senaite /
senaite.core
| Total Complexity | 97 |
| Total Lines | 511 |
| Duplicated Lines | 29.94 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like build.bika.lims.exportimport.instruments.panalytical.omnia often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | # -*- coding: utf-8 -*- |
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| 2 | # |
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| 3 | # This file is part of SENAITE.CORE |
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| 4 | # |
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| 5 | # Copyright 2018 by it's authors. |
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| 6 | # Some rights reserved. See LICENSE.rst, CONTRIBUTORS.rst. |
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| 7 | |||
| 8 | """ Omnia Axios XRF |
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| 9 | """ |
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| 10 | from datetime import datetime |
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| 11 | from bika.lims.utils import to_unicode |
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| 12 | from bika.lims import bikaMessageFactory as _ |
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| 13 | from bika.lims.exportimport.instruments.resultsimport import \ |
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| 14 | AnalysisResultsImporter, InstrumentCSVResultsFileParser |
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| 15 | |||
| 16 | class AxiosXrfCSVMultiParser(InstrumentCSVResultsFileParser): |
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| 17 | |||
| 18 | def __init__(self, csv): |
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| 19 | InstrumentCSVResultsFileParser.__init__(self, csv) |
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| 20 | self._end_header = False |
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| 21 | self._columns = [] |
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| 22 | self.columns_name = False #To know if the next line contains |
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| 23 | #analytic's columns name |
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| 24 | |||
| 25 | |||
| 26 | def _parseline(self, line): |
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| 27 | # Process the line differenly if it pertains at header or results block |
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| 28 | if self._end_header == False: |
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| 29 | sline = line.strip(',') |
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| 30 | return self.parse_headerline(sline) |
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| 31 | else: |
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| 32 | return self.parse_resultline(line) |
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| 33 | |||
| 34 | def splitLine(self, line): |
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| 35 | # If pertains at header it split the line by ':' and then remove ',' |
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| 36 | # Else split by ',' and remove blank spaces |
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| 37 | if self._end_header == False: |
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| 38 | sline = line.split(':') |
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| 39 | return [token.strip(',') for token in sline] |
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| 40 | |||
| 41 | return [token.strip() for token in line.split(',')] |
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| 42 | |||
| 43 | def csvDate2BikaDate(self,DateTime): |
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| 44 | #11/03/2014 14:46:46 --> %d/%m/%Y %H:%M %p |
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| 45 | dtobj = datetime.strptime(DateTime,"%d/%m/%Y %H:%M:%S") |
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| 46 | return dtobj.strftime("%Y%m%d %H:%M:%S") |
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| 47 | |||
| 48 | def parse_headerline(self, line): |
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| 49 | #Process incoming header line |
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| 50 | """11/03/2014 14:46:46 |
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| 51 | PANalytical |
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| 52 | Results quantitative - Omnian 2013, |
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| 53 | |||
| 54 | Selected archive:,Omnian 2013 |
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| 55 | Number of results selected:,4 |
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| 56 | """ |
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| 57 | |||
| 58 | # Save each header field (that we know) and its own value in the dict |
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| 59 | if line.startswith('Results quantitative'): |
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| 60 | line = to_unicode(line) |
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| 61 | if len(self._header) == 0: |
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| 62 | self.err("Unexpected header format", numline=self._numline) |
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| 63 | return -1 |
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| 64 | |||
| 65 | line = line.replace(',', "") |
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| 66 | splitted = line.split(' - ') |
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| 67 | self._header['Quantitative'] = splitted[1] |
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| 68 | return 1 |
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| 69 | |||
| 70 | View Code Duplication | if line.startswith('Selected archive'): |
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|
Duplication
introduced
by
|
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| 71 | if len(self._header) == 0: |
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| 72 | self.err("No header found", numline=self._numline) |
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| 73 | return -1 |
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| 74 | |||
| 75 | splitted = self.splitLine(line) |
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| 76 | if len(splitted) > 1: |
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| 77 | self._header['Archive'] = splitted[1].replace('"', '').strip() |
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| 78 | else: |
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| 79 | self.warn('Unexpected header format', numline=self._numline) |
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| 80 | return 0 |
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| 81 | |||
