeNoLeap(1989, 8, 16, 12, 0, 0, 0, 6, 228),
netcdftime._netcdftime.DatetimeNoLeap(1989, 9, 16, 0, 0, 0, 0, 2, 259),
netcdftime._netcdftime.DatetimeNoLeap(1989, 10, 16, 12, 0, 0, 0, 4, 289),
netcdftime._netcdftime.DatetimeNoLeap(1989, 11, 16, 0, 0, 0, 0, 0, 320),
netcdftime._netcdftime.DatetimeNoLeap(1989, 12, 16, 12, 0, 0, 0, 2, 350),
netcdftime._netcdftime.DatetimeNoLeap(1990, 1, 16, 12, 0, 0, 0, 5, 16),
netcdftime._netcdftime.DatetimeNoLeap(1990, 2, 15, 0, 0, 0, 0, 0, 46),
netcdftime._netcdftime.DatetimeNoLeap(1990, 3, 16, 12, 0, 0, 0, 1, 75),
netcdftime._netcdftime.DatetimeNoLeap(1990, 4, 16, 0, 0, 0, 0, 4, 106),
netcdftime._netcdftime.DatetimeNoLeap(1990, 5, 16, 12, 0, 0, 0, 6, 136),
netcdftime._netcdftime.DatetimeNoLeap(1990, 6, 16, 0, 0, 0, 0, 2, 167),
netcdftime._netcdftime.DatetimeNoLeap(1990, 7, 16, 12, 0, 0, 0, 4, 197),
netcdftime._netcdftime.DatetimeNoLeap(1990, 8, 16, 12, 0, 0, 0, 0, 228),
netcdftime._netcdftime.DatetimeNoLeap(1990, 9, 16, 0, 0, 0, 0, 3, 259),
netcdftime._netcdftime.DatetimeNoLeap(1990, 10, 16, 12, 0, 0, 0, 5, 289),
netcdftime._netcdftime.DatetimeNoLeap(1990, 11, 16, 0, 0, 0, 0, 1, 320),
netcdftime._netcdftime.DatetimeNoLeap(1990, 12, 16, 12, 0, 0, 0, 3, 350),
netcdftime._netcdftime.DatetimeNoLeap(1991, 1, 16, 12, 0, 0, 0, 6, 16),
netcdftime._netcdftime.DatetimeNoLeap(1991, 2, 15, 0, 0, 0, 0, 1, 46),
netcdftime._netcdftime.DatetimeNoLeap(1991, 3, 16, 12, 0, 0, 0, 2, 75),
netcdftime._netcdftime.DatetimeNoLeap(1991, 4, 16, 0, 0, 0, 0, 5, 106),
netcdftime._netcdftime.DatetimeNoLeap(1991, 5, 16, 12, 0, 0, 0, 0, 136),
netcdftime._netcdftime.DatetimeNoLeap(1991, 6, 16, 0, 0, 0, 0, 3, 167),
netcdftime._netcdftime.DatetimeNoLeap(1991, 7, 16, 12, 0, 0, 0, 5, 197),
netcdftime._netcdftime.DatetimeNoLeap(1991, 8, 16, 12, 0, 0, 0, 1, 228),
netcdftime._netcdftime.DatetimeNoLeap(1991, 9, 16, 0, 0, 0, 0, 4, 259),
netcdftime._netcdftime.DatetimeNoLeap(1991, 10, 16, 12, 0, 0, 0, 6, 289),
netcdftime._netcdftime.DatetimeNoLeap(1991, 11, 16, 0, 0, 0, 0, 2, 320),
netcdftime._netcdftime.DatetimeNoLeap(1991, 12, 16, 12, 0, 0, 0, 4, 350),
netcdftime._netcdftime.DatetimeNoLeap(1992, 1, 16, 12, 0, 0, 0, 0, 16),
netcdftime._netcdftime.DatetimeNoLeap(1992, 2, 15, 0, 0, 0, 0, 2, 46),
netcdftime._netcdftime.DatetimeNoLeap(1992, 3, 16, 12, 0, 0, 0, 3, 75),
netcdftime._netcdftime.DatetimeNoLeap(1992, 4, 16, 0, 0, 0, 0, 6, 106),
netcdftime._netcdftime.DatetimeNoLeap(1992, 5, 16, 12, 0, 0, 0, 1, 136),
netcdftime._netcdftime.DatetimeNoLeap(1992, 6, 16, 0, 0, 0, 0, 4, 167),
netcdftime._netcdftime.DatetimeNoLeap(1992, 7, 16, 12, 0, 0, 0, 6, 197),
netcdftime._netcdftime.DatetimeNoLeap(1992, 8, 16, 12, 0, 0, 0, 2, 228),
