id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
124300184,MDU6SXNzdWUxMjQzMDAxODQ=,690,hourofyear,12929592,closed,0,,,4,2015-12-30T03:36:37Z,2022-05-12T21:22:37Z,2019-01-29T22:44:38Z,NONE,,,,"Is there a way to use 'hourofyear' in the same way 'dayofyear' works? I want the calculate the mean temperature value for a 2d dataset for each hour of the year based on 40 years of hourly data. I realise this might be a pandas question but if I receive an answer from a pandas forum I don't know if I would be able to work out how to apply it to an xray dataset. 

Below is the code I would use to calculate 'dayofyear' but with the word 'hour' used to replace 'day'. Obviously it does not work!
Any feedback will be greatly appreciated

ds=xray.open_mfdataset('/DATA/*TEMP.nc')
ds_variable=ds['TEMP']
hourofyear=ds_variable.groupby('time.hourofyear').mean('time')
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/690/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
105519744,MDU6SXNzdWUxMDU1MTk3NDQ=,563,Upsampling - fill,12929592,open,0,,,4,2015-09-09T05:05:27Z,2019-07-13T01:02:24Z,,NONE,,,,"Will the next version of xray have the capability to fill when upsampling?
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/563/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
98074194,MDU6SXNzdWU5ODA3NDE5NA==,501,xray methods using shapefile as mask?,12929592,closed,0,,,17,2015-07-30T02:49:35Z,2019-06-18T10:49:53Z,2016-12-29T01:42:03Z,NONE,,,,"Can we set a shapefile as a mask for each netcdf file and run xray methods for values within the shapefile region?

for example if I want to create a timeseries of monthly mean temperature for 'mystate' from a netcdf file that contains data for the whole country:

filepath = r""DATA/temp/_/_temp.nc""
shapefile = r""DATA/mystate.shp""

ds=xray.open_mfdataset(filepath) 
ds_variable=ds['temp']
monthlymean=ds_variable.resample('1MS', dim='time', how='mean')
meanmonthlyofmystate=monthlymean.groupby('time').mean() #add somewhere here the shapefile
meanmonthlyofmystate.to_pandas().plot()
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/501/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
88897697,MDU6SXNzdWU4ODg5NzY5Nw==,436,Examples combining multiple files,12929592,closed,0,,,4,2015-06-17T03:12:45Z,2019-01-15T20:10:37Z,2019-01-15T20:10:37Z,NONE,,,,"Are you able to provide more examples of combining and working with multiple netcdf files. All of the examples appear to be working with the one netcdf file. 
I would like to create time series plots and spatial plots of anomalies of climate data for hundreds of netcdf files separated by month.  
I must admit I am not very experienced but I think xray may be a better option than how I have been processing netcdf files in the past. 
Thanks
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/436/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
325933825,MDU6SXNzdWUzMjU5MzM4MjU=,2178,if 10% of ds meets criteria then count,12929592,closed,0,,,2,2018-05-24T01:40:54Z,2018-05-24T03:03:17Z,2018-05-24T03:03:17Z,NONE,,,,"Can I please have help in calculating the total number of days that 10% of my dataset (on each day) is equal to or greater than a given value (e.g. 35). I will then use the where function and loop this through a number of regions and periods but first need help figuring out how to apply the 10% condition. 
Any help will be greatly appreciate.
Sarah

#open dataset
ds=xr.open_mfdataset('/DATA/WRF/sample10years/*/*/*FFDI.nc')

#select period
fireseason=ds['FFDI'].sel(time=slice('2008-09-01', '2009-05-01'))

#resample to daily max
dailymax=fireseason.resample(time='1D').max('time')

#count number of days with dailymax>=35 if at least 10% of that day meets that criteria
dailycountTOTAL = (if 10% of dailymax >= 35).count()
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2178/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
238731491,MDU6SXNzdWUyMzg3MzE0OTE=,1466,Rasterio - Attribute Error,12929592,closed,0,,,5,2017-06-27T04:01:08Z,2017-06-28T13:23:54Z,2017-06-27T04:35:18Z,NONE,,,,"I am able to open a tif using rasterio but when I try to open the same tif using rasterio in xarray I receive the following error message:

