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| id ▼ | node_id | number | state | locked | title | user | body | created_at | updated_at | closed_at | merged_at | merge_commit_sha | assignee | milestone | draft | head | base | author_association | auto_merge | repo | url | merged_by |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 106899035 | MDExOlB1bGxSZXF1ZXN0MTA2ODk5MDM1 | 1277 | closed | 0 | Restored dim order in DataArray.rolling().reduce() | fujiisoup 6815844 | - [x] closes #1125 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew Added 1 line to fix #1125. I hope this is enough. If another care is necessary, please let me know. | 2017-02-19T12:14:55Z | 2017-07-09T23:53:15Z | 2017-02-27T17:11:02Z | 2017-02-27T17:11:02Z | 5e50c0dc4d0e8238437963cd79d31daaddd41cd8 | 0 | 8aa40159f34464fc561bcd189f0f7c418fdabba0 | 1cafb14cb4726da14abfb8976d22e6e2b5f3ae24 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1277 | ||||
| 108710689 | MDExOlB1bGxSZXF1ZXN0MTA4NzEwNjg5 | 1289 | closed | 0 | Added a support for Dataset.rolling. | fujiisoup 6815844 | - [x] closes #859 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry There seems to be two approaches to realize Dataset.rolling, 1. Apply rolling in each DataArrays and then combine them. 2. Apply Dataset directoly with some DataArrays that do not depend on `dim` kept aside, then merge them later. I chose the latter approach to reuse existing `Rolling` object as much as possible, but it results in some duplicates in `ImplementsRollingDatasetReduce`. Any feedbacks and comments are very welcome. | 2017-03-02T08:40:03Z | 2017-07-09T23:53:13Z | 2017-03-31T03:10:45Z | 2017-03-31T03:10:45Z | 09ef2c280677c45593d4f93a67962afc42abacf1 | 0 | 37c58f4c84f0d8743e3e175d0d2ca982bedb4425 | 371d034372bc7522098a142a0debf93916c49102 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1289 | ||||
| 113807418 | MDExOlB1bGxSZXF1ZXN0MTEzODA3NDE4 | 1347 | closed | 0 | Support for DataArray.expand_dims() | fujiisoup 6815844 | - [x] closes #1326 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry I added a DataArray's method `expand_dims` based on the discussion in #1326 . The proposed API is similar to `numpy.expand_dims` and slightly different from `Variables.expand_dims`, which requires whole sequences of `dims` of the result array. My concern is that I do not yet fully understand the lazy data manipulation in xarray. Does Variable.expand_dims do it? | 2017-04-02T06:36:37Z | 2017-04-10T02:05:38Z | 2017-04-10T01:01:54Z | 2017-04-10T01:01:54Z | 444fce8a7ae26546e283a6876f003aafb84b7552 | 0 | dd9f573112f376ad5ff061756c2fa599058899d9 | 79b61ccdd0c9c1822fbec52d1dc488a4dfd0c8af | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1347 | ||||
| 115014590 | MDExOlB1bGxSZXF1ZXN0MTE1MDE0NTkw | 1364 | closed | 0 | Fix a typo | fujiisoup 6815844 | - [x] closes #1363 Fixes typos in reshaping.rst. Is there a good way to check docs before merge? | 2017-04-10T02:14:56Z | 2017-07-09T23:53:03Z | 2017-04-10T02:24:00Z | 2017-04-10T02:24:00Z | f87bb0beadd937e3e9657e6d686a20b2bb288d2b | 0 | e65faf553d6c2a61d847aade9f4399eb536734ae | 444fce8a7ae26546e283a6876f003aafb84b7552 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1364 | ||||
| 119303797 | MDExOlB1bGxSZXF1ZXN0MTE5MzAzNzk3 | 1400 | closed | 0 | Patch isel points | fujiisoup 6815844 | - [x] closes #1337 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry A small fix for the bug reported in #1337, where unselected coords were wrongly assigned as `data_vars` by `sel_points`. I hope I did not forget anything. | 2017-05-06T14:59:51Z | 2017-07-09T23:53:06Z | 2017-05-09T02:31:52Z | 2017-05-09T02:31:52Z | e1982faf8e906ccdcb16b07462ffa77fd13bf69c | 0 | a5e9e62125f2681d668fab1a6b1d420481b6109e | a9a12b0aca862d5ab19180594f616b8efab13308 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1400 | ||||
| 121071107 | MDExOlB1bGxSZXF1ZXN0MTIxMDcxMTA3 | 1412 | closed | 0 | Multiindex scalar coords, fixes #1408 | fujiisoup 6815844 | - [x] Closes #1408 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API To fix #1408, This modification works, but actually I do not fully satisfied yet. There are `if` statements in many places. The major changes I made are 1. `variable.__getitem__` now returns an OrderedDict if a single element is selected from MultiIndex. 2. `indexing.remap_level_indexers` also returns `selected_dims` which is a map from the original dimension to the selected dims which will be a scalar coordinate. Change 1 keeps level-coordinates even after `ds.isel(yx=0)`. Change 2 enables to track which levels are selected, then the selected levels are changed to a scalar coordinate. I guess much smarter solution should exist. I would be happy if anyone gives me a comment. | 2017-05-17T14:25:50Z | 2017-05-25T11:04:55Z | 2017-05-25T11:04:55Z | aace43cba61ca4f45e2ec4e53571d604f77dd0a1 | 0 | 185abd0ed70996fafea5ad23f36d867703b81203 | d5c7e0612e8243c0a716460da0b74315f719f2df | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1412 | |||||
| 122418207 | MDExOlB1bGxSZXF1ZXN0MTIyNDE4MjA3 | 1426 | closed | 0 | scalar_level in MultiIndex | fujiisoup 6815844 | - [x] Closes #1408 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API [Edit for more clarity] I restarted a new branch to fix #1408 (I closed the older one #1412).  Because the changes I made is relatively large, here I summarize this PR. # Sumamry In this PR, I newly added two kinds of levels in MultiIndex, `index-level` and `scalar-level`. `index-level` is an ordinary level in MultiIndex (as in current implementation), while `scalar-level` indicates dropped level (which is newly added in this PR). # Changes in behaviors. 1. Indexing a scalar at a particular level changes that level to `scalar-level` instead of dropping that level (changed from #767). 2. Indexing a scalar from a MultiIndex, the selected value now becomes a `MultiIndex-scalar` rather than a scalar of tuple. 3. Enabled indexing along a `index-level` if the MultiIndex has only a single `index-level`. Examples of the output are shown below. Any suggestions for these behaviors are welcome. ```python In [1]: import numpy as np ...: import xarray as xr ...: ...: ds1 = xr.Dataset({'foo': (('x',), [1, 2, 3])}, {'x': [1, 2, 3], 'y': 'a'}) ...: ds2 = xr.Dataset({'foo': (('x',), [4, 5, 6])}, {'x': [1, 2, 3], 'y': 'b'}) ...: # example data ...: ds = xr.concat([ds1, ds2], dim='y').stack(yx=['y', 'x']) ...: ds Out[1]: <xarray.Dataset> Dimensions: (yx: 6) Coordinates: * yx (yx) MultiIndex - y (yx) object 'a' 'a' 'a' 'b' 'b' 'b' # <--- this is index-level - x (yx) int64 1 2 3 1 2 3 # <--- this is also index-level Data variables: foo (yx) int64 1 2 3 4 5 6 In [2]: # 1. indexing a scalar converts `index-level` x to `scalar-level`. ...: ds.sel(x=1) Out[2]: <xarray.Dataset> Dimensions: (yx: … | 2017-05-25T11:03:05Z | 2019-01-14T21:20:28Z | 2019-01-14T21:20:27Z | 5821b1de3713a3513bdce890e77999fd4c4b0688 | 0 | 38dbbbca748b0f22d1c49d63e5e5524ac093295f | bb87a9441d22b390e069d0fde58f297a054fd98a | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1426 | |||||
