NumPy

Motivation

Currently lots of C++ based unit tests are executed directly from test.sh/win-test.sh for example:
https://github.com/pytorch/pytorch/blob/0bd8d0951dcb4063c0f7552a7404bd7f0e7b6e6f/.jenkins/pytorch/test.sh#L317

Which have following drawbacks:

• It excluded those test runtime from auto-sharding/auto-categorization
• Make them subject of running on only particular platform (

I notice that there are still a fair number LGTM alerts, many of which look pretty simple. I wonder if they would be a good source of issues for the next NumPy sprint?

dask.data

In [12]: import dask.dataframe as dd, pandas as pd

In [13]: df = dd.from_pandas(pd.DataFrame({"A": [1, 2]}), npartitions=1)

In [14]: df.head()
/home/taugspurger/miniconda3/envs/stac-table/lib/python3.9/site-packages/dask/dataframe/core.py:6778: UserWarning: Insufficient elements for `head`. 5 elements requested, only 2 elements available. Try passing larger `npartiti

The conversion for np.ascontiguousarray doesn't appear to handle scalar inputs:

import numpy as np
import numba as nb


@nb.njit
def test_fn(x):
    return np.ascontiguousarray(x)


test_fn(1)
# ...
# TypingError: Failed in nopython mode pipeline (step: nopython frontend)
# No implementation of function Function(<function ascontiguousarray at 0x7fdd294aab00>) found for si

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

What's your use case?
In other words, what's your pain point?

Variable names and their icons are shown as vertical header. This

• is ugly,
• doesn't show the selection properly,
• doesn't allow sorting by variable names,
• doesn't allow selection by dragging across a range of variables (though one can drag across rows in the table itself),
• and possibly something else.

<img wi

hi,
if possible, please add these indicators as well:
TDI (Traders Dynamic Index)
chandelier exit
pivot points
BOP (balance of power)
CTM (Chande trend meter)
Coppock Curve
Correlation Coefficient
PMO (DecisionPoint Price Momentum Oscillator)
Ulcer Index
most of them except TDI are available on stockcharts.com
thanks

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

Support Series.median()

As far as I know, there's no way to see what options are enabled.

It'd be nice to add either xr.get_options or xr.show_options or xr.options so that users can see what is enabled.

I think it would be nice to have a one-line description under each option

"arithmetic_join": "outer"
    controls DataArray/Dataset alignment in binary operations.