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add regression citt, add new datafields to citt

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This commit is contained in:
Markus Clauß
2023-06-16 09:43:33 +02:00
parent 959358e38f
commit cd9bd08863
7 changed files with 244 additions and 29 deletions
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1
src/paveit/datamodels/__init__.py
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@@ -13,3 +13,4 @@ from .sheartest import *
from .taskmanager import *
from .usermanagement import *
from .workpackage import *
from .regression import *

6
src/paveit/datamodels/citt.py
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@@ -26,6 +26,7 @@ class CyclicIndirectTensileTest(Document):
filehash = StringField(required=True)
speciment_name = StringField(required=True, default=None)
meta = {
@@ -45,6 +46,9 @@ class CITTSiffnessResults(CyclicIndirectTensileTest):
f_set = FloatField()
sigma_set = FloatField()
T_set = FloatField()
speciment_diameter = FloatField(required=True)
speciment_height = FloatField(required=True)
N_from = IntField()
N_to = IntField()
@@ -55,6 +59,8 @@ class CITTSiffnessResults(CyclicIndirectTensileTest):
stiffness = FloatField()
nu = FloatField()
phase = FloatField()
el_strains = FloatField()
sigma_calc = FloatField()
#required parameter
## F
F_amp = FloatField()

44
src/paveit/datamodels/regression.py Executable file
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@@ -0,0 +1,44 @@
import datetime
from mongoengine import *
from .taskmanager import TaskManagerBase
class RegressionBase(Document):
date = DateTimeField(default=datetime.datetime.now(),
wtf_options={"render_kw": {
"step": "60"
}})
task_id = LazyReferenceField(TaskManagerBase, required=True)
#statistische Werte
stat_r2 = FloatField(required=False)
meta = {
'allow_inheritance': True,
'index_opts': {},
'index_background': True,
'index_cls': False,
'auto_create_index': True,
'collection': 'regression',
"db_alias": 'dblabtests',
}
class RegCITT(RegressionBase):
nsamples = IntField()
Emax = FloatField(min_value=0, max_value=150000)
Emin = FloatField()
T0 = FloatField(min_value=-100, max_value=100)
phi = FloatField()
z0 = FloatField()
z1 = FloatField()

18
src/paveit/labtest/base.py
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@@ -163,7 +163,6 @@ class DataSineLoad():
self._logger.debug(f'columns: {colnames}')
print(self.data.head())
print(self.data['s_piston'].head())
self._logger.debug(f'standardize_data: {self.data.columns}')
@@ -171,10 +170,11 @@ class DataSineLoad():
def _standardize_meta(self):
self._logger.debug('run _standardize_meta')
# remove "\r\n" ending from Windows
# remove "\r\n" ending from Windows and whitespace
for col in list(self.metadata.keys()):
col_mod = col.replace('\r\n', '')
col_mod = col_mod.strip()
if col != col_mod:
self.metadata[col_mod] = self.metadata[col]
@@ -189,6 +189,13 @@ class DataSineLoad():
self.metadata.pop(name)
break
# stip data
for key in self.metadata.keys():
try:
self.metadata[key] = self.metadata[key].strip()
except:
pass
self._logger.debug(f'meta (stand.): {self.metadata}')
@@ -247,6 +254,13 @@ class DataSineLoad():
for col in ['time']:
self.data[col] = self.data[col].mul(self.unit_t)
try:
self.data['f'] = self.data['f'].mul(self.unit_freq)
except:
pass
return True