| 128471998 | MDExOlB1bGxSZXF1ZXN0MTI4NDcxOTk4 | 1469 | closed | 0 | Argmin indexes | fujiisoup 6815844 | - [x] Closes #1388 - [x] Tests added / passed - [x] Passes ``git diff master | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API With this PR, ValueError raises if `argmin()` is called by a multi-dimensional array. `argmin_indexes()` method is also added for `xr.DataArray`. Current API design for `argmin_indexes()` returns the argmin-indexes as an `OrderedDict` of `DataArray`s. Example: ```python In [1]: import xarray as xr ...: da = xr.DataArray([[1, 2], [-1, 40], [5, 6]], ...: [('x', ['c', 'b', 'a']), ('y', [1, 0])]) ...: ...: da.argmin_indexes() ...: Out[1]: OrderedDict([('x', <xarray.DataArray 'x' ()> array(1)), ('y', <xarray.DataArray 'y' ()> array(0))]) In [2]: da.argmin_indexes(dims='y') Out[2]: OrderedDict([('y', <xarray.DataArray 'y' (x: 3)> array([0, 0, 0]) Coordinates: * x (x) <U1 'c' 'b' 'a')]) ``` (Because the returned object is an `OrderedDict`, it is not beautifully printed. The returned type can be a `xr.Dataset` if we want.) Although in #1388 `argmin_indexes()` was originally suggested so that we can pass the result into `isel_point`, ```python da.isel_points(**da.argmin_indexes()) ``` current implementation of `isel_points` does **NOT** work for this case. This is mainly because 1. `isel_points` currently does not work for 0-dimensional or multi-dimensional input. 2. Even for 1-dimensional input (the second one in the above examples), we should also pass `x` as an indexer rather than the coordinate of indexer. For 1, I have prepared modification of `isel_points` to accept multi-dimensional arrays, but I guess it should be in another PR after the API decision. (It is related in #475, and #974.) For 2, we should either + change API of `argmin_indexes` to return not only the indicated dimension but also all the dimensions, like ```python In [2]: da.argmin_i… | 2017-07-01T01:23:31Z | 2020-06-29T19:36:25Z | 2020-06-29T19:36:25Z | ea61e19d4afcb3988eecbafdad28e1320995ce2c | 0 | 81c61b733ea8892464c69b5c75aabb57b5e60989 | bb87a9441d22b390e069d0fde58f297a054fd98a | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1469 | |||||
| 137907421 | MDExOlB1bGxSZXF1ZXN0MTM3OTA3NDIx | 1530 | closed | 0 | Deprecate old pandas support | fujiisoup 6815844 | - [x] Closes #1512 - [x] Tests passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Explicitly deprecated old pandas (< 0.18) and old numpy (< 1.11) supports. Some backported functions in `npcompat` are removed because numpy == 1.11 already has them. | 2017-08-28T09:40:02Z | 2017-11-04T09:51:51Z | 2017-08-31T17:25:10Z | 2017-08-31T17:25:10Z | 0b2424a1813bf1af712780c360a94a5588523adf | 0.10 2415632 | 0 | da5c16e98193addd1d856e6772b0f521a66ef209 | b190501a011f3427ae6a3220d72a8d972cb7c203 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1530 | |||
| 140043201 | MDExOlB1bGxSZXF1ZXN0MTQwMDQzMjAx | 1564 | closed | 0 | Uint support in reduce methods with skipna | fujiisoup 6815844 | - [x] Closes #1562 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fixes #1562 | 2017-09-08T13:54:54Z | 2017-11-04T09:51:49Z | 2017-09-08T16:12:23Z | 2017-09-08T16:12:23Z | a993317be46e6cba96424faa9fbcc54d3753d571 | 0 | f15ae04686f592038b1a8672b403181ae5758595 | 3a81942eb0cc38129208a52c391f7150af6f2538 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1564 | ||||
| 143131673 | MDExOlB1bGxSZXF1ZXN0MTQzMTMxNjcz | 1594 | closed | 0 | Remove unused version check for pandas. | fujiisoup 6815844 | - [x] Closes #1593 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [n.a.] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Currently some tests fail due to dask bug in #1591 | 2017-09-26T13:16:42Z | 2017-11-04T09:51:45Z | 2017-09-27T02:10:58Z | 2017-09-27T02:10:58Z | 25d1855e737444c156f50d1f37a67d9674a8bac5 | 0 | a4625c678913a3908a841f2774202037bd16a73d | 3a91442afe2a805b6aea5a3b9be3f72eb7245354 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1594 | ||||
| 146498388 | MDExOlB1bGxSZXF1ZXN0MTQ2NDk4Mzg4 | 1632 | closed | 0 | Support autocompletion dictionary access in ipython. | fujiisoup 6815844 | - [x] Closes #1628 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Support #1628. | 2017-10-13T16:19:35Z | 2017-11-04T16:05:02Z | 2017-10-22T17:49:21Z | 2017-10-22T17:49:21Z | 9763a66e0e4675e7adc3fff3830c62f0e31a2bb3 | 0 | 93130463edc5e27e0b63bad4fa8e1fbc69ac7f6d | 2949558b75a65404a500a237ec54834fd6946d07 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1632 | ||||
| 147688319 | MDExOlB1bGxSZXF1ZXN0MTQ3Njg4MzE5 | 1639 | closed | 0 | indexing with broadcasting | fujiisoup 6815844 | - [x] Closes #1444, #1436 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API This is a duplicate of #1473 originally opened by @shoyer Thanks, @shoyer, for giving me github's credit. I enjoyed this PR. I really appreciate your help to finish up this PR. | 2017-10-19T23:22:14Z | 2017-11-04T08:29:55Z | 2017-10-19T23:52:50Z | 2017-10-19T23:52:50Z | 9a0c744c8015345a6e892039d73eff40119bb66b | 0 | 170abc515bfc7112c212032ab8cecd50804acdb6 | 4c3c3328a7ea8269e1411c5119dd0b3d4d972cc4 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1639 | ||||
| 149814156 | MDExOlB1bGxSZXF1ZXN0MTQ5ODE0MTU2 | 1676 | closed | 0 | Support orthogonal indexing in MemoryCachedArray (Fix for #1429) | fujiisoup 6815844 | - [x] Closes #1429 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API This bug originates from the complicated structure around the array wrappers and their indexing, i.e. different array wrappers support different indexing types, and moreover, some can store another array wrapper in it. I made some cleanups. + Now every array wrapper is a subclass of `NDArrayIndexable` + Every array wrapper should implement its own `__getitem__` or just store another `NDArrayIndexable`. I think I added enough test for it, but I am not yet fully accustomed with xarray's backend. There might be many combinations of their hierarchical relation. I would appreciate any comments. | 2017-10-31T15:10:59Z | 2017-11-09T13:47:38Z | 2017-11-06T17:21:56Z | 2017-11-06T17:21:55Z | 2a1d3928a0aa0e66fe0a2211a6c9f1d079404dff | 0 | 7bba356573e778692b397f0d0a095fcc04a40819 | acae757d869af776a4b2bd980fb77a1873f4c510 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1676 | ||||
| 149933325 | MDExOlB1bGxSZXF1ZXN0MTQ5OTMzMzI1 | 1677 | closed | 0 | Removed `.T` from __dir__ explicitly | fujiisoup 6815844 | - [x] Closes #1675 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Remved `T` from `xr.Dataset.__dir__` to suppress a deprecation warning in Ipython autocompletion. | 2017-10-31T23:43:42Z | 2017-11-04T09:51:21Z | 2017-11-01T00:48:42Z | 2017-11-01T00:48:42Z | f83361c76b6aa8cdba8923080bb6b98560cf3a96 | 0 | 41755910dead906ab0011b298bb56e8945e045ef | 17956ea5de2cf5029992e8f83460fcc878e3d024 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1677 | ||||
| 150669865 | MDExOlB1bGxSZXF1ZXN0MTUwNjY5ODY1 | 1692 | closed | 0 | Bugfix in broadcast indexes | fujiisoup 6815844 | - [x] Closes #1688 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fixes #1688. It is caused that `Variable._broadcast_indexes` returns a wrong type of `Indexer`. Now it supports the orthogonal-indexing with `LazilyIndexedArray`. | 2017-11-04T09:49:11Z | 2017-11-04T09:51:37Z | 2017-11-04T09:49:22Z | fa2d863c009507f58ec608091e1e68e0ceb9c961 | 0 | 6615838eef90f1bf9bd46976842fab37c68bf942 | f83361c76b6aa8cdba8923080bb6b98560cf3a96 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1692 | |||||
