703 lines
20 KiB
Python
703 lines
20 KiB
Python
import os
|
|
import tempfile
|
|
|
|
import lmfit as lm
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pandas as pd
|
|
import py7zr
|
|
import scipy.fft as sfft
|
|
import seaborn as sns
|
|
|
|
from pytestpavement.analysis.regression import fit_cos, fit_cos_eval
|
|
from pytestpavement.io.geosys import read_geosys
|
|
from pytestpavement.io.minio import MinioClient
|
|
from pytestpavement.labtests.base import DataSineLoad
|
|
from pytestpavement.models.data import DataSheartest
|
|
from pytestpavement.models.sheartest import DynamicShearTestExtension
|
|
|
|
|
|
class ShearTest(DataSineLoad):
|
|
"""
|
|
Dynamic Shear Bounding Test
|
|
"""
|
|
|
|
def __init__(self,
|
|
fname: str,
|
|
debug: bool = False,
|
|
gap_width: float = 1.0,
|
|
roundtemperature: bool = True,
|
|
archive_file=False,
|
|
s3_params: dict = {}):
|
|
|
|
#set parameter
|
|
self.gap_width = gap_width
|
|
self.debug = debug
|
|
self.file = fname
|
|
self.roundtemperature = roundtemperature
|
|
self.archive_file = archive_file
|
|
self.s3_params = s3_params
|
|
|
|
# process file
|
|
self._run()
|
|
|
|
def plot_fited_data(self, opath=None, pkname=None, r2min=0.99):
|
|
|
|
ylabel_dict = {
|
|
'F': 'Kraft in N',
|
|
's_vert_sum': 'norm. mittlerer Scherweg\n $S_{mittel}$ in mm',
|
|
's_piston': 'norm. Kolbenweg\n in mm',
|
|
's_vert_1': 'Scherweg\n $S_1$ in mm',
|
|
's_vert_2': 'Scherweg\n $S_2$ in mm'
|
|
}
|
|
|
|
columns_analyse = [
|
|
'F',
|
|
's_vert_sum',
|
|
's_vert_1',
|
|
's_vert_2',
|
|
's_piston',
|
|
]
|
|
|
|
if not (opath is None) & (pkname is None):
|
|
showplot = False
|
|
|
|
opath = os.path.join(opath, pkname, 'raw_data')
|
|
if not os.path.exists(opath):
|
|
os.makedirs(opath)
|
|
|
|
else:
|
|
showplot = True
|
|
|
|
for i, fit in self.fit.iterrows():
|
|
|
|
if not any([fit['r2_F'] < r2min, fit['r2_s_vert_sum'] < r2min]):
|
|
continue
|
|
|
|
data = self.data[int(fit['idx_data'])]
|
|
|
|
if data is None:
|
|
continue
|
|
|
|
freq = data['f'].unique()[0]
|
|
sigma = data['sigma_normal'].unique()[0]
|
|
s = data['extension'].unique()[0]
|
|
T = data['T'].unique()[0]
|
|
|
|
fig, axs = plt.subplots(len(columns_analyse),
|
|
1,
|
|
figsize=(8, len(columns_analyse) * 2),
|
|
sharex=True)
|
|
|
|
for idxcol, col in enumerate(columns_analyse):
|
|
x, y = data.index, data[col]
|
|
|
|
#add fit
|
|
f = self.fit.iloc[i]
|
|
parfit = {}
|
|
for k in ['amp', 'freq', 'phase', 'offset', 'slope']:
|
|
parfit[k] = f[f'fit_{k}_{col}']
|
|
|
|
yreg = fit_cos_eval(x, parfit)
|
|
|
|
if col in ['s_piston', 's_vert_sum']:
|
|
y = y - np.mean(y)
|
|
yreg = yreg - np.mean(yreg)
|
|
|
|
plt.sca(axs[idxcol])
|
|
plt.plot(x, y, label='Messdaten')
|