| 82 | View Code Duplication | if line.startswith('Number of'): |
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| 83 | if len(self._header) == 0: |
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| 84 | self.err("No header found", numline=self._numline) |
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| 85 | return -1 |
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| 86 | |||
| 87 | splitted = self.splitLine(line) |
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| 88 | if len(splitted) > 1: |
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| 89 | self._header['NumResults'] = splitted[1].replace('"', '').strip() |
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| 90 | else: |
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| 91 | self.warn('Unexpected header format', numline=self._numline) |
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| 92 | return 0 |
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| 93 | |||
| 94 | if line.startswith('Seq.'): |
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| 95 | if len(self._header) == 0: |
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| 96 | self.err("No header found", numline=self._numline) |
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| 97 | return -1 |
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| 98 | #Grab column names |
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| 99 | self._columns = line.split(',') |
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| 100 | self._end_header = True |
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| 101 | return 1 |
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| 102 | |||
| 103 | else: |
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| 104 | self._header['Date'] = line |
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| 105 | return 1 |
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| 106 | |||
| 107 | |||
| 108 | |||
| 109 | def parse_resultline(self, line): |
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| 110 | # Process incoming results line |
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| 111 | if not line.strip(): |
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| 112 | return 0 |
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| 113 | if line.startswith(',,'): |
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| 114 | return 0 |
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| 115 | |||
| 116 | rawdict = {} |
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| 117 | # Split by "," |
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| 118 | splitted = self.splitLine(line.strip(";")) |
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| 119 | |||
| 120 | errors = '' |
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| 121 | |||
| 122 | # Adjunt separated values from split by ',' |
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| 123 | for idx, result in enumerate(splitted): |
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| 124 | if result.startswith('"'): |
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| 125 | # It means that is the value's firts part |
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| 126 | # Consequently we take second part and append both |
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| 127 | result = (splitted[idx].strip('"') + "," + splitted[idx+1].strip('"')) |
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| 128 | splitted[idx] = result |
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| 129 | splitted.remove(splitted[idx+1]) |
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| 130 | |||
| 131 | result_type = '' |
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| 132 | result_sum = '' |
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| 133 | for idx, result in enumerate(splitted): |
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| 134 | if self._columns[idx] == 'Result type': |
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| 135 | result_type = result |
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| 136 | elif self._columns[idx].startswith('Sample name'): |
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| 137 | rid = result |
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| 138 | elif self._columns[idx].startswith('Seq.'): |
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| 139 | pass |
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| 140 | elif self._columns[idx] == 'Sum': |
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| 141 | result_sum = result |
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| 142 | else: |
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| 143 | rawdict[self._columns[idx]] = {'DefaultResult':result_type, |
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| 144 | # Replace to obtain UK values from default |
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| 145 | 'Concentration':result.replace(',','.'), |
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| 146 | 'Sum':result_sum} |
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| 147 | try: |
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| 148 | rawdict['DateTime'] = {'DateTime':self.csvDate2BikaDate(self._header['Date']), |
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| 149 | 'DefaultValue':'DateTime'} |
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| 150 | except: |
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| 151 | pass |
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| 152 | if not rid: |
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| 153 | self.err("No Sample defined", numline=self._numline) |
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| 154 | return 0 |
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| 155 | |||
| 156 | self._addRawResult(rid, rawdict, True) |
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| 157 | return 0 |
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| 158 | |||
| 159 | |||
| 160 | def getAttachmentFileType(self): |
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| 161 | return "PANalytical - Omnia Axios XRF" |
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| 162 | |||
| 163 | class AxiosXrfCSVParser(InstrumentCSVResultsFileParser): |
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| 164 | |||
| 165 | def __init__(self, csv): |
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| 166 | InstrumentCSVResultsFileParser.__init__(self, csv) |
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| 167 | self._end_header = False |
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| 168 | self._columns = [] |
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| 169 | self.columns_name = False #To know if the next line contains |
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| 170 | #analytic's columns name |
