html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/501#issuecomment-503052359,https://api.github.com/repos/pydata/xarray/issues/501,503052359,MDEyOklzc3VlQ29tbWVudDUwMzA1MjM1OQ==,37119533,2019-06-18T10:49:52Z,2019-06-18T10:49:52Z,NONE,"I am doing a [project](https://github.com/eduardogfma/ml_fire/blob/master/regionalSegmentation/FWI_geographicalSegmentation.ipynb) where we need to segment a geospatial data. The data set comprises the Fire Weather Index (FWI) -- a fire hazard classification system -- for the whole world from the 1980's to 2018. We want to segment data according to the NUTS (or GADM) in order to be able to predict and investigate possible patterns over time.
We have to our disposal:
* the FWI dataset (resolution: 0.7 degrees ~ 80x80 Km)
* the NUTS (and GADM) shape files
Our methodology was the following:
1. import shape files with geopandas
2. import the FWI dataset with xarray
3. rasterize the shapefiles (adapting @shoyer code)
4. get FWI data segmented by NUTS/GADM
A couple of issues were identified (as shown in the picture below):
As can be seen there seems to exist an **offset between the segmented data and the actual map areas**. Moreover, there are **some islands that are not even captured by the segmentation**.
The only modification to the original code was the introduction of the argument `all_touched=True` to the `features.rasterize` method.
Does anyone ever got a similar error?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194
https://github.com/pydata/xarray/issues/501#issuecomment-378252406,https://api.github.com/repos/pydata/xarray/issues/501,378252406,MDEyOklzc3VlQ29tbWVudDM3ODI1MjQwNg==,19677300,2018-04-03T13:36:25Z,2018-04-03T13:36:25Z,NONE,"@shoyer ， the website can't opem. Can you share the code or new website? Moreover, is there any new way to solve the nc file extracting?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194
https://github.com/pydata/xarray/issues/501#issuecomment-146031940,https://api.github.com/repos/pydata/xarray/issues/501,146031940,MDEyOklzc3VlQ29tbWVudDE0NjAzMTk0MA==,12929592,2015-10-06T23:22:50Z,2015-10-06T23:22:50Z,NONE,"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
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194
https://github.com/pydata/xarray/issues/501#issuecomment-143899899,https://api.github.com/repos/pydata/xarray/issues/501,143899899,MDEyOklzc3VlQ29tbWVudDE0Mzg5OTg5OQ==,12929592,2015-09-28T23:20:04Z,2015-09-28T23:20:04Z,NONE,"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.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194
https://github.com/pydata/xarray/issues/501#issuecomment-143116745,https://api.github.com/repos/pydata/xarray/issues/501,143116745,MDEyOklzc3VlQ29tbWVudDE0MzExNjc0NQ==,12929592,2015-09-25T03:49:53Z,2015-09-25T03:49:53Z,NONE,"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()
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194
https://github.com/pydata/xarray/issues/501#issuecomment-142788988,https://api.github.com/repos/pydata/xarray/issues/501,142788988,MDEyOklzc3VlQ29tbWVudDE0Mjc4ODk4OA==,12929592,2015-09-24T02:52:55Z,2015-09-24T02:52:55Z,NONE,"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
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194
https://github.com/pydata/xarray/issues/501#issuecomment-128216372,https://api.github.com/repos/pydata/xarray/issues/501,128216372,MDEyOklzc3VlQ29tbWVudDEyODIxNjM3Mg==,12929592,2015-08-06T02:24:17Z,2015-08-06T02:24:17Z,NONE,"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
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,98074194