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import types

class DictToObject:
    def __init__(self, dictionary):
        for key, value in dictionary.items():
            if callable(value):
                # If the value is callable (a function/method), bind it to the instance
                setattr(self, key, types.MethodType(value, self))
            else:
                # Otherwise, set it as an attribute

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"""Demo model of SimpleKit usage."""
from simplekit import SimpleKit
from collections import deque
from numpy.random import default_rng
import sys
 
class MMk(SimpleKit):
    """Implementation of an M/M/k queueing model using SimpleKit."""
 
    def __init__(self, arrivalRate, serviceRate, maxServers, numCustomers, seed = 1234567):

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class WeekDayConstrainedDist(ciw.dists.Distribution):

    def __init__(self, dist, offset=0, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.dist = dist
        self.offset = offset

    def sample(self, t=None, ind=None):
        tau = (np.floor(t) - self.offset + 7) % 7
        if tau < 5:

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def weekday_server_sched(offset):
    return [[1 if ((i - offset + 7) % 7) < 5 else 0, i+1] for i in range(7)]

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# Ugly one-liner from an interactive session... sorry!
import numpy as np;
from scipy.stats import ttest_1samp as t;
import matplotlib.pyplot as plt;
[
  plt.scatter(
    np.arange(1,101), 
    [t(np.random.normal(size=n+1), 1).pvalue for n in range(100)],
    alpha=1/np.sqrt(100), 
    color='g')

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import matplotlib.pyplot as plt
import networkx as nx
import queueing_tool as qt

g = qt.generate_random_graph(100, seed=3)
q = qt.QueueNetwork(g, seed=3)
q.max_agents = 20000
q.initialize(100)

q.start_collecting_data()

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import ciw

import pandas as pd

N = ciw.create_network(
    arrival_distributions={
        "Baby": [ciw.dists.Exponential(rate=1.0), None, None],
        "Child": [ciw.dists.Exponential(rate=2.0), None, None],
    },
    service_distributions={

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import numpy as np
ds = pd.date_range('2023-01-01', '2027-03-02')
df = pd.DataFrame(dict(ds=ds, y=np.random.poisson(np.arange(ds.size) / 10 + 1)))
df = features.add_day_of_year_features(df, 'ds')
df['GROUP'] = ['ALL'] * ds.size

param_grid = {
                'changepoint_prior_scale': [0.001],
                'seasonality_prior_scale': [0.01],
                'holidays_prior_scale': [0.01]

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import socket
import pickle

data_structure = {'key': 'value', 'numbers': [1, 2, 3]}

# Serialize the data structure
serialized_data = pickle.dumps(data_structure)

# Send the serialized data to script2.py
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:

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from dataclasses import dataclass
import random

import ciw
from hciw.waitlist import create_existing_customers_from_list
from hciw.service_disciplines import lex_priority_benchmark_with_threshold_switch
import numpy as np
import pandas as pd