markus/lib-paveit-demo
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move function calc_E to functions/citt

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This commit is contained in:
Markus Clauß
2023-07-03 08:31:30 +02:00
parent 28255116f2
commit f482a72ca5
4 changed files with 89 additions and 89 deletions
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2
src/paveit/datamodels/taskmanager.py
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@@ -47,7 +47,7 @@ class TaskManagerBase(Document):
"db_alias": 'dblabtests', "db_alias": 'dblabtests',
} }
class TaskCITTStiffness(TaskManagerBase): class TaskCITTStiffness(TaskManagerBase):
material = LazyReferenceField(Material, required=True) material = LazyReferenceField(Material, required=True)

87
src/paveit/functions/citt.py
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@@ -14,3 +14,90 @@ def calc_nu(T):
#TODO: Prüfen ob Formel stimmt! #TODO: Prüfen ob Formel stimmt!
nu = 0.15 + (0.35) / (1 + np.exp(3.1849 - 0.04233 * (9 / 5 * T + 32))) nu = 0.15 + (0.35) / (1 + np.exp(3.1849 - 0.04233 * (9 / 5 * T + 32)))
return nu return nu
def calc_E(data, metadata, columns_analyse):
data.index = data.index - data.index[0]
res_temp = {}
x = data.index.values
freq = np.round(float(data['f'].unique()), 2)
sigma = float(data['sigma'].unique())
temperature = float(data['T'].unique())
for idxcol, col in enumerate(columns_analyse):
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
# 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
res_temp['sigma_set'] = sigma
res_temp['T_set'] = temperature
res_temp['N_from'] = data['N'].min()
res_temp['N_to'] = data['N'].max()
res_temp['n_samples_per_cycle'] = int(
len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))
## Stiffness
deltaF = res_temp['fit_F_amp']
deltaU = res_temp['fit_s_hor_sum_amp']
h = float(metadata['speciment_height'])
d = float(metadata['speciment_diameter'])
nu = calc_nu(temperature)
res_temp['nu'] = nu
#nach TP Asphalt 26
res_temp['stiffness'] = deltaF /(h * deltaU) * (4.0/np.pi -1 + nu)
## Elastische hori. Dehnung
res_temp['el_strains'] = 2*2*deltaU/d * (1+3*nu)/(4 + np.pi*nu - np.pi) * 1000.0 # 2*2 daher, da deltaU nur Ampl. nicht Gesamtkraft ist
# TODO: Überarbeiten und erweitern (ISSUE #2)
res_temp['phase'] = res_temp['fit_F_phase'] - res_temp['fit_s_hor_sum_phase']
return res_temp

87
src/paveit/labtest/citt.py
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@@ -13,93 +13,6 @@ from paveit.labtest import DataSineLoad
from paveit.labtest.citt_fatigue import CittAnalyseFatigue from paveit.labtest.citt_fatigue import CittAnalyseFatigue
def calc_E(data, metadata, columns_analyse):
data.index = data.index - data.index[0]
res_temp = {}
x = data.index.values
freq = np.round(float(data['f'].unique()), 2)
sigma = float(data['sigma'].unique())
temperature = float(data['T'].unique())
for idxcol, col in enumerate(columns_analyse):
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
# 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
res_temp['sigma_set'] = sigma
res_temp['T_set'] = temperature
res_temp['N_from'] = data['N'].min()
res_temp['N_to'] = data['N'].max()
res_temp['n_samples_per_cycle'] = int(
len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))
## Stiffness
deltaF = res_temp['fit_F_amp']
deltaU = res_temp['fit_s_hor_sum_amp']
h = float(metadata['speciment_height'])
d = float(metadata['speciment_diameter'])
nu = calc_nu(temperature)
res_temp['nu'] = nu
#nach TP Asphalt 26
res_temp['stiffness'] = deltaF /(h * deltaU) * (4.0/np.pi -1 + nu)
## Elastische hori. Dehnung
res_temp['el_strains'] = 2*2*deltaU/d * (1+3*nu)/(4 + np.pi*nu - np.pi) * 1000.0 # 2*2 daher, da deltaU nur Ampl. nicht Gesamtkraft ist
# TODO: Überarbeiten und erweitern (ISSUE #2)
res_temp['phase'] = res_temp['fit_F_phase'] - res_temp['fit_s_hor_sum_phase']
return res_temp
class CITTBase(DataSineLoad): class CITTBase(DataSineLoad):
def _set_parameter(self): def _set_parameter(self):

2
src/paveit/labtest/citt_fatigue.py
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@@ -1,7 +1,7 @@
import numpy as np import numpy as np
import pandas as pd import pandas as pd
from paveit.labtest.citt import calc_E from paveit.functions.citt import calc_E
class CittAnalyseFatigue(): class CittAnalyseFatigue():