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Base Model für CITT erstellt, PTM Dortmund ergänzt, Tests hinzugefügt

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Markus Clauß
2023-02-28 13:56:11 +01:00
parent b248a7e9b1
commit e861dbf10e
17 changed files with 917 additions and 103 deletions
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194
src/paveit/labtest/citt.py
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@@ -5,6 +5,7 @@ from csv import reader
import numpy as np
import pandas as pd
from paveit.labtest import DataSineLoad
from torch import isin
class CITTBase(DataSineLoad):
@@ -15,9 +16,9 @@ class CITT_KIT(DataSineLoad):
def _calc(self):
return (self.df.mean().mean(), self.df.max().max())
def _bytes_to_df(self):
def _process_data(self):
logger.debug('convert bytes to pandas.DataFrame')
self.data.seek(0)
with io.TextIOWrapper(self.data, encoding='latin-1') as read_obj:
csv_reader = reader(read_obj, delimiter=';')
@@ -93,29 +94,50 @@ class CITT_KIT(DataSineLoad):
#res = res.sort_values(['f', 'ZEIT'])
#define in class
self.df = res.reset_index()
class CITT_PTMDortmund(DataSineLoad):
def _calc(self):
return (self.df.mean().mean(), self.df.max().max())
self.data = res.reset_index()
def _bytes_to_df(self):
class CITT_PTMDortmund(DataSineLoad):
def _define_units(self):
self.unit_s = 1 #mm
self.unit_F = 1000. #N
self.unit_t = 1. #s
def update_parameter(self):
self.meta_names_of_parameter = {'sigma': ['Max. Spannung', 'Max Stress'],
'f': ['Frequenz', 'Frequency'],
'T': ['Versuchstemperatur', 'Target Test Temperature'],
'Nfrom': ['Erster Aufzeichnungslastwechsel', 'Start Cycle'],
'Nto': ['Letzer Aufzeichnungslastwechsel', 'Last Cycle'],
't': ['Zeitfolgen', 'Time Series'],
'speciment_diameter': ['Durchmesser (mm)', 'Diameter (mm)'],
'speciment_height': ['Länge (mm)', 'Length (mm)'],
} #list of names
self.data_column_names = {
'time': ['Time Series'],
'F': ['Load Series'],
's_hor_1': ['LVDT1 Series'],
's_hor_2': ['LVDT2 Series'],
}
def _process_data(self):
res = []
xl = pd.ExcelFile(self.data)
num_sheets = len(xl.sheet_names)
print(num_sheets)
diameter = []
height = []
for sheetid in range(num_sheets):
temp = pd.read_excel(self.data, sheetid, skiprows=97)
temp = temp.drop(index=0)
#convert data to numerical data
#convert data to numerical data
for col in temp.columns:
temp[col] = pd.to_numeric(temp[col])
@@ -124,53 +146,118 @@ class CITT_PTMDortmund(DataSineLoad):
meta = pd.read_excel(self.data, sheetid,
skiprows=1,
nrows=90)
nrows=80)
meta = meta[meta.columns[[0, 2]]]
meta = meta.set_index(
meta.columns[0]).to_dict()[meta.columns[1]]
meta.columns[0])
temp['sigma'] = float(meta['Max. Spannung'])
temp['T'] = float(meta['Versuchstemperatur'])
freq = float(meta['Frequenz'])
dt = 1 / freq
temp['f'] = freq
meta = meta.dropna(axis=0)
meta = meta[meta.columns[0]]
meta = meta.to_dict()
#remove whitespace in dict keys:
meta = {x.strip(): v for x, v in meta.items() if isinstance(x, str)}
Nfrom = int(meta['Erster Aufzeichnungslastwechsel'])
Nto = int(meta['Letzer Aufzeichnungslastwechsel'])
frequency_test = None
# add metadata to dataframe
for par in ['sigma', 'f', 'T']:
names = self.meta_names_of_parameter[par]
v = None
for name in names:
try:
v = np.round(float(meta[name]),5)
if par == 'f':
v = np.round(v,2)
break
except:
pass
assert v is not None
temp[par] = v
if par == 'f':
frequency_test = v
# read additional parameters
names = self.meta_names_of_parameter['Nfrom']
for name in names:
try:
Nfrom = int(meta[name])
break
except:
Nfrom = None
assert Nfrom is not None
names = self.meta_names_of_parameter['Nto']
for name in names:
try:
Nto = int(meta[name])
break
except:
Nto = None
assert Nto is not None
#add cycle number to dataframe
time_idx = temp['Zeitfolgen'].values
N = np.zeros_like(time_idx)
self._logger.debug(len(N))
names = self.meta_names_of_parameter['t']
for name in names:
try:
time_idx = temp[name].values
break
except:
time_idx = None
assert time_idx is not None
temp['N'] = 0
self._logger.info(f'cycles from {Nfrom} to {Nto}')
#BUG: Ist in Messdatei falsch definiert und wird von PTM angepasst. '''
#for cycle in range(Nfrom, Nto+1):
for cycle in range(10):
dt = 1.0/frequency_test
tmax = dt
max_timeindex = max(time_idx)
cycle = 0
while tmax < max_timeindex:
# time window
tmin = (cycle) * dt
tmin = (cycle) * dt
tmax = (cycle + 1) * dt
#filter data
idx = temp[(time_idx >= tmin)
& (time_idx < tmax)].index
#FIX: siehe bug oben
if any(idx)>=500:
idx = idx[idx<500]
#set cycle number
N[idx] = cycle
temp.loc[idx, 'N'] = cycle
cycle += 1
temp['N'] = N
# add diameter and height to list
diameter.append(float(meta['Durchmesser (mm)']))
height.append(float(meta['Länge (mm)']))
names = self.meta_names_of_parameter['speciment_diameter']
for name in names:
try:
v = float(meta[name])
break
except:
v = None
assert v is not None
diameter.append(v)
names = self.meta_names_of_parameter['speciment_height']
for name in names:
try:
v = float(meta[name])
break
except:
v = None
assert v is not None
height.append(v)
#append data to final dataframe
res.append(temp)
@@ -178,15 +265,18 @@ class CITT_PTMDortmund(DataSineLoad):
res = pd.concat(res)
# add data from speciment to metadata
#if not 'speciment_diameter' in self.metadata:
# self.metadata['speciment_diameter'] = np.mean(diameter)
#if not 'speciment_height' in self.metadata:
# self.metadata['speciment_height'] = np.mean(height)
if not 'diameter' in self.metadata:
self.metadata['diameter'] = np.mean(diameter)
if not 'height' in self.metadata:
self.metadata['height'] = np.mean(height)
#define in class
self.df = res.reset_index()
self.data = res.reset_index()
self.metadata.update(meta)
# log infos
logger.debug(self.metadata)
logger.debug(self.df.head())
self._logger.debug(self.metadata)
self._logger.debug(self.data.head())