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| 171 | |||
| 172 | def _parseline(self, line): |
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| 173 | # Process the line differenly if it pertains at header or results block |
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| 174 | if self._end_header == False: |
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| 175 | sline = line.strip(',') |
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| 176 | return self.parse_headerline(sline) |
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| 177 | else: |
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| 178 | return self.parse_resultline(line) |
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| 179 | |||
| 180 | def csvDate2BikaDate(self,DateTime): |
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| 181 | #11/03/2014 14:46:46 --> %d/%m/%Y %H:%M %p |
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| 182 | dtobj = datetime.strptime(DateTime,"%d/%m/%Y %H:%M:%S") |
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| 183 | return dtobj.strftime("%Y%m%d %H:%M:%S") |
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| 184 | |||
| 185 | def splitLine(self, line): |
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| 186 | # If pertains at header it split the line by ':' and then remove ',' |
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| 187 | # Else split by ',' and remove blank spaces |
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| 188 | if self._end_header == False: |
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| 189 | sline = line.split(':') |
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| 190 | return [token.strip(',') for token in sline] |
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| 191 | |||
| 192 | return [token.strip() for token in line.split(',')] |
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| 193 | |||
| 194 | def parse_headerline(self, line): |
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| 195 | #Process incoming header line |
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| 196 | """ |
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| 197 | 29/11/2013 10:15:44 |
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| 198 | PANalytical |
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| 199 | "Quantification of sample ESFERA CINZA - 1g H3BO3 - 1:0,5 - NO PPC", |
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| 200 | |||
| 201 | R.M.S.:,"0,035" |
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| 202 | Result status:, |
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| 203 | Sum before normalization:,"119,5 %" |
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| 204 | Normalised to:,"100,0 %" |
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| 205 | Sample type:,Pressed powder |
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| 206 | Initial sample weight (g):,"2,000" |
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| 207 | Weight after pressing (g):,"3,000" |
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| 208 | Correction applied for medium:,No |
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| 209 | Correction applied for film:,No |
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| 210 | Used Compound list:,Oxides |
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| 211 | Results database:,omnian 2013 |
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| 212 | Results database in:,c:\panalytical\superq\userdata |
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| 213 | """ |
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| 214 | |||
| 215 | if line.startswith('"Quantification of sample') or line.startswith('Quantification of sample'): |
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| 216 | line = to_unicode(line) |
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| 217 | if len(self._header) == 0: |
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| 218 | self.warn('Unexpected header format', numline=self._numline) |
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| 219 | return -1 |
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| 220 | # Remove non important string and double comas to obtein |
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| 221 | # the sample name free |
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| 222 | line = line.replace("Quantification of sample ", "") |
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| 223 | line = line.replace('"', "") |
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| 224 | splitted = line.split(' - ') |
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| 225 | |||
| 226 | if len(splitted) > 3:# Maybe we don't need this, i could be all the sample's identifier... |
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| 227 | self._header['Sample'] = splitted[0].strip(' ') |
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| 228 | self._header['Quantity'] = splitted[1] |
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| 229 | self._header['????'] = splitted[2]# At present we |
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| 230 | # don't know what |
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| 231 | # is that |
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| 232 | self._header['PPC'] = splitted[3] |
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| 233 | |||
| 234 | elif len(splitted) == 1: |
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| 235 | self._header['Sample'] = splitted[0].replace('Quantification of sample','').strip(' ') |
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| 236 | |||
| 237 | else: |
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| 238 | self.warn('Unexpected header format', numline=self._numline) |
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| 239 | return 1 |
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| 240 | # Save each header field (that we know) and its own value in the dict |
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| 241 | View Code Duplication | if line.startswith('R.M.S.'): |
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| 242 | |||
| 243 | if len(self._header) == 0: |
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| 244 | self.err("No header found", numline=self._numline) |
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| 245 | return -1 |
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| 246 | |||
| 247 | splitted = self.splitLine(line) |
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| 248 | if len(splitted) > 1: |
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| 249 | self._header['R.M.S.'] = splitted[1].replace('"', '').strip() |
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| 250 | else: |
