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import logging

logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(name)s %(threadName)s %(message)s')
logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO)

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# run it like `docker-compose up -d`
# then go to http://localhost:15672 and login with user and password `guest`
version: '3.8'

services:
  rabbitmq:
    image: rabbitmq:3.10-management-alpine
    restart: always
#    environment:
#      - RABBITMQ_DEFAULT_USER=guest

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"""
useful for local development and unit tests when tasks are run syncronously
"""

from your_app.settings import TESTING
from celery import Task
from celery.canvas import Signature

def run_task(task_signature: Signature):
    if TESTING:

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# put custom modules to ./custom_addons
version: '2'
services:
  postgresql:
    image: docker.io/bitnami/postgresql:13
    volumes:
      - 'postgresql_data:/bitnami/postgresql'
    environment:
      # ALLOW_EMPTY_PASSWORD is recommended only for development.
      - ALLOW_EMPTY_PASSWORD=yes

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{
  "consumer": {
    "name": "OrderService"
  },
  "provider": {
    "name": "UserService"
  },
  "interactions": [
    {
      "description": "GetUser API call for UserA",

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def format_exception_as_python_does(e: BaseException):
    # taken from https://stackoverflow.com/a/35498685
    return traceback.format_exception(type(e), e, e.__traceback__)

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# pip install saga_py
from saga import SagaBuilder, SagaError

counter1 = 0
counter2 = 0

def _print_counters():
    global counter1, counter2
    print(f'After this action, {counter1=}, {counter2=} \n')

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def dict_without_keys(data_: dict, keys: list):
    """
    Example:
    
    from dictdiffer import diff
    
    assert list(diff(
        dict_without_keys(listing.to_dict(), ['_id', '_cls', 'created_date', 'updated_date', 'rooms[].id', 'photos']),
        dict_without_keys(normal_listing_dict, ['photos'])

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# this method works well on ALL data
# see https://towardsdatascience.com/my-notes-for-singular-value-decomposition-with-interactive-code-feat-peter-mills-7584f4f2930a
import numpy as np 
from sklearn import preprocessing
np.set_printoptions(precision=2)

X = np.array([
    [1,2,3],
    [4,10,1],    
    [5,3,2],    

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def visualize_grid_cv_params(grid_cv):
    df = pd.DataFrame(grid_cv.cv_results_['params'])
    df['score'] = grid_cv.cv_results_['mean_test_score']

    fig, axes = plt.subplots(1, len(grid_cv.param_grid), sharey=True, figsize=(15,4))

    i = 0
    for param in grid_cv.param_grid:
        data = df.groupby(param).mean()['score'].to_dict()
        param_values = list(data.keys())