netcdftime._netcdftime.DatetimeNoLeap(1992, 9, 16, 0, 0, 0, 0, 5, 259),
netcdftime._netcdftime.DatetimeNoLeap(1992, 10, 16, 12, 0, 0, 0, 0, 289),
netcdftime._netcdftime.DatetimeNoLeap(1992, 11, 16, 0, 0, 0, 0, 3, 320),
netcdftime._netcdftime.DatetimeNoLeap(1992, 12, 16, 12, 0, 0, 0, 5, 350),
netcdftime._netcdftime.DatetimeNoLeap(1993, 1, 16, 12, 0, 0, 0, 1, 16),
netcdftime._netcdftime.DatetimeNoLeap(1993, 2, 15, 0, 0, 0, 0, 3, 46),
netcdftime._netcdftime.DatetimeNoLeap(1993, 3, 16, 12, 0, 0, 0, 4, 75),
netcdftime._netcdftime.DatetimeNoLeap(1993, 4, 16, 0, 0, 0, 0, 0, 106),
netcdftime._netcdftime.DatetimeNoLeap(1993, 5, 16, 12, 0, 0, 0, 2, 136),
netcdftime._netcdftime.DatetimeNoLeap(1993, 6, 16, 0, 0, 0, 0, 5, 167),
netcdftime._netcdftime.DatetimeNoLeap(1993, 7, 16, 12, 0, 0, 0, 0, 197),
netcdftime._netcdftime.DatetimeNoLeap(1993, 8, 16, 12, 0, 0, 0, 3, 228),
netcdftime._netcdftime.DatetimeNoLeap(1993, 9, 16, 0, 0, 0, 0, 6, 259),
netcdftime._netcdftime.DatetimeNoLeap(1993, 10, 16, 12, 0, 0, 0, 1, 289),
netcdftime._netcdftime.DatetimeNoLeap(1993, 11, 16, 0, 0, 0, 0, 4, 320),
netcdftime._netcdftime.DatetimeNoLeap(1993, 12, 16, 12, 0, 0, 0, 6, 350),
netcdftime._netcdftime.DatetimeNoLeap(1994, 1, 16, 12, 0, 0, 0, 2, 16),
netcdftime._netcdftime.DatetimeNoLeap(1994, 2, 15, 0, 0, 0, 0, 4, 46),
netcdftime._netcdftime.DatetimeNoLeap(1994, 3, 16, 12, 0, 0, 0, 5, 75),
netcdftime._netcdftime.DatetimeNoLeap(1994, 4, 16, 0, 0, 0, 0, 1, 106),
netcdftime._netcdftime.DatetimeNoLeap(1994, 5, 16, 12, 0, 0, 0, 3, 136),
netcdftime._netcdftime.DatetimeNoLeap(1994, 6, 16, 0, 0, 0, 0, 6, 167),
netcdftime._netcdftime.DatetimeNoLeap(1994, 7, 16, 12, 0, 0, 0, 1, 197),
netcdftime._netcdftime.DatetimeNoLeap(1994, 8, 16, 12, 0, 0, 0, 4, 228),
netcdftime._netcdftime.DatetimeNoLeap(1994, 9, 16, 0, 0, 0, 0, 0, 259),
netcdftime._netcdftime.DatetimeNoLeap(1994, 10, 16, 12, 0, 0, 0, 2, 289),
netcdftime._netcdftime.DatetimeNoLeap(1994, 11, 16, 0, 0, 0, 0, 5, 320),
netcdftime._netcdftime.DatetimeNoLeap(1994, 12, 16, 12, 0, 0, 0, 0, 350),
netcdftime._netcdftime.DatetimeNoLeap(1995, 1, 16, 12, 0, 0, 0, 3, 16),
netcdftime._netcdftime.DatetimeNoLeap(1995, 2, 15, 0, 0, 0, 0, 5, 46),
netcdftime._netcdftime.DatetimeNoLeap(1995, 3, 16, 12, 0, 0, 0, 6, 75),
netcdftime._netcdftime.DatetimeNoLeap(1995, 4, 16, 0, 0, 0, 0, 2, 106),
netcdftime._netcdftime.DatetimeNoLeap(1995, 5, 16, 12, 0, 0, 0, 4, 136),
netcdftime._netcdftime.DatetimeNoLeap(1995, 6, 16, 0, 0, 0, 0, 0, 167),
netcdftime._netcdftime.DatetimeNoLeap(1995, 7, 16, 12, 0, 0, 0, 2, 197),
netcdftime._netcdftime.DatetimeNoLeap(1995, 8, 16, 12, 0, 0, 0, 5, 228),
netcdftime._netcdftime.DatetimeNoLeap(1995, 9, 16, 0, 0, 0, 0, 1, 259),