| 150670128 | MDExOlB1bGxSZXF1ZXN0MTUwNjcwMTI4 | 1693 | closed | 0 | Bugfix in broadcast_indexes | fujiisoup 6815844 | - [x] Closes #1688, #1694 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fixes #1688. It is caused that `Variable._broadcast_indexes` returns a wrong type of `Indexer`. Now it supports the orthogonal-indexing with `LazilyIndexedArray`. | 2017-11-04T09:58:43Z | 2017-11-07T20:41:53Z | 2017-11-07T20:41:44Z | 2017-11-07T20:41:44Z | fb6e13ec15e85bbeceedbcd754e063f6e5696bf7 | 0 | 307c84ad3c7c3b1bf52d246061e5cc06f3e9a97e | 2a1d3928a0aa0e66fe0a2211a6c9f1d079404dff | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1693 | ||||
| 151356843 | MDExOlB1bGxSZXF1ZXN0MTUxMzU2ODQz | 1700 | closed | 0 | Add dropna test. | fujiisoup 6815844 | - [x] Closes #1694 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API This PR simply adds a particular test pointed out in #1694 . | 2017-11-08T11:25:18Z | 2017-11-09T07:56:19Z | 2017-11-09T07:56:13Z | 2017-11-09T07:56:13Z | dbf7b01cb4a4d9fb00882e0457523e4bb806820c | 0 | eb2dd717cea9fb35bee72408c144c23c13b96884 | fb6e13ec15e85bbeceedbcd754e063f6e5696bf7 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1700 | ||||
| 153223232 | MDExOlB1bGxSZXF1ZXN0MTUzMjIzMjMy | 1724 | closed | 0 | Fix unexpected loading after ``print`` | fujiisoup 6815844 | - [x] Closes #1720 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Only a single missing underscore causes this issue :) Added tests. | 2017-11-17T06:20:28Z | 2017-11-17T16:44:40Z | 2017-11-17T16:44:40Z | 2017-11-17T16:44:40Z | 6463504ae7c6fd0c2250237a2a74baf1b707723a | 0 | 0fb6a01376abd84ad9e9b6802f980a4e4013a53c | 1a012080e0910f3295d0fc26806ae18885f56751 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1724 | ||||
| 155221247 | MDExOlB1bGxSZXF1ZXN0MTU1MjIxMjQ3 | 1746 | closed | 0 | Fix in vectorized item assignment | fujiisoup 6815844 | - [x] Closes #1743, #1744 - [x] Tests added / passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Found bugs in `nputils.NumpyVindexAdapter.__setitem__` and `DataArray.__setitem__`. I will add more tests later. Test case suggestions would be appreciated. | 2017-11-29T00:37:41Z | 2017-12-09T03:29:35Z | 2017-12-09T03:29:35Z | 2017-12-09T03:29:35Z | 5e801894886b2060efa8b28798780a91561a29fd | 0 | 6906eebfc7645d06ee807773f5df9215634addef | 4b8339b53f1b9dcd79f2a9060933713328a13b90 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1746 | ||||
| 157856511 | MDExOlB1bGxSZXF1ZXN0MTU3ODU2NTEx | 1776 | closed | 0 | [WIP] Fix pydap array wrapper | fujiisoup 6815844 | - [x] Closes #1775 (remove if there is no corresponding issue, which should only be the case for minor changes) - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` (remove if you did not edit any Python files) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) I am trying to fix #1775, but tests are still failing. Any help would be appreciated. | 2017-12-12T15:22:07Z | 2019-09-25T15:44:19Z | 2018-01-09T01:48:13Z | 2018-01-09T01:48:13Z | ab0db05a58fd47fe895d1a85c09c37d96263d3b7 | 0.10.3 3008859 | 0 | e27c043adb96e027ac51e9d1abdf88e20db8dd7b | c368ee734945bbc736c33463ea561311bbdc1e9b | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1776 | |||
| 163657424 | MDExOlB1bGxSZXF1ZXN0MTYzNjU3NDI0 | 1837 | closed | 0 | Rolling window with `as_strided` | fujiisoup 6815844 | - [x] Closes #1831, #1142, #819 - [x] Tests added - [x] Tests passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API I started to work for refactoring rollings. As suggested in [#1831 comment](https://github.com/pydata/xarray/issues/1831#issuecomment-357828636), I implemented `rolling_window` methods based on `as_strided`. I got more than 1,000 times speed up! yey! ```python In [1]: import numpy as np ...: import xarray as xr ...: ...: da = xr.DataArray(np.random.randn(10000, 3), dims=['x', 'y']) ``` with the master ```python %timeit da.rolling(x=5).reduce(np.mean) 1 loop, best of 3: 9.68 s per loop ``` with the current implementation ```python %timeit da.rolling(x=5).reduce(np.mean) 100 loops, best of 3: 5.29 ms per loop ``` and with the bottleneck ```python %timeit da.rolling(x=5).mean() 100 loops, best of 3: 2.62 ms per loop ``` My current concerns are + Can we expose the new `rolling_window` method of `DataArray` and `Dataset` to the public? I think this method itself is useful for many usecases, such as short-term-FFT and convolution. This also gives more flexible rolling operation, such as windowed moving average, strided rolling, and ND-rolling. + Is there any dask's equivalence to numpy's `as_strided`? Currently, I just use a slice->concatenate path, but I don't think it is very efficient. (Is it already efficient, as dask utilizes out-of-core computation?) Any thoughts are welcome. | 2018-01-18T09:18:19Z | 2018-06-22T22:27:11Z | 2018-03-01T03:39:19Z | 2018-03-01T03:39:19Z | dc3eebf3a514cfdc1039b63f2a542121d1328ba9 | 0 | aeabdf5fc7ead2f2ae24b59045cc987f6feb5033 | f3bbb3ef6badcfe5d1f3b77c231846f0e79a93ea | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1837 | ||||
| 163896260 | MDExOlB1bGxSZXF1ZXN0MTYzODk2MjYw | 1841 | closed | 0 | Add dtype support for reduce methods. | fujiisoup 6815844 | - [x] Closes #1838changes) - [x] Tests added - [x] Tests passed - [x] Passes ``git diff upstream/master **/*py | flake8 --diff`` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fixes #1838. The new rule for reduce is + If dtype is not None and different from array's dtype, use numpy's aggregation function instead of bottleneck's. + If out is not None, raise an error. as suggested in [this comments](https://github.com/pydata/xarray/issues/1838#issuecomment-358851474). | 2018-01-19T06:40:41Z | 2018-01-20T18:29:02Z | 2018-01-20T18:29:02Z | 2018-01-20T18:29:02Z | 3bd704a4815ad2281e61eedcee3c7935789d410b | 0 | 0da3a635e2996098dbb35969001c6033a11b26a8 | 74d8318c68be884134d449afad18dfe731d48b72 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1841 | ||||
| 164452988 | MDExOlB1bGxSZXF1ZXN0MTY0NDUyOTg4 | 1851 | closed | 0 | Indexing benchmarking | fujiisoup 6815844 | - [x] Relates to #1771 Just added some benchmarks for basic, outer, and vectorized indexing and assignments. | 2018-01-23T00:27:29Z | 2018-01-24T08:10:19Z | 2018-01-24T08:10:19Z | 2018-01-24T08:10:19Z | 04974b99113d3f449c5592abc01a5701ba2382e4 | 0 | 57da6e5c3da679ca7528ec29b570122d20e0727e | e31cf43e8d183c63474b2898a0776fda72abc82c | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1851 | ||||
| 165099396 | MDExOlB1bGxSZXF1ZXN0MTY1MDk5Mzk2 | 1858 | closed | 0 | Adding a link to asv benchmark. | fujiisoup 6815844 | As discussed in #1851, I added a link in doc/installing.rst and a badge on README. | 2018-01-25T11:56:56Z | 2018-01-25T21:55:24Z | 2018-01-25T17:46:12Z | 2018-01-25T17:46:12Z | 009291139fde0c859ee565141cdb3b6a3d28cba0 | 0 | 416ddfacdca2e1946823a4292db41e1d4f2c1aec | 0a0593d78fad6c0b776d4c3c6b32a24b2bdfba35 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1858 | ||||
| 166925359 | MDExOlB1bGxSZXF1ZXN0MTY2OTI1MzU5 | 1883 | closed | 0 | Support nan-ops for object-typed arrays | fujiisoup 6815844 | - [x] Closes #1866 - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API I am working to add aggregation ops for object-typed arrays, which may make #1837 cleaner. I added some tests but maybe not sufficient. Any other cases which should be considered? e.g. `[True, 3.0, np.nan]` etc... | 2018-02-02T23:16:39Z | 2018-02-15T22:03:06Z | 2018-02-15T22:03:01Z | 2018-02-15T22:03:01Z | b6a0d60e720f5a19d6e00b11fc7f3d485e52a80c | 0 | e46d07de2dcaf7df1bf12e94c8ad70aa8a7cb10b | 2aa5b8a5c094593569f5bd9ae220d1f2fc0ecda0 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1883 | ||||