|
|
|
r2 = np.round(f[f'r2_{col}'], 3)
|
|
plt.plot(x,
|
|
yreg,
|
|
alpha=0.7,
|
|
label=f'Regression ($R^2 = {r2}$)')
|
|
|
|
if not ('F' in col):
|
|
s = f['extension']
|
|
parline = dict(lw=0.4,
|
|
ls='--',
|
|
color='lightgrey',
|
|
alpha=0.4,
|
|
label='Bereich des zul. Scherweges')
|
|
plt.axhspan(-s, s, **parline)
|
|
|
|
if idxcol == len(columns_analyse) - 1:
|
|
plt.xlabel('Zeit in s')
|
|
|
|
plt.ylabel(ylabel_dict[col])
|
|
plt.legend()
|
|
|
|
plt.tight_layout()
|
|
|
|
if showplot:
|
|
plt.show()
|
|
break
|
|
|
|
else:
|
|
ofile = f'{T}deg_{sigma}MPa_{freq}Hz_{s}mm'.replace('.', 'x')
|
|
ofile = os.path.join(opath, ofile + '.pdf')
|
|
|
|
plt.savefig(ofile)
|
|
plt.close()
|
|
|
|
|
|
class ShearTestExtension(ShearTest):
|
|
|
|
def runfit(self):
|
|
self._fit_data()
|
|
|
|
def file_in_db(self):
|
|
|
|
n = DynamicShearTestExtension.objects(filehash=self.filehash).count()
|
|
|
|
if n > 0:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def save(self, material1, material2, bounding, meta: dict):
|
|
|
|
for i, fit in self.fit.iterrows():
|
|
|
|
data = self.data[int(fit['idx_data'])]
|
|
|
|
#check if data in db
|
|
n = DynamicShearTestExtension.objects(
|
|
f=fit['f'],
|
|
sigma_normal=fit['sigma_normal'],
|
|
T=fit['T'],
|
|
|
|
extension=fit['extension'],
|
|
material1=material1,
|
|
material2=material2,
|
|
bounding=bounding,
|
|
filehash=self.filehash,
|
|
).count()
|
|
if n > 0: continue
|
|
|
|
# save fit
|
|
|
|
values = {}
|
|
for col in ['F', 's_vert_1', 's_vert_2', 's_vert_sum']:
|
|
values[f'fit_amp_{col}'] = fit[f'fit_amp_{col}']
|
|
values[f'fit_freq_{col}'] = fit[f'fit_freq_{col}']
|
|
values[f'fit_phase_{col}'] = fit[f'fit_phase_{col}']
|
|
values[f'fit_offset_{col}'] = fit[f'fit_offset_{col}']
|
|
values[f'fit_slope_{col}'] = fit[f'fit_slope_{col}']
|
|
values[f'r2_{col}'] = fit[f'r2_{col}']
|
|
|
|
values.update(meta)
|
|
|
|
try:
|
|
r = DynamicShearTestExtension(
|
|
#metadata
|
|
f=fit['f'],
|
|
sigma_normal=fit['sigma_normal'],
|
|
T=fit['T'],
|
|
extension=fit['extension'],
|
|
filehash=self.filehash,
|
|
material1=material1,
|
|
material2=material2,
|
|
bounding=bounding,
|
|
#results
|
|
stiffness=fit['G'],
|
|
#
|
|
**values).save()
|
|
|
|
#save raw data
|
|
rdata = DataSheartest(
|
|
result_id=r.id,
|
|
time=data.index.values,
|
|
F=data['F'].values,
|
|
N=data['N'].values,
|
|
s_vert_1=data['s_vert_1'].values,
|
|
s_vert_2=data['s_vert_2'].values,
|
|
s_vert_sum=data['s_vert_sum'].values,
|
|
s_piston=data['s_piston'].values,
|
|
).save()
|
|
|
|
except:
|
|
print('error saving data')
|
|
raise
|
|
rdata.delete()
|
|
|
|
if self.archive_file:
|
|
mclient = MinioClient(self.s3_params['S3_URL'],
|
|
self.s3_params['S3_ACCESS_KEY'],