netcdftime._netcdftime.DatetimeNoLeap(1995, 10, 16, 12, 0, 0, 0, 3, 289),
netcdftime._netcdftime.DatetimeNoLeap(1995, 11, 16, 0, 0, 0, 0, 6, 320),
netcdftime._netcdftime.DatetimeNoLeap(1995, 12, 16, 12, 0, 0, 0, 1, 350),
netcdftime._netcdftime.DatetimeNoLeap(1996, 1, 16, 12, 0, 0, 0, 4, 16),
netcdftime._netcdftime.DatetimeNoLeap(1996, 2, 15, 0, 0, 0, 0, 6, 46),
netcdftime._netcdftime.DatetimeNoLeap(1996, 3, 16, 12, 0, 0, 0, 0, 75),
netcdftime._netcdftime.DatetimeNoLeap(1996, 4, 16, 0, 0, 0, 0, 3, 106),
netcdftime._netcdftime.DatetimeNoLeap(1996, 5, 16, 12, 0, 0, 0, 5, 136),
netcdftime._netcdftime.DatetimeNoLeap(1996, 6, 16, 0, 0, 0, 0, 1, 167),
netcdftime._netcdftime.DatetimeNoLeap(1996, 7, 16, 12, 0, 0, 0, 3, 197),
netcdftime._netcdftime.DatetimeNoLeap(1996, 8, 16, 12, 0, 0, 0, 6, 228),
netcdftime._netcdftime.DatetimeNoLeap(1996, 9, 16, 0, 0, 0, 0, 2, 259),
netcdftime._netcdftime.DatetimeNoLeap(1996, 10, 16, 12, 0, 0, 0, 4, 289),
netcdftime._netcdftime.DatetimeNoLeap(1996, 11, 16, 0, 0, 0, 0, 0, 320),
netcdftime._netcdftime.DatetimeNoLeap(1996, 12, 16, 12, 0, 0, 0, 2, 350),
netcdftime._netcdftime.DatetimeNoLeap(1997, 1, 16, 12, 0, 0, 0, 5, 16),
netcdftime._netcdftime.DatetimeNoLeap(1997, 2, 15, 0, 0, 0, 0, 0, 46),
netcdftime._netcdftime.DatetimeNoLeap(1997, 3, 16, 12, 0, 0, 0, 1, 75),
netcdftime._netcdftime.DatetimeNoLeap(1997, 4, 16, 0, 0, 0, 0, 4, 106),
netcdftime._netcdftime.DatetimeNoLeap(1997, 5, 16, 12, 0, 0, 0, 6, 136),
netcdftime._netcdftime.DatetimeNoLeap(1997, 6, 16, 0, 0, 0, 0, 2, 167),
netcdftime._netcdftime.DatetimeNoLeap(1997, 7, 16, 12, 0, 0, 0, 4, 197),
netcdftime._netcdftime.DatetimeNoLeap(1997, 8, 16, 12, 0, 0, 0, 0, 228),
netcdftime._netcdftime.DatetimeNoLeap(1997, 9, 16, 0, 0, 0, 0, 3, 259),
netcdftime._netcdftime.DatetimeNoLeap(1997, 10, 16, 12, 0, 0, 0, 5, 289),
netcdftime._netcdftime.DatetimeNoLeap(1997, 11, 16, 0, 0, 0, 0, 1, 320),
netcdftime._netcdftime.DatetimeNoLeap(1997, 12, 16, 12, 0, 0, 0, 3, 350),
netcdftime._netcdftime.DatetimeNoLeap(1998, 1, 16, 12, 0, 0, 0, 6, 16),
netcdftime._netcdftime.DatetimeNoLeap(1998, 2, 15, 0, 0, 0, 0, 1, 46),
netcdftime._netcdftime.DatetimeNoLeap(1998, 3, 16, 12, 0, 0, 0, 2, 75),
netcdftime._netcdftime.DatetimeNoLeap(1998, 4, 16, 0, 0, 0, 0, 5, 106),
netcdftime._netcdftime.DatetimeNoLeap(1998, 5, 16, 12, 0, 0, 0, 0, 136),
netcdftime._netcdftime.DatetimeNoLeap(1998, 6, 16, 0, 0, 0, 0, 3, 167),
netcdftime._netcdftime.DatetimeNoLeap(1998, 7, 16, 12, 0, 0, 0, 5, 197),
netcdftime._netcdftime.DatetimeNoLeap(1998, 8, 16, 12, 0, 0, 0, 1, 228),
netcdftime._netcdftime.DatetimeNoLeap(1998, 9, 16, 0, 0, 0, 0, 4, 259),
netcdftime._netcdftime.DatetimeNoLeap(1998, 10, 16, 12, 0, 0, 0, 6, 289),
