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')

Will the next version of xray have the capability to fill when upsampling?

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()

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

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()

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

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 ... ```

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

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.

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()

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