|
|
self.s3_params['S3_SECRET_KEY'])
|
|
|
|
bucket = str(meta['org_id'])
|
|
mclient.create_bucket(bucket)
|
|
|
|
extension = os.path.splitext(self.file)[-1]
|
|
ofilename = self.filehash + extension
|
|
outpath = 'sheartest'
|
|
|
|
metadata_s3 = {
|
|
'project_id': str(meta['project_id']),·
|
|
'user_id': str(meta['user_id']),
|
|
'filename': os.path.split(self.file)[-1],
|
|
'speciment': meta['speciment_name']
|
|
}
|
|
|
|
#compress data to tmpfolder
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
|
|
ofilename_compressed = ofilename + '.7z'
|
|
|
|
compressed_file = os.path.join(tmpdirname,
|
|
ofilename_compressed)
|
|
|
|
with py7zr.SevenZipFile(compressed_file, 'w') as archive:
|
|
archive.writeall(self.file)
|
|
|
|
mclient.upload_file(bucket,
|
|
compressed_file,
|
|
ofilename_compressed,
|
|
outpath=outpath,
|
|
content_type="application/raw",
|
|
metadata=metadata_s3)
|
|
|
|
def _set_parameter(self):
|
|
|
|
self.split_data_based_on_parameter = [
|
|
'T', 'sigma_normal', 'f', 'extension'
|
|
]
|
|
|
|
self.col_as_int = ['N']
|
|
self.col_as_float = ['T', 'F', 'f', 's_vert_sum']
|
|
|
|
self.val_col_names = ['time', 'T', 'f', 'N', 'F', 's_vert_sum']
|
|
# Header names after standardization; check if exists
|
|
self.val_header_names = ['speciment_diameter']
|
|
|
|
self.columns_analyse = [
|
|
'F', 's_vert_sum', 's_vert_1', 's_vert_2', 's_piston'
|
|
]
|
|
|
|
self.number_of_load_cycles_for_analysis = 5
|
|
|
|
def _calc_missiong_values(self):
|
|
|
|
cols = self.data.columns
|
|
|
|
for c in ['vert']:
|
|
if not f's_{c}_sum' in cols:
|
|
self.data[f's_{c}_sum'] = self.data[[f's_{c}_1', f's_{c}_2'
|
|
]].sum(axis=1).div(2.0)
|
|
|
|
def _fit_data(self):
|
|
|
|
self.fit = []
|
|
|
|
for idx_data, data in enumerate(self.data):
|
|
|
|
if data is None: continue
|
|
|
|
data.index = data.index - data.index[0]
|
|
|
|
res = {}
|
|
res['idx_data'] = int(idx_data)
|
|
|
|
# Fitting
|
|
freq = float(np.round(data['f'].mean(), 4))
|
|
if (self.debug):
|
|
sigma_normal = np.round(data['sigma_normal'].mean(), 3)
|
|
T = np.round(data['T'].mean(), 3)
|
|
|
|
for idxcol, col in enumerate(self.columns_analyse):
|
|
|
|
if not col in data.columns: continue
|
|
|
|
x = data.index.values
|
|
y = data[col].values
|
|
|
|
# Fourier Transformation
|
|
"""
|
|
dt = np.diff(x).mean() #mean sampling rate
|
|
n = len(x)
|
|
|
|
res[f'psd_{col}'] = sfft.rfft(y) #compute the FFT
|
|
res[f'freq_{col}'] = sfft.rfftfreq(n, dt)
|
|
"""
|
|
|
|
res_fit = fit_cos(x, y, freq=freq, constfreq=True)
|
|
|
|
res[f'r2_{col}'] = res_fit['r2']
|
|
|
|
res[f'fit_amp_{col}'] = res_fit['amp']
|
|
res[f'fit_freq_{col}'] = res_fit['freq']
|
|
res[f'fit_phase_{col}'] = res_fit['phase']
