wirelessr

"""
Install the Google AI Python SDK

$ pip install google-generativeai

See the getting started guide for more information:
https://ai.google.dev/gemini-api/docs/get-started/python
"""

import os

wirelessr

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from ruptures import Pelt
from scipy.stats import ttest_ind

# Generate synthetic data with clear changepoints
np.random.seed(42)
timestamps = pd.date_range('2023-10-26', periods=60, freq='D')
latency = np.concatenate([

wirelessr

from copy import deepcopy
import json

from confluent_kafka.schema_registry import SchemaRegistryClient
from confluent_kafka.schema_registry.avro import AvroSerializer, AvroDeserializer
from confluent_kafka.serialization import StringSerializer, SerializationContext, MessageField

KAFKA_HOST = 'localhost'

schema_registry_conf = {'url': f'http://{KAFKA_HOST}:8081'}

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import logging, sys
from pyflink.common import WatermarkStrategy, Row
from pyflink.common.serialization import Encoder
from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.connectors import FileSink, OutputFileConfig, NumberSequenceSource
from pyflink.datastream.execution_mode import RuntimeExecutionMode
from pyflink.datastream.functions import KeyedProcessFunction, RuntimeContext, MapFunction, CoMapFunction, CoFlatMapFunction
from pyflink.datastream.state import MapStateDescriptor, ValueStateDescriptor
from pyflink.common import JsonRowDeserializationSchema, JsonRowSerializationSchema

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flowchart LR
    core[(Core)]
    cart[(Cart)]
    other[(Others)]
    bq[(BigQuery)]
    rt[[Realtime events including CDC]]
    pg[(Postgres)]
    api[Shoplytics]
    user(((User)))
    

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describe("fast-check test", () => {
  before(async () => {
      // generate 10 random records
  });
    
  it("#1", async () => {
    const result = await getMoney(100);
    expect(result.length).to.be.equal(10);
  });

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const events = require('events');
const emitter = new events.EventEmitter();

emitter.on('purchased', function(user, total) {
    if (total >= 1000) {
        giveCoupon(cart.user, total);
    }
});
emitter.on('purchased', function(user, total) {
    if (ok && total >= 5000) {

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const expect = require("chai").expect;

const table = [10, 10, 15, 15, 30, 30, 100];
const boxTable = [0, 1, 0, 0, 1, 0, 1];

const dateDiff = (sD1, sD2) => {
  const d1 = new Date(sD1);
  const d2 = new Date(sD2);
  d1.setHours(0, 0, 0, 0);
  d2.setHours(0, 0, 0, 0);

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const os = require('os');

// Take the first CPU, considering every CPUs have the same specs
// and every NodeJS process only uses one at a time.
const cpus = os.cpus();
const cpu = cpus[0];

// Accumulate every CPU times values
const total = Object.values(cpu.times).reduce(
    (acc, tv) => acc + tv, 0

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let lastid = "0-0";
let checkBacklog = true
while (true) {
    const myid = checkBacklog ? lastid : ">";
    const items = await redis.xreadgroup('GROUP',GroupName,ConsumerName,'BLOCK','2000','COUNT','10','STREAMS',StreamName,myid);

    if (!items) continue;
    
    checkBacklog = !(items[0][1].length == 0);