DatetimeNoLeap(2000, 3, 1), cftime.DatetimeNoLeap(2000, 3, 2)] curve = Curve((gregorian_dates, [1, 2, 3])) plot = bokeh_renderer.get_plot(curve) xs = plot.handles['cds'].data['x'] self.assertEqual(xs.astype('int64'), np.array([951696000000, 951868800000, 951955200000])) ...
cftime.DatetimeNoLeap 对象无法使用 pandas.to_datetime() 进行转换 我正在使用 xarray 读取多个气候模型数据,自从升级 python 以来,我的代码现在已停止工作。 它失败了,因为某些模型数据具有不同的日历: In[15]:ds.coords['time'].values[0]Out[15]:cftime.DatetimeNoLeap(1861, 1, 16, 12, 0, 0, 0, ...
datetime.time is an idealized time that assumes there are 86,400 seconds per day with no leap seconds. This object stores the hour, minute, second, microsecond, and tzinfo (time zone information). datetime.datetime is a combination of a date and a time. It has all the attributes of both...
center=True).mean().plot(label='GridMet',color='r')(ds_Austin_CPC.tmin*1.8+32).rolling(time=15,center=True).mean().plot(label='CPC',color='b')plt.grid()plt.xticks(ticks=[datetime.date(2021,x,1)forxinrange(1,13)],fontsize=12)plt.xlim([datetime.date(2021,1,1),datetime.date...
一种可能性是将cftime 转换为 datetime,但这可能会给非标准 cftime 日历(例如 NoLeap)带来奇怪的结果。因此,我正在尝试将日期时间转换为 cftime。 我可以按如下方式暴力破解它,但是有可用的内置方法吗? >>> import pandas as pd >>> import xarray as xr >>> >>> da = xr.DataArray( ... [1, 2]...
2/2/2021: Version 1.4.1 released. Restore use of calendar-specific subclasses incftime.num2date,cftime.datetime.__add__, andcftime.datetime.__sub__. The use of this will be removed in a later release. Add 'fromordinal' static method to create a cftime.datetime instance from a julian da...
()function to view the timeseries since it doesn't seem to understand the cftime format. I would like to get all the datetimes of all of my datasets in the same format because others have datetime64[nc], cftime.DatetimeNoLeap() etc. and I would like to standardise my datetime format...
def test_assert_has_data_for_time_cftime_datetimes(calendar, date_type): time_bounds = np.array([[0, 2], [2, 4], [4, 6]]) nv = np.array([0, 1]) time = np.array([1, 3, 5]) data = np.zeros((3)) var_name = 'a' ds = xr.DataArray(data, coords=[time], dims=...