netcdftime._netcdftime.DatetimeNoLeap(1998, 11, 16, 0, 0, 0, 0, 2, 320),
netcdftime._netcdftime.DatetimeNoLeap(1998, 12, 16, 12, 0, 0, 0, 4, 350),
netcdftime._netcdftime.DatetimeNoLeap(1999, 1, 16, 12, 0, 0, 0, 0, 16),
netcdftime._netcdftime.DatetimeNoLeap(1999, 2, 15, 0, 0, 0, 0, 2, 46),
netcdftime._netcdftime.DatetimeNoLeap(1999, 3, 16, 12, 0, 0, 0, 3, 75),
netcdftime._netcdftime.DatetimeNoLeap(1999, 4, 16, 0, 0, 0, 0, 6, 106),
netcdftime._netcdftime.DatetimeNoLeap(1999, 5, 16, 12, 0, 0, 0, 1, 136),
netcdftime._netcdftime.DatetimeNoLeap(1999, 6, 16, 0, 0, 0, 0, 4, 167),
netcdftime._netcdftime.DatetimeNoLeap(1999, 7, 16, 12, 0, 0, 0, 6, 197),
netcdftime._netcdftime.DatetimeNoLeap(1999, 8, 16, 12, 0, 0, 0, 2, 228),
netcdftime._netcdftime.DatetimeNoLeap(1999, 9, 16, 0, 0, 0, 0, 5, 259),
netcdftime._netcdftime.DatetimeNoLeap(1999, 10, 16, 12, 0, 0, 0, 0, 289),
netcdftime._netcdftime.DatetimeNoLeap(1999, 11, 16, 0, 0, 0, 0, 3, 320),
netcdftime._netcdftime.DatetimeNoLeap(1999, 12, 16, 12, 0, 0, 0, 5, 350),
netcdftime._netcdftime.DatetimeNoLeap(2000, 1, 16, 12, 0, 0, 0, 1, 16),
netcdftime._netcdftime.DatetimeNoLeap(2000, 2, 15, 0, 0, 0, 0, 3, 46),
netcdftime._netcdftime.DatetimeNoLeap(2000, 3, 16, 12, 0, 0, 0, 4, 75),
netcdftime._netcdftime.DatetimeNoLeap(2000, 4, 16, 0, 0, 0, 0, 0, 106),
netcdftime._netcdftime.DatetimeNoLeap(2000, 5, 16, 12, 0, 0, 0, 2, 136),
netcdftime._netcdftime.DatetimeNoLeap(2000, 6, 16, 0, 0, 0, 0, 5, 167),
netcdftime._netcdftime.DatetimeNoLeap(2000, 7, 16, 12, 0, 0, 0, 0, 197),
netcdftime._netcdftime.DatetimeNoLeap(2000, 8, 16, 12, 0, 0, 0, 3, 228),
netcdftime._netcdftime.DatetimeNoLeap(2000, 9, 16, 0, 0, 0, 0, 6, 259),
netcdftime._netcdftime.DatetimeNoLeap(2000, 10, 16, 12, 0, 0, 0, 1, 289),
netcdftime._netcdftime.DatetimeNoLeap(2000, 11, 16, 0, 0, 0, 0, 4, 320),
netcdftime._netcdftime.DatetimeNoLeap(2000, 12, 16, 12, 0, 0, 0, 6, 350),
netcdftime._netcdftime.DatetimeNoLeap(2001, 1, 16, 12, 0, 0, 0, 2, 16),
netcdftime._netcdftime.DatetimeNoLeap(2001, 2, 15, 0, 0, 0, 0, 4, 46),
netcdftime._netcdftime.DatetimeNoLeap(2001, 3, 16, 12, 0, 0, 0, 5, 75),
netcdftime._netcdftime.DatetimeNoLeap(2001, 4, 16, 0, 0, 0, 0, 1, 106),
netcdftime._netcdftime.DatetimeNoLeap(2001, 5, 16, 12, 0, 0, 0, 3, 136),
netcdftime._netcdftime.DatetimeNoLeap(2001, 6, 16, 0, 0, 0, 0, 6, 167),
netcdftime._netcdftime.DatetimeNoLeap(2001, 7, 16, 12, 0, 0, 0, 1, 197),
netcdftime._netcdftime.DatetimeNoLeap(2001, 8, 16, 12, 0, 0, 0, 4, 228),
netcdftime._netcdftime.DatetimeNoLeap(2001, 9, 16, 0, 0, 0, 0, 0, 259),
netcdftime._netcdftime.DatetimeNoLeap(2001, 10, 16, 12, 0, 0, 0, 2, 289),