|
|
res[f'fit_offset_{col}'] = res_fit['offset']
|
|
res[f'fit_slope_{col}'] = res_fit['slope']
|
|
|
|
## Schersteifigkeit berechnen
|
|
deltaF = res['fit_amp_F']
|
|
deltaS = res['fit_amp_s_vert_sum']
|
|
|
|
A = np.pi * self.meta['speciment_diameter']**2 / 4
|
|
tau = deltaF / A
|
|
gamma = deltaS / self.gap_width
|
|
|
|
res['G'] = tau / gamma
|
|
|
|
#metadaten
|
|
for c in ['T', 'extension', 'sigma_normal', 'f']:
|
|
res[c] = data[c][0]
|
|
|
|
self.fit.append(res)
|
|
|
|
if (self.debug) & (len(self.fit) > 5):
|
|
break
|
|
|
|
self.fit = pd.DataFrame.from_records(self.fit)
|
|
|
|
def plot_results(self, opath=None, pkname=None, r2min=0.96):
|
|
if not (opath is None) & (pkname is None):
|
|
showplot = False
|
|
|
|
opath = os.path.join(opath, pkname)
|
|
if not os.path.exists(opath):
|
|
os.makedirs(opath)
|
|
else:
|
|
showplot = True
|
|
|
|
dfplot = self.fit.copy()
|
|
for col in ['extension', 'fit_amp_s_vert_sum']:
|
|
dfplot[col] = dfplot[col].mul(1000)
|
|
|
|
fig, ax = plt.subplots()
|
|
|
|
xticks = list(dfplot['extension'].unique())
|
|
|
|
df = dfplot
|
|
df = df[(df['r2_F'] >= r2min) & (df['r2_s_vert_sum'] >= r2min)]
|
|
|
|
sns.scatterplot(
|
|
data=df,
|
|
x='fit_amp_s_vert_sum',
|
|
y='G',
|
|
hue='T',
|
|
ax=ax,
|
|
alpha=0.7,
|
|
#size=150,
|
|
size="G",
|
|
sizes=(50, 160),
|
|
edgecolor='k',
|
|
palette='muted',
|
|
zorder=10)
|
|
|
|
df = dfplot
|
|
df = df[(df['r2_F'] < r2min) & (df['r2_s_vert_sum'] < r2min)]
|
|
|
|
if not df.empty:
|
|
sns.scatterplot(data=df,
|
|
x='fit_amp_s_vert_sum',
|
|
y='G',
|
|
facecolor='grey',
|
|
alpha=0.5,
|
|
legend=False,
|
|
zorder=1,
|
|
ax=ax)
|
|
|
|
ax.set_xlabel('gemessene Scherwegamplitude in $\mu m$')
|
|
ax.set_ylabel('Scherseteifigkeit in MPa/mm')
|
|
|
|
ax.set_xticks(xticks)
|
|
ax.grid()
|
|
|
|
if not showplot:
|
|
ofile = os.path.join(opath, 'shearstiffness.pdf')
|
|
|
|
plt.savefig(ofile)
|
|
plt.show()
|
|
|
|
def plot_stats(self, opath=None, pkname=None, r2min=0.96):
|
|
if not (opath is None) & (pkname is None):
|
|
showplot = False
|
|
|
|
opath = os.path.join(opath, pkname)
|
|
if not os.path.exists(opath):
|
|
os.makedirs(opath)
|
|
else:
|
|
showplot = True
|
|
|
|
dfplot = self.fit.copy()
|
|
for col in ['extension', 'fit_amp_s_vert_sum']:
|
|
dfplot[col] = dfplot[col].mul(1000)
|
|
|
|
#r2
|
|
|
|
df = self.fit
|
|
|
|
fig, axs = plt.subplots(1, 2, sharey=True, sharex=True)
|
|
|
|
parscatter = dict(palette='muted', alpha=0.7, edgecolor='k', lw=0.3)
|
|
|
|
# r2
|
|
ax = axs[0]
|
|
sns.scatterplot(data=df,
|
|
x='fit_amp_s_vert_sum',
|
|
y='r2_F',
|
|
hue='T',
|
|
ax=ax,
|
|
**parscatter)
|
|
ax.set_ylabel('Bestimmtheitsmaß $R^2$')
|
|
ax.set_title('Kraft')
|
|
|
|
ax = axs[1]
|
|
