Source code for pyqtgraph.widgets.ColorMapWidget

from ..Qt import QtGui, QtCore
from .. import parametertree as ptree
import numpy as np
from ..pgcollections import OrderedDict
from .. import functions as fn

__all__ = ['ColorMapWidget']

[docs]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)
[docs] def __init__(self, parent=None): ptree.ParameterTree.__init__(self, parent=parent, 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) def widgetGroupInterface(self): return (self.sigColorMapChanged, self.saveState, self.restoreState) def saveState(self): return self.params.saveState() def restoreState(self, state): self.params.restoreState(state)
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': item = RangeColorMapItem(name, self.fields[name]) elif mode == 'enum': item = EnumColorMapItem(name, self.fields[name]) self.addChild(item) return item 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. ============== ================================================================= """ if isinstance(data, dict): data = np.array([tuple(data.values())], dtype=[(k, float) for k in data.keys()]) 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 def saveState(self): items = OrderedDict() for item in self: itemState = item.saveState(filter='user') itemState['field'] = item.fieldName items[item.name()] = itemState state = {'fields': self.fields, 'items': items} return state def restoreState(self, state): if 'fields' in state: self.setFields(state['fields']) for itemState in state['items']: item = self.addNew(itemState['field']) item.restoreState(itemState) class RangeColorMapItem(ptree.types.SimpleParameter): mapType = 'range' 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): mapType = 'enum' 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