# Copyright (c) 2016-2017, System Engineering Software Society
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from __future__ import (print_function, division, unicode_literals,
absolute_import)
import json
import numpy as np
from sympathy import api
from sympathy.api import node as synode
from sympathy.api import qt2 as qt_compat
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags, adjust
from sympathy.api.nodeconfig import join_doc
from sympathy.platform.exceptions import SyConfigurationError
from sylib.old_figure import util, gui
# OVERVIEW_DOCSTRING = """
# The
# :ref:`Figure Compressor` node allows to compress a list of Figures into one
# single Figure, while the :ref:`Layout Figures in Subplots` generates a Figure
# with subplots. Figures can be exported using the :ref:`Export Figures` node.
# """
TREEVIEW_DOCSTRING = """
.. note::
This node is being deprecated by
:ref:`org.sysess.sympathy.visualize.figure`,
please avoid using it for new flows.
Both of the nodes :ref:`org.sysess.sympathy.visualize.figuretabletreegui` and
:ref:`org.sysess.sympathy.visualize.figurestablestreegui` are used to configure
figures in a graphical user interface. They both output the figure(s) on the
upper port and a configuration table an optional lower port. The configuration
table can be used in the nodes
:ref:`org.sysess.sympathy.visualize.figurefromtablewithtable` and
:ref:`org.sysess.sympathy.visualize.figuresfromtableswithtable`.
The configuration gui for these nodes consists of a toolbar and a tree view.
The tree view has two columns: one for the configuration items and one for
their values.
You can add plots to the figure by clicking on its corresponding button in the
toolbar, or by pressing on a plot button and dragging it to where in the tree
view you want it (possible drop locations will be shown in green). The plot
will be added with some basic properties depending on which plot type you added
(e.g. *X Data* and *Y Data* for line plot). Almost all configuration items
support more than the default properties. To add more, right-click on a
configuration item and choose "Add..." or "Add property" depending on what you
want to add.
Properties that allow free text are interpreted as python code and executed. In
this python evironment the input data table is available under the name ``arg``
(``table`` can also be used for historical reasons). For example one can refer
to data columns in a connected Table by writing something like
``arg['My data column']``. Have a look at the :ref:`datatypeapis` to see all
the available methods and attributes for the data type that you connect to the
node.
Use the node :ref:`Export figures` to write any figures you produce to files.
"""
CONF_TABLE_DOCSTRING = """
Having the full configuration for the figure as a table on an input port allow
you to programmatically configure a figure. If you are looking for an eaiser
but slightly less powerful way to configure a figure you should instead use one
of the nodes :ref:`org.sysess.sympathy.visualize.figuretabletreegui` or
:ref:`org.sysess.sympathy.visualize.figurestablestreegui` where you can
configure the figure in a graphical user interface.
The configuration table consists of one parameter column and one value column.
Both column should be of text type. The easiest way to learn how to create a
specific figure with this node is to first build the same figure using the node
:ref:`org.sysess.sympathy.visualize.figuretabletreegui` and look at the
configuration table that that node produces.
Here is a simple example of a configuration table for a line plot:
=========================== ===================
Parameters Values
=========================== ===================
axes.axes-1.xaxis_position bottom
axes.axes-1.yaxis_position left
axes.axes-1.title Plot title
axes.axes-1.xlabel The xlabel
axes.axes-1.ylabel The ylabel
line.line-1.axes axes-1
line.line-1.xdata ``table.col('x').data``
line.line-1.ydata ``table.col('y').data``
=========================== ===================
**Plots**
Every line/scatter is addressed with a unique
identifier *{id}*, which can be any string without a '.'. A
line parameter is constructed as with *line.{id}.{property}*
in the parameter column and the corresponding value in the
value column. Every line needs to have at least the 'xdata'
and 'ydata' specified. All line properties, except the 'ydata',
can also be given on a *global* level like *line.{property}*.
All properties given on a global level with be copied to all
configured lines without overriding locally declared properties.
Currently supported properties are (some properties allow
alternative names *longname/shortname*):
===================== =====================
Property Type
===================== =====================
xdata unicode
ydata unicode
axes *axes id* (see below)
label unicode
marker matplotlib marker: o, ., ^, d, etc
markersize float
markeredgecolor mpl color (see below)
markeredgewidth float
markerfacecolor mpl color (see below)
linestyle matplotlib line style: -, --, .-, etc
linewidth float
color mpl color (see below)
alpha float [0., 1.]
zorder number
drawstyle matplotlib drawstyle: default, steps, etc
===================== =====================
Please see the matplotlib_ documentation for sensible values of the
different types.
