ca3fbe2ff9
* Added HDF5 exporter
* CSV exporter gets (x,y,y,y) export mode
* Updates to SVG, Matplotlib exporter
* Console can filter exceptions by string
* Added tick context menu to GradientEditorItem
* Added export feature to imageview
* Parameter trees:
- Option to save only user-editable values
- Option to set visible title of parameters separately from name
- Added experimental ParameterSystem for handling large systems of
interdependent parameters
- Auto-select editable portion of spinbox when editing
* Added Vector.__abs__
* Added replacement garbage collector for avoiding crashes on multithreaded Qt
* Fixed "illegal instruction" caused by closing file handle 7 on OSX
* configfile now reloads QtCore objects, Point, ColorMap, numpy arrays
* Avoid triggering recursion issues in exception handler
* Various bugfies and performance enhancements
250 lines
9.7 KiB
Python
250 lines
9.7 KiB
Python
from ..Qt import QtGui, QtCore
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from .. import parametertree as ptree
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import numpy as np
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from ..pgcollections import OrderedDict
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from .. import functions as fn
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__all__ = ['ColorMapWidget']
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class ColorMapWidget(ptree.ParameterTree):
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"""
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This class provides a widget allowing the user to customize color mapping
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for multi-column data. Given a list of field names, the user may specify
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multiple criteria for assigning colors to each record in a numpy record array.
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Multiple criteria are evaluated and combined into a single color for each
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record by user-defined compositing methods.
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For simpler color mapping using a single gradient editor, see
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:class:`GradientWidget <pyqtgraph.GradientWidget>`
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"""
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sigColorMapChanged = QtCore.Signal(object)
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def __init__(self, parent=None):
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ptree.ParameterTree.__init__(self, parent=parent, showHeader=False)
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self.params = ColorMapParameter()
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self.setParameters(self.params)
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self.params.sigTreeStateChanged.connect(self.mapChanged)
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## wrap a couple methods
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self.setFields = self.params.setFields
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self.map = self.params.map
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def mapChanged(self):
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self.sigColorMapChanged.emit(self)
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def widgetGroupInterface(self):
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return (self.sigColorMapChanged, self.saveState, self.restoreState)
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def saveState(self):
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return self.params.saveState()
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def restoreState(self, state):
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self.params.restoreState(state)
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class ColorMapParameter(ptree.types.GroupParameter):
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sigColorMapChanged = QtCore.Signal(object)
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def __init__(self):
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self.fields = {}
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ptree.types.GroupParameter.__init__(self, name='Color Map', addText='Add Mapping..', addList=[])
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self.sigTreeStateChanged.connect(self.mapChanged)
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def mapChanged(self):
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self.sigColorMapChanged.emit(self)
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def addNew(self, name):
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mode = self.fields[name].get('mode', 'range')
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if mode == 'range':
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item = RangeColorMapItem(name, self.fields[name])
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elif mode == 'enum':
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item = EnumColorMapItem(name, self.fields[name])
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self.addChild(item)
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return item
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def fieldNames(self):
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return self.fields.keys()
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def setFields(self, fields):
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"""
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Set the list of fields to be used by the mapper.
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The format of *fields* is::
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[ (fieldName, {options}), ... ]
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============== ============================================================
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Field Options:
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mode Either 'range' or 'enum' (default is range). For 'range',
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The user may specify a gradient of colors to be applied
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linearly across a specific range of values. For 'enum',
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the user specifies a single color for each unique value
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(see *values* option).
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units String indicating the units of the data for this field.
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values List of unique values for which the user may assign a
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color when mode=='enum'. Optionally may specify a dict
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instead {value: name}.
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============== ============================================================
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"""
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self.fields = OrderedDict(fields)
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#self.fields = fields
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#self.fields.sort()
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names = self.fieldNames()
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self.setAddList(names)
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def map(self, data, mode='byte'):
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"""
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Return an array of colors corresponding to *data*.
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============== =================================================================
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**Arguments:**
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data A numpy record array where the fields in data.dtype match those
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defined by a prior call to setFields().
