init package and add first subpackages

src/paveit/labtest/citt.py

@@ -0,0 +1,192 @@
+import io
+import os
+from csv import reader
+
+import numpy as np
+import pandas as pd
+from paveit.labtest import DataSineLoad
+
+
+class CITTBase(DataSineLoad):
+    def _calc(self):
+        return (self.df.mean().mean(), self.df.max().max())
+
+class CITT_KIT(DataSineLoad):
+    def _calc(self):
+        return (self.df.mean().mean(), self.df.max().max())
+
+    def _bytes_to_df(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=';')
+
+            read = False
+
+            data = []
+            temp = []
+
+            for idx_row, row in enumerate(csv_reader):
+                if row == ['*****']:
+
+                    if read == False:
+                        read = True
+                    else:
+                        read = False
+                        data.append(temp)
+
+                        temp = []
+
+                    continue
+
+                if read:
+
+                    row = [r.replace(',', '.') for r in row]
+
+                    temp.append(row)
+
+        #convert to pandas
+
+        res = []
+
+        freqs = [10.0, 5.0, 1.0, 0.1, 10.0]
+
+        for idx_data, d in enumerate(data):
+
+            t = pd.DataFrame(d[3:])
+            t.columns = d[1]
+
+            freq = freqs[idx_data]
+            t['f'] = freq
+
+            for col in t.columns:
+                t[col] = pd.to_numeric(t[col])
+
+            # add cycle number
+            dt = 1. / freq
+
+            Nmax = int(np.ceil(t['ZEIT'].max() / dt))
+
+            N = np.zeros_like(t['ZEIT'])
+
+            for i in range(Nmax):
+                if i == 0:
+                    tmin = 0
+                    tmax = dt
+                else:
+                    tmax = (i + 1) * dt
+                    tmin = (i) * dt
+
+                idx = t[(t['ZEIT'] >= tmin) & (t['ZEIT'] < tmax)].index
+                N[idx] = i
+
+
+            t['N'] = N
+
+            res.append(t)
+
+        #remove second 10 Hz
+        res = pd.concat(res[:-1])
+
+        res['T'] = self.temperature
+        #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())
+
+    def _bytes_to_df(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            
+            for col in temp.columns:
+                temp[col] = pd.to_numeric(temp[col])
+
+
+            #read metadata from file
+
+            meta = pd.read_excel(self.data, sheetid,
+                                      skiprows=1,
+                                      nrows=90)
+
+            meta = meta[meta.columns[[0, 2]]]
+            meta = meta.set_index(
+                meta.columns[0]).to_dict()[meta.columns[1]]
+            
+            temp['sigma'] = float(meta['Max. Spannung'])
+            temp['T'] = float(meta['Versuchstemperatur'])
+            freq = float(meta['Frequenz'])
+            dt = 1 / freq
+            temp['f'] = freq
+
+            Nfrom = int(meta['Erster Aufzeichnungslastwechsel'])
+            Nto = int(meta['Letzer Aufzeichnungslastwechsel'])
+
+            #add cycle number to dataframe
+            time_idx = temp['Zeitfolgen'].values
+            N = np.zeros_like(time_idx)
+            self._logger.debug(len(N))
+            
+            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):
+                
+                # time window
+                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['N'] = N
+                
+            # add diameter and height to list
+            diameter.append(float(meta['Durchmesser (mm)']))
+            height.append(float(meta['Länge (mm)']))
+                
+            #append data to final dataframe
+            res.append(temp)
+
+        #concat all parts to single dataframe
+        res = pd.concat(res)
+
+        # add data from speciment to metadata
+        
+        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()
+        
+        # log infos
+        logger.debug(self.metadata)
+        logger.debug(self.df.head())