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from google.cloud import bigquery
import random
import uuid
from datetime import datetime, timedelta
import sys

# Ejectuar el script en la terminal
# $>  python  bigquery_insert_parametrized_events_table.py "20230606"

if len(sys.argv) > 1:

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import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import Slider

def likelihood_func(θ,y,n,togle_var):
    if(togle_var):
        return np.log(np.power(1-θ,y-n) * np.power(θ,n))
    return np.power(1-θ,y-n) * np.power(θ,n)
    
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

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import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
import numpy as np

fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 1, 5, 2])

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from pyspark.context import SparkContext

spark = SparkContext.getOrCreate()

rdd1 = spark.textFile("inversiones.csv")

mapping_function = lambda linea : (linea.split(",")[3],1)

filter_function  = lambda dato : (dato[0] != "date")

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for(let i=0 ; i<10 ; i++){

        let printN = ()=> console.log(i);

        setTimeout(printN , i*1000);
}

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class myAwesomeElement extends HTMLElement {


    constructor(){
        super();

        this.attachShadow({mode:"open"});

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import numpy as np 
import matplotlib.pyplot as plt 
from scipy.special import gamma
from math import exp as e

def gaussian_distribution(vector_x,a,l):
    result = [((l*(l*x)**(a-1))*e(-l*x))/gamma(a) for x in vector_x]
    
    return np.array(result)
	

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