| 168214895 | MDExOlB1bGxSZXF1ZXN0MTY4MjE0ODk1 | 1899 | closed | 0 | Vectorized lazy indexing | fujiisoup 6815844 | - [x] Closes #1897 - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) I tried to support lazy vectorised indexing inspired by #1897. More tests would be necessary but I want to decide whether it is worth to continue. My current implementation is + For outer/basic indexers, we combine successive indexers (as we are doing now). + For vectorised indexers, we just store them as is and index sequentially when the evaluation. The implementation was simpler than I thought, but it has a clear limitation. It requires to load array before the vectorised indexing (I mean, the evaluation time). If we make a vectorised indexing for a large array, the performance significantly drops and it is not noticeable until the evaluation time. I appreciate any suggestions. | 2018-02-09T11:22:01Z | 2018-06-08T01:21:06Z | 2018-03-06T22:00:57Z | 2018-03-06T22:00:57Z | 54468e1924174a03e7ead3be8545f687f084f4dd | 0 | 8e967105194d7b4208bcac22127cd0cb01a7a484 | dc3eebf3a514cfdc1039b63f2a542121d1328ba9 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1899 | ||||
| 169631557 | MDExOlB1bGxSZXF1ZXN0MTY5NjMxNTU3 | 1919 | closed | 0 | Remove flake8 from travis | fujiisoup 6815844 | - [x] Closes #1912 The removal of flake8 from travis would increase the clearer separation between style-issue and test failure. | 2018-02-16T14:03:46Z | 2018-05-01T07:24:04Z | 2018-05-01T07:24:00Z | 2018-05-01T07:24:00Z | 39b2a37207fc8e6c5199ba9386831ba7eb06d82b | 0 | 04c55cd0a92ae8d274fe4d60f41389ec8e91642e | d191352b6c1e15a2b6105b4b76552fe974231396 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1919 | ||||
| 169812105 | MDExOlB1bGxSZXF1ZXN0MTY5ODEyMTA1 | 1922 | closed | 0 | Support indexing with 0d-np.ndarray | fujiisoup 6815844 | - [x] Closes #1921 - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) Now Variable accepts 0d-np.ndarray indexer. | 2018-02-18T02:46:27Z | 2018-02-18T07:26:33Z | 2018-02-18T07:26:30Z | 2018-02-18T07:26:30Z | 2ff7b4c4e394bfe73445f8cf471f0df8b79417bf | 0 | ebfc09681441da6b6278a50f5db91350a8308859 | e0621c7d66c13b486b1890f67a126caec2990da7 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1922 | ||||
| 171396650 | MDExOlB1bGxSZXF1ZXN0MTcxMzk2NjUw | 1942 | closed | 0 | Fix precision drop when indexing a datetime64 arrays. | fujiisoup 6815844 | - [x] Closes #1932 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API This precision drop was caused when converting `pd.Timestamp` to `np.array` ```python In [7]: ts = pd.Timestamp(np.datetime64('2018-02-12 06:59:59.999986560')) In [11]: np.asarray(ts, 'datetime64[ns]') Out[11]: array('2018-02-12T06:59:59.999986000', dtype='datetime64[ns]') ``` We need to call `to_datetime64` explicitly. | 2018-02-26T14:53:57Z | 2018-06-08T01:21:07Z | 2018-02-27T01:13:45Z | 2018-02-27T01:13:45Z | d8ccc7a999dce1a9ac205452e327bab5aa5f99f0 | 0 | f58aaa046d64315ae231fa77d7aa9e6713628742 | f530e668fa50665245988be2a00748b9b3ccc0a8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1942 | ||||
| 171557279 | MDExOlB1bGxSZXF1ZXN0MTcxNTU3Mjc5 | 1943 | closed | 0 | Fix rtd link on readme | fujiisoup 6815844 | Typo in url. | 2018-02-27T03:52:56Z | 2018-02-27T04:31:59Z | 2018-02-27T04:27:24Z | 2018-02-27T04:27:24Z | 243093cf814ffaae2a9ce08215632500fbebcf52 | 0 | d31024bb4b25b4ab581bae5718d7015b9686e74f | d8ccc7a999dce1a9ac205452e327bab5aa5f99f0 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1943 | ||||
| 172394913 | MDExOlB1bGxSZXF1ZXN0MTcyMzk0OTEz | 1950 | closed | 0 | Fix doc for missing values. | fujiisoup 6815844 | - [x] Closes #1944 | 2018-03-02T00:47:23Z | 2018-03-03T06:58:33Z | 2018-03-02T20:17:29Z | 2018-03-02T20:17:29Z | 350e97793f89ddd4097b97e0c4af735a5144be24 | 0 | 0ca721f51e812eacd04a166238e1a4d72979fd8c | dc3eebf3a514cfdc1039b63f2a542121d1328ba9 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1950 | ||||
| 172670528 | MDExOlB1bGxSZXF1ZXN0MTcyNjcwNTI4 | 1957 | closed | 0 | Numpy 1.13 for rtd | fujiisoup 6815844 | - [x] Partly closes #1944 I noticed [this](https://github.com/pydata/xarray/pull/1950#issuecomment-370125253) is due to the use of old numpy on rtd. xref #1956 | 2018-03-03T14:51:21Z | 2018-03-03T22:22:54Z | 2018-03-03T22:22:49Z | 7d63d9b43c9f4ebf02c3af846bd09a3150fdea73 | 0 | b035efc79252545015a5b0be9ea9667d91c7a664 | 350e97793f89ddd4097b97e0c4af735a5144be24 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1957 | |||||
| 173170675 | MDExOlB1bGxSZXF1ZXN0MTczMTcwNjc1 | 1968 | closed | 0 | einsum for xarray | fujiisoup 6815844 | - [x] Closes #1951 - [x] Tests added - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) Currently, lazy-einsum for dask is not yet working. @shoyer I think `apply_ufunc` supports lazy computation, but I did not yet figure out how to do this. Can you give me a help? | 2018-03-06T14:18:22Z | 2018-03-12T06:42:12Z | 2018-03-12T06:42:08Z | 2018-03-12T06:42:08Z | 8271dffc63ec2b12fa81b11381981c9f900449e7 | 0 | 2bd06ef56d6a4aca4fc742fd9a6ad85d9f3e25bd | aa83d0ec5a0da9e8880d3194864ff212d5990d6b | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1968 | ||||
| 175403318 | MDExOlB1bGxSZXF1ZXN0MTc1NDAzMzE4 | 1994 | closed | 0 | Make constructing slices lazily. | fujiisoup 6815844 | - [x] Closes #1993 - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes. Quick fix of #1993. With this fix, the script shown in #1993 runs Bottleneck: 0.08317923545837402 s Pandas: 1.3338768482208252 s Xarray: 1.1349339485168457 s | 2018-03-15T23:15:26Z | 2018-03-18T08:56:31Z | 2018-03-18T08:56:27Z | 2018-03-18T08:56:27Z | 1d0fbe6fe36d5e8a650d416cce85e7994b32e796 | 0 | 3f8aad371b8b06ebe2e620952954e6568b345fb2 | e1dc51572e971567fd3562db0e9f662e3de80898 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/1994 | ||||
| 184495028 | MDExOlB1bGxSZXF1ZXN0MTg0NDk1MDI4 | 2087 | closed | 0 | Drop conflicted coordinate when assignment. | fujiisoup 6815844 | - [x] Closes #2068 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) After this, when assigning a dataarray to a dataset, non-dimensional and conflicted coordinates of the dataarray are dropped. example ``` In [2]: ds = xr.Dataset({'da': ('x', [0, 1, 2])}, ...: coords={'y': (('x',), [0.1, 0.2, 0.3])}) ...: ds ...: Out[2]: <xarray.Dataset> Dimensions: (x: 3) Coordinates: y (x) float64 0.1 0.2 0.3 Dimensions without coordinates: x Data variables: da (x) int64 0 1 2 In [3]: other = ds['da'] ...: other['y'] = 'x', [0, 1, 2] # conflicted non-dimensional coordinate ...: ds['da'] = other ...: ds ...: Out[3]: <xarray.Dataset> Dimensions: (x: 3) Coordinates: y (x) float64 0.1 0.2 0.3 # 'y' is not overwritten Dimensions without coordinates: x Data variables: da (x) int64 0 1 2 ``` | 2018-04-27T00:12:43Z | 2018-05-02T05:58:41Z | 2018-05-02T02:31:02Z | 2018-05-02T02:31:02Z | 0cc64a08c672e6361d05acea3fea9f34308b62ed | 0 | 6e26ad1df7ba1309cd547896b3c571e2dd5b2a40 | d1e1440dc5d0bc9c341da20fde85b56f2a3c1b5b | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2087 | ||||
