<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>
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