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from pyqtgraph.Qt import QtGui, QtCore
import pyqtgraph.parametertree as ptree
import numpy as np
from pyqtgraph.pgcollections import OrderedDict
import pyqtgraph.functions as fn
__all__ = ['ColorMapWidget']
class ColorMapWidget(ptree.ParameterTree):
"""
This class provides a widget allowing the user to customize color mapping
for multi-column data. Given a list of field names, the user may specify
multiple criteria for assigning colors to each record in a numpy record array.
Multiple criteria are evaluated and combined into a single color for each
record by user-defined compositing methods.
For simpler color mapping using a single gradient editor, see
:class:`GradientWidget <pyqtgraph.GradientWidget>`
"""
sigColorMapChanged = QtCore.Signal(object)
def __init__(self):
ptree.ParameterTree.__init__(self, showHeader=False)
self.params = ColorMapParameter()
self.setParameters(self.params)
self.params.sigTreeStateChanged.connect(self.mapChanged)
## wrap a couple methods
self.setFields = self.params.setFields
self.map = self.params.map
def mapChanged(self):
self.sigColorMapChanged.emit(self)
class ColorMapParameter(ptree.types.GroupParameter):
sigColorMapChanged = QtCore.Signal(object)
def __init__(self):
self.fields = {}
ptree.types.GroupParameter.__init__(self, name='Color Map', addText='Add Mapping..', addList=[])
self.sigTreeStateChanged.connect(self.mapChanged)
def mapChanged(self):
self.sigColorMapChanged.emit(self)
def addNew(self, name):
mode = self.fields[name].get('mode', 'range')
if mode == 'range':
self.addChild(RangeColorMapItem(name, self.fields[name]))
elif mode == 'enum':
self.addChild(EnumColorMapItem(name, self.fields[name]))
def fieldNames(self):
return self.fields.keys()
def setFields(self, fields):
"""
Set the list of fields to be used by the mapper.
The format of *fields* is::
[ (fieldName, {options}), ... ]
============== ============================================================
Field Options:
mode Either 'range' or 'enum' (default is range). For 'range',
The user may specify a gradient of colors to be applied
linearly across a specific range of values. For 'enum',
the user specifies a single color for each unique value
(see *values* option).
units String indicating the units of the data for this field.
values List of unique values for which the user may assign a
color when mode=='enum'. Optionally may specify a dict
instead {value: name}.
============== ============================================================
"""
self.fields = OrderedDict(fields)
#self.fields = fields
#self.fields.sort()
names = self.fieldNames()
self.setAddList(names)
def map(self, data, mode='byte'):
"""
Return an array of colors corresponding to *data*.
========= =================================================================
Arguments
data A numpy record array where the fields in data.dtype match those
defined by a prior call to setFields().
mode Either 'byte' or 'float'. For 'byte', the method returns an array
of dtype ubyte with values scaled 0-255. For 'float', colors are
returned as 0.0-1.0 float values.
========= =================================================================
"""
colors = np.zeros((len(data),4))
for item in self.children():
if not item['Enabled']:
continue
chans = item.param('Channels..')
mask = np.empty((len(data), 4), dtype=bool)
for i,f in enumerate(['Red', 'Green', 'Blue', 'Alpha']):
mask[:,i] = chans[f]
colors2 = item.map(data)
op = item['Operation']
if op == 'Add':
colors[mask] = colors[mask] + colors2[mask]
elif op == 'Multiply':
colors[mask] *= colors2[mask]
elif op == 'Overlay':
a = colors2[:,3:4]
c3 = colors * (1-a) + colors2 * a
c3[:,3:4] = colors[:,3:4] + (1-colors[:,3:4]) * a
colors = c3
elif op == 'Set':
colors[mask] = colors2[mask]
colors = np.clip(colors, 0, 1)
if mode == 'byte':
colors = (colors * 255).astype(np.ubyte)
return colors
class RangeColorMapItem(ptree.types.SimpleParameter):
def __init__(self, name, opts):
self.fieldName = name
units = opts.get('units', '')
ptree.types.SimpleParameter.__init__(self,
name=name, autoIncrementName=True, type='colormap', removable=True, renamable=True,
children=[
#dict(name="Field", type='list', value=name, values=fields),
dict(name='Min', type='float', value=0.0, suffix=units, siPrefix=True),
dict(name='Max', type='float', value=1.0, suffix=units, siPrefix=True),
dict(name='Operation', type='list', value='Overlay', values=['Overlay', 'Add', 'Multiply', 'Set']),
dict(name='Channels..', type='group', expanded=False, children=[
dict(name='Red', type='bool', value=True),
dict(name='Green', type='bool', value=True),
dict(name='Blue', type='bool', value=True),
dict(name='Alpha', type='bool', value=True),
]),
dict(name='Enabled', type='bool', value=True),
dict(name='NaN', type='color'),
])
def map(self, data):
data = data[self.fieldName]
scaled = np.clip((data-self['Min']) / (self['Max']-self['Min']), 0, 1)
cmap = self.value()
colors = cmap.map(scaled, mode='float')
mask = np.isnan(data) | np.isinf(data)
nanColor = self['NaN']
nanColor = (nanColor.red()/255., nanColor.green()/255., nanColor.blue()/255., nanColor.alpha()/255.)
colors[mask] = nanColor
return colors
class EnumColorMapItem(ptree.types.GroupParameter):
def __init__(self, name, opts):
self.fieldName = name
vals = opts.get('values', [])
if isinstance(vals, list):
vals = OrderedDict([(v,str(v)) for v in vals])
childs = [{'name': v, 'type': 'color'} for v in vals]
childs = []
for val,vname in vals.items():
ch = ptree.Parameter.create(name=vname, type='color')
ch.maskValue = val
childs.append(ch)
ptree.types.GroupParameter.__init__(self,
name=name, autoIncrementName=True, removable=True, renamable=True,
children=[
dict(name='Values', type='group', children=childs),
dict(name='Operation', type='list', value='Overlay', values=['Overlay', 'Add', 'Multiply', 'Set']),
dict(name='Channels..', type='group', expanded=False, children=[
dict(name='Red', type='bool', value=True),
dict(name='Green', type='bool', value=True),
dict(name='Blue', type='bool', value=True),
dict(name='Alpha', type='bool', value=True),
]),
dict(name='Enabled', type='bool', value=True),
dict(name='Default', type='color'),
])
def map(self, data):
data = data[self.fieldName]
colors = np.empty((len(data), 4))
default = np.array(fn.colorTuple(self['Default'])) / 255.
colors[:] = default
for v in self.param('Values'):
mask = data == v.maskValue
c = np.array(fn.colorTuple(v.value())) / 255.
colors[mask] = c
#scaled = np.clip((data-self['Min']) / (self['Max']-self['Min']), 0, 1)
#cmap = self.value()
#colors = cmap.map(scaled, mode='float')
#mask = np.isnan(data) | np.isinf(data)
#nanColor = self['NaN']
#nanColor = (nanColor.red()/255., nanColor.green()/255., nanColor.blue()/255., nanColor.alpha()/255.)
#colors[mask] = nanColor
return colors
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