/Users/slburns/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/xarray/backends/rasterio_.pyc in open_rasterio(filename, chunks, cache, lock)
    141         # CRS is a dict-like object specific to rasterio
    142         # We convert it back to a PROJ4 string using rasterio itself
--> 143         attrs['crs'] = riods.crs.to_string()
    144     # Maybe we'd like to parse other attributes here (for later)
    145 

AttributeError: 'dict' object has no attribute 'to_string' 

Is there some other step I should be doing first?
Thanks



","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1466/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
182168383,MDU6SXNzdWUxODIxNjgzODM=,1043,combine datasets and replace,12929592,closed,0,,,2,2016-10-11T04:02:29Z,2016-10-12T02:25:58Z,2016-10-12T02:25:58Z,NONE,,,,"I would like to replace the time in one dataset with the time in another dataset. I have tried .concat(), .merge() and .update() with various errors. Details of the errors for each of those steps and the datasets are posted below. 
Any feedback on how I might resolve this will be greatly appreciated.

```
ds = xray.open_mfdataset('/DATA/WRF/*/*/*T_SFC.nc')               

time=ds['time'].to_index()

time_utc = time.tz_localize('UTC')
au_tz = pytz.timezone('Australia/Sydney')
#convert UTC to local time
time_local = time_utc.tz_convert(au_tz)
time_local=time_local.tz_localize(None)

local_series=time_local.to_series()
local_df=local_series.to_frame()
local_df.columns=['localtime']

local_ds=xray.Dataset.from_dataframe(local_df)

newconcat_ds=xray.concat(ds, local_ds['localtime']) #TypeError: can only concatenate xray Dataset and DataArray objects 

newmerge_ds=ds.merge(local_ds) #InvalidIndexError: Reindexing only valid with uniquely valued Index objects 

newupdate_ds=ds.update(ds['time'],local_ds['time']) #TypeError: unhashable type: 'DataArray'
```

I would like to replace the time in this dataset:

```
In[333]:ds
Out[333]: 
<xray.Dataset>
Dimensions:    (latitude: 106, longitude: 193, time: 17520)
Coordinates:
  * latitude   (latitude) float32 -39.2 -39.1495 -39.099 -39.0486 -38.9981 ...
  * longitude  (longitude) float32 140.8 140.848 140.896 140.944 140.992 ...
  * time       (time) datetime64[ns] 2009-01-01 2009-01-01T01:00:00 ...

Data variables:

T_SFC      (time, latitude, longitude) float64 13.83 13.86 13.89 13.92 ...
Attributes:
    creationTime: 1431922712
    creationTimeString: Sun May 17 21:18:32 PDT 2015
    Conventions: COARDS
```

