FerusAndBeyond

import os
import time

for k in ["MY_PUBLIC_VARIABLE", "MY_SECRET_VARIABLE"]:
    print(k, os.getenv(k))

FerusAndBeyond

version: '3'
services:
  my_component:
    # Here you can specify an env-file with variables
    # that can be used in the docker-container,
    # BUT these variables can not be used here in the
    # docker-compose using ${...} UNLESS it is a file named
    # ".env". This file (in the same directory) is always
    # loaded into the docker-compose, but not into the 
    # docker-containers unless the variables are mapped in the 

FerusAndBeyond

MY_SECRET_VARIABLE = "I'm batman!"

FerusAndBeyond

FROM python:3.8-alpine
COPY app .
CMD python -u main.py

FerusAndBeyond

from dotenv import load_dotenv
import os

# by default .env will be loaded
load_dotenv()
# if we had another file called .env2
# we could load it using
# load_dotenv(".env2")

# now we can use os.getenv as before:

FerusAndBeyond

import os

# first way
os.environ['TERM_PROGRAM']
# => 'Apple_Terminal'
# a non-existing variable will lead to error
os.environ["THIS_VARIABLE_DOESNT_EXIST"]
# => KeyError: 'THIS_VARIABLE_DOESNT_EXIST'

# second way

FerusAndBeyond

from scipy.signal import correlate
from scipy.signal import correlation_lags

# Function to calculate cross-correlation,
# extract the best matching shift and then shift
# one of the series appropriately.
def shift_for_maximum_correlation(x, y):
    correlation = correlate(x, y, mode="full")
    lags = correlation_lags(x.size, y.size, mode="full")
    lag = lags[np.argmax(correlation)]

FerusAndBeyond

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Generate two series that are correlated
original_x = pd.Series(np.random.uniform(size=100))
original_y = 1.3*original_x + np.random.normal(0, 0.1, size=100)

# Now create shifted versions,
# I create two examples, one where x is shifted

FerusAndBeyond

from collections import defaultdict

# by default a dict
a = defaultdict(dict)
assert a[5] == {}
a[5]["a"] = 5
assert a[5] == { "a": 5 }

# by default a list
a = defaultdict(list)

FerusAndBeyond

from collections import Counter
import random

counter = Counter()
# add lists with random number of a, b and c.
counter.update([random.choice(["a", "b", "c"]) for _ in range(100)])
counter.update([random.choice(["a", "b"]) for _ in range(100)])
# merge with counts
counter.update({ "a": 10000, "b": 1 })
# add directly