| 185343180 | MDExOlB1bGxSZXF1ZXN0MTg1MzQzMTgw | 2100 | closed | 0 | Fix a bug introduced in #2087 | fujiisoup 6815844 | - [x] Closes #2099 - [x] Tests added - [x] Tests passed A quick fix for #2099 | 2018-05-02T06:07:01Z | 2018-05-14T00:01:15Z | 2018-05-02T21:59:34Z | 2018-05-02T21:59:34Z | b9f40cc1da9c45b3dd33a3434b69c3d8fce57138 | 0 | 3a830bf8aeb97a25c40517a54efa4ca66b7e42dd | 0cc64a08c672e6361d05acea3fea9f34308b62ed | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2100 | ||||
| 185983977 | MDExOlB1bGxSZXF1ZXN0MTg1OTgzOTc3 | 2104 | closed | 0 | implement interp() | fujiisoup 6815844 | - [x] Closes #2079 (remove if there is no corresponding issue, which should only be the case for minor changes) - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) I started working to add `interpolate_at` to xarray, as discussed in issue #2079 (but without caching). I think I need to take care of more edge cases, but before finishing up this PR, I want to discuss what the best API is. I would like to this method working similar to `isel`, which may support *vectorized* interpolation. Currently, this works as follwos ```python In [1]: import numpy as np ...: import xarray as xr ...: ...: da = xr.DataArray([0, 0.1, 0.2, 0.1], dims='x', coords={'x': [0, 1, 2, 3]}) ...: In [2]: # simple linear interpolation ...: da.interpolate_at(x=[0.5, 1.5]) ...: Out[2]: <xarray.DataArray (x: 2)> array([0.05, 0.15]) Coordinates: * x (x) float64 0.5 1.5 In [3]: # with cubic spline interpolation ...: da.interpolate_at(x=[0.5, 1.5], method='cubic') ...: Out[3]: <xarray.DataArray (x: 2)> array([0.0375, 0.1625]) Coordinates: * x (x) float64 0.5 1.5 In [4]: # interpolation at one single position ...: da.interpolate_at(x=0.5) ...: Out[4]: <xarray.DataArray ()> array(0.05) Coordinates: x float64 0.5 In [5]: # interpolation with broadcasting ...: da.interpolate_at(x=xr.DataArray([[0.5, 1.0], [1.5, 2.0]], dims=['y', 'z'])) ...: Out[5]: <xarray.DataArray (y: 2, z: 2)> array([[0.05, 0.1 ], [0.15, 0.2 ]]) Coordinates: x (y, z) float64 0.5 1.0 1.5 2.0 Dimensions without coordinates: y, z In [6]: da = xr.DataArray([[0, 0.1, 0.2], [1.0, 1.1, 1.2]], ...: dims=… | 2018-05-04T13:28:38Z | 2018-06-11T13:01:21Z | 2018-06-08T00:33:52Z | 2018-06-08T00:33:52Z | e39729928544204894e65c187d66c1a2b1900fea | 0 | 60e2ca3b056a623b1e35042f7fc3d13668c11fa5 | 21a9f3d7e3a5dd729aeafd08dda966c365520965 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2104 | ||||
| 187477383 | MDExOlB1bGxSZXF1ZXN0MTg3NDc3Mzgz | 2119 | closed | 0 | Support keep_attrs for apply_ufunc for xr.Variable | fujiisoup 6815844 | - [x] Closes #2114 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fixes 2114. | 2018-05-11T14:18:51Z | 2018-05-11T22:54:48Z | 2018-05-11T22:54:44Z | 2018-05-11T22:54:43Z | d63001cdbc3bd84f4d6d90bd570a2215ea9e5c2e | 0 | 6f9094f0741403c021774b605eadbc8315dc2630 | 6d8ac11ca0a785a6fe176eeca9b735c321a35527 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2119 | ||||
| 187600342 | MDExOlB1bGxSZXF1ZXN0MTg3NjAwMzQy | 2122 | closed | 0 | Fixes centerized rolling with bottleneck | fujiisoup 6815844 | - [x] Closes #2113 - [x] Tests added (for all bug fixes or enhancements) - [x] Tests passed (for all non-documentation changes) - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Two bugs were found and fixed. 1. rolling a dask-array with center=True and bottleneck 2. rolling an integer dask-array with bottleneck | 2018-05-12T02:28:21Z | 2018-05-13T00:27:56Z | 2018-05-12T06:15:55Z | 2018-05-12T06:15:55Z | a52540505f606bd7619536d82d43f19f2cbe58b5 | 0 | fc1c2f1987079c6e63fabd6b771693f7bd79894f | d63001cdbc3bd84f4d6d90bd570a2215ea9e5c2e | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2122 | ||||
| 187657188 | MDExOlB1bGxSZXF1ZXN0MTg3NjU3MTg4 | 2124 | closed | 0 | Raise an Error if a coordinate with wrong size is assigned to a dataarray | fujiisoup 6815844 | - [x] Closes #2112 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API. Now uses `dataset_merge_method` when a new coordinate is assigned to a xr.DataArray | 2018-05-13T07:50:15Z | 2018-05-16T02:10:48Z | 2018-05-15T16:39:22Z | 2018-05-15T16:39:22Z | 9a48157b525d9e346e73f358a99ceb52717fd3ea | 0 | 923cd1363d424c1904fbe0b6deac051d81361551 | ebe0dd03187a5c3138ea12ca4beb13643679fe21 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2124 | ||||
| 187657264 | MDExOlB1bGxSZXF1ZXN0MTg3NjU3MjY0 | 2125 | closed | 0 | Reduce pad size in rolling | fujiisoup 6815844 | - [ ] Closes #N.A. - [x] Tests added (for all bug fixes or enhancements) - [ ] Tests N.A. - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API I noticed `rolling` with dask array and with bottleneck can be slightly improved by reducing the padding depth in `da.ghost.ghost(a, depth=depth, boundary=boundary)`. @jhamman , can you kindly review this? | 2018-05-13T07:52:50Z | 2018-05-14T22:43:24Z | 2018-05-13T22:37:48Z | 2018-05-13T22:37:48Z | f861186cbd11bdbfb2aab8289118a59283a2d7af | 0 | a7adc7e20dd8909220a4bee79e15c7e1aeb95733 | ebe0dd03187a5c3138ea12ca4beb13643679fe21 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2125 | ||||
| 190509999 | MDExOlB1bGxSZXF1ZXN0MTkwNTA5OTk5 | 2185 | closed | 0 | weighted rolling mean -> weighted rolling sum | fujiisoup 6815844 | An example of weighted rolling mean in doc is actually weighted rolling *sum*. It is a little bit misleading [SO](https://stackoverflow.com/questions/50520835/xarray-simple-weighted-rolling-mean-example-using-construct/50524093#50524093), so I propose to change `weighted rolling mean` -> `weighted rolling sum` | 2018-05-25T08:03:59Z | 2018-05-25T10:38:52Z | 2018-05-25T10:38:48Z | 2018-05-25T10:38:48Z | 04df50efefecaea729133c14082eb5e24491633e | 0 | 428b970fb28e11ebc17a1d1780a107307ab00daa | b48e0969670f17857a314b5a755b1a1bf7ee38df | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2185 | ||||
| 191653297 | MDExOlB1bGxSZXF1ZXN0MTkxNjUzMjk3 | 2205 | closed | 0 | Support dot with older dask | fujiisoup 6815844 | - [x] Related with #2203 - [x] Tests added - [x] Tests passed - [x] Fully documented Related with #2203, I think it is better if `xr.DataArray.dot()` is working even with older dask, at least in the simpler case (as this is a very primary operation). The cost is a slight complication of the code. Any comments are welcome. | 2018-05-31T06:13:48Z | 2018-06-01T01:01:37Z | 2018-06-01T01:01:34Z | 2018-06-01T01:01:34Z | 9d60897a6544d3a2d4b9b3b64008b2bc316d8b98 | 0 | ea5d4e90e286b807f0289fee9b7605f08b1b5e55 | 7036eb5b629f2112da9aa13538aecb07f0f83f5a | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2205 | ||||
| 193486763 | MDExOlB1bGxSZXF1ZXN0MTkzNDg2NzYz | 2220 | closed | 0 | Reduce memory usage in doc.interpolation.rst | fujiisoup 6815844 | I noticed an example I added to doc in #2104 consumes more than 1 GB memory, and it results in the failing in readthedocs build. This PR changes this to a much lighter example. | 2018-06-08T01:23:13Z | 2018-06-08T01:45:11Z | 2018-06-08T01:31:19Z | 2018-06-08T01:31:19Z | 98e6a4b84dd2cf4296a3e0aa9710bb79411354e4 | 0 | 843893c4167eb85bfe2b70db33fd38b56b6743b4 | e39729928544204894e65c187d66c1a2b1900fea | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2220 | ||||
