# Copyright (c) 2018 Combine Control Systems AB
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#
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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.
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# 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
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
from sympathy.api import node as synode
from sympathy.api import dtypes
from sympathy.api.nodeconfig import (Port, Ports, Tag, Tags,
adjust)
from sympathy.api import exceptions as syexc
class ConvertError(Exception):
pass
MASKED_NODEIDS = [
'org.sysess.sympathy.table.fillmaskedvalues',
'org.sysess.sympathy.table.maskvalues',
'org.sysess.sympathy.table.dropmaskvalues',
]
_config_error_msg = (
'Failure in column {column}: could not convert {name}: '
'"{value}" to {type}.')
def _convert(value, dtype, name):
try:
return dtypes.numpy_value_from_dtype_str(dtype, value)
except ValueError:
raise ConvertError(
dict(name=name, value=value, type=dtypes.typename_from_kind(
dtype.kind)))
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.
"""
author = 'Erik der Hagopian'
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'
related = (MASKED_NODEIDS
+ ['org.sysess.sympathy.data.table.holdvaluetable'])
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['columns']._passthrough = 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 = dtypes.numpy_dtype_factory_for_dtype(
column.dtype)
value = _convert(fill.value, dtype, 'Value')
return column.filled(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):
try:
output_table.set_column_from_array(
name, fill_conv(array))
output_table.set_column_attributes(
name, input_table.get_column_attributes(name))
except ConvertError as ce:
raise syexc.SyConfigurationError(_config_error_msg.format(
column=name, **ce.args[0]))
else:
output_table.update_column(name, input_table, name)
[docs]class MaskTable(synode.Node):
"""
Mask values in Table.
"""
author = 'Erik der Hagopian'
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'
related = MASKED_NODEIDS
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['columns']._passthrough = 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 = dtypes.numpy_dtype_factory_for_dtype(
column.dtype)
value = _convert(fill.value, dtype, '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):
try:
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))
except ConvertError as ce:
raise syexc.SyConfigurationError(_config_error_msg.format(
column=name, **ce.args[0]))
[docs]class DropMaskTable(synode.Node):
author = 'Erik der Hagopian'
description = 'Drop either rows or columns with any masked values.'
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'
related = MASKED_NODEIDS + ['org.sysess.sympathy.data.table.dropnantable']
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['columns']._passthrough = 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):
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))
