namoshizun

import time
import random
from elasticapm import Client, instrument
from elasticapm.base import get_client as get_apm_client
from elasticapm.contrib.celery import register_exception_tracking, register_instrumentation
from elasticapm.traces import execution_context


from celery import Celery
from celery.canvas import group

namoshizun

[
  {
    "database": "load_planner",
    "index": "saas-base",
    "nodes": {
      "table": "saas_api_user",
      "schema": "public",
      "columns": [],
      "transform": {
        "rename": {

namoshizun

---
apiVersion: v1
kind: ConfigMap
metadata:
  name: metricbeat-daemonset-modules
  namespace: kube-system
  labels:
    k8s-app: metricbeat
data:
  system.yml: |-

namoshizun

import React from 'react';
import { useState, useEffect } from 'react'

function App() {
  const [count, setCount] = useState(0)
  
  // Should print the latest count a render happens
  console.log(count)
  useEffect(() => {
    console.log('In effect')

namoshizun

library(heatmaply)
library(shiny)
library(plotly)


ui <- fluidPage(
    titlePanel("Old Faithful Geyser Data"),
    mainPanel(
        plotlyOutput(outputId = 'heatmap')
    )

namoshizun

from vid_utils import Video, concatenate_videos

videos = [
  Video(speed=1.0, path="C:/temp/my_video_1.mp4"),
  Video(speed=2.0, path="C:/temp/my_video_2.mp4"),
  Video(speed=0.5, path="C:/temp/my_video_3.mp4"),
]

concatenate_videos(videos=videos, output_file=f"C:/temp/output_video.mp4")

namoshizun

import functools
import inspect
from tqdm import tqdm


def print_progress(total=None, enabled=True):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            if not enabled or total is None:

namoshizun

# Install GPFlow
!git clone https://github.com/namoshizun/GPflow.git
!mv ./GPflow/gpflow ./
!rm -rf GPflow
!pip install multipledispatch
!pip install pytest


# Install Other libraries
!pip install edward

namoshizun

from __future__ import division
from random import random
import numpy as np
import pandas as pd

'''
    Use regret-matching algorithm to play Scissors-Rock-Paper.
'''

class RPS:

namoshizun

"""
Implement a very simple gaussian process for regression task.
Credit : http://katbailey.github.io/post/gaussian-processes-for-dummies/
"""

import numpy as np
import matplotlib.pyplot as pl


def prepare_data(n, fn):