69
src/paveit/labtest/citt.py
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@@ -54,6 +54,8 @@ class CITTBase(DataSineLoad):
}
def _sel_df(self, df, num=5, shift=-1):
print(df.head())
N = df['N'].unique()
n_N = len(N)
@@ -61,7 +63,7 @@ class CITTBase(DataSineLoad):
min_N = min(N)
freq = float(df['f'].unique()[0])
# define cycles to select
if freq == 10.0:
Nfrom = 98
@@ -84,6 +86,10 @@ class CITTBase(DataSineLoad):
else:
Nfrom = None
Nto = None
self._logger.debug(f'{min_N}, {max_N}, {n_N}, {num}, {shift}')
self._logger.debug(f'Frequenz: {freq}, Nfrom: {Nfrom}, Nto: {Nto}')
# Fall 1: nur num Lastwechsel
if n_N < num:
@@ -229,18 +235,27 @@ class CITTBase(DataSineLoad):
## Stiffness
deltaF = res_temp['fit_F_amp']
deltaU = res_temp['fit_s_hor_sum_amp']
h = float(self.metadata['speciment_height'])
d = float(self.metadata['speciment_diameter'])
nu = calc_nu(temperature)
res_temp['nu'] = nu
h = float(self.metadata['speciment_height'])
deltaU = res_temp['fit_s_hor_sum_amp']
res_temp['stiffness'] = (deltaF * (0.274 + nu)) / (h * deltaU)
#nach TP Asphalt 26
res_temp['stiffness'] = deltaF /(h * deltaU) (4.0/np.pi -1 + nu)
## Elastische hori. Dehnung
res_temp['el_strains'] = 2*deltaU/d * (1+3*nu)/(4 + np.pi*nu - np.pi) * 1000.0
## maximale Zugspannung im Probekörpermittelpunkt
res_temp['sigma_calc'] = (2*deltaF)/(np.pi*d*h)
# TODO: Überarbeiten und erweitern (ISSUE #2)
res_temp['phase'] = res_temp['fit_F_phase'] - res_temp[
'fit_s_hor_sum_phase']
res_temp['phase'] = res_temp['fit_F_phase'] - res_temp['fit_s_hor_sum_phase']
except Exception as e:
self._logger.exception(e)
res_temp = None
@@ -284,6 +299,9 @@ class CITTBase(DataSineLoad):
else:
meta['speciment_name'] = self.filename
meta['speciment_diameter'] = self.metadata['speciment_diameter']
meta['speciment_height'] = self.metadata['speciment_height']
#check if result in db
#n = CITTSiffness.objects(**meta).count()
@@ -346,7 +364,7 @@ class CITT_TUDresdenWille(CITTBase):
't': ['Zeit'],
'speciment_diameter': ['Probekörberdurchmesser', 'Probekörberbreite'],
'speciment_height': ['Probekörperhöhe'],
'speciment_name': ['Probekörper-Nummer Bezeichnung'],
'speciment_name': ['Probekörper', 'Probekörper-Nummer Bezeichnung'],
} #list of names
self.data_column_names = {
@@ -387,7 +405,7 @@ class CITT_TUDresdenGeosysOne(CITTBase):
't': ['Zeit'],
'speciment_diameter': ['Probekörberdurchmesser', 'Probekörberbreite'],
'speciment_height': ['Probekörperhöhe'],
'speciment_name': ['Probekörper-Nummer Bezeichnung'],
'speciment_name': ['Probekörper', 'Probekörper-Nummer Bezeichnung'],
} #list of names
self.data_column_names = {
@@ -806,28 +824,31 @@ class CITT_LaborHart(CITTBase):
def _define_units(self):
self.unit_s = 1.0 #mm
self.unit_F = 1.0 #N
self.unit_s = 1.0/1000.0/10.0 #mm 10fach überhöht abgelegt
self.unit_F = 1.0/10.0 #N 10fach überhöht abgelegt
self.unit_t = 1. / 1000.0 #s
self.unit_freq = 1.0/10. #s 10fach überhöht abgelegt
def update_parameter(self):
self.meta_names_of_parameter = {
'sigma': ['Oberspannung'],
'T': ['Solltemperatur'],
't': ['TIME'],
't': ['Time_ms'],
'speciment_diameter': ['Probendurchmesser'],
'speciment_height': ['Probenhöhe'],
'speciment_name': ['Probenbezeichnung'],
} #list of names
self.data_column_names = {
'time': ['TIME'],
'f': ['FREQUENZ'],
'F': ['Load'],
's_hor_1': ['SENSOR 4'],
's_hor_2': ['SENSOR Extension'],
's_piston': ['Position'],
'N': ['Impulsnummer'],
'time': ['Time_ms'],
'f': ['FREQUENZ_x10'],
'F': ['Load_N_x10'],
's_hor_1': ['SENSOR_EXT_µm_x10'],
's_hor_2': ['SENSOR_S4_µm_x10'],
's_piston': ['POSITION_µm'],
'N': ['Zyklen_fortlaufend'],
}
def _process_data(self):
@@ -884,13 +905,7 @@ class CITT_LaborHart(CITTBase):
# FIX: Sigma nicht in Metadaten oder Messdaten enthalten
print(meta)
if not "sigma" in self.metadata:
sigma = float(
os.path.split(self.filename)[-1].split('MPa')[0].strip().replace(
',', '.'))
meta['sigma'] = sigma
#clean data
data = data.dropna(axis=1)