sns.scatterplot(data=df,
|
|
x='fit_amp_s_vert_sum',
|
|
y='r2_s_vert_sum',
|
|
hue='T',
|
|
legend=False,
|
|
ax=ax,
|
|
**parscatter)
|
|
ax.set_ylabel('$R^2$ (S_{mittel})')
|
|
ax.set_title('mittlerer Scherweg')
|
|
|
|
for ax in axs.flatten():
|
|
ax.grid()
|
|
ax.set_xlabel('gemessene Scherwegamplitude in $\mu m$')
|
|
|
|
plt.tight_layout()
|
|
|
|
if not showplot:
|
|
ofile = os.path.join(opath, 'stats_r2.pdf')
|
|
plt.savefig(ofile)
|
|
plt.show()
|
|
|
|
|
|
class ShearTestExtensionLaborHart(ShearTestExtension):
|
|
|
|
def _define_units(self):
|
|
|
|
self.unit_F = 1 / 1000.0 #N
|
|
self.unit_t = 1 / 1000. #s
|
|
|
|
def _set_units(self):
|
|
|
|
#for col in ['F']:
|
|
# self.data[col] = self.data[col].mul(self.unit_F)
|
|
|
|
for col in ['time']:
|
|
self.data[col] = self.data[col].mul(self.unit_t)
|
|
|
|
return True
|
|
|
|
def _read_data(self):
|
|
"""
|
|
read data from Labor Hart
|
|
"""
|
|
|
|
# parameter
|
|
encoding = 'latin-1'
|
|
skiprows = 14
|
|
hasunits = True
|
|
splitsign = ':;'
|
|
|
|
# metadata from file
|
|
meta = {}
|
|
|
|
with open(self.file, 'r', encoding=encoding) as f:
|
|
count = 0
|
|
|
|
for line in f:
|
|
count += 1
|
|
|
|
#remove whitespace
|
|
linesplit = line.strip()
|
|
linesplit = linesplit.split(splitsign)
|
|
|
|
if len(linesplit) == 2:
|
|
|
|
meta[linesplit[0]] = linesplit[1]
|
|
|
|
if count >= skiprows:
|
|
break
|
|
|
|
# data
|
|
data = pd.read_csv(self.file,
|
|
encoding=encoding,
|
|
skiprows=skiprows,
|
|
decimal=',',
|
|
sep=';')
|
|
|
|
## add header to df
|
|
with open(self.file, 'r', encoding=encoding) as f:
|
|
count = 0
|
|
|
|
for line in f:
|
|
count += 1
|
|
|
|
if count >= skiprows:
|
|
break
|
|
|
|
head = line.split(';')
|
|
data.columns = head
|
|
|
|
#clean data
|
|
data = data.dropna(axis=1)
|
|
|
|
#define in class
|
|
self.meta = meta
|
|
self.data = data
|
|
return True
|
|
|
|
def _standardize_meta(self):
|
|
|
|
keys = list(self.meta.keys())
|
|
for key in keys:
|
|
|
|
if any(map(key.__contains__, ['Probenbezeichnung'])):
|
|
self.meta['speciment'] = self.meta.pop(key)
|
|
|
|
elif any(map(key.__contains__, ['Datum/Uhrzeit'])):
|
|
self.meta['datetime'] = self.meta.pop(key)
|
|
try:
|
|
self.meta['datetime'] = pd.to_datetime(
|
|
self.meta['datetime'])
|
|
except:
|
|
pass
|
|
|
|
elif any(map(key.__contains__, ['Probenhöhe'])):
|
|
self.meta['speciment_height'] = float(
|
|
self.meta.pop(key).replace(',', '.'))
|
|
elif any(map(key.__contains__, ['Probendurchmesser'])):
|
|
self.meta['speciment_diameter'] = float(
|
|
self.meta.pop(key).replace(',', '.'))
|
|
elif any(map(key.__contains__, ['Solltemperatur'])):
|
|
self.meta['temperature'] = float(
|
|
self.meta.pop(key).replace(',', '.'))
