bhuiyanmobasshir94

import face_recognition
import cv2
import numpy as np

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.

bhuiyanmobasshir94

import face_recognition
import cv2
import numpy as np

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.

bhuiyanmobasshir94

import pandas as pd
import pdfkit
from PyPDF2 import PdfFileMerger
import os
import math

df = pd.read_excel('CALCOLATORE PREVENTIVI.xls', sheet_name='CALCOLATORE') # col = 1, row = 2

var1 = df.loc[3][7]
raw_var1 = var1.split('\n')

bhuiyanmobasshir94

import requests
import pandas as pd
import datetime
import os
import logging
from bs4 import BeautifulSoup
import re

now = datetime.datetime.now()
todays_date = str(now).split()[0]

bhuiyanmobasshir94

Using Git

Global Settings

• Related Setup: https://gist.github.com/hofmannsven/6814278
• Related Pro Tips: https://ochronus.com/git-tips-from-the-trenches/
• Interactive Beginners Tutorial: http://try.github.io/
• Git Cheatsheet by GitHub: https://services.github.com/on-demand/downloads/github-git-cheat-sheet/

bhuiyanmobasshir94

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1

bhuiyanmobasshir94

There is a known method to emulate a container for variables, which support both methods of access: by a variable's name and a string key.

class Vars:
    def __init__(self, **kw):
        self.__dict__.update(kw)

    def __getitem__(self, key):
        return self.__dict__[key]

    def __setitem__(self, key, val):

bhuiyanmobasshir94

# http://en.wikipedia.org/wiki/Extreme_points_of_the_United_States#Westernmost
top = 49.3457868 # north lat
left = -124.7844079 # west long
right = -66.9513812 # east long
bottom =  24.7433195 # south lat

def cull(l):
    c = []
    for (lat, lng) in l:
        if bottom <= lat <= top and left <= lng <= right:

bhuiyanmobasshir94

import requests

def get_weather(latitude_,longitude_):
    # latitude = latitude_
    # longitude = longitude_
    # url = 'http://api.openweathermap.org/data/2.5/weather?lat={}&lon={}&appid=8f47bbfcc82517d109015de292ab80cd&units=metric'.format(latitude, longitude)
    # res = requests.get(url)
    # data = res.json()
    response = dict(main="Haze",desc="haze",temp=18,pressure=1014,humidity=72,temp_min=18,temp_max=18,wind_speed=1.5,wind_degree=310,datetime=1544556600,clouds_all=20,sys_sunrise=1544488256,sys_sunset=1544526753)
    return response

bhuiyanmobasshir94

import os
import pandas as pd
import numpy as np
import random
import django
from django.utils import timezone
from populate_weather import get_location,get_weather

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'categorized_affect_map.settings')
django.setup()