4
src/paveit/postprocessing/__init__.py Executable file
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@@ -0,0 +1,4 @@
from .citt import citt
__all__ = ['citt',
]

131
src/paveit/postprocessing/citt.py Normal file
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@@ -0,0 +1,131 @@
import datetime
import numpy as np
from bson import ObjectId
from paveit.datamodels import CITTSiffnessResults, RegCITT
import lmfit as lm
import pandas as pd
import scipy.special as sf
from scipy.optimize import curve_fit
def temp_freq_equivalence(T, f, phi, T0=20.0):
alphaT = np.exp(phi * ((1 / (T + 273.15)) - (1 / (T0 + 273.15))))
x = np.log(f * alphaT) / np.log(10)
return x
def stiffness_tp26(T, f, phi, Emax, Emin, z0, z1, T0=20.0):
x = temp_freq_equivalence(T, f, phi, T0)
E = Emin + (Emax - Emin) / (1 + np.exp(z1 * x + z0))
return E
def calc_nu(T):
#TODO: Prüfen ob Formel stimmt!
nu = 0.15 + (0.35) / (1 + np.exp(3.1849 - 0.04233 * (9 / 5 * T + 32)))
return nu
def citt(task_id: str):
"""
Postprocessing task
"""
print('postprocessing')
task_id = ObjectId(task_id)
# read all data
data = []
parlist = ['f_set', 'T_set', 'stiffness', 'phase']
for obj in CITTSiffnessResults.objects(task_id=task_id).only(*parlist):
data.append(dict((k, obj[k]) for k in parlist ))
data = pd.DataFrame.from_records(data)
#Emax/Emin
line_mod = lm.models.LinearModel()
out = line_mod.fit(data.stiffness, x=data.phase)
print(out.best_values)
Emax = line_mod.eval(out.params, x=0.0)
Emin = 0
assert Emin < Emax
print(data.head())
# Fit data
mod = lm.models.Model(stiffness_tp26, independent_vars=['f','T'])
mod.set_param_hint(
'Emin',
value=Emin,
min=0,
max=0.9*Emax,
vary=True,
)
mod.set_param_hint(
'Emax',
value=Emax,
min=0.9*Emax,
max=1.1*Emax,
vary=True,
)
mod.set_param_hint(
'T0',
value=20.0,
vary=False,
)
mod.set_param_hint('phi', value=25000, min=15000, max=35000, vary=True)
mod.set_param_hint('z0', value=1,min=1e-10, max=1000., vary=True)
mod.set_param_hint('z1', value=-1, min=-1000., max=-1e-10, vary=True)
parms_fit = [
mod.param_hints['Emin']['value'], mod.param_hints['Emax']['value'],
mod.param_hints['phi']['value'], mod.param_hints['z0']['value'],
mod.param_hints['z1']['value']
]
## run fit
results = []
r2 = []
methods = ['leastsq', 'powell']
for method in methods:
result = mod.fit(data.stiffness, T=data.T_set, f=data.f_set, method=method, verbose=False)
r2temp = 1.0 - result.redchi / np.var(data.stiffness.values, ddof=2)
r2.append(r2temp)
results.append(result)
best = np.nanargmax(r2)
res = results[best].best_values
res['nsamples'] = len(data)
res['task_id'] = task_id
res['stat_r2'] = r2[best]
res['date'] = datetime.datetime.now()
print(res)
# save results to db
doc = RegCITT.objects(task_id=task_id).modify(upsert=True, **res)
return True