Schersteifigkeit für TUD und Labor Hart ergänzt. Auswertung läuft

2023-03-03 15:03:23 +01:00
parent e1dd4c7c00
commit ecaf97bbb3
3 changed files with 109 additions and 59 deletions
--- a/src/paveit/datamodels/data.py
+++ b/src/paveit/datamodels/data.py
@@ -37,7 +37,6 @@ class DataSheartest(RawData):
    N = ListField(IntField())
    s_vert_1 = ListField(FloatField())
    s_vert_2 = ListField(FloatField())
-    s_vert_sum = ListField(FloatField(), required=False)
    s_piston = ListField(FloatField(), required=False)
    s_hor_1 = ListField(FloatField(), required=False)
    s_hor_2 = ListField(FloatField(), required=False)
--- a/src/paveit/labtest/base.py
+++ b/src/paveit/labtest/base.py
@@ -201,7 +201,7 @@ class DataSineLoad():

        for col in [
                's_hor_sum', 's_hor_1', 's_hor_2', 's_vert_sum', 's_vert_1',
-                's_vert_2'
+                's_vert_2', 's_piston', 'extension',
        ]:
            if col in self.data.columns:
                self.data[col] = self.data[col].mul(self.unit_s)
@@ -232,12 +232,16 @@ class DataSineLoad():
        cols = self.data.columns

        if not 's_hor_sum' in cols:
-            self.data['s_hor_sum'] = self.data[['s_hor_1',
-                                                's_hor_2']].sum(axis=1)
+            if ('s_hor_1' in self.data.columns) & ('s_hor_2'
+                                                   in self.data.columns):
+                self.data['s_hor_sum'] = self.data[['s_hor_1',
+                                                    's_hor_2']].sum(axis=1)

        if not 's_vert_sum' in cols:
-            self.data['s_vert_sum'] = self.data[['s_vert_1',
-                                                 's_vert_2']].sum(axis=1)
+            if ('s_vert_1' in self.data.columns) & ('s_vert_2'
+                                                    in self.data.columns):
+                self.data['s_vert_sum'] = self.data[['s_vert_1',
+                                                     's_vert_2']].sum(axis=1)

    def _post_opt_data(self):
        #set dtypes:
--- a/src/paveit/labtest/sheartest.py
+++ b/src/paveit/labtest/sheartest.py
@@ -79,11 +79,12 @@ class ShearTest(DataSineLoad):
        self.col_as_float = ['T', 'F', 's_piston', 's_hor_1', 's_hor_2']

        self.val_col_names = [
-            'time', 'T', 'f', 'sigma_normal', 'extension', 'N', 'F', 's_hor_1',
-            's_hor_2', 's_vert_1', 's_vert_2'
+            'time', 'T', 'f', 'sigma_normal', 'extension', 'N', 'F',
+            's_vert_1', 's_vert_2'
        ]

-        self.round_values = [('T', 1), ('sigma_normal', 1), ('f', 1)]
+        self.round_values = [('T', 1), ('sigma_normal', 1), ('f', 1),
+                             ('extension', 6)]

        self.columns_analyse = [
            'F', 's_vert_sum', 's_vert_1', 's_vert_2', 's_hor_1', 's_hor_2',
@@ -91,9 +92,7 @@ class ShearTest(DataSineLoad):
        ]

        # Header names after standardization; check if exists
-        self.val_header_names = [
-            'speciment_height', 'speciment_diameter', 'broken'
-        ]
+        self.val_header_names = ['speciment_diameter', 'broken']

        self.number_of_load_cycles_for_analysis = 5

@@ -172,9 +171,8 @@ class ShearTestExtension(ShearTest):
                time=data.index,
                F=list(data['F']),
                N=list(data['N']),
-                s_vert_1=list(data['s_hor_1']),
-                s_vert_2=list(data['s_hor_2']),
-                s_vert_sum=list(data['s_hor_sum']),
+                s_vert_1=list(data['s_vert_1']),
+                s_vert_2=list(data['s_vert_2']),
            )

            # add optional datas
@@ -230,74 +228,77 @@ class ShearTestExtension(ShearTest):
        Calculate Results        
        """

-        self._logger.info('run _calc base')
-        print('run BASE')
+        self._logger.info('run _calc')

        self.fit = []

        for idx_data, data in enumerate(self.data):

-            if data is None: continue
+            try:
+                if data is None: continue

-            data.index = data.index - data.index[0]
+                data.index = data.index - data.index[0]

-            res_temp = {}
-            res_temp['idx'] = idx_data
+                res_temp = {}
+                res_temp['idx'] = idx_data

-            # Fitting
-            freq = data['f'].mean()
-            sigma_normal = data['sigma_normal'].mean()
-            T = data['T'].mean()
-            extension = data['extension'].mean()
+                # Fitting
+                freq = data['f'].mean()
+                sigma_normal = data['sigma_normal'].mean()
+                T = data['T'].mean()
+                extension = data['extension'].mean()

