Пример #1
0
    def test_clean_data_obj_success(self):
        rows = [{
            'name': 'John',
            'age': '40',
            'active': '1957-1980',
            'rate': '9.7'
        }]

        expected_data = {
            'name': 'John',
            'age': 40,
            'active': '1957-1980',
            'rate': 9.7
        }

        result = clean_data_obj(rows)
        self.assertEqual(result[0], expected_data)
Пример #2
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    def test_clean_data_obj_javascript(self):
        rows = [{
            'name': 'John',
            'age': '40',
            'active': '<script src=/hacker/bad.js></script>',
            'rate': '9.7'
        }]

        expected_data = {
            'name': 'John',
            'age': 40,
            'active': '&lt;script src=/hacker/bad.js&gt;&lt;/script&gt;',
            'rate': 9.7
        }

        result = clean_data_obj(rows)
        self.assertEqual(result[0], expected_data)
Пример #3
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    def test_clean_data_obj_list_data(self):
        row = ['John', '40', '1957-1980', '9.7']
        expected_data = ['John', 40, '1957-1980', 9.7]

        result = clean_data_obj(row)
        self.assertEqual(result, expected_data)
Пример #4
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 def test_clean_data_obj_empty_obj(self):
     result = clean_data_obj({})
     self.assertEqual(result, {})
Пример #5
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def import_from_gsheet_helper(user,
                              silo_id,
                              silo_name,
                              spreadsheet_id,
                              sheet_id=None,
                              partialcomplete=False):
    msgs = []
    if spreadsheet_id is None:
        msgs.append({
            'level': messages.ERROR,
            'msg': 'A Google Spreadsheet is not selected to import data from.',
            'redirect': reverse('index')
        })

    credential_obj = _get_credential_object(user)
    if not isinstance(credential_obj, OAuth2Credentials):
        msgs.append(credential_obj)
        return msgs

    defaults = {
        'name': silo_name,
        'description': 'Google Sheet Import',
        'public': False,
        'owner': user
    }
    silo, created = Silo.objects.get_or_create(
        pk=None if silo_id == '0' else silo_id, defaults=defaults)
    msgs.append({'silo_id': silo.id})

    metadata, error = _get_gsheet_metadata(credential_obj, spreadsheet_id,
                                           user)
    if error:
        if 'credential' in error:
            return error.get('credential')
        else:
            msgs.append(error)
            return msgs

    spreadsheet_name = metadata.get('properties', {}).get('title', '')
    gsheet_read = _get_or_create_read('GSheet Import', spreadsheet_name,
                                      'Google Spreadsheet Import',
                                      spreadsheet_id, user, silo)
    if sheet_id:
        gsheet_read.gsheet_id = sheet_id
        gsheet_read.save()

    sheet_name = 'Sheet1'
    if gsheet_read.gsheet_id:
        sheets = metadata.get('sheets', None)
        for sheet in sheets:
            properties = sheet.get('properties', None)
            if properties:
                if str(properties.get('sheetId')) == str(
                        gsheet_read.gsheet_id):
                    sheet_name = properties.get('title')

    values, error = _fetch_data_gsheet(credential_obj, spreadsheet_id,
                                       sheet_name)
    if error:
        msgs.append(error)
        return msgs

    unique_fields = silo.unique_fields.all()
    skipped_rows = set()
    headers = []
    lvss = []

    # get the column names
    header = values.pop(0)
    for h in header:
        h = cleanKey(h)
        headers.append(h)

    data = _convert_gsheet_data(headers, values)

    for r, row in enumerate(data):
        filter_criteria = {}
        # build filter_criteria if unique field(s) have been setup for this silo
        for uf in unique_fields:
            try:
                if str(row[uf.name]).isdigit():
                    filter_criteria.update({str(uf.name): int(row[uf.name])})
                else:
                    filter_criteria.update({str(uf.name): str(row[uf.name])})
            except AttributeError as e:
                logger.warning(e)
        if filter_criteria:
            filter_criteria.update({'silo_id': silo.id})
            try:
                lvs = LabelValueStore.objects.get(**filter_criteria)
                lvs.edit_date = timezone.now()
            except LabelValueStore.DoesNotExist:
                lvs = LabelValueStore()
            except LabelValueStore.MultipleObjectsReturned:
                for k, v in filter_criteria.iteritems():
                    skipped_rows.add('{}={}'.format(k, v))
                continue
        else:
            lvs = LabelValueStore()

        # add the key and values to the document
        row = clean_data_obj(row)
        for key, val in row.iteritems():
            val = smart_str(val, strings_only=True)
            setattr(lvs, key, val)

        lvs.silo_id = silo.id
        lvs.read_id = gsheet_read.id
        lvs.create_date = timezone.now()
        lvs = calculateFormulaCell(lvs, silo)
        if partialcomplete:
            lvss.append(lvs)
        else:
            lvs.save()
    addColsToSilo(silo, headers)

    if skipped_rows:
        skipped_str = ','.join(str(s) for s in skipped_rows)
        msg = 'Skipped updating/adding records where {} because there are ' \
              'already multiple records.'.format(skipped_str)
        msgs.append({'level': messages.WARNING, 'msg': msg})

    msgs.append({'level': messages.SUCCESS, 'msg': 'Operation successful'})
    if partialcomplete:
        return lvss, msgs
    return msgs