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# download iot core iso file | |
# curl http://go.microsoft.com/fwlink/?LinkId=616847 | |
curl http://download.microsoft.com/download/8/C/B/8CBE5D09-B5C5-462B-8043-DAC64938FDAC/IOT%20Core%20RPi.ISO > IOT-Core-RPi.iso | |
# install p7zip for decompress *.iso and *.msi files | |
sudo port install p7zip | |
# decompress files | |
7z x IOT-Core-RPi.ISO | |
7z x Windows_10_IoT_Core_RPi2.msi |
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<?xml version="1.0" encoding="UTF-8"?> | |
<configuration> | |
<system.webServer> | |
<staticContent> | |
<mimeMap fileExtension="woff" mimeType="application/font-woff" /> | |
<mimeMap fileExtension="woff2" mimeType="application/font-woff" /> | |
</staticContent> | |
<rewrite> | |
<rules> | |
<rule name="Handle History Mode and custom 404/500" stopProcessing="true"> |
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# https://stackoverflow.com/questions/25191620/ | |
# creating-lowpass-filter-in-scipy-understanding-methods-and-units | |
import numpy as np | |
from scipy.signal import butter, lfilter, freqz | |
from matplotlib import pyplot as plt | |
def butter_lowpass(cutoff, fs, order=5): | |
nyq = 0.5 * fs |
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/* | |
Written by Amit Seal Ami | |
*/ | |
#include <stdio.h> | |
#include <string.h> | |
/* | |
Instructions | |
============ |
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from flask import send_file | |
from pandas import DataFrame | |
class MyModelView(ModelView): | |
@expose('/export') | |
def export(self): | |
# Grab parameters from URL | |
page, sort_idx, sort_desc, search, filters = self._get_list_extra_args() | |
sort_column = self._get_column_by_idx(sort_idx) | |
if sort_column is not None: | |
sort_column = sort_column[0] |
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version: '2.3' | |
services: | |
kaggle: | |
image: kaggle/python-gpu-build | |
runtime: nvidia | |
container_name: kaggle | |
user: root | |
ports: | |
- "8888:8888" | |
volumes: |
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__author__ = 'artemr' | |
''' | |
왜 파이썬 데코레이터를 만들때, @wraps어노테이션을 쓰는 것을 권장하는 걸까? | |
이유인 즉슨, 데코레이터 내부에서 인자로 전달받은 함수가 익명함수 처럼 취급되어 버리므로 디버깅이 난해해지는 단점이 있었기 때문이다. | |
자세한 설명은 아래의 링크에 첨부되어 있다. | |
원본: http://artemrudenko.wordpress.com/2013/04/15/python-why-you-need-to-use-wraps-with-decorators/ | |
사본: https://www.evernote.com/shard/s174/sh/78eaad5f-a8f2-4496-b984-e3385fb963c0/922d9ab4b5cd23ac7b85aab42536aa4f | |
''' | |
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version: '2' | |
services: | |
kaggle: | |
image: kaggle/python | |
container_name: kaggle | |
command: jupyter notebook --allow-root --no-browser --ip="0.0.0.0" --notebook-dir=/notebooks --NotebookApp.token='' --allow-root | |
user: root | |
ports: | |
- "8888:8888" | |
volumes: |
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# Shell to use with Make | |
SHELL := /bin/bash | |
# Set important Paths | |
PROJECT := # Set to your project name | |
LOCALPATH := $(CURDIR)/$(PROJECT) | |
PYTHONPATH := $(LOCALPATH)/ | |
PYTHON_BIN := $(VIRTUAL_ENV)/bin | |
# Export targets not associated with files |
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