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| 251 | self.warn('Unexpected header format', numline=self._numline) |
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| 252 | return 0 |
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| 253 | |||
| 254 | View Code Duplication | if line.startswith('Result status'): |
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| 255 | if len(self._header) == 0: |
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| 256 | self.err("No header found", numline=self._numline) |
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| 257 | |||
| 258 | splitted = self.splitLine(line) |
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| 259 | if len(splitted) > 1: |
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| 260 | self._header['Result status'] = splitted[1].replace('"', '').strip() |
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| 261 | else: |
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| 262 | self.warn('Unexpected header format', numline=self._numline) |
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| 263 | |||
| 264 | return 0 |
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| 265 | |||
| 266 | View Code Duplication | if line.startswith('Sum before normalization'): |
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| 267 | if len(self._header) == 0: |
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| 268 | self.err("No header found", numline=self._numline) |
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| 269 | return -1 |
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| 270 | |||
| 271 | splitted = self.splitLine(line) |
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| 272 | if len(splitted) > 1: |
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| 273 | self._header['Sum'] = splitted[1].replace('"', '').strip() |
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| 274 | else: |
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| 275 | self.warn('Unexpected header format', numline=self._numline) |
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| 276 | |||
| 277 | return 0 |
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| 278 | |||
| 279 | View Code Duplication | if line.startswith('Normalised to'): |
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| 280 | if len(self._header) == 0: |
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| 281 | self.err("No header found", numline=self._numline) |
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| 282 | return -1 |
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| 283 | |||
| 284 | splitted = self.splitLine(line) |
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| 285 | if len(splitted) > 1: |
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| 286 | self._header['Normalized'] = splitted[1].replace('"', '').strip() |
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| 287 | else: |
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| 288 | self.warn('Unexpected header format', numline=self._numline) |
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| 289 | |||
| 290 | return 0 |
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| 291 | |||
| 292 | View Code Duplication | if line.startswith('Sample type'): |
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| 293 | if len(self._header) == 0: |
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| 294 | self.err("No header found", numline=self._numline) |
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| 295 | return -1 |
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| 296 | |||
| 297 | splitted = self.splitLine(line) |
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| 298 | if len(splitted) > 1: |
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| 299 | self._header['Sample type'] = splitted[1].strip() |
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| 300 | else: |
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| 301 | self.warn('Unexpected header format', numline=self._numline) |
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| 302 | |||
| 303 | return 0 |
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| 304 | |||
| 305 | View Code Duplication | if line.startswith('Initial sample weight (g)'): |
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| 306 | if len(self._header) == 0: |
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| 307 | self.err("No header found", numline=self._numline) |
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| 308 | return -1 |
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| 309 | |||
| 310 | splitted = self.splitLine(line) |
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| 311 | if len(splitted) > 1: |
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| 312 | self._header['Initial sample weight'] = splitted[1].replace('"', '').strip() |
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| 313 | else: |
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| 314 | self.warn('Unexpected header format', numline=self._numline) |
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| 315 | |||
| 316 | return 0 |
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| 317 | |||
| 318 | View Code Duplication | if line.startswith('Weight after pressing (g)'): |
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| 319 | if len(self._header) == 0: |
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| 320 | self.err("No header found", numline=self._numline) |
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| 321 | return -1 |
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| 322 | |||
| 323 | splitted = self.splitLine(line) |
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| 324 | if len(splitted) > 1: |
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| 325 | self._header['Weight after pressing'] = splitted[1].replace('"', '').strip() |
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| 326 | else: |
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| 327 | self.warn('Unexpected header format', numline=self._numline) |
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| 328 | |||
| 329 | return 0 |
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| 330 | |||
| 331 | View Code Duplication | if line.startswith('Correction applied for medium'): |
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| 332 | if len(self._header) == 0: |
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| 333 | self.warn('Unexpected header format', numline=self._numline) |
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| 334 | return -1 |
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| 335 | |||
| 336 | splitted = self.splitLine(line) |
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| 337 | if len(splitted) > 1: |
