vaclavcadek

def animate(i):
    return plt.plot(x[:i], y[:i], c='r')

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)
fig = plt.figure(figsize=(12, 8))
plt.xlim(0, 2 * np.pi)
plt.ylim(-1, 1)
anim = animation.FuncAnimation(fig, animate, frames=100)
display(HTML(anim.to_html5_video()))

vaclavcadek

from kafka import KafkaConsumer

consumer = KafkaConsumer('fast-messages', bootstrap_servers='localhost:9092')
for message in consumer:
    print(message)

vaclavcadek

FROM ubuntu:latest
MAINTAINER Vaclav Cadek

RUN apt-get update
RUN apt-get install python-virtualenv -y

RUN virtualenv -p /usr/bin/python3 ~/venv
RUN . ~/venv/bin/activate
RUN ~/venv/bin/pip install numpy pandas matplotlib jupyter

vaclavcadek

from scipy import stats
import matplotlib.pyplot as plt
import numpy as np

def PoissonPP( rt, Dx, Dy=None ):
    '''
    Determines the number of events `N` for a rectangular region,
    given the rate `rt` and the dimensions, `Dx`, `Dy`.
    Returns a <2xN> NumPy array.
    '''

vaclavcadek

from ortools.sat.python import cp_model


class SolutionPrinter(cp_model.CpSolverSolutionCallback):

    def __init__(self, variables):
        super().__init__()
        self.variables = variables
        self.solutions = []

vaclavcadek

def rgb2hex(rgb):
    return ''.join([format(val, '02X') for val in rgb])

vaclavcadek

51
27 68
30 48
43 67
58 48
58 27
37 69
38 46
46 10
61 33

vaclavcadek

from scipy.integrate import simpson
import matplotlib.pyplot as plt
import numpy as np


def almost_fourier_transform(g: np.ndarray, t: np.ndarray, f: float):
    # approximate center of mass as a mean
    t1 = np.min(t)
    t2 = np.max(t)
    scaling_factor = (1 / (t2 - t1))

vaclavcadek

FROM python:3.8-alpine

# Define global args
ARG FUNCTION_DIR="/home/app/"

RUN apk add --no-cache \
    libstdc++

# Install aws-lambda-cpp build dependencies
RUN apk add --no-cache \

vaclavcadek

import json
import pickle

class Foo:
    """ An old Foo."""

    def __init__(self, a, b):
        self.a = a
        self.b = b