-            x = data.index.values
+                x = data.index.values

-            for idxcol, col in enumerate(self.columns_analyse):
+                for idxcol, col in enumerate(self.columns_analyse):

-                if not col in data.columns: continue
+                    if not col in data.columns: continue

-                y = data[col].values
+                    y = data[col].values

-                res = fit_cos(x, y, freq=freq)
+                    res = fit_cos(x, y, freq=freq)

-                for key, value in res.items():
-                    res_temp[f'fit_{col}_{key}'] = value
+                    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)
+                    res_temp[f'fit_{col}_max'] = max(y)
+                    res_temp[f'fit_{col}_min'] = min(y)

-            # add more metadata
-            res_temp['f_set'] = freq
-            res_temp['sigma_normal'] = sigma_normal
-            res_temp['T_set'] = T
-            res_temp['extension'] = extension
-            res_temp['broken'] = self.metadata['broken']
+                # add more metadata
+                res_temp['f_set'] = freq
+                res_temp['sigma_normal'] = sigma_normal
+                res_temp['T_set'] = T
+                res_temp['extension'] = extension
+                res_temp['broken'] = self.metadata['broken']

-            res_temp['N_from'] = int(data['N'].min())
-            res_temp['N_to'] = int(data['N'].max())
-            res_temp['N_tot'] = int(self.max_N_in_data[idx_data])
+                res_temp['N_from'] = int(data['N'].min())
+                res_temp['N_to'] = int(data['N'].max())
+                res_temp['N_tot'] = int(self.max_N_in_data[idx_data])

-            res_temp['n_samples_per_cycle'] = int(
-                len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))
+                res_temp['n_samples_per_cycle'] = int(
+                    len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))

-            ## Schersteifigkeit berechnen
-            deltaF = res_temp['fit_F_amp']
-            deltaS = res_temp['fit_s_vert_sum_amp']
+                ## Schersteifigkeit berechnen
+                deltaF = res_temp['fit_F_amp']
+                deltaS = res_temp['fit_s_vert_sum_amp']

-            A = np.pi * self.metadata['speciment_diameter']**2 / 4
-            tau = deltaF / A
-            gamma = deltaS / self.gap_width
+                A = np.pi * self.metadata['speciment_diameter']**2 / 4
+                tau = deltaF / A
+                gamma = deltaS / self.gap_width

-            res_temp['G'] = tau / gamma
+                res_temp['G'] = tau / gamma

-            #metadaten
-            #for c in ['T', 'extension', 'sigma_normal', 'f']:
-            #    res_temp[c] = res_temp[c][0]
+                #metadaten
+                #for c in ['T', 'extension', 'sigma_normal', 'f']:
+                #    res_temp[c] = res_temp[c][0]

-            self.fit.append(res_temp)
+                self.fit.append(res_temp)

-            if (self.debug) & (len(self.fit) > 5):
-                break
+                if (self.debug) & (len(self.fit) > 5):
+                    break
+
+            except Exception as e:
+                self._logger.critical(e, exc_info=True)

        self.fit = pd.DataFrame.from_records(self.fit)

@@ -401,6 +402,52 @@ class ShearTestExtensionLaborHart(ShearTestExtension):
        self.data = data
        self.metadata.update(meta)

+        # log infos
+        self._logger.info(self.metadata)
+        self._logger.info(self.data.head())
+
+
+class ShearTestExtensionTUDresden(ShearTestExtension):
+
+    def _define_units(self):
+
+        self.unit_s = 1 / 1000.  #mm
+        self.unit_F = 1.0  #N
+        self.unit_t = 1.0  #s
+
+    def update_parameter(self):
+
+        self.meta_names_of_parameter = {
+            't': ['TIME'],
+            'speciment_diameter': ['Diameter of specimen\r\n'],
+        }  #list of names
+
+        self.data_column_names = {
+            'time': ['TIME'],
+            'f': ['soll frequency'],
+            'T': ['soll temperature'],
+            'sigma_normal': ['soll sigma'],
+            'extension': ['soll extension'],
+            'F': ['vertical load from hydraulic pressure'],
+            's_vert_1': ['Vertical position from LVDT 1'],
+            's_vert_2': ['Vertical position from LVDT 2'],
+            's_piston': ['vertical position from hydraulic pressure'],
+            'N': ['Number of vertical cycles'],
+        }
+
+    def _process_data(self):
+
+        meta, data = read_geosys(
+            self.data, '015', metadata_ids=['001', '003', '005', '007', '009'])
+
+        #convert sigma
+        f = 900.0 / 355.0
+        data['soll sigma'] = data['soll sigma'].mul(f)
+
+        #define in class
+        self.data = data.reset_index()
+        self.metadata.update(meta)
+
        # log infos
        self._logger.info(self.metadata)
        self._logger.info(self.data.head())