markus/lib-paveit-demo
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Datenstrukturen und Auswertung angepasst

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Markus Clauß
2023-03-17 14:54:19 +01:00
parent ecaf97bbb3
commit 471fa8dabf
42 changed files with 613 additions and 131 deletions
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src/paveit/labtest/citt.py Normal file → Executable file
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@@ -5,6 +5,7 @@ from csv import reader
import numpy as np
import pandas as pd
from bson import ObjectId
from paveit import calc_nu, fit_cos
from paveit.datamodels import CITTSiffness, CITTSiffnessResults
from paveit.io import read_geosys
@@ -135,14 +136,43 @@ class CITTBase(DataSineLoad):
if not col in data.columns: continue
y = data[col].values
res = fit_cos(x, y, freq=freq)
for key, value in res.items():
res_temp[f'fit_{col}_{key}'] = value
res_temp[f'fit_{col}_max'] = max(y)
res_temp[f'fit_{col}_min'] = min(y)
# analyse cycle data
cycle_min = []
cycle_max = []
cycle_mean = []
cycle_diff = []
for N, data_cycle in data.groupby('N'):
y = data_cycle[col].values
cycle_min.append(y.min())
cycle_max.append(y.max())
cycle_mean.append(y.mean())
cycle_diff.append(cycle_max[-1] - cycle_min[-1])
res_temp[f'fit_{col}_cycle_min'] = cycle_min
res_temp[f'fit_{col}_min'] = np.mean(cycle_min)
res_temp[f'fit_{col}_min_std'] = np.std(cycle_min)
res_temp[f'fit_{col}_min_diff_rel'] = (np.max(cycle_min) - np.min(cycle_min))/np.mean(cycle_min)
res_temp[f'fit_{col}_cycle_max'] = cycle_max
res_temp[f'fit_{col}_max'] = np.mean(cycle_max)
res_temp[f'fit_{col}_max_std'] = np.std(cycle_max)
res_temp[f'fit_{col}_max_diff_rel'] = (np.max(cycle_max) - np.min(cycle_max))/np.mean(cycle_max)
res_temp[f'fit_{col}_cycle_mean'] = cycle_mean
res_temp[f'fit_{col}_mean'] = np.mean(cycle_mean)
res_temp[f'fit_{col}_mean_std'] = np.std(cycle_mean)
res_temp[f'fit_{col}_mean_diff_rel'] = (np.max(cycle_mean) - np.min(cycle_mean))/np.mean(cycle_mean)
res_temp[f'fit_{col}_cycle_diff'] = cycle_diff
res_temp[f'fit_{col}_diff'] = np.mean(cycle_diff)
res_temp[f'fit_{col}_diff_std'] = np.std(cycle_diff)
res_temp[f'fit_{col}_diff_diff_rel'] = (np.max(cycle_diff) - np.min(cycle_diff))/np.mean(cycle_diff)
# add more metadata
res_temp['f_set'] = freq
@@ -186,12 +216,9 @@ class CITTBase(DataSineLoad):
self._logger.debug(self.fit['stiffness'])
def save(self,
org_id: ObjectId,
project_id: ObjectId,
material_id: ObjectId,
user_id: ObjectId,
meta: dict = {},
wp_id: ObjectId | None = None):
task_id: ObjectId,
meta: dict = {}
):
"""
save results to mongodb
"""
@@ -200,16 +227,13 @@ class CITTBase(DataSineLoad):
raise
# precheck data and results
assert len(self.data) == len(self.fit)
#assert len(self.data) == len(self.fit)
for idx_fit, fit in self.fit.iterrows():
data = self.data[idx_fit]
meta['filehash'] = self.filehash
meta['org_id'] = org_id
meta['project_id'] = project_id
meta['material'] = material_id
meta['user_id'] = user_id
meta['task_id'] = task_id
#check if result in db
#n = CITTSiffness.objects(**meta).count()
@@ -726,11 +750,13 @@ class CITT_LaborHart(CITTBase):
data.columns = head
# FIX: Sigma nicht in Metadaten oder Messdaten enthalten
sigma = float(
os.path.split(self.filename)[-1].split('MPa')[0].strip().replace(
',', '.'))
print(meta)
if not "sigma" in self.metadata:
sigma = float(
os.path.split(self.filename)[-1].split('MPa')[0].strip().replace(
',', '.'))
meta['sigma'] = sigma
meta['sigma'] = sigma
#clean data
data = data.dropna(axis=1)
@@ -847,3 +873,100 @@ class CITT_BAGKoeln(CITTBase):
# log infos
self._logger.info(self.metadata)
self._logger.info(self.data.head())
class CITT_Braunschweig(CITTBase):
def _define_units(self):
self.unit_s = 1.0 #mm
self.unit_F = -1000.0 #N
self.unit_t = 1.0
def update_parameter(self):
self.meta_names_of_parameter = {
't': ['t [s]'],
} #list of names
self.data_column_names = {
'time': ['t [s]'],
'f': [ 'FREQUENZ_x10'],
'F': [ 'F act [kN]'],
's_hor_1': ['r 1A [mm]'],
's_hor_2': ['r 1B [mm]'],
's_piston': ['s piston [mm]'],
}
def _process_data(self):
self._logger.info(self.metadata)
meta = {}
encoding = 'latin-1'
self.data.seek(0)
head = pd.read_csv(self.data,
skiprows=14,
sep=';',
nrows=1,
encoding=encoding,
decimal=',',
thousands='.',
skip_blank_lines=True).columns
head = [h.strip() for h in head]
self.data.seek(0)
data = pd.read_csv(self.data,
skiprows=20,
sep=';',
encoding=encoding,
decimal=',',
thousands='.',
header=None,
skip_blank_lines=True)
data.columns = head
# add frequency
## [start, ncycles]
sel = {0.1: [0, 11], 1.0: [11, 20], 5.0: [30, 100], 10: [130, 111]}
N_f_dict = {}
Ns = 0
for freq in sel.keys():
par = sel[freq]
N = par[0]
dN = par[1]
for i in range(N, N + dN):
N_f_dict[Ns] = freq
Ns += 1
data['f'] = data['N'].replace(N_f_dict)
data = data[data['f'] <= 10.0]
#sigma
data['sigma'] = self.metadata['sigma']
data['T'] = self.metadata['temperature']
#clean data
#data = data.dropna(axis=0)
#data = data.dropna(axis=1)
data = data.drop(columns=['Date Time'])
#define in class
self.data = data
self.metadata.update(meta)
# log infos
self._logger.info(self.metadata)
self._logger.info(self.data.head())