netcdftime._netcdftime.DatetimeNoLeap(2001, 11, 16, 0, 0, 0, 0, 5, 320),
netcdftime._netcdftime.DatetimeNoLeap(2001, 12, 16, 12, 0, 0, 0, 0, 350),
netcdftime._netcdftime.DatetimeNoLeap(2002, 1, 16, 12, 0, 0, 0, 3, 16),
netcdftime._netcdftime.DatetimeNoLeap(2002, 2, 15, 0, 0, 0, 0, 5, 46),
netcdftime._netcdftime.DatetimeNoLeap(2002, 3, 16, 12, 0, 0, 0, 6, 75),
netcdftime._netcdftime.DatetimeNoLeap(2002, 4, 16, 0, 0, 0, 0, 2, 106),
netcdftime._netcdftime.DatetimeNoLeap(2002, 5, 16, 12, 0, 0, 0, 4, 136),
netcdftime._netcdftime.DatetimeNoLeap(2002, 6, 16, 0, 0, 0, 0, 0, 167),
netcdftime._netcdftime.DatetimeNoLeap(2002, 7, 16, 12, 0, 0, 0, 2, 197),
netcdftime._netcdftime.DatetimeNoLeap(2002, 8, 16, 12, 0, 0, 0, 5, 228),
netcdftime._netcdftime.DatetimeNoLeap(2002, 9, 16, 0, 0, 0, 0, 1, 259),
netcdftime._netcdftime.DatetimeNoLeap(2002, 10, 16, 12, 0, 0, 0, 3, 289),
netcdftime._netcdftime.DatetimeNoLeap(2002, 11, 16, 0, 0, 0, 0, 6, 320),
netcdftime._netcdftime.DatetimeNoLeap(2002, 12, 16, 12, 0, 0, 0, 1, 350),
netcdftime._netcdftime.DatetimeNoLeap(2003, 1, 16, 12, 0, 0, 0, 4, 16),
netcdftime._netcdftime.DatetimeNoLeap(2003, 2, 15, 0, 0, 0, 0, 6, 46),
netcdftime._netcdftime.DatetimeNoLeap(2003, 3, 16, 12, 0, 0, 0, 0, 75),
netcdftime._netcdftime.DatetimeNoLeap(2003, 4, 16, 0, 0, 0, 0, 3, 106),
netcdftime._netcdftime.DatetimeNoLeap(2003, 5, 16, 12, 0, 0, 0, 5, 136),
netcdftime._netcdftime.DatetimeNoLeap(2003, 6, 16, 0, 0, 0, 0, 1, 167),
netcdftime._netcdftime.DatetimeNoLeap(2003, 7, 16, 12, 0, 0, 0, 3, 197),
netcdftime._netcdftime.DatetimeNoLeap(2003, 8, 16, 12, 0, 0, 0, 6, 228),
netcdftime._netcdftime.DatetimeNoLeap(2003, 9, 16, 0, 0, 0, 0, 2, 259),
netcdftime._netcdftime.DatetimeNoLeap(2003, 10, 16, 12, 0, 0, 0, 4, 289),
netcdftime._netcdftime.DatetimeNoLeap(2003, 11, 16, 0, 0, 0, 0, 0, 320),
netcdftime._netcdftime.DatetimeNoLeap(2003, 12, 16, 12, 0, 0, 0, 2, 350),
netcdftime._netcdftime.DatetimeNoLeap(2004, 1, 16, 12, 0, 0, 0, 5, 16),
netcdftime._netcdftime.DatetimeNoLeap(2004, 2, 15, 0, 0, 0, 0, 0, 46),
netcdftime._netcdftime.DatetimeNoLeap(2004, 3, 16, 12, 0, 0, 0, 1, 75),
netcdftime._netcdftime.DatetimeNoLeap(2004, 4, 16, 0, 0, 0, 0, 4, 106),
netcdftime._netcdftime.DatetimeNoLeap(2004, 5, 16, 12, 0, 0, 0, 6, 136),
netcdftime._netcdftime.DatetimeNoLeap(2004, 6, 16, 0, 0, 0, 0, 2, 167),
netcdftime._netcdftime.DatetimeNoLeap(2004, 7, 16, 12, 0, 0, 0, 4, 197),
netcdftime._netcdftime.DatetimeNoLeap(2004, 8, 16, 12, 0, 0, 0, 0, 228),
netcdftime._netcdftime.DatetimeNoLeap(2004, 9, 16, 0, 0, 0, 0, 3, 259),
netcdftime._netcdftime.DatetimeNoLeap(2004, 10, 16, 12, 0, 0, 0, 5, 289),
netcdftime._netcdftime.DatetimeNoLeap(2004, 11, 16, 0, 0, 0, 0, 1, 320),