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| 338 | self._header['Correction medium'] = splitted[1].replace('"', '').strip() |
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| 339 | else: |
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| 340 | self.warn('Unexpected header format', numline=self._numline) |
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| 341 | |||
| 342 | return 0 |
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| 343 | |||
| 344 | View Code Duplication | if line.startswith('Correction applied for film'): |
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| 345 | if len(self._header) == 0: |
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| 346 | self.err("No header found", numline=self._numline) |
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| 347 | return -1 |
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| 348 | |||
| 349 | splitted = self.splitLine(line) |
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| 350 | if len(splitted) > 1: |
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| 351 | self._header['Correction film'] = splitted[1].replace('"', '').strip() |
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| 352 | else: |
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| 353 | self.warn('Unexpected header format', numline=self._numline) |
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| 354 | |||
| 355 | return 0 |
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| 356 | |||
| 357 | View Code Duplication | if line.startswith('Used Compound list'): |
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| 358 | if len(self._header) == 0: |
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| 359 | self.err("No header found", numline=self._numline) |
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| 360 | return -1 |
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| 361 | |||
| 362 | splitted = self.splitLine(line) |
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| 363 | if len(splitted) > 1: |
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| 364 | self._header['Used compound'] = splitted[1].replace('"', '').strip() |
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| 365 | else: |
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| 366 | self.warn('Unexpected header format', numline=self._numline) |
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| 367 | |||
| 368 | return 0 |
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| 369 | View Code Duplication | if line.startswith('Results database:'): |
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| 370 | if len(self._header) == 0: |
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| 371 | self.err("No header found", numline=self._numline) |
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| 372 | return -1 |
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| 373 | |||
| 374 | splitted = self.splitLine(line) |
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| 375 | if len(splitted) > 1: |
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| 376 | self._header['Result database'] = splitted[1].replace('"', '').strip() |
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| 377 | else: |
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| 378 | self.warn('Unexpected header format', numline=self._numline) |
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| 379 | |||
| 380 | return 0 |
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| 381 | |||
| 382 | |||
| 383 | if self.columns_name: |
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| 384 | if len(self._header) == 0: |
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| 385 | self.err("No header found", numline=self._numline) |
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| 386 | return -1 |
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| 387 | |||
| 388 | #Grab column names |
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| 389 | self._end_header = True |
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| 390 | self._columns = self.splitLine(line) |
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| 391 | return 1 |
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| 392 | |||
| 393 | if line.startswith('Results database in'): |
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| 394 | if len(self._header) == 0: |
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| 395 | self.err("No header found", numline=self._numline) |
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| 396 | return -1 |
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| 397 | |||
| 398 | splitted = self.splitLine(line) |
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| 399 | if len(splitted) > 1: |
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| 400 | self._header['Database path'] = splitted[1]+splitted[2] |
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| 401 | self.columns_name = True |
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| 402 | else: |
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| 403 | self.warn('Unexpected header format', numline=self._numline) |
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| 404 | |||
| 405 | return 1 |
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| 406 | |||
| 407 | else: |
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| 408 | self._header['Date'] = line |
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| 409 | return 1 |
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| 410 | |||
| 411 | def parse_resultline(self, line): |
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| 412 | # Process incoming results line |
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| 413 | if not line.strip(): |
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| 414 | return 0 |
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| 415 | |||
| 416 | rawdict = {} |
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| 417 | # Split by "," |
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| 418 | splitted = self.splitLine(line.strip(";")) |
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| 419 | # Look to know if the first value is an enumerate field |
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| 420 | try: |
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| 421 | int(splitted[0]) |
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| 422 | rawdict["num"] = splitted[0] |
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| 423 | splitted = splitted[1:] |
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| 424 | except ValueError: |
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| 425 | pass |
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| 426 | |||
| 427 | # Enumerate the list to obtain: [(0,data0),(1,data1),...] |
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| 428 | e_splitted = list(enumerate(splitted)) |