| 193762231 | MDExOlB1bGxSZXF1ZXN0MTkzNzYyMjMx | 2222 | closed | 0 | implement interp_like | fujiisoup 6815844 | - [x] Closes #2218 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API. This adds `interp_like`, that behaves like `reindex_like` but using interpolation. | 2018-06-09T06:46:48Z | 2018-06-20T01:39:40Z | 2018-06-20T01:39:24Z | 2018-06-20T01:39:23Z | 59ad782f29a0f4766bac7802be6650be61f018b8 | 0 | 134bf835f010cc86e59b299f23914d013565d1f9 | 66be9c5db7d86ea385c3a4cd4295bfce67e3f25b | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2222 | ||||
| 195508617 | MDExOlB1bGxSZXF1ZXN0MTk1NTA4NjE3 | 2236 | closed | 0 | Refactor nanops | fujiisoup 6815844 | - [x] Closes #2230 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) In #2230, the addition of `min_count` keywords for our reduction methods was discussed, but our `duck_array_ops` module is becoming messy (mainly due to nan-aggregation methods for dask, bottleneck and numpy) and it looks a little hard to maintain them. I tried to refactor them by moving nan-aggregation methods to `nanops` module. I think I still need to take care of more edge cases, but I appreciate any comment for the current implementation. Note: In my implementation, **bottleneck is not used when `skipna=False`**. bottleneck would be advantageous when `skipna=True` as numpy needs to copy the entire array once, but I think numpy's method is still OK if `skipna=False`. | 2018-06-18T12:27:31Z | 2018-09-26T12:42:55Z | 2018-08-16T06:59:33Z | 2018-08-16T06:59:33Z | 0b9ab2d12ae866a27050724d94facae6e56f5927 | 0 | b72a1c852add254a4cdd49408fe4e9c934ceece6 | 4df048c146b8da7093faf96b3e59fb4d56945ec5 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2236 | ||||
| 204585059 | MDExOlB1bGxSZXF1ZXN0MjA0NTg1MDU5 | 2326 | closed | 0 | fix doc build error after #2312 | fujiisoup 6815844 | I merged #2312 without making sure the building test passing, but there was a typo. Ths PR fixes it. | 2018-07-28T09:15:20Z | 2018-07-28T10:05:53Z | 2018-07-28T10:05:50Z | 2018-07-28T10:05:50Z | ded0a684136540962bcc409e6272b1cebb5af30a | 0 | 326dc820ea662972599d522f36bdbf1b7565f21c | 2fa9dded34e06104379ad1a12c6967913998889b | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2326 | ||||
| 206224273 | MDExOlB1bGxSZXF1ZXN0MjA2MjI0Mjcz | 2342 | closed | 0 | apply_ufunc now raises a ValueError when the size of input_core_dims is inconsistent with number of argument | fujiisoup 6815844 | - [x] Closes #2341 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Now raises a ValueError when the size of input_core_dims is inconsistent with number of argument. | 2018-08-05T06:20:03Z | 2018-08-06T22:38:57Z | 2018-08-06T22:38:53Z | 2018-08-06T22:38:53Z | 0b181226bbb1c26adfdd5d47d567fb78d0a450fa | 0 | 1e64344507c9db30ba746e29369d299fda39e61d | 56381ef444c5e699443e8b4e08611060ad5c9507 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2342 | ||||
| 206226854 | MDExOlB1bGxSZXF1ZXN0MjA2MjI2ODU0 | 2343 | closed | 0 | local flake8 | fujiisoup 6815844 | Trivial changes to pass local flake8 tests. | 2018-08-05T07:47:38Z | 2018-08-05T23:47:00Z | 2018-08-05T23:47:00Z | 2018-08-05T23:47:00Z | f217a7d8675062aff14f3dc6fb008af0cba8da49 | 0 | 1b275b5737287860d6c68614d32f891150bf1f11 | 56381ef444c5e699443e8b4e08611060ad5c9507 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2343 | ||||
| 206537474 | MDExOlB1bGxSZXF1ZXN0MjA2NTM3NDc0 | 2349 | closed | 0 | dask.ghost -> dask.overlap | fujiisoup 6815844 | Dask renamed `dask.ghost` -> `dask.overlap` in dask/dask#3830. This PR follows up this. | 2018-08-06T22:54:46Z | 2018-08-08T01:14:04Z | 2018-08-08T01:14:02Z | 2018-08-08T01:14:02Z | 04458670782c0b6fdba7e7021055155b2a6f284a | 0 | 108381f9c88526e676fff193a4a7f70e7c9204ec | 0b181226bbb1c26adfdd5d47d567fb78d0a450fa | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2349 | ||||
| 206864758 | MDExOlB1bGxSZXF1ZXN0MjA2ODY0NzU4 | 2353 | closed | 0 | Raises a ValueError for a confliction between dimension names and level names | fujiisoup 6815844 | - [x] Closes #2299 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API. Now it raises an Error to assign new dimension with the name conflicting with an existing level name. Therefore, it is not allowed ```python b = xr.Dataset(coords={'dim0': ['a', 'b'], 'dim1': [0, 1]}) b = b.stack(dim_stacked=['dim0', 'dim1']) # This should raise an errors even though its length is consistent with `b['dim0']` b['c'] = (('dim0',), [10, 11, 12, 13]) # This is OK b['c'] = (('dim_stacked',), [10, 11, 12, 13]) ``` | 2018-08-08T00:52:29Z | 2018-08-13T22:16:36Z | 2018-08-13T22:16:31Z | 2018-08-13T22:16:31Z | e3350fd724c30bb3695f755316f9b840445a0af6 | 0 | 82475aff193036c4b1493081414fc66befbfc150 | 846e28f8862b150352512f8e3d05bcb9db57a1a3 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2353 | ||||
| 206867230 | MDExOlB1bGxSZXF1ZXN0MjA2ODY3MjMw | 2354 | closed | 0 | Mark some tests related to cdat-lite as xfail | fujiisoup 6815844 | I just mark some to_cdms2 tests xfail. See #2332 for the details. It is a temporal workaround and we may need to keep #2332 open until it is solved. | 2018-08-08T01:13:25Z | 2018-08-10T16:09:30Z | 2018-08-10T16:09:30Z | 2018-08-10T16:09:30Z | fe99a22ca7bcb1f854c22f5f6894d3c5d40774a6 | 0 | b81ca434f6b93c8eca5d44b16f5a03ec060db382 | 04458670782c0b6fdba7e7021055155b2a6f284a | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2354 | ||||
| 208144841 | MDExOlB1bGxSZXF1ZXN0MjA4MTQ0ODQx | 2366 | closed | 0 | Future warning for default reduction dimension of groupby | fujiisoup 6815844 | - [ ] Closes #xxxx - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Started to fix #2363. Now warns a futurewarning in groupby if default reduction dimension is not specified. As a side effect, I added `xarray.ALL_DIMS`. With `dim=ALL_DIMS` always reduces along all the dimensions. | 2018-08-14T01:16:34Z | 2018-09-28T06:54:30Z | 2018-09-28T06:54:30Z | 2018-09-28T06:54:30Z | 638b251c622359b665208276a2cb23b0fbc5141b | 0 | 68d7c04e5ebf1e6c7c34a71ee73da6a7f30ca4a2 | 04253f271c66a12366a82d357c2a889dd3eea42f | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2366 | ||||
| 209078448 | MDExOlB1bGxSZXF1ZXN0MjA5MDc4NDQ4 | 2372 | closed | 0 | [MAINT] Avoid using duck typing | fujiisoup 6815844 | - [x] Closes #2179 - [x] Tests passed - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) | 2018-08-17T08:26:31Z | 2018-08-20T01:13:26Z | 2018-08-20T01:13:16Z | 2018-08-20T01:13:16Z | 8378d3af259d7d1907359fc087dd0a6ca7e5ef17 | 0 | 6b206c771b7ebe1bf6eeed7ef0cb50fffbf8df9e | 0b9ab2d12ae866a27050724d94facae6e56f5927 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2372 | ||||
| 209145472 | MDExOlB1bGxSZXF1ZXN0MjA5MTQ1NDcy | 2373 | closed | 0 | More support of non-string dimension names | fujiisoup 6815844 | - [x] Tests passed (for all non-documentation changes) Following to #2174 In some methods, consistency of the dictionary arguments and keyword arguments are checked twice in `Dataset` and `Variable`. Can we change the API of Variable so that it does not take kwargs-type argument for dimension names? | 2018-08-17T13:18:18Z | 2018-08-20T01:13:02Z | 2018-08-20T01:12:37Z | 2018-08-20T01:12:37Z | 725bd57ffa64d7e391ceef2b056fa8122ec09e8d | 0 | 48a2f3170b907f2e2253fd484d1b323a1f1b51ad | 0b9ab2d12ae866a27050724d94facae6e56f5927 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2373 | ||||
