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| { | |
| "__inputs": [], | |
| "__requires": [ | |
| { | |
| "type": "grafana", | |
| "id": "grafana", | |
| "name": "Grafana", | |
| "version": "4.6.3" | |
| }, | |
| { |
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| FROM python:2-alpine | |
| RUN pip install \ | |
| beautifulsoup4 \ | |
| requests | |
| COPY papers.py /usr/local/bin/ | |
| RUN chmod +x /usr/local/bin/papers.py | |
| WORKDIR /root |
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| import dask | |
| import dask.array as da | |
| import dask.dataframe as dd | |
| import sparse | |
| @dask.delayed(pure=True) | |
| def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9): | |
| return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh)) | |
| def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9): |
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| import pandas as pd | |
| import dask.dataframe as dd | |
| import numpy as np | |
| import dask.array as da | |
| import inspect | |
| from numpydoc.docscrape import NumpyDocString | |
| import pydoc | |
| def maybe_build_signature(obj, name): |
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