Metadata-Version: 2.1
Name: pipeline_csv
Version: 1.1
Summary: Pipeline inline inspection data as CSV file.
Home-page: https://github.com/vb64/pipeline.csv
Author: Vitaly Bogomolov
Author-email: mail@vitaly-bogomolov.ru
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/vb64/pipeline.csv/issues
Description: # PipelineCsv library
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        [In Russian](READMEru.md)
        
        The free, open source PipelineCsv library is designed to work with the results of analysis of in-line flaw detection data in the form of a CSV file.
        
        The library provides a set of high-level operations with CSV file.
        
        Data can be
        
        -   mirrored
        -   glued together from several CSV files
        -   stretched/compressed along the distance according to a given set of intermediate points
        -   interpreted as an iterable sequence of pipes with geodata
        
        ## Installation
        
        ```bash
        pip install pipeline-csv
        ```
        
        ## Usage
        
        It is necessary to define the sets of defects and markers used in your project.
        To do this, you need to define your class for CSV row by deriving it from the `pipeline_csv.csvfile.row.Row` class and
        override two methods of this class: `defekts_dict` and `lineobj_dict`.
        
        ```python
        from pipeline_csv.csvfile.row import Row
        
        class TypeMarker:
            VALVE = 0
            CASE_START = 1
            CASE_END = 2
        
        LINEOBJ = {
          TypeMarker.VALVE: "Valve",
          TypeMarker.CASE_START: "Casing start",
          TypeMarker.CASE_END: "Casing end",
        }
        
        class TypeDefekt:
            CORROZ = 0
            DENT = 1
        
        DEFEKTS = {
          TypeDefekt.CORROZ: "Corrosion",
          TypeDefekt.DENT: "Dent",
        }
        
        class MyRow(Row):
        
            @staticmethod
            def defekts_dict():
                return DEFEKTS
        
            @staticmethod
            def lineobj_dict():
                return LINEOBJ
        ```
        
        For the data mirroring operation, you need to override the `markers_reverse` method, which returns a dictionary that specifies the rules for replacing when mirroring.
        
        ```python
        REVERSE_MARKER = {
          TypeMarker.CASE_START: TypeMarker.CASE_END,
          TypeMarker.CASE_END: TypeMarker.CASE_START,
        }
        
        class MyRow(Row):
        
            @staticmethod
            def markers_reverse():
                return REVERSE_MARKER
        ```
        
        Further, the MyRow class can be used in operations with data of CSV files.
        
        ### Creating a CSV file
        
        Construct new csv file from scratch.
        
        ```python
        from pipeline_csv.csvfile import File
        
        csv_file = File()
        ```
        
        Define tube at distance 1.0 m length = 11.0 m, thick = 10.5 mm with one seam with orientation 3 hour 00 minutes.
        
        ```python
        from pipeline_csv import TypeHorWeld
        from pipeline_csv.orientation import Orientation
        
        csv_file.data = [
          MyRow.as_weld(1000),
          MyRow.as_thick(1010, 105),
          MyRow.as_seam(1020, TypeHorWeld.HORIZONTAL, Orientation(3, 0), None),
          MyRow.as_weld(12000),
        ]
        ```
        
        Add outside defect to tube at distance 5.0 m from left tube weld,
        length = 20 mm, width = 10 mm, depth = 30% tube wall thickness,
        orientation from 4 hours 00 minutes to 5 hours 00 minutes,
        maximum depth point at distance 5.01 m from left tube weld, orientation 4 hours 30 minutes
        with comment 'metal loss'.
        
        ```python
        from pipeline_csv import DefektSide
        
        csv_file.data.append(MyRow.as_defekt(
          6000,
          TypeDefekt.CORROZ,
          DefektSide.OUTSIDE
          '20', '10', '30',
          Orientation(4, 0), Orientation(5, 0),
          6010, Orientation(4, 30),
          'metal loss'
        ))
        ```
        
        Save csv to file.
        
        ```python
        import os
        
        csv_file.to_file('example.csv')
        assert os.path.getsize('example.csv') > 0
        ```
        
        ### Data mirroring
        
        Create copy from saved file.
        
        ```python
        csv_copy = File.from_file('example.csv')
        ```
        
        Check distance of the last object in copy and defect orientation.
        
        ```python
        assert csv_copy.total_length == 12000
        assert len(csv_copy.data) == 5
        
        defect_row = csv_copy.data[3]
        assert defect_row.is_defect
        assert defect_row.orient_td == '4,00'
        assert defect_row.orient_bd == '5,00'
        ```
        
        Reverse copy.
        
        ```python
        csv_copy.reverse()
        ```
        
        Relative position of defekt must change and defect orientation must be mirrored.
        
        ```python
        defect_row = csv_copy.data[2]
        assert defect_row.is_defect
        
        assert defect_row.orient_td == '7,00'
        assert defect_row.orient_bd == '8,00'
        ```
        
        Save reversed copy to file.
        
        ```python
        csv_file.to_file('reversed.csv')
        assert os.path.getsize('reversed.csv') > 0
        ```
        
        ### Docking new data and compression/stretching
        
        Append to initial CSV empty pipe with length 10.0 m and reversed copy from the file.
        
        ```python
        csv_file.join([10000, 'reversed.csv'])
        assert csv_file.total_length == 28000
        assert len(csv_file.data) == 11
        ```
        
        Compress distances and length of all objects in half.
        
        ```python
        csv_file.dist_modify(
          # table of corrections
          # each node define as pair 'existing distance', 'new distance'
          [[0, 0],
          [28000, 14000],
        ])
        assert csv_file.total_length == 14000
        ```
        
        Save file with compress distances.
        
        ```python
        csv_file.to_file('transformed.csv')
        assert os.path.getsize('transformed.csv') > 0
        ```
        
        ### Pipe sequence
        
        Iterate by pipes.
        
        ```python
        csv_trans = File.from_file('transformed.csv')
        warnings = []
        current_dist = 0
        for i in csv_trans.get_tubes(warnings):
            assert i.dist >= current_dist
            current_dist = i.dist
            tube = i
        
        assert not warnings
        ```
        
        Set geodata for tube
        
        ```python
        assert tube.latitude == ''
        assert tube.longtitude == ''
        assert tube.altitude == ''
        
        tube.set_geo(10, 11, 12)
        
        assert tube.latitude == 10
        assert tube.longtitude == 11
        assert tube.altitude == 12
        
        csv_trans.to_file('geo.csv')
        assert os.path.getsize('geo.csv') > 0
        ```
        
        Load from saved file and check geodata from last pipe.
        
        ```python
        csv_geo = File.from_file('geo.csv')
        last_tube = list(csv_geo.get_tubes(warnings))[-1]
        
        assert last_tube.latitude == '10'
        assert last_tube.longtitude == '11'
        assert last_tube.altitude == '12'
        ```
        
        ## Development
        
        ```
        $ git clone git@github.com:vb64/pipeline.csv.git
        $ cd pipeline.csv
        $ make setup PYTHON_BIN=/path/to/python3
        $ make tests
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