| 212889732 | MDExOlB1bGxSZXF1ZXN0MjEyODg5NzMy | 2398 | closed | 0 | implement Gradient | fujiisoup 6815844 | - [x] Closes #1332 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Added `xr.gradient`, `xr.DataArray.gradient`, and `xr.Dataset.gradient` according to #1332. | 2018-09-04T08:11:52Z | 2018-09-21T20:02:43Z | 2018-09-21T20:02:43Z | 2018-09-21T20:02:43Z | ab96954883200f764a0dd50870e4db240c119265 | 0 | 528bcab00920a40a49643d412ea0d9c8a2d2102c | 66a8f8dd7f5a2997ff614f3966d1951587915e7e | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2398 | ||||
| 218706745 | MDExOlB1bGxSZXF1ZXN0MjE4NzA2NzQ1 | 2446 | closed | 0 | fix:2445 | fujiisoup 6815844 | - [x] Closes #2445 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes It is a regression after #2360. | 2018-09-27T16:00:17Z | 2018-09-28T18:24:42Z | 2018-09-28T18:24:36Z | 2018-09-28T18:24:35Z | 23d1cda3b7da5c73a5f561a5c953b50beaa2bfe6 | 0 | a49e5d7afa60b53a4c4ee1f65443231577fecbcd | c2b09d697c741b5d6ddede0ba01076c0cb09cf19 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2446 | ||||
| 218721452 | MDExOlB1bGxSZXF1ZXN0MjE4NzIxNDUy | 2447 | closed | 0 | restore ddof support in std | fujiisoup 6815844 | - [x] Closes #2440 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes It looks that I wrongly remove `ddof` option for `nanstd` in #2236. This PR fixes this. | 2018-09-27T16:51:44Z | 2018-10-03T12:44:55Z | 2018-09-28T13:44:29Z | 2018-09-28T13:44:29Z | 458cf51ce20e8d924b38b59c8fbc3bb10f39148e | 0 | a50d8ac2f39ed996b25793c50946ccef90ce5974 | 78058e2c1f39cbfae6eddb30e3b7d4a81b54ad8b | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2447 | ||||
| 220272833 | MDExOlB1bGxSZXF1ZXN0MjIwMjcyODMz | 2462 | closed | 0 | pep8speaks | fujiisoup 6815844 | - [x] Closes #2428 I installed pep8speaks as suggested in #2428. It looks they do not need a yml file, but it may be safer to add this (just renamed from `.stickler.yml`) | 2018-10-04T07:17:34Z | 2018-10-07T22:40:15Z | 2018-10-07T22:40:08Z | 2018-10-07T22:40:08Z | cf1e6c73d0366124485c1d767b89ac1cc301705b | 0 | 9b620892593672e881cb91f22431179ddde05508 | bb87a9441d22b390e069d0fde58f297a054fd98a | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2462 | ||||
| 221311770 | MDExOlB1bGxSZXF1ZXN0MjIxMzExNzcw | 2477 | closed | 0 | Inhouse LooseVersion | fujiisoup 6815844 | - [x] Closes #2468 - [x] Tests added - [N.A.] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later) A fix for #2468. | 2018-10-09T05:23:56Z | 2018-10-10T13:47:31Z | 2018-10-10T13:47:23Z | 2018-10-10T13:47:23Z | 7f20a20aa278d2bb056403d665c10e29968755cd | 0 | 7aec9fe1517c74f2711289d073b40d46fff0e233 | 289b377129b18e7dc6da8336e958a85be868acbe | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2477 | ||||
| 238972759 | MDExOlB1bGxSZXF1ZXN0MjM4OTcyNzU5 | 2612 | closed | 0 | Added Coarsen | fujiisoup 6815844 | - [x] Closes #2525 - [x] Tests added - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Started to implement `corsen`. The API is currently something like ```python actual = ds.coarsen(time=2, x=3, side='right', coordinate_func={'time': np.max}).max() ``` Currently, it is not working for a datetime coordinate, since `mean` does not work for this dtype. e.g. ```python da = xr.DataArray(np.linspace(0, 365, num=365), dims='time', coords={'time': pd.date_range('15/12/1999', periods=365)}) da['time'].mean() # -> TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('<M8[ns]') ``` I am not familiar with datetime things. Any advice will be appreciated. | 2018-12-16T15:28:31Z | 2019-01-06T09:13:56Z | 2019-01-06T09:13:46Z | 2019-01-06T09:13:46Z | ede3e0101bae2f45c3f4634a1e1ecb8e2ccd0258 | 0 | 1523292b876ec5578b806c9c2cc43ce80d73a061 | dba299befbdf19b02612573b218bcc1e97d4e010 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2612 | ||||
| 239784815 | MDExOlB1bGxSZXF1ZXN0MjM5Nzg0ODE1 | 2621 | closed | 0 | Fix multiindex selection | fujiisoup 6815844 | - [x] Closes #2619 - [x] Tests added - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fix using ` MultiIndex.remove_unused_levels()` | 2018-12-19T10:30:15Z | 2018-12-24T15:37:27Z | 2018-12-24T15:37:27Z | 2018-12-24T15:37:27Z | b5059a538ee2efda4d753cc9a49f8c09cd026c19 | 0 | 61d1d494ab370126519fbb9285014f947f2dfe2b | c2ce5ea83b5924302653c8dfba7de68c7d98c942 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2621 | ||||
| 242435203 | MDExOlB1bGxSZXF1ZXN0MjQyNDM1MjAz | 2653 | closed | 0 | Implement integrate | fujiisoup 6815844 | - [x] Closes #1288 - [x] Tests added - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API I would like to add `integrate`, which is essentially an xarray-version of `np.trapz`. I know there was variety of discussions in #1288, but I think it would be nice to limit us within that numpy provides by `np.trapz`, i.e., 1. only for `trapz` not `rectangle` or `simps` 2. do not care `np.nan` 3. do not support `bounds` Most of them (except for 1) can be solved by combining several existing methods. | 2019-01-05T11:22:10Z | 2019-01-31T17:31:31Z | 2019-01-31T17:30:31Z | 2019-01-31T17:30:31Z | 492303924f4573173029aa9cf5a785413ee9d2ed | 0 | 056111372d4c26cefe7d3bb9a40df86c406ec037 | ede3e0101bae2f45c3f4634a1e1ecb8e2ccd0258 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2653 | ||||
| 244162181 | MDExOlB1bGxSZXF1ZXN0MjQ0MTYyMTgx | 2668 | closed | 0 | fix datetime_to_numeric and Variable._to_numeric | fujiisoup 6815844 | - [x] Closes #2667 - [x] Tests added - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Started to fixing #2667 | 2019-01-11T22:02:07Z | 2019-02-11T11:58:22Z | 2019-02-11T09:47:09Z | 2019-02-11T09:47:09Z | 4cd56a9edb083a3eb8d11e7a367dfb9bda76fc2e | 0 | 0b266156c839a66166a06c110ffdc5e18fbe7571 | 6d2076688d4f5466cf77ace2b196e910c1c0fbb8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2668 | ||||
| 276346147 | MDExOlB1bGxSZXF1ZXN0Mjc2MzQ2MTQ3 | 2942 | closed | 0 | Fix rolling operation with dask and bottleneck | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #2940 - [x] Tests added - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Fix for #2940 It looks that there was a bug in the previous logic, but I am not sure why it was working... | 2019-05-06T21:23:41Z | 2019-06-30T00:34:57Z | 2019-06-30T00:34:57Z | 7ba929d15cc77c718c8dbb4f96582820fa98a861 | 0 | ca96cc3b709ef043a7fa54030c6ddf26da8b4089 | 5aaa6547cd14a713f89dfc7c22643d86fce87916 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/2942 | |||||
| 340541733 | MDExOlB1bGxSZXF1ZXN0MzQwNTQxNzMz | 3520 | closed | 0 | Fix set_index when an existing dimension becomes a level | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3512 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API There was a bug in `set_index`, where an old dimension was not updated if it becomes a level of MultiIndex. | 2019-11-13T16:06:50Z | 2019-11-14T11:56:25Z | 2019-11-14T11:56:18Z | 2019-11-14T11:56:18Z | c0ef2f616e87e9f924425bcd373ac265f14203cb | 0 | 18fa5ec46da318d76488ea2994e9654e9683bce9 | 8b240376fd91352a80b068af606850e8d57d1090 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3520 | ||||
| 341746408 | MDExOlB1bGxSZXF1ZXN0MzQxNzQ2NDA4 | 3541 | closed | 0 | Added fill_value for unstack | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3518 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Added an option `fill_value` for `unstack`. I am trying to add `sparse` option too, but it may take longer. Probably better to do in a separate PR? | 2019-11-16T11:10:56Z | 2019-11-16T14:42:31Z | 2019-11-16T14:36:44Z | 2019-11-16T14:36:43Z | 56c16e4bf45a3771fd9acba76d802c0199c14519 | 0 | 5c574ecebc76df7f1f55811acd7b7531ed8dba86 | 52d48450f6291716a90f4f7e93e15847942e0da0 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3541 | ||||
