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Installing pgextwlist

# Dockerfile
ENV PGEXTWLIST_VERSION 1.7
RUN wget -q -O pgextwlist-${PGEXTWLIST_VERSION}.tar.gz https://github.com/dimitri/pgextwlist/archive/v${PGEXTWLIST_VERSION}.tar.gz
RUN tar -xzf pgextwlist-${PGEXTWLIST_VERSION}.tar.gz && \
    cd pgextwlist-${PGEXTWLIST_VERSION} && \
    make -j${CPUS} && make install && \
 export pkg=$(pg_config --pkglibdir) && mkdir $pkg/plugins && cp $pkg/pgextwlist.so $pkg/plugins

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#!/usr/bin/env python
from pprint import pprint
from functools import reduce
import operator

import numpy as np

from affine import Affine
from rasterio.crs import CRS
import rasterio

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import random
import time
import json
import threading
import click


def send_message(msg, async=True):
    """Logs the message to an output stream.

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import sys


def log_calls(fh=sys.stderr):
    """Logs the function, args, kwargs and return value to a file handle
    """
    def decorator(func):
        def wrapper(*args, **kwargs):
            retval = func(*args, **kwargs)
            print(func, args, kwargs, retval, file=fh)

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FROM python:3.6-slim-stretch

ADD requirements.txt /tmp/requirements.txt

RUN apt-get update && \
    apt-get install -y \
        build-essential \
        make \
        gcc \
        locales \

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#!/usr/bin/env python
import warnings
from functools import wraps


def deprecated_kwarg(old, new, version):
    def decorator(func):
        @wraps(func)
        def func_wrapper(*args, **kwargs):
            new_kwargs = kwargs.copy()

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#!/usr/bin/env python
from fitparse import FitFile
import click
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator


def get_records(fitfile, fields=None):
    if fields is None:

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Parallelism and Concurrency in Python

There should be one-- and preferably only one --obvious way to do it.

-- Tim Peters, Zen of Python

When we need our programs to run faster, the first place we often look is parallelism and concurrency. By doing more things at once, we hope for significant speed gains. Python has a number of techniques optimized for different use cases; there is no one obvious way to do it! So how do you decide the correct approach?

Here's my quick take on the primary tools for parallel execution in Python, their strengths and weaknesses. I'll focus mainly on the tools in the standard library, but I'll touch on a few third-party modules that provide interesting features.

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all: deps clean install test

.PHONY: docs

install:
	python setup.py build_ext
	pip install -e .[all]

deps:
	pip install -r requirements-dev.txt