|
|
elif any(map(key.__contains__, ['Prüfbedingungen'])):
|
|
self.meta['test_version'] = self.meta.pop(key)
|
|
elif any(map(key.__contains__, ['Name des VersAblf'])):
|
|
self.meta['test'] = self.meta.pop(key)
|
|
elif any(map(key.__contains__, ['Prüfer'])):
|
|
self.meta['examiner'] = self.meta.pop(key)
|
|
|
|
return True
|
|
|
|
def _standardize_data(self):
|
|
|
|
colnames = list(self.data.columns)
|
|
|
|
for i, col in enumerate(colnames):
|
|
if col == 'TIME':
|
|
colnames[i] = 'time'
|
|
|
|
#set values
|
|
elif col == 'Sollwert Frequenz':
|
|
colnames[i] = 'f'
|
|
elif col == 'SollTemperatur':
|
|
colnames[i] = 'T'
|
|
elif col == 'Max Scherweg':
|
|
colnames[i] = 'extension'
|
|
elif col == 'Sollwert Normalspannung':
|
|
colnames[i] = 'sigma_normal'
|
|
elif col == 'Impulsnummer':
|
|
colnames[i] = 'N'
|
|
|
|
# measurements
|
|
|
|
elif col == 'Load':
|
|
colnames[i] = 'F'
|
|
elif col == 'Position':
|
|
colnames[i] = 's_piston'
|
|
|
|
elif col == 'VERTIKAL Links':
|
|
colnames[i] = 's_vert_1'
|
|
elif col == 'VERTIKAL Rechts':
|
|
colnames[i] = 's_vert_2'
|
|
|
|
elif col == 'HORIZONTAL links':
|
|
colnames[i] = 's_hor_1'
|
|
|
|
elif col == 'HOIZONTAL Rechts':
|
|
colnames[i] = 's_hor_2'
|
|
|
|
self.data.columns = colnames
|
|
|
|
|
|
class ShearTestExtensionTUDresdenGeosys(ShearTestExtension):
|
|
|
|
def _define_units(self):
|
|
|
|
self.unit_S = 1 / 1000.0 #N
|
|
|
|
def _set_units(self):
|
|
|
|
for col in [
|
|
's_vert_sum', 's_vert_1', 's_vert_2', 's_piston', 'extension'
|
|
]:
|
|
self.data[col] = self.data[col].mul(self.unit_S)
|
|
|
|
#convert internal units to global
|
|
f = np.mean([0.9 / 355, 0.6 / 234.0, 0.3 / 116.0])
|
|
|
|
self.data['sigma_normal'] = self.data['sigma_normal'].mul(f).apply(
|
|
lambda x: np.round(x, 1))
|
|
|
|
return True
|
|
|
|
def _read_data(self):
|
|
"""
|
|
read data from Labor Hart
|
|
"""
|
|
|
|
# parameter
|
|
encoding = 'latin-1'
|
|
skiprows = 14
|
|
hasunits = True
|
|
splitsign = ':;'
|
|
|
|
head, data = read_geosys(self.file, '015')
|
|
|
|
#define in class
|
|
self.meta = head
|
|
self.data = data
|
|
return True
|
|
|
|
def _standardize_meta(self):
|
|
|
|
keys = list(self.meta.keys())
|
|
for key in keys:
|
|
|
|
if key == 'd':
|
|
self.meta['speciment_diameter'] = self.meta.pop(key)
|
|
|
|
return True
|
|
|
|
def _standardize_data(self):
|
|
|
|
colnames = list(self.data.columns)
|
|
|
|
for i, col in enumerate(colnames):
|
|
|
|
#set values
|
|
if col == 'soll temperature':
|
|
colnames[i] = 'T'
|
|
elif col == 'soll extension':
|
|
colnames[i] = 'extension'
|
|
elif col == 'soll sigma':
|
|
colnames[i] = 'sigma_normal'
|
|
elif col == 'soll frequency':
|
|
colnames[i] = 'f'
|
|
|
|
elif col == 'Number of vertical cycles':
|
|
colnames[i] = 'N'
|
|
|
|
# measurements
|
|
elif col == 'vertical load from hydraulic pressure':
|
|
colnames[i] = 'F'
|
|
elif col == 'vertical position from hydraulic pressure':
|
|
colnames[i] = 's_piston'
|
|
|
|
elif col == 'Vertical position from LVDT 1':
|
|
colnames[i] = 's_vert_1'
|
|
elif col == 'Vertical position from LVDT 2':
|
|
colnames[i] = 's_vert_2'
|
|
|
|
self.data.columns = colnames
|