netcdftime._netcdftime.DatetimeNoLeap(2004, 12, 16, 12, 0, 0, 0, 3, 350),
netcdftime._netcdftime.DatetimeNoLeap(2005, 1, 16, 12, 0, 0, 0, 6, 16),
netcdftime._netcdftime.DatetimeNoLeap(2005, 2, 15, 0, 0, 0, 0, 1, 46),
netcdftime._netcdftime.DatetimeNoLeap(2005, 3, 16, 12, 0, 0, 0, 2, 75),
netcdftime._netcdftime.DatetimeNoLeap(2005, 4, 16, 0, 0, 0, 0, 5, 106),
netcdftime._netcdftime.DatetimeNoLeap(2005, 5, 16, 12, 0, 0, 0, 0, 136),
netcdftime._netcdftime.DatetimeNoLeap(2005, 6, 16, 0, 0, 0, 0, 3, 167),
netcdftime._netcdftime.DatetimeNoLeap(2005, 7, 16, 12, 0, 0, 0, 5, 197),
netcdftime._netcdftime.DatetimeNoLeap(2005, 8, 16, 12, 0, 0, 0, 1, 228),
netcdftime._netcdftime.DatetimeNoLeap(2005, 9, 16, 0, 0, 0, 0, 4, 259),
netcdftime._netcdftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, 6, 289),
netcdftime._netcdftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, 2, 320),
netcdftime._netcdftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, 4, 350),
netcdftime._netcdftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, 0, 16),
netcdftime._netcdftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, 2, 46),
netcdftime._netcdftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, 3, 75),
netcdftime._netcdftime.DatetimeNoLeap(2006, 4, 16, 0, 0, 0, 0, 6, 106),
netcdftime._netcdftime.DatetimeNoLeap(2006, 5, 16, 12, 0, 0, 0, 1, 136),
netcdftime._netcdftime.DatetimeNoLeap(2006, 6, 16, 0, 0, 0, 0, 4, 167),
netcdftime._netcdftime.DatetimeNoLeap(2006, 7, 16, 12, 0, 0, 0, 6, 197),
netcdftime._netcdftime.DatetimeNoLeap(2006, 8, 16, 12, 0, 0, 0, 2, 228),
netcdftime._netcdftime.DatetimeNoLeap(2006, 9, 16, 0, 0, 0, 0, 5, 259),
netcdftime._netcdftime.DatetimeNoLeap(2006, 10, 16, 12, 0, 0, 0, 0, 289),
netcdftime._netcdftime.DatetimeNoLeap(2006, 11, 16, 0, 0, 0, 0, 3, 320),
netcdftime._netcdftime.DatetimeNoLeap(2006, 12, 16, 12, 0, 0, 0, 5, 350),
netcdftime._netcdftime.DatetimeNoLeap(2007, 1, 16, 12, 0, 0, 0, 1, 16),
netcdftime._netcdftime.DatetimeNoLeap(2007, 2, 15, 0, 0, 0, 0, 3, 46),
netcdftime._netcdftime.DatetimeNoLeap(2007, 3, 16, 12, 0, 0, 0, 4, 75),
netcdftime._netcdftime.DatetimeNoLeap(2007, 4, 16, 0, 0, 0, 0, 0, 106),
netcdftime._netcdftime.DatetimeNoLeap(2007, 5, 16, 12, 0, 0, 0, 2, 136),
netcdftime._netcdftime.DatetimeNoLeap(2007, 6, 16, 0, 0, 0, 0, 5, 167),
netcdftime._netcdftime.DatetimeNoLeap(2007, 7, 16, 12, 0, 0, 0, 0, 197),
netcdftime._netcdftime.DatetimeNoLeap(2007, 8, 16, 12, 0, 0, 0, 3, 228),
netcdftime._netcdftime.DatetimeNoLeap(2007, 9, 16, 0, 0, 0, 0, 6, 259),
netcdftime._netcdftime.DatetimeNoLeap(2007, 10, 16, 12, 0, 0, 0, 1, 289),
netcdftime._netcdftime.DatetimeNoLeap(2007, 11, 16, 0, 0, 0, 0, 4, 320),