.. _matplotlib: http://matplotlib.org/api/lines_api.html
Example
^^^^^^^
An example assigning the 'index' column as x values and the 'signal' column as
y values to a line with id 'line-1', as well as drawing it in red with a
circular marker:
==================== ==================
Parameters Values
==================== ==================
line.line-1.xdata ``table.col('index').data``
line.line-1.ydata ``table.col('signal').data``
line.line-1.color red
line.line-1.marker o
==================== ==================
**Axes**
Axes are defined similarly to lines. All axes are overlaid on top of each
other. Every axes also has a unique identifier *{id}* (without '.'). The
parameter name is constructed as *axes.{id}.{property}* on the local level
or *axes.{property}* for global properties, valid for all defined axes.
===================== =====================
Property Type
===================== =====================
xaxis_position bottom, top
yaxis_position left, right
title unicode
xlabel unicode
ylabel unicode
xlim str of two comma separated numbers
ylim str of two comma separated numbers
xscale linear, log
yscale linear, log
aspect auto, equal, float
grid GRID (see below)
legend LEGEND (see below)
===================== =====================
**Grid**
Every axes can also have a grid with the following optional
properties:
===================== =====================
Property Type
===================== =====================
show bool
color mpl color (see below)
linestyle matplotlib line style: -, --, .-, etc
linewidth float
which major, minor, both
axis both, x, y
===================== =====================
**Legend**
Every axes can also have a legend defined with the following
optional properties:
===================== =====================
Property Type
===================== =====================
show bool
loc mpl legend location (e.g. best, upper left)
ncol int
fontsize e.g. x-small, medium, x-large, etc
markerfirst bool
frameon bool
title unicode
===================== =====================
Example
^^^^^^^
The example defines two axes, one (id=xy) with the y axis on the left and the
other (id=foo) with the y axis on the right while sharing the bottom x axis.
Since the xaxis_position is shared between the two axes, it is defined on the
global level. For *xy*, a legend will be shown in the upper left corner, while
the *foo* axes will have a green grid.
======================= ===================
Parameters Values
======================= ===================
axes.xaxis_position bottom
axes.xy.yaxis_position left
axes.xy.xlabel The xy xlabel
axes.xy.ylabel The xy ylabel
axes.xy.legend.show True
axes.xy.legend.loc upper left
axes.foo.yaxis y2
axes.foo.ylabel The y2 ylabel
axes.foo.grid.show True
axes.foo.grid.color green
======================= ===================
**MPL colors**
All properties with *mpl colors* values expect a string with
either a hex color (with or without extra alpha channel), 3 or 4
comma separated integers for the RGBA values (range [0, 255]),
3 or 4 comma separated floats for the RGBA values (range [0., 1.])
or a matplotlib color_ name (e.g. r, red, blue, etc.).
.. _color: http://matplotlib.org/examples/color/named_colors.html
"""
QtCore = qt_compat.QtCore
QtGui = qt_compat.QtGui
qt_compat.backend.use_matplotlib_qt()
class SuperNodeFigureWithTable(synode.Node):
author = 'Benedikt Ziegler'
version = '0.1'
icon = 'figure.svg'
tags = Tags(Tag.Visual.Figure)
parameters = synode.parameters()
parameters.set_list(
'parameters', label='Parameters:',
description='The column containing the parameter names.',
editor=synode.Util.combo_editor(edit=True, filter=True))
parameters.set_list(
'values', label='Values:',
description='The column containing the parameter values.',
editor=synode.Util.combo_editor(edit=True, filter=True))
def verify_parameters(self, node_context):
parameters = node_context.parameters
param_list = [] != parameters['parameters'].list
value_list = [] != parameters['values'].list
return param_list and value_list
def adjust_parameters(self, node_context):
config_input = node_context.input['config']
adjust(node_context.parameters['parameters'], config_input)
adjust(node_context.parameters['values'], config_input)
def execute(self, node_context):
config_table = node_context.input['config']
parameters = node_context.parameters
param_col = parameters['parameters'].selected
value_col = parameters['values'].selected
if param_col is None or value_col is None:
raise SyConfigurationError(
"No columns were selected or the columns you have selected "
"are not present in the input please make sure to use data "
"that contains the selected columns before executing this "
"node.")
param_names = config_table.get_column_to_array(param_col)
param_values = config_table.get_column_to_array(value_col)
configuration = list(zip(param_names, param_values))
figure_param = util.parse_configuration(configuration)
self._create_figure(node_context, figure_param)
def _create_figure(self, node_context, figure_param):
raise NotImplementedError()
