xalelax

class FakeContextManager:
    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        pass


with FakeContextManager() as context:
    print("Hello")

xalelax

appnope==0.1.3
arrow==1.3.0
arviz==0.16.1
asttokens==2.4.1
attrs==23.1.0
backcall==0.2.0
black==23.11.0
bokeh==3.3.0
boto3==1.28.80
botocore==1.31.80

xalelax

# %% [markdown]
# # Generate synthetic data
# %%
import numpy as np

np.random.seed(123)

N_SAMPLES = 500

x = np.random.uniform(low=-1, high=1, size=N_SAMPLES)

xalelax

from itertools import product

faces = list(range(1, 7))


def contains_consecutive_sixes(outcome):
    for i, j in zip(outcome, outcome[1:]):
        if i == j == 6:
            return True

xalelax

import random

def generate_random_hypergraph(order, size):
    nodes = set(range(order))
    edges = set()
    while len(edges) < size:
        edge_size = random.randint(2, order)
        edge = frozenset(random.sample(nodes, k=edge_size))
        if edge not in edges:
          edges.add(edge)

xalelax

import networkx as nx
from networkx import NetworkXException
import matplotlib.pyplot as plt


def plot_hypergraph_components(hypergraph):
    decomposed_graph = decompose_edges_by_len(hypergraph)
    decomposed_edges = decomposed_graph['edges']
    nodes = decomposed_graph['nodes']

xalelax

from collections import defaultdict

def decompose_edges_by_len(hypergraph):
    decomposed_edges = defaultdict(list)
    for edge in hypergraph['edges']:
        decomposed_edges[len(edge)].append(edge)
    decomposition = {
        'nodes': hypergraph['nodes'],
        'edges': decomposed_edges
    }

xalelax

test_hypergraph = {
    'nodes': ['a', 'b', 'c', 'd','e','f'],
    'edges': [
        ('a', 'b'),
        ('b', 'c'),
        ('c', 'd'),
        ('a', 'c'),
        ('b', 'f'),
        ('f', 'c'),
        ('e', 'f'),

xalelax

from model import Company

def simulate_sample_company(n_steps, **kwargs):
    """
    Runs a model for n_steps and returns a pandas.DataFrame
    containing the data collected at each step.
    kwargs are forwarded to init of company.
    """
    model = Company(**kwargs)
    for i in range(n_steps):

xalelax

class Company(Model):
    """
    Model of a company; pyramidal structure with n different levels;
    workers at different levels weigh differently on the efficiency of the
    company as a whole.
    """
    def pick_for_promotion_from(self, source_level):
        if self.promotion_strategy == 'best':
            return max(source_level, key=lambda e: e.competency)
        elif self.promotion_strategy == 'worst':