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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
188996339 MDU6SXNzdWUxODg5OTYzMzk= 1115 Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data hrishikeshac 6334793 closed 0     31 2016-11-13T21:29:04Z 2020-05-25T16:57:48Z 2020-05-25T16:57:48Z NONE      

As a earth scientist regularly dealing with 3D data (time, latitude, longitude), I believe it would be great to be able to perform cross-correlation on DataArrays by specifying the axis. It's usage could look like: a.corr(b, axis = 0). It would be even more useful if the two arrays need not have the same dimensions (e.g. 'b' could be a time series).

Currently, the only way to compute this that I am aware of, is by looping through each grid, converting the time series to pd.Series(), and then computing the correlation. This takes a long time. Would also appreciate suggestions to a faster algorithm. 

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   completed xarray 13221727 issue
396102183 MDExOlB1bGxSZXF1ZXN0MjQyMzk5MjY1 2652 cov() and corr() hrishikeshac 6334793 closed 0     20 2019-01-04T23:30:44Z 2020-05-25T16:57:31Z 2020-05-25T16:57:31Z FIRST_TIMER   0 pydata/xarray/pulls/2652

Added da.corr() and da.cov() to dataarray.py. Test added in test_dataarray.py, and tested using pytest. Concerns issue #1115

The test is based on demo data and can be readily added to the user guide. 

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     xarray 13221727 pull
307903558 MDU6SXNzdWUzMDc5MDM1NTg= 2009 Removing inter-subplot spaces when using cartopy projections hrishikeshac 6334793 closed 0     2 2018-03-23T05:02:59Z 2018-03-23T16:45:15Z 2018-03-23T16:45:15Z NONE      

Code Sample, a copy-pastable example if possible

```python import cartopy.crs as ccrs import xarray as xr import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec

projection = True

ts = xr.tutorial.load_dataset('air_temperature').air[0, ...] ncols, nrows = 2, 2 fig = plt.figure()

ny,nx = ts.shape

dx, dy = nx/ny, 1

figsize = plt.figaspect(float(dy * ncols) / float(dx * nrows))

fig = plt.figure(figsize=figsize)

gs = gridspec.GridSpec(ncols, nrows)

for i in range(4): if projection: ax = plt.subplot(gs[i], projection=ccrs.PlateCarree()) ax.coastlines() ts.plot(ax=ax, add_colorbar=False, add_labels=False, transform=ccrs.PlateCarree()) else: ax = plt.subplot(gs[i]) ts.plot(ax=ax, add_colorbar=False, add_labels=False) ax.set_xticks([]) ax.set_yticks([]) # ax.set_aspect('auto', adjustable='box-forced') if (i == 0) or (i == 1): ax.set_title('title') if (i == 0) or (i == 2): ax.set_ylabel('ylabel')

plt.tight_layout()

fig.subplots_adjust(wspace=0, hspace=0)

plt.show()

```

Problem description

In the above script, the subplots get plotted with no in-between spaces if no projection information is provided (i.e. projection=False).

But when projection info is provided, there is pretty much no way of removing the inter-column (or inter-row) spaces.

Commented lines are the different ways that I tried to remove the spaces.

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.4.final.0 python-bits: 64 OS: Darwin OS-release: 16.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: None LOCALE: None.None xarray: 0.10.0 pandas: 0.22.0 numpy: 1.14.0 scipy: 1.0.0 netCDF4: 1.3.1 h5netcdf: 0.5.0 Nio: None bottleneck: 1.2.1 cyordereddict: None dask: 0.16.1 matplotlib: 2.1.2 cartopy: 0.15.1 seaborn: 0.8.1 setuptools: 38.4.0 pip: 9.0.3 conda: 4.4.10 pytest: 3.3.2 IPython: 6.2.1 sphinx: 1.6.6 
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   completed xarray 13221727 issue

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