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| 429 | errors = '' |
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| 430 | |||
| 431 | com = False |
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| 432 | for idx, result in e_splitted: |
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| 433 | if result.startswith('"'): |
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| 434 | # It means that is the first value part |
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| 435 | # Consequently we take second part and append both |
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| 436 | result = (e_splitted[idx][1].strip('"') + "," + e_splitted[idx+1][1].strip('"')) |
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| 437 | e_splitted[idx] = (idx,result) |
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| 438 | e_splitted.remove(e_splitted[idx+1]) |
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| 439 | com = True |
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| 440 | rawdict[self._columns[idx]] = result |
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| 441 | conc = self._columns[idx] # Main value's name |
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| 442 | |||
| 443 | |||
| 444 | elif com:# We have rm the 2nd part value, consequently we |
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| 445 | # need to decrement idx |
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| 446 | if len(self._columns) <= idx-1: |
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| 447 | self.err("Orphan value in column ${index}", |
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| 448 | mapping={"index":str(idx + 1)}, |
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| 449 | numline=self._numline) |
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| 450 | break |
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| 451 | # We add and sync the result with its value's name |
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| 452 | rawdict[self._columns[idx-1]] = result |
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| 453 | |||
| 454 | else: |
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| 455 | if len(self._columns) <= idx: |
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| 456 | self.err("Orphan value in column ${index}", |
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| 457 | mapping={"index":str(idx + 1)}, |
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| 458 | numline=self._numline) |
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| 459 | break |
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| 460 | rawdict[self._columns[idx]] = result |
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| 461 | |||
| 462 | aname = rawdict[self._columns[0]]# The fisrt column is analytic name |
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| 463 | if not aname: |
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| 464 | self.err("No Analysis Name defined", numline=self._numline) |
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| 465 | return 0 |
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| 466 | elif aname == "<H>": |
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| 467 | # <H> maybe is data error header? We need more examples... |
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| 468 | errors = rawdict.get('Compound') |
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| 469 | notes = rawdict.get('Calibration') |
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| 470 | rawdict['Notes'] = notes |
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| 471 | |||
| 472 | rid = self._header['Sample'] |
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| 473 | if not rid: |
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| 474 | self.err("No Sample defined", numline=self._numline) |
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| 475 | return 0 |
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| 476 | |||
| 477 | notes = rawdict.get('Notes', '') |
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| 478 | notes = "Notes: %s" % notes if notes else '' |
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| 479 | rawdict['DefaultResult'] = conc |
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| 480 | # Replace to obtain UK values from default |
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| 481 | rawdict[conc] = rawdict[conc].replace(',','.') |
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| 482 | rawdict['Remarks'] = ' '.join([errors, notes]) |
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| 483 | rawres = self.getRawResults().get(rid, []) |
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| 484 | raw = rawres[0] if len(rawres) > 0 else {} |
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| 485 | raw[aname] = rawdict |
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| 486 | if not 'DateTime' in raw: |
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| 487 | try: |
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| 488 | raw['DateTime'] = {'DateTime':self.csvDate2BikaDate(self._header['Date']), |
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| 489 | 'DefaultValue':'DateTime'} |
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| 490 | except: |
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| 491 | pass |
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| 492 | |||
| 493 | self._addRawResult(rid, raw, True) |
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| 494 | return 0 |
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| 495 | |||
| 496 | |||
| 497 | def getAttachmentFileType(self): |
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| 498 | return "PANalytical - Omnia Axios XRF" |
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| 499 | |||
| 500 | |||
| 501 | class AxiosXrfImporter(AnalysisResultsImporter): |
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| 502 | |||
| 503 | def __init__(self, parser, context, override, |
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| 504 | allowed_ar_states=None, allowed_analysis_states=None, |
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| 505 | instrument_uid=None): |
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| 506 | AnalysisResultsImporter.__init__(self, parser, context, |
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| 507 | override, |
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| 508 | allowed_ar_states, |
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| 509 | allowed_analysis_states, |
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| 510 | instrument_uid) |
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| 511 |
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