<jupyter_start><jupyter_text>Calculation of heat demand and load duration curves  Imports of popular packages and often used settings.<jupyter_code>import sys
import logging

import numpy as np
import scipy as sp
import sklearn
import statsmodels.api as sm
from statsmodels.formula.api import ols

%load_ext autoreload
%autoreload 2

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import seaborn as sns
sns.set_context("poster")
sns.set(rc={'figure.figsize': (16, 9.)})
sns.set_style("whitegrid")

import pandas as pd
pd.set_option("display.max_rows", 120)
pd.set_option("display.max_columns", 120)

logging.basicConfig(level=logging.INFO, stream=sys.stdout)
                                            # General setup of data science project directory structure
directories_and_pathes = [['data',''],      # /data
                          ['raw',''],       # /data/raw
                          ['ref',''],       # /data/ref
                          ['results',''],   # /data/results
                          ['work',''],      # /data/work
                          ['docs',''],      # /docs
                          ['notebooks',''], # /notebooks
                          ['src',''],       # /src
                          ['tests','']]     # /tests
# polulate dataframe 'working_directories' - note that column 'absolute_path' remains empty until function check_dirs() has been executed
working_directories = pd.DataFrame(directories_and_pathes,columns=['critical_dirs','absolute_path'])
from ecogenie.model import check_dirs
check_dirs(working_directories) 
working_directories<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11222681630242173, 'fraction_numerical': 0.005906674542232723, 'mean_word_length': 2.596602972399151, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '406331', 'license': 'MIT', 'n_tokens_mistral': 498, 'n_tokens_neox': 486, 'n_words': 123, 'path': 'notebooks/sandbox.ipynb', 'repo_name': 'PeetsB/ecogenie', 'size': '1693'}