<jupyter_start><jupyter_text>MDL DB roll decay evaluationThis notebook is assempling the measurement files on your computer<jupyter_code>from jupyterthemes import jtplot
jtplot.style(theme='chesterish', context='notebook', ticks=True, grid=False)
%matplotlib inline
%load_ext autoreload
%autoreload 2
import warnings
import pandas as pd
pd.set_option("display.max_columns", None)
import numpy as np
import os
import matplotlib.pyplot as plt

import data
import copy
from mdldb.mdl_db import MDLDataBase
from mdldb.tables import Base, Model, LoadingCondition, Run, RolldecayLinear, RolldecayDirect
from mdldb.tables import RolldecayNorwegian, RolldecayDirectImproved, RolldecayLinearAnalytical
from mdldb.tables import RolldecaySimplifiedIkeda
from mdldb import mdl_to_evaluation
from evaluation.run_dynamic import RunDynamic
from evaluation.run_manoeuvring import RunZigZag

from rolldecayestimators.direct_estimator import DirectEstimator
from rolldecayestimators.direct_estimator_improved import DirectEstimatorImproved
from rolldecayestimators.direct_estimator_cubic import EstimatorCubic,EstimatorQuadratic,EstimatorLinear
from rolldecayestimators.direct_linear_estimator import DirectLinearEstimator
from rolldecayestimators.norwegian_estimator import NorwegianEstimator
from rolldecayestimators.analytical_linear_estimator import AnalyticalLinearEstimator
from rolldecayestimators.ikeda_estimator import IkedaQuadraticEstimator, IkedaEstimatorFitError
from rolldecayestimators.simplified_ikeda import SimplifiedIkedaInputError

from rolldecayestimators.transformers import CutTransformer, LowpassFilterDerivatorTransformer, ScaleFactorTransformer, OffsetTransformer
#from rolldecay.equations_lambdify import calculate_acceleration, calculate_velocity
from sklearn.pipeline import Pipeline

import signal_lab.mdl_to_evaluation
import rolldecay.database as database
db = database.get_db()
for db_run in db.session.query(Run).filter((Run.Körfallstyp=='Kursstyrning') & 
                                           (Run.test_type== 'roll decay')):
    db_run.load(save_temp=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.04739336492890995, 'fraction_numerical': 0.00047393364928909954, 'mean_word_length': 8.060085836909872, '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': '129877', 'license': 'MIT', 'n_tokens_mistral': 693, 'n_tokens_neox': 654, 'n_words': 149, 'path': 'notebooks/rolldecay/02_roll_damping_DB/04.7_maa_mdl_db_build_temp_data.ipynb', 'repo_name': 'martinlarsalbert/rolldecay', 'size': '2110'}