issues

Table actions

5 rows where repo = 13221727, state = "closed" and user = 6645714 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: milestone, comments, updated_at, created_at (date), updated_at (date), closed_at (date)

type 2

state 1

  • closed · 5 ✖

repo 1

  • xarray · 5 ✖
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
199032440 MDU6SXNzdWUxOTkwMzI0NDA= 1193 Reading netcdf containing empty N-d string arrays causes exception petercable 6645714 closed 0     4 2017-01-05T19:00:22Z 2019-08-30T23:18:04Z 2019-08-30T23:18:04Z CONTRIBUTOR      

For example:

``` import xarray as xr import numpy as np

a = np.array([[]], 'str')

ds = xr.Dataset() ds['x'] = (['dim0', 'dim1'], a, {}) ds.to_netcdf('test.nc')

ds = xr.open_dataset('test.nc') ds.load() ```

The exception occurs in the call to conventions.char_to_string:

def char_to_string(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') kind = arr.dtype.kind if kind not in ['U', 'S']: raise ValueError('argument must be a string') return arr.view(kind + str(arr.shape[-1]))[..., 0]

... return arr.view(kind + str(arr.shape[-1]))[..., 0] TypeError: data-type must not be 0-sized

I am able to avoid the exception locally by testing that the last dimension is > 0 prior to creating a view:

def char_to_string(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') kind = arr.dtype.kind if kind not in ['U', 'S']: raise ValueError('argument must be a string') if arr.shape[-1] > 0: return arr.view(kind + str(arr.shape[-1]))[..., 0] return arr

 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/1193/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   completed xarray 13221727 issue
112385728 MDExOlB1bGxSZXF1ZXN0NDgyMTA5ODU= 632 Fix a couple of minor typos petercable 6645714 closed 0   0.6.1 1307323 1 2015-10-20T14:48:54Z 2015-10-21T07:08:00Z 2015-10-20T16:58:10Z CONTRIBUTOR   0 pydata/xarray/pulls/632 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/632/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
     xarray 13221727 pull
112424213 MDExOlB1bGxSZXF1ZXN0NDgyMzUyMTM= 633 Remove all mutable arguments from functions petercable 6645714 closed 0   0.6.1 1307323 3 2015-10-20T17:55:36Z 2015-10-21T07:08:00Z 2015-10-20T21:35:02Z CONTRIBUTOR   0 pydata/xarray/pulls/633

http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments

Not sure if you want this or not. I didn't see any cases where this was being used intentionally to store state between calls.

 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/633/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
     xarray 13221727 pull
100601294 MDU6SXNzdWUxMDA2MDEyOTQ= 526 64-bit values are coerced to 32-bit when writing as NETCDF4_CLASSIC petercable 6645714 closed 0     5 2015-08-12T18:06:42Z 2015-09-01T21:02:08Z 2015-09-01T21:02:08Z CONTRIBUTOR      

``` import xray import numpy

ds = xray.Dataset() time_data = numpy.arange(10.0, 100.0, .5) raw_data = time_data.copy() times = xray.Variable('time', time_data) data = xray.DataArray(raw_data, dims=['time'], coords={'time': times}) ds['data'] = data ```

If you examine ds now, you’ll see:

<xray.Dataset> Dimensions: (time: 180) Coordinates: * time (time) float64 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ... Data variables: data (time) float64 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ...

Now, if we dump to netcdf and reload:

ds.to_netcdf('test1.nc', format='NETCDF4_CLASSIC') ds1 = xray.open_dataset('test1.nc')

The formerly 64-bit values are now 32-bit:

<xray.Dataset> Dimensions: (time: 180) Coordinates: * time (time) float32 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ... Data variables: data (time) float32 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ...

It looks to me like coerce_nc3_dtype is being called for all formats that aren't straight NETCDF4.

 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/526/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   completed xarray 13221727 issue
100692054 MDExOlB1bGxSZXF1ZXN0NDIzMTU4Nzg= 529 Issue #526 F8 values are incorrectly coerced to F4 when writing NETCD… petercable 6645714 closed 0   0.6 1213895 3 2015-08-13T05:18:22Z 2015-08-19T18:29:04Z 2015-08-19T18:28:59Z CONTRIBUTOR   0 pydata/xarray/pulls/529

…F3-compatible formats

 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/529/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
     xarray 13221727 pull

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issues] (
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [number] INTEGER,
   [title] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [state] TEXT,
   [locked] INTEGER,
   [assignee] INTEGER REFERENCES [users]([id]),
   [milestone] INTEGER REFERENCES [milestones]([id]),
   [comments] INTEGER,
   [created_at] TEXT,
   [updated_at] TEXT,
   [closed_at] TEXT,
   [author_association] TEXT,
   [active_lock_reason] TEXT,
   [draft] INTEGER,
   [pull_request] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [state_reason] TEXT,
   [repo] INTEGER REFERENCES [repos]([id]),
   [type] TEXT
);
CREATE INDEX [idx_issues_repo]
    ON [issues] ([repo]);
CREATE INDEX [idx_issues_milestone]
    ON [issues] ([milestone]);
CREATE INDEX [idx_issues_assignee]
    ON [issues] ([assignee]);
CREATE INDEX [idx_issues_user]
    ON [issues] ([user]);