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"""
 Prints CSV of all fields of a model.
"""

import csv
import logging
import sys

from django.core.management.base import BaseCommand

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import $ from "jquery";
import plugin from "./plugin";

class ScrollToTop {
	constructor(element, options) {
		const $element = $(element);

		$(window).scroll(function () {
			if ($(this).scrollTop() > options.offset) {
				$element.fadeIn();

edison7500

import $ from "jquery";

/**
 * Generate a jQuery plugin
 * @param pluginName [string] Plugin name
 * @param className [object] Class of the plugin
 * @param shortHand [bool] Generate a shorthand as $.pluginName
 *
 * @example
 * import plugin from 'plugin';

edison7500

# how to calculate BMI 
def BMI(height, weight): 
    bmi = weight/(height**2) 
    return bmi 
  
# Driver code 
height = 1.79832
weight = 70
  
# calling the BMI function 

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import re
import logging
from django.conf import settings
from django.urls import LocaleRegexURLResolver
from django.utils.translation import get_language

logger = logging.getLogger("django")


class NoPrefixLocaleRegexURLResolver(LocaleRegexURLResolver):

edison7500

Raspberry Pi InfluxDB: The solution for IoT Data storage

Raspberry Pi is costeffect linux computer very commonly used for IoT home automation projects.

Here are the 3 problems with conventional databases for IoT data store applications:

• Too much or complex configuration
• Unable to expire data / set retentional policies
• Not tailor made of Time Series Data

edison7500

from pprint import pprint 


words = {'Bakkt': 2, 'Telegram': 1, 'Dfinity': 2, '大公司': 5, 'EOS': 2, 'BTC': 7, '开发者': 4, '联盟链': 1, '期货合约': 1, '投资': 3, 'ETH': 2, '安全': 1, '矿业': 3, '交易所': 1, '政策': 1, '公链': 1, '分叉': 1}

# sorted with value
sorted_words = sorted(words.items(), key=lambda x: x[1], reverse=True) 

pprint(sorted_words, indent=2)

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import math
from datetime import datetime, timedelta

from django.conf import settings
from django.core.management.base import BaseCommand
from apps.articles.models import Article

idf_path = getattr(settings, "IDF_PATH", None)

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import shutil
import tempfile
from datetime import datetime, timedelta

import jieba
import pandas as pd
from django.conf import settings
from django.core.management.base import BaseCommand
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer

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#!/usr/bin/env python
# coding: utf-8
import numpy as np
from PIL import Image


# 得到需要裁剪边框的距离
def remove_image_rim(np_img, boundary=0.8):
    _np_img = np_img
    h, w, c = np_img.shape