| 341761585 | MDExOlB1bGxSZXF1ZXN0MzQxNzYxNTg1 | 3542 | closed | 0 | sparse option to reindex and unstack | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3518 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Added `sparse` option to `reindex` and `unstack`. I just added a minimal set of codes necessary to `unstack` and `reindex`. There is still a lot of space to complete the sparse support as discussed in #3245. | 2019-11-16T14:41:00Z | 2019-11-19T22:40:34Z | 2019-11-19T16:23:34Z | 2019-11-19T16:23:34Z | 220adbc65e0b8c46feddaa6984df4a3a1ce0af6b | 0 | 92ce6cdbfc0ff59a1963933bdb46612908ab4de2 | 56c16e4bf45a3771fd9acba76d802c0199c14519 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3542 | ||||
| 344805747 | MDExOlB1bGxSZXF1ZXN0MzQ0ODA1NzQ3 | 3566 | closed | 0 | Make 0d-DataArray compatible for indexing. | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3562 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Now 0d-DataArray can be used for indexing. | 2019-11-23T12:43:32Z | 2023-08-31T02:06:21Z | 2023-08-31T02:06:21Z | ee41a090c44d58d89d2761d92e3ce84ecae3aacb | 0 | 2d738536efcbcbac3ff75aeb5bf680900cd0f886 | d1e4164f3961d7bbb3eb79037e96cae14f7182f8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3566 | |||||
| 347592715 | MDExOlB1bGxSZXF1ZXN0MzQ3NTkyNzE1 | 3587 | open | 0 | boundary options for rolling.construct | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #2007, #2011 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Added some boundary options for rolling.construct. Currently, the option names are inherited from `np.pad`, `['edge' | 'reflect' | 'symmetric' | 'wrap']`. Do we want a more intuitive name, such as `periodic`? | 2019-12-02T12:11:44Z | 2022-06-09T14:50:17Z | ad596b643eb6e63870b222debe1067821002460f | 0 | 56760379d32efc104541c9f3a0f0133e0fa916a4 | d1e4164f3961d7bbb3eb79037e96cae14f7182f8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3587 | ||||||
| 360395968 | MDExOlB1bGxSZXF1ZXN0MzYwMzk1OTY4 | 3670 | closed | 0 | sel with categorical index | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3669, #3674 - [x] Tests added - [x] Passes `black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API It is a bit surprising that no members have used xarray with CategoricalIndex... If there is anything missing additionally, please feel free to point it out. | 2020-01-08T10:51:06Z | 2020-01-25T22:38:28Z | 2020-01-25T22:38:21Z | 2020-01-25T22:38:20Z | cc142f430f9f468c990b6607ddf3424b0facf054 | 0 | 27f35059038c6ab74e6352932ac58759f2aca5b0 | 9c7286639136f52aee877f44de8c89d7c8f41068 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3670 | ||||
| 400511693 | MDExOlB1bGxSZXF1ZXN0NDAwNTExNjkz | 3953 | closed | 0 | Fix wrong order of coordinate converted from pd.series with MultiIndex | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3951 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API It looks `dataframe.set_index(index).index == index` is not always true. Added a workaround for this... | 2020-04-07T21:28:04Z | 2020-04-08T05:49:46Z | 2020-04-08T02:19:11Z | 2020-04-08T02:19:10Z | 1eedc5c146d9e6ebd46ab2cc8b271e51b3a25959 | 0 | b79a96e506e02e549255c6afdd8eeefe6c37b102 | f07adb293e67ae01d305fd1c8fb42f5bad2238e7 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/3953 | ||||
| 413872842 | MDExOlB1bGxSZXF1ZXN0NDEzODcyODQy | 4036 | closed | 0 | support darkmode | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #4024 - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Now it looks like  I'm pretty sure that this workaround is not the best (maybe the second worst), as it only supports the dark mode of vscode but not other environments. I couldn't find a good way to make a workaround for the general dark-mode. Any advice is welcome. | 2020-05-06T04:39:07Z | 2020-05-21T21:06:15Z | 2020-05-07T20:36:32Z | 2020-05-07T20:36:32Z | 69548df9826cde9df6cbdae9c033c9fb1e62d493 | 0 | 6cd140ba5924d067c77a30552c524a3f88206b4d | 9ec3f7b44d50ffa2298a9796847e69953ae96cbd | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/4036 | ||||
| 418912877 | MDExOlB1bGxSZXF1ZXN0NDE4OTEyODc3 | 4069 | closed | 0 | Improve interp performance | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #2223 - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API Now n-dimensional interp works sequentially if possible. It may speed up some cases. | 2020-05-16T04:23:47Z | 2020-05-25T20:02:41Z | 2020-05-25T20:02:37Z | 2020-05-25T20:02:36Z | d1f7cb8fd95d588d3f7a7e90916c25747b90ad5a | 0 | 1a7d738ea82cf714a28b4b2f8dcdc711d5c39fc6 | 2542a63f6ebed1a464af7fc74b9f3bf302925803 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/4069 | ||||
| 447892617 | MDExOlB1bGxSZXF1ZXN0NDQ3ODkyNjE3 | 4219 | closed | 0 | nd-rolling | fujiisoup 6815844 | - [x] Closes #4196 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [ ] New functions/methods are listed in `api.rst` I noticed that the implementation of nd-rolling is straightforward. The core part is implemented but I am wondering what the best API is, with keeping it backward-compatible. Obviously, it is basically should look like ```python da.rolling(x=3, y=3).mean() ``` A problem is other parameters, `centers` and `min_periods`. In principle, they can depend on dimension. For example, we can have `center=True` only for `x` but not for `y`. So, maybe we allow dictionary for them? ```python da.rolling(x=3, y=3, center={'x': True, 'y': False}, min_periods={'x': 1, 'y': None}).mean() ``` The same thing happens for `.construct` method. ```python da.rolling(x=3, y=3).construct(x='x_window', y='y_window', stride={'x': 2, 'y': 1}) ``` I'm afraid if this dictionary argument was a bit too redundant. Does anyone have another idea? | 2020-07-12T12:19:19Z | 2020-08-08T07:23:51Z | 2020-08-08T04:16:27Z | 2020-08-08T04:16:27Z | 1d3dee08291c83d13c46c9b4ede99020942df2f1 | 0 | f44dd5db5ee54cb01f1c6cb6a3d662f93932cd1d | e04e21d6160f43bc44e999b6f54f9fe4682f9b81 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/4219 | ||||
| 465085685 | MDExOlB1bGxSZXF1ZXN0NDY1MDg1Njg1 | 4329 | closed | 0 | ndrolling repr fix | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #4328 - [x] Tests added - [x] Passes `isort . && black . && mypy . && flake8` There was a bug in `rolling.__repr__` but it was not tested. Fixed and tests are added. | 2020-08-08T23:34:37Z | 2020-08-09T13:15:50Z | 2020-08-09T11:57:38Z | 2020-08-09T11:57:37Z | df7b2eae3a26c1e86bd5f1dd7dab9cc8c4e53914 | 0 | 3b9cf9819679a9080a26ba469b78563981a3a9d1 | f02ca53714de06a4fc035f9dbc75b55be6fa3297 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/4329 | ||||
| 581821524 | MDExOlB1bGxSZXF1ZXN0NTgxODIxNTI0 | 4974 | closed | 0 | implemented pad with new-indexes | fujiisoup 6815844 | <!-- Feel free to remove check-list items aren't relevant to your change --> - [x] Closes #3868 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` Now we use a tuple of indexes for `DataArray.pad` and `Dataset.pad`. | 2021-03-01T07:50:08Z | 2023-09-14T02:47:24Z | 2023-09-14T02:47:24Z | 1c150b58f2d05749bcec5de1a10889289e390b85 | 0 | 30391c64c809686bfefd3bb878ca66eaf86016a5 | d1e4164f3961d7bbb3eb79037e96cae14f7182f8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/4974 |
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