issue_comments

@spencerkclark Very helpful!!! Thanks a million! :)

@spencerkclark Thank you very much for your help! I will install the development version on my local machine. Currently I am using NCAR Cheyenne to manipulate the climate data. What I am doing on Cheyenne as a detour is: xarray.assign_coords(time = xarray.indexes['time'].to_datetimeindex()) xarray.resample(time="D").mean("time") I hope NCAR will support the next release of xarray. A follow-up question is that when we using xarray to manipulate the large dataset such as <xarray.DataArray (time: 14600, lat: 192, lon: 288)> and want to save the results for further machine learning applications (e.g., using sklearn or XGBoost, even deep learning), what will be a good format to store the data on server or local machine that will be easily used by sklearn or XGBoost?

@zzheng93 this will be possible in the next release of xarray, so not quite yet, but soon. If you're in a hurry you could install the development version.

@spencerkclark Thank you very much :) I am new to the Xarray community. I am wondering if there is any instruction regarding installing the latest development version and how to implement the daily resampling function.

Hi folks, I have some data like 2000-01-01 00:00:00, 2000-01-01 12:00:00, 2000-01-02 00:00:00, 2000-01-02 12:00:00. The index is cftime And I want to take the average within the same date and save the results. I am wondering if it is possible to resample them at a daily level (e.g., the results will be 2000-01-01 00:00:00 and 2000-01-02 00:00:00)?

Yes, when open_mfdataset decides to convert to CFTime this is much faster. When time is in datetime64, I get: ```

AttributeError Traceback (most recent call last) <ipython-input-72-a96fa0263d3e> in <module>() 9 dss = xr.open_mfdataset(files,decode_times=True,autoclose=True) 10 #month_start = [DatetimeNoLeap(date.dt.year, date.dt.month, 1) for date in dss.time] ---> 11 month_start = [DatetimeNoLeap(date.year, date.month, 1) for date in dss.time.values] 12 #month_start = [DatetimeNoLeap(yr, mon, 1) for yr,mon in zip(dss.time.dt.year,dss.time.dt.month)] 13 #break

<ipython-input-72-a96fa0263d3e> in <listcomp>(.0) 9 dss = xr.open_mfdataset(files,decode_times=True,autoclose=True) 10 #month_start = [DatetimeNoLeap(date.dt.year, date.dt.month, 1) for date in dss.time] ---> 11 month_start = [DatetimeNoLeap(date.year, date.month, 1) for date in dss.time.values] 12 #month_start = [DatetimeNoLeap(yr, mon, 1) for yr,mon in zip(dss.time.dt.year,dss.time.dt.month)] 13 #break

AttributeError: 'numpy.datetime64' object has no attribute 'year' ``` You can see I made a feeble attempt to fix it to work for all the CMIP5 calendars, but is just as slow. Any suggestions?

@spencerkclark thanks! I hadn't figured out that particular workaround, but it works, albeit quite slow. For now it will get me to the next step, but just changing to first-of-the-month takes longer than regridding all models to a common grid!

I am trying to combine the monthly CMIP5 rcp85 ts datasets (go past 2064AD) with the myriad calendars, so I love the new CFTimeIndex! But I need resample(time='MS') in order to force them all to start on the first of each month thanks!