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mode Either 'byte' or 'float'. For 'byte', the method returns an array
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of dtype ubyte with values scaled 0-255. For 'float', colors are
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returned as 0.0-1.0 float values.
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============== =================================================================
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"""
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if isinstance(data, dict):
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data = np.array([tuple(data.values())], dtype=[(k, float) for k in data.keys()])
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colors = np.zeros((len(data),4))
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for item in self.children():
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if not item['Enabled']:
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continue
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chans = item.param('Channels..')
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mask = np.empty((len(data), 4), dtype=bool)
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for i,f in enumerate(['Red', 'Green', 'Blue', 'Alpha']):
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mask[:,i] = chans[f]
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colors2 = item.map(data)
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op = item['Operation']
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if op == 'Add':
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colors[mask] = colors[mask] + colors2[mask]
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elif op == 'Multiply':
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colors[mask] *= colors2[mask]
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elif op == 'Overlay':
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a = colors2[:,3:4]
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c3 = colors * (1-a) + colors2 * a
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c3[:,3:4] = colors[:,3:4] + (1-colors[:,3:4]) * a
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colors = c3
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elif op == 'Set':
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colors[mask] = colors2[mask]
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colors = np.clip(colors, 0, 1)
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if mode == 'byte':
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colors = (colors * 255).astype(np.ubyte)
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return colors
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def saveState(self):
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items = OrderedDict()
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for item in self:
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itemState = item.saveState(filter='user')
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itemState['field'] = item.fieldName
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items[item.name()] = itemState
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state = {'fields': self.fields, 'items': items}
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return state
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def restoreState(self, state):
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if 'fields' in state:
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self.setFields(state['fields'])
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for itemState in state['items']:
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item = self.addNew(itemState['field'])
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item.restoreState(itemState)
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class RangeColorMapItem(ptree.types.SimpleParameter):
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mapType = 'range'
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def __init__(self, name, opts):
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self.fieldName = name
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units = opts.get('units', '')
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ptree.types.SimpleParameter.__init__(self,
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name=name, autoIncrementName=True, type='colormap', removable=True, renamable=True,
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children=[
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#dict(name="Field", type='list', value=name, values=fields),
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dict(name='Min', type='float', value=0.0, suffix=units, siPrefix=True),
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dict(name='Max', type='float', value=1.0, suffix=units, siPrefix=True),
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dict(name='Operation', type='list', value='Overlay', values=['Overlay', 'Add', 'Multiply', 'Set']),
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dict(name='Channels..', type='group', expanded=False, children=[
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dict(name='Red', type='bool', value=True),
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dict(name='Green', type='bool', value=True),
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dict(name='Blue', type='bool', value=True),
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dict(name='Alpha', type='bool', value=True),
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]),
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dict(name='Enabled', type='bool', value=True),
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dict(name='NaN', type='color'),
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])
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def map(self, data):
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data = data[self.fieldName]
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scaled = np.clip((data-self['Min']) / (self['Max']-self['Min']), 0, 1)
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cmap = self.value()
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colors = cmap.map(scaled, mode='float')
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mask = np.isnan(data) | np.isinf(data)
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nanColor = self['NaN']
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nanColor = (nanColor.red()/255., nanColor.green()/255., nanColor.blue()/255., nanColor.alpha()/255.)
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colors[mask] = nanColor
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return colors
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class EnumColorMapItem(ptree.types.GroupParameter):
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mapType = 'enum'
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def __init__(self, name, opts):
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self.fieldName = name
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vals = opts.get('values', [])
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if isinstance(vals, list):
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vals = OrderedDict([(v,str(v)) for v in vals])
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childs = [{'name': v, 'type': 'color'} for v in vals]
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childs = []
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for val,vname in vals.items():
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ch = ptree.Parameter.create(name=vname, type='color')
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ch.maskValue = val
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childs.append(ch)
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ptree.types.GroupParameter.__init__(self,
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name=name, autoIncrementName=True, removable=True, renamable=True,
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children=[
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dict(name='Values', type='group', children=childs),
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dict(name='Operation', type='list', value='Overlay', values=['Overlay', 'Add', 'Multiply', 'Set']),
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dict(name='Channels..', type='group', expanded=False, children