home / github

Menu
  • Search all tables
  • GraphQL API

issues

Table actions
  • GraphQL API for issues

3 rows where user = 5497186 sorted by updated_at descending

✖
✖

✎ View and edit SQL

This data as json, CSV (advanced)

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

type 1

  • issue 3

state 1

  • closed 3

repo 1

  • xarray 3
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
178887041 MDU6SXNzdWUxNzg4ODcwNDE= 1016 DataArray coords: tuple vs. list jonathanstrong 5497186 closed 0     2 2016-09-23T14:08:15Z 2018-10-31T16:56:47Z 2018-10-31T16:56:47Z NONE      

is there a reason the coords dictionary values cannot be a tuple? I found this fairly baffling to debug when it tripped me up.

```

this works

xarray.DataArray(np.random.random((3, 3, 3)), dims=('one', 'two', 'three'), coords={ 'one': ['four', 'five', 'six'], } )

this throws the following error

ValueError: dimensions ('four',) must have the same length as the number of data dimensions, ndim=0

xarray.DataArray(np.random.random((3, 3, 3)), dims=('one', 'two', 'three'), coords={ 'one': ('four', 'five', 'six'), } ) ```

even if there was a clearer error it would help quite a bit. As it stands you are thinking, 'what?! four isn't a dimension!'

using 0.8.2

{
    "url": "https://api.github.com/repos/pydata/xarray/issues/1016/reactions",
    "total_count": 2,
    "+1": 2,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  completed xarray 13221727 issue
134376872 MDU6SXNzdWUxMzQzNzY4NzI= 768 save/load DataArray to numpy npz functions jonathanstrong 5497186 closed 0     11 2016-02-17T19:29:31Z 2016-12-24T11:55:40Z 2016-12-24T11:55:40Z NONE      

hey -

Apologies if this is bad form: I wanted to pass this along but don't have time to do a proper pull request.

I have found pickle to be really problematic for serializing data, so wrote these two functions to save to numpy's binary npz format and retrieve it. Generally, the numpy format is much less likely to bomb when attempting to load on another computer because of some unseen dependency. If there's interest, I could probably add this as a serialization method to DataArray in the next month or so.

``` python def to_npz(da, file_or_buffer): if 'dims' in da.dims: raise ValueError('Can\'t use "dims" as a dim name.') if 'values' in da.dims: raise ValueError('Can\'t use "values" as a dim name.') arrays = {} arrays['dims'] = da.dims for dim in da.dims: arrays[dim] = da.indexes[dim] arrays['values'] = da.values np.savez(file_or_buffer, **arrays)

def from_npz(file_or_buffer): data = np.load(file_or_buffer) assert hasattr(data, 'keys'), "np.load returned a {}, not a dict-like object".format(type(data)) assert 'dims' in data, 'Can\'t locate "dims" key in file' assert 'values' in data, 'Can\'t locate "values" key in file' for dimname in data['dims']: assert dimname in data, 'Can\'t locate "{}" key in file'.format(dimname) return xray.DataArray(data['values'], dims=data['dims'], coords=dict(zip(data['dims'], [data[dimname] for dimname in data['dims']]))) ```

it's pretty speedy, here is an example for a (3, 4, 5) shaped DataArray:

In [42]: def save_and_load_again(da): with open('/path/to/datarray.npz', 'w') as f: to_npz(da, f) with open('/path/to/datarray.npz', 'r') as f: a = from_npz(f) return a %time (save_and_load_again(da) == da).all() CPU times: user 12.6 ms, sys: 0 ns, total: 12.6 ms Wall time: 26.2 ms Out[42]: <xray.DataArray ()> array(True, dtype=bool)

{
    "url": "https://api.github.com/repos/pydata/xarray/issues/768/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  completed xarray 13221727 issue
126205116 MDU6SXNzdWUxMjYyMDUxMTY= 716 pandas date_range as index causes TypeError on repr jonathanstrong 5497186 closed 0     2 2016-01-12T15:33:20Z 2016-01-13T22:17:00Z 2016-01-13T22:17:00Z NONE      

love this library btw, much thanks.

```

import pandas as pd import numpy as np import xray pd.version u'0.17.1' np.version '1.10.4' xray.version '0.6.1' import pytz import datetime datetime_index = pd.date_range(start=datetime.datetime.now(), tz=pytz.timezone('America/New_York'), periods=32, freq='1h') da = xray.DataArray(data=a, dims=['example', 'channel', 'row', 'column'], coords={'example': datetime_index, 'channel': np.arange(1), 'row': np.arange(28), 'column': np.arange(28)}) print da Traceback (most recent call last): [...] TypeError: data type not understood ```

{
    "url": "https://api.github.com/repos/pydata/xarray/issues/716/reactions",
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  completed xarray 13221727 issue

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]);
Powered by Datasette · Queries took 3667.303ms · About: xarray-datasette
  • Sort ascending
  • Sort descending
  • Facet by this
  • Hide this column
  • Show all columns
  • Show not-blank rows