# Copyright (c) 2018 Combine Control Systems AB
# All rights reserved.
#
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# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the Combine Control Systems AB nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED.
# IN NO EVENT SHALL COMBINE CONTROL SYSTEMS AB BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
from sympathy.api import node as synode
from sympathy.api.nodeconfig import (Port, Ports, Tag, Tags,
adjust)
def selected_columns_op(input_table, output_table, columns, set_progress,
update=True):
if update:
output_table.set_name(input_table.get_name())
output_table.set_table_attributes(input_table.get_table_attributes())
column_names = input_table.column_names()
selected_names = set(columns.selected_names(column_names))
n_column_names = len(column_names)
for i, name in enumerate(column_names):
set_progress(i * (100. / n_column_names))
if name in selected_names:
yield name
elif update:
output_table.update_column(name, input_table, name)
[docs]class FillMaskedTable(synode.Node):
"""
Fill masked values in Table.
:Opposite. nodes: :ref:`Mask values in Table`
"""
author = 'Erik der Hagopian <erik.hagopian@combine.se>'
copyright = '(c) 2018 Combine Control Systems AB'
description = 'Fill masked values in Table.'
icon = 'select_table_columns.svg'
name = 'Fill masked values in Table'
nodeid = 'org.sysess.sympathy.table.fillmaskedvalues'
tags = Tags(Tag.DataProcessing.Select)
version = '1.0'
inputs = Ports([Port.Table('Input')])
outputs = Ports([Port.Table('Output')])
parameters = synode.parameters()
parameters.set_list(
'columns', label='Select columns', description='Select columns.',
value=[], editor=synode.Editors.multilist_editor(edit=True))
parameters.set_string(
'value', label='Value', description='Specified fill value',
value='')
def adjust_parameters(self, node_context):
adjust(node_context.parameters['columns'], node_context.input[0])
def execute(self, node_context):
in_table = node_context.input[0]
out_table = node_context.output[0]
self.fill_columns(
in_table, out_table, node_context.parameters['columns'],
self.set_progress, node_context.parameters['value'])
@staticmethod
def fill_columns(input_table, output_table, columns, set_progress, fill):
def fill_conv(column):
dtype = column.dtype
if dtype.kind in ['U', 'S']:
dtype = np.dtype(dtype.kind)
return column.filled(dtype.type(fill.value))
for name in selected_columns_op(input_table, output_table, columns,
set_progress):
array = input_table.get_column_to_array(name)
if isinstance(array, np.ma.MaskedArray):
output_table.set_column_from_array(
name, fill_conv(array))
output_table.set_column_attributes(
name, input_table.get_column_attributes(name))
else:
output_table.update_column(name, input_table, name)
[docs]class MaskTable(synode.Node):
"""
Mask values in Table.
:Opposite. nodes: :ref:`Fill masked values in Table`
"""
author = 'Erik der Hagopian <erik.hagopian@combine.se>'
copyright = '(c) 2018 Combine Control Systems AB'
description = 'Mask values in Table.'
icon = 'select_table_columns.svg'
name = 'Mask values in Table'
nodeid = 'org.sysess.sympathy.table.maskvalues'
tags = Tags(Tag.DataProcessing.Select)
version = '1.0'
inputs = Ports([Port.Table('Input')])
outputs = Ports([Port.Table('Output')])
parameters = synode.parameters()
parameters.set_list(
'columns', label='Select columns', description='Select columns.',
value=[], editor=synode.Editors.multilist_editor(edit=True))
parameters.set_string(
'value', label='Value', description='Specified fill value',
value='')
def adjust_parameters(self, node_context):
adjust(node_context.parameters['columns'], node_context.input[0])
def execute(self, node_context):
in_table = node_context.input[0]
out_table = node_context.output[0]
self.mask_columns(
in_table, out_table, node_context.parameters['columns'],
self.set_progress, node_context.parameters['value'])
@staticmethod
def mask_columns(input_table, output_table, columns, set_progress, fill):
def mask_conv(column):
dtype = column.dtype
if dtype.kind in ['U', 'S']:
dtype = np.dtype(dtype.kind)
value = dtype.type(fill.value)
if dtype.kind == 'f' and np.isnan(value):
mask = np.isnan(column)
elif dtype.kind in ['m', 'M'] and np.isnat(value):
mask = np.isnat(column)
else:
mask = column == value
if isinstance(column, np.ma.MaskedArray):
mask |= column.mask
res = np.ma.MaskedArray(column.data, mask, dtype=dtype)
else:
res = np.ma.MaskedArray(column, mask, dtype=dtype)
return res
for name in selected_columns_op(input_table, output_table, columns,
set_progress):
output_table.set_column_from_array(
name, mask_conv(input_table.get_column_to_array(name)))
output_table.set_column_attributes(
name, input_table.get_column_attributes(name))
[docs]class DropMaskTable(synode.Node):
"""
Drop masked values in Table.
:Ref. nodes: :ref:`Fill masked values in Table`,
:ref:`Mask values in Table`
"""
author = 'Erik der Hagopian <erik.hagopian@combine.se>'
copyright = '(c) 2018 Combine Control Systems AB'
description = 'Drop masked values in Table.'
icon = 'select_table_columns.svg'
name = 'Drop masked values in Table'
nodeid = 'org.sysess.sympathy.table.dropmaskvalues'
tags = Tags(Tag.DataProcessing.Select)
version = '1.0'
inputs = Ports([Port.Table('Input')])
outputs = Ports([Port.Table('Output')])
parameters = synode.parameters()
parameters.set_list(
'columns', label='Select columns', description='Select columns.',
value=[], editor=synode.Editors.multilist_editor(edit=True))
directions = ['Rows', 'Columns']
parameters.set_string(
'direction', label='Drop',
value=directions[0],
description='Select along which axis to drop values',
editor=synode.Editors.combo_editor(options=directions))
def adjust_parameters(self, node_context):
adjust(node_context.parameters['columns'], node_context.input[0])
def execute(self, node_context):
in_table = node_context.input[0]
out_table = node_context.output[0]
self.drop_columns(
in_table, out_table, node_context.parameters['columns'],
self.set_progress, node_context.parameters['direction'])
@staticmethod
def drop_columns(input_table, output_table, columns, set_progress,
direction):
def mask_conv(column):
dtype = column.dtype
if dtype.kind in ['U', 'S']:
dtype = np.dtype(dtype.kind)
if direction.value == 'Columns':
for name in selected_columns_op(input_table, output_table, columns,
set_progress):
array = input_table.get_column_to_array(name)
if isinstance(array, np.ma.MaskedArray):
if not np.any(array.mask):
output_table.set_column_from_array(
name, array.data)
output_table.set_column_attributes(
name, input_table.get_column_attributes(name))
else:
output_table.update_column(name, input_table, name)
elif direction.value == 'Rows':
mask = np.zeros(input_table.number_of_rows(), dtype=bool)
for name in selected_columns_op(input_table, output_table, columns,
set_progress, update=False):
array = input_table.get_column_to_array(name)
if isinstance(array, np.ma.MaskedArray):
mask |= array.mask
if not np.any(mask):
output_table.update(input_table)
else:
for name in input_table.column_names():
array = input_table.get_column_to_array(name)
array = array[~mask]
if isinstance(array, np.ma.MaskedArray):
if not np.any(array.mask):
array = array.data
output_table.set_column_from_array(
name, array)
output_table.set_column_attributes(
name, input_table.get_column_attributes(name))
