issue_comments

I am trying not to be annoying but is there any chance you were able to fix this bug? I checked on the examples page but could not find anything. thanks

Thank you for fixing the last line, I can get your example to run with my own data without any errors being raised however the output does not appear to have any masked applied! I can use where() to plot a selected area but when it comes to being used with another dataset it doesn't appear to be working for me. Is there an example that shows something like temperature data over time for different states, extracted using the method above? This method will be so useful for me - if I can get it to work! Any feedback will be greatly appreciated.

Thank you, but can we use the mask and apply it to another xray dataset - so you only take the values from one dataset that fall in the region of the mask)? I have tried below (but this doesn't work). Thanks

ds.states.where(ds.states == state_ids['California']).plot()

dstemp=xray.open_mfdataset(filepath) ds_variable=dstemp['temp'] monthlymean=ds_variable.resample('1MS', dim='time', how='mean') meanmonthlycaliforniatemp=ds.states.where(ds.states==state_ids['California']).monthlymean.groupby('time').mean() meanmonthlycaliforniatemp.to_pandas().plot()

Now that I am using xray version 0.6.0 I cannot find any examples that use where() to mask out values from one xray dataset using a rasterized shapefile that has been turned into an xray dataset.

Referring to my original post in this thread can I resample a timeseries, find the mean, groupby by time and plot using only the values that fall within one state?

Any feedback will be greatly appreciated

This example is very helpful. Thank you.

I think the where() method you refer to will be very useful - When will the version 0.5.3 be released?

Thanks Sarah

On 31 July 2015 at 06:00, Stephan Hoyer notifications@github.com wrote:

rasterio and geopandas can be combined with xray to make converting shapefiles into raster masks pretty easy. Here's a quick demo: ``python import geopandas from rasterio import features from affine import Affine

def transform_from_latlon(lat, lon): lat = np.asarray(lat) lon = np.asarray(lon) trans = Affine.translation(lon[0], lat[0]) scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0]) return trans * scale

def rasterize(shapes, coords, fill=np.nan, *

_kwargs): """Rasterize a list of (geometry, fill_value) tuples onto the given xray coordinates. This only works for 1d latitude and longitude arrays. """ transform = transform_from_latlon(coords['latitude'], coords['longitude']) out_shape = (len(coords['latitude']), len(coords['longitude'])) raster = features.rasterize(shapes, out_shape=out_shape, fill=fill, transform=transform, *_kwargs) return xray.DataArray(raster, coords=coords, dims=('latitude', 'longitude'))

states = geopandas.read_file('/Users/shoyer/Downloads/ne_10m_admin_1_states_provinces_lakes') geometries = states.query("admin == 'United States of America'").geometry shapes = [(shape, n) for n, shape in enumerate(geometries)]

ds = xray.Dataset(coords={'longitude': np.linspace(-125, -65, num=2000), 'latitude': np.linspace(50, 25, num=1000)}) ds['states'] = rasterize(shapes, ds.coords) plotting requires the dev version of xray

ds.states.plot()

Once you have the rasterized geometries, you can use them as arrays to do arithmetic: https://github.com/xray/xray/issues/503

When we figure out how to represent coordinate reference systems properly in xray we might add in a direct wrapper for some of these rasterio functions.

— Reply to this email directly or view it on GitHub https://github.com/xray/xray/issues/501#issuecomment-126461466.

Dr Sarah Harris Research Fellow School of Earth, Atmosphere and Environment Room 227, 9 Rainforest Walk Monash University Ph: 03 9902 4243 Email: sarah.harris@monash.edu Email%3Asarah.harris@monash.edu