I would like to use the time in this dataset to replace the time in the first dataset:

```
In[334]: local_ds
Out[334]: 
<xray.Dataset>
Dimensions:    (time: 17520)
Coordinates:
  * time       (time) datetime64[ns] 2009-01-01T11:00:00 2009-01-01T12:00:00 ...

Data variables:

 localtime  (time) datetime64[ns] 2009-01-01T11:00:00 2009-01-01T12:00:00 ...
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1043/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
181005061,MDU6SXNzdWUxODEwMDUwNjE=,1036,convert xarray dataset to local timezone,12929592,closed,0,,,2,2016-10-04T21:07:02Z,2016-10-11T04:02:51Z,2016-10-11T04:02:51Z,NONE,,,,"Can I convert an xarray dataset to a different timezone?
I have tried using similar steps that I would use in pandas to convert from UTC to 'Australia/Sydney'. I have pasted below some of these steps, along with a small section of the dataset I am working with.
Any feedback will be greatly appreciated.

ds = xray.open_mfdataset('/DATA/WRF/_/_/*T_SFC.nc')

import pytz  
ds_utc = ds['time'].tz_localize(pytz.UTC)
au_tz = pytz.timezone('Australia/Sydney')
ds_local = ds_utc.astimezone(au_tz)

<xray.Dataset>
Dimensions:    (latitude: 106, longitude: 193, time: 17520)

Coordinates:
- latitude   (latitude) float32 -39.2 -39.1495 -39.099 -39.0486 -38.9981 ...
- longitude  (longitude) float32 140.8 140.848 140.896 140.944 140.992 ...
- time       (time) datetime64[ns] 2009-01-01 2009-01-01T01:00:00 ...

Data variables:
    T_SFC      (time, latitude, longitude) float64 13.83 13.86 13.89 13.92 ...

Attributes:
    creationTime: 1431922712
    creationTimeString: Sun May 17 21:18:32 PDT 2015
    Conventions: COARDS
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1036/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
180503054,MDU6SXNzdWUxODA1MDMwNTQ=,1025,Extract value for given time latitude and longitude,12929592,closed,0,,,5,2016-10-02T09:04:32Z,2016-10-03T09:07:31Z,2016-10-03T09:07:31Z,NONE,,,,"I would like to loop through a list of dates, latitudes and longitudes and extract the maximum daily temperature from an hourly dataset of netcdf files. This appears more difficult than I thought it would be because I cannot seem to use the given latitude and longitude (even though I know the latitude and longitude matching the grid point). 

ds = xray.open_mfdataset('/DATA/WRF/_/_/*Temp.nc')
ds_variable=ds['Temp']
dailymax=ds_variable.resample('D', dim='time', how='max')

MaxTempattime=dailymax.sel(time='2015-02-01') 
MaxTempatpoint=MaxTempattime.isel(latitude=-39.1495, longitude=140.848) #this is where the problem occurs

print MaxTempatpoint.values

I see 'slice' can take a given latitude and longitude but I can't set a range for each of the thousands of points I need. Should I be using some type of index for latitude and longitude?
Any feedback on the best approach for extracting a value at a given time, latitude and longitude will be greatly appreciated. 
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1025/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
121336727,MDU6SXNzdWUxMjEzMzY3Mjc=,673,resampling with missing data,12929592,closed,0,,,2,2015-12-09T20:55:09Z,2015-12-13T00:27:43Z,2015-12-13T00:27:24Z,NONE,,,,"I regularly use resample and groupby to analyse a 40 year hourly 2D dataset with no problems. However, a new dataset that I am working with is missing some leap year days and the output is wrong with what seems like months have been swapped around. Is this because the number of days in the month is used to divide to get the mean? 
So my actual question is - how is the mean taken when using groupby or resample, does it count the number hours or days in the dataset and how does it deal with missing data?

Some of the steps I follow:

``` Python
ds=xray.open_mfdataset(filepath)    
dsvariable=ds[variable]
resampledaily=(dsvariable.resample('D', dim='time', how='max'))
resamplemonthly=(resampledaily.resample('1MS', dim='time', how='mean'))
monthly_ts=resamplemonthly.groupby('time').mean()
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/673/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
93416316,MDU6SXNzdWU5MzQxNjMxNg==,456,Percentiles,12929592,closed,0,,,2,2015-07-07T01:48:40Z,2015-09-09T00:59:04Z,2015-09-09T00:59:04Z,NONE,,,,"Is there a command to calculate percentiles?

I am currently using xray to create max, min and mean plots for each season for 40 years of data. I have tried replacing where I use 'max' with 'percentile' or 'quantile' and then change ('time') to ('time, 90) but there is no attribute 'percentile' or 'quantile'  

ds = xray.open_mfdataset('myfiles/_/_/*temp.nc')
mytemp=ds['temp']
eachseasonmax=mytemp.groupby('time.season').max('time')

Also is this the correct place for these types of questions?

Thanks
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