netcdftime._netcdftime.DatetimeNoLeap(2007, 12, 16, 12, 0, 0, 0, 6, 350),
netcdftime._netcdftime.DatetimeNoLeap(2008, 1, 16, 12, 0, 0, 0, 2, 16),
netcdftime._netcdftime.DatetimeNoLeap(2008, 2, 15, 0, 0, 0, 0, 4, 46),
netcdftime._netcdftime.DatetimeNoLeap(2008, 3, 16, 12, 0, 0, 0, 5, 75),
netcdftime._netcdftime.DatetimeNoLeap(2008, 4, 16, 0, 0, 0, 0, 1, 106),
netcdftime._netcdftime.DatetimeNoLeap(2008, 5, 16, 12, 0, 0, 0, 3, 136),
netcdftime._netcdftime.DatetimeNoLeap(2008, 6, 16, 0, 0, 0, 0, 6, 167),
netcdftime._netcdftime.DatetimeNoLeap(2008, 7, 16, 12, 0, 0, 0, 1, 197),
netcdftime._netcdftime.DatetimeNoLeap(2008, 8, 16, 12, 0, 0, 0, 4, 228),
netcdftime._netcdftime.DatetimeNoLeap(2008, 9, 16, 0, 0, 0, 0, 0, 259),
netcdftime._netcdftime.DatetimeNoLeap(2008, 10, 16, 12, 0, 0, 0, 2, 289),
netcdftime._netcdftime.DatetimeNoLeap(2008, 11, 16, 0, 0, 0, 0, 5, 320),
netcdftime._netcdftime.DatetimeNoLeap(2008, 12, 16, 12, 0, 0, 0, 0, 350),
netcdftime._netcdftime.DatetimeNoLeap(2009, 1, 16, 12, 0, 0, 0, 3, 16),
netcdftime._netcdftime.DatetimeNoLeap(2009, 2, 15, 0, 0, 0, 0, 5, 46),
netcdftime._netcdftime.DatetimeNoLeap(2009, 3, 16, 12, 0, 0, 0, 6, 75),
netcdftime._netcdftime.DatetimeNoLeap(2009, 4, 16, 0, 0, 0, 0, 2, 106),
netcdftime._netcdftime.DatetimeNoLeap(2009, 5, 16, 12, 0, 0, 0, 4, 136),
netcdftime._netcdftime.DatetimeNoLeap(2009, 6, 16, 0, 0, 0, 0, 0, 167),
netcdftime._netcdftime.DatetimeNoLeap(2009, 7, 16, 12, 0, 0, 0, 2, 197),
netcdftime._netcdftime.DatetimeNoLeap(2009, 8, 16, 12, 0, 0, 0, 5, 228),
netcdftime._netcdftime.DatetimeNoLeap(2009, 9, 16, 0, 0, 0, 0, 1, 259),
netcdftime._netcdftime.DatetimeNoLeap(2009, 10, 16, 12, 0, 0, 0, 3, 289),
netcdftime._netcdftime.DatetimeNoLeap(2009, 11, 16, 0, 0, 0, 0, 6, 320),
netcdftime._netcdftime.DatetimeNoLeap(2009, 12, 16, 12, 0, 0, 0, 1, 350)], standard_name=u'time', calendar=u'365_day', long_name=u'time', var_name='time')
我为观察到的数据得到了这个：
DimCoord([datetime.datetime(1901, 1, 16, 0, 0),
datetime.datetime(1901, 2, 15, 0, 0),
datetime.datetime(1901, 3, 16, 0, 0), ...,
datetime.datetime(2015, 10, 16, 0, 0),
datetime.datetime(2015, 11, 16, 0, 0),
datetime.datetime(2015, 12, 16, 0, 0)], standard_name='time', calendar=u'gregorian', long_name=u'time', var_name='time')
所以我有点困惑，为什么它昨天起作用但今天不起作用。你认为你可以帮忙吗？
以下是我正在运行的代码的简化版本
import matplotlib.pyplot as plt
import iris
import iris.coord_categorisation as iriscc
import iris.plot as iplt
import iris.quickplot as qplt
import iris.analysis.cartography
import matplotlib.dates as mdates
#the first part of the code is all about the CORDEX models, the second half brings in the observed data, and the third plots the annual temperature in a line graph.
