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class CompilerLauncherMixin: | |
"""Add "compiler launchers" to distutils. | |
We use this to be able to run the Pandas build using "ccache". | |
A compiler launcher is a program that is invoked instead of invoking the | |
compiler directly. It is passed the full compiler invocation command line. | |
A similar feature exists in CMake, see | |
https://cmake.org/cmake/help/latest/prop_tgt/LANG_COMPILER_LAUNCHER.html. |
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def snowflake_fix_fetch( | |
df, | |
*, | |
parse_variant_columns=(), | |
lower_column_names=True, | |
convert_int64=True, | |
): | |
"""Apply Snowflake-specific fixes to a dataframe fetched from Snowflake. | |
- Lower all column names | |
- Parse VARIANT columns |
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from pyspark.sql import SparkSession | |
def local_pyspark_cluster(n_cpus=1, memory_mb=512) -> SparkSession: | |
"""Start a local PySpark cluster with default settings. | |
Returns a client to that session. | |
""" | |
return ( | |
SparkSession.builder.master(f"local[{n_cpus}]") |
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import pytest | |
def mark_fixture(mark, *args, **kwargs): | |
"""Decorator to mark a fixture. | |
Usage: | |
@mark_fixture(pytest.mark.slow, scope="session", ...) | |
def my_fixture(): | |
... |
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import torch | |
from torch.nn import * | |
def pointwise(in_channels, out_channels): | |
return Sequential( | |
Conv2d(in_channels, out_channels, 1, 1), | |
BatchNorm2d(out_channels), | |
ReLU(), | |
) |
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hop | dev | dtype | win | impl | error (dB) | |
---|---|---|---|---|---|---|
32 | cuda | torch.float32 | ones | torch.stft | 92 | |
32 | cuda | torch.float32 | ones | stft_fb | 30 | |
32 | cuda | torch.float32 | ones | torch_stft_fb | 92 | |
32 | cuda | torch.float32 | ones | librosa | 92 | |
32 | cuda | torch.float32 | librosa | torch.stft | 92 | |
32 | cuda | torch.float32 | librosa | stft_fb | 29 | |
32 | cuda | torch.float32 | librosa | torch_stft_fb | 92 | |
32 | cuda | torch.float32 | librosa | librosa | 92 | |
32 | cuda | torch.float32 | asteroid | torch.stft | 92 |
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import json | |
import os | |
import requests | |
from collections import Counter | |
username = "jonashaag" | |
password = "..." | |
cache_name = f"involves-{username}.json" | |
if not os.path.exists(cache_name): |
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def bin_freqs(x, nbins): | |
return ( | |
x.T.reshape((-1, int(x.shape[0] / nbins))).mean(axis=-1).reshape((-1, nbins)).T | |
) | |
def compact_freqs(x): | |
return np.vstack( | |
( | |
x[:16], |