def main():
#PART 1: CORDEX MODELS
#bring in all the models we need and give them a name
CCCma = '/exports/csce/datastore/geos/users/s0xxxx/Climate_Modelling/AFR_44_tas/ERAINT/1979-2012/tas_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'
#Load exactly one cube from given file
CCCma = iris.load_cube(CCCma)
#remove flat latitude and longitude and only use grid latitude and grid longitude to make consistent with the observed data, also make sure all of the longitudes are monotonic
lats = iris.coords.DimCoord(CCCma.coord('latitude').points[:,0], \
standard_name='latitude', units='degrees')
lons = CCCma.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, \
standard_name='longitude', units='degrees')
CCCma.remove_coord('latitude')
CCCma.remove_coord('longitude')
CCCma.remove_coord('grid_latitude')
CCCma.remove_coord('grid_longitude')
CCCma.add_dim_coord(lats, 1)
CCCma.add_dim_coord(lons, 2)
#we are only interested in the latitude and longitude relevant to Malawi
Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
latitude=lambda v: -17. <= v <= -9.)
CCCma = CCCma.extract(Malawi)
#time constraignt to make all series the same
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
CCCma = CCCma.extract(t_constraint)
#data is in Kelvin, but we would like to show it in Celcius
CCCma.convert_units('Celsius')
#We are interested in plotting the graph with time along the x ais, so we need a mean of all the coordinates, i.e. mean temperature across whole country
iriscc.add_year(CCCma, 'time')
CCCma = CCCma.aggregated_by('year', iris.analysis.MEAN)
CCCma.coord('latitude').guess_bounds()
CCCma.coord('longitude').guess_bounds()
CCCma_grid_areas = iris.analysis.cartography.area_weights(CCCma)
CCCma_mean = CCCma.collapsed(['latitude', 'longitude'],
iris.analysis.MEAN,
weights=CCCma_grid_areas)
#PART 2: OBSERVED DATA
#bring in all the files we need and give them a name
CRU= '/exports/csce/datastore/geos/users/s0xxxx/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.tmp.dat.nc'
#Load exactly one cube from given file
CRU = iris.load_cube(CRU, 'near-surface temperature')
#define the latitude and longitude
lats = iris.coords.DimCoord(CRU.coord('latitude').points, \
standard_name='latitude', units='degrees')
lons = CRU.coord('longitude').points
#we are only interested in the latitude and longitude relevant to Malawi
Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
latitude=lambda v: -17. <= v <= -9.)
CRU = CRU.extract(Malawi)
#time constraignt to make all series the same
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
CRU = CRU.extract(t_constraint)
#We are interested in plotting the graph with time along the x ais, so we need a mean of all the coordinates, i.e. mean temperature across whole country
iriscc.add_year(CRU, 'time')
CRU = CRU.aggregated_by('year', iris.analysis.MEAN)
CRU.coord('latitude').guess_bounds()
CRU.coord('longitude').guess_bounds()
CRU_grid_areas = iris.analysis.cartography.area_weights(CRU)
CRU_mean = CRU.collapsed(['latitude', 'longitude'],
iris.analysis.MEAN,
weights=CRU_grid_areas)
#PART 3: PLOT LINE GRAPH - ANNUAL
#set major plot indicators for x-axis
plt.gca().xaxis.set_major_locator(mdates.YearLocator(5))
#assign the line colours
qplt.plot(CCCma_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')
qplt.plot(CRU_mean, label='Observed', lw=2, color='black')
#create a legend and set its location to under the graph
plt.legend(loc="upper center", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)
#create a title
plt.title('Mean Near Surface Temperature for Malawi 1989-2008', fontsize=11)
#add grid lines
plt.grid()
#save the image of the graph and include full legend
plt.savefig('ERAINT_Temperature_LineGraph_Annual', bbox_inches='tight')
#show the graph in the console
iplt.show()
if __name__ == '__main__':
main()
谢谢！ 梨香
2 个答案:
答案 0 :(得分：1)
我不知道你的time坐标会发生什么，但解决方法可能是专门针对你的year坐标进行绘制：
qplt.plot(CCCma_mean.coord('year'), CCCma_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')
qplt.plot(CRU_mean.coord('year'), CRU_mean, label='Observed', lw=2, color='black')
答案 1 :(得分：0)
谢谢@RuthC。这就是诀窍。它似乎是绘制时间作为默认值，因为一些数据是自1949年以来的天数，另一个是自1900年以来的天数，它在x轴上绘制了相隔49年的时间。虽然它前一天工作很奇怪！我的软件必须改变一些东西。
请注意，我还必须删除此代码：
Re-enter new password:
... Failed! Error: File './mysql/user.MYD' not found (Errcode: 13 - Permission denied)
否则x轴不会显示底部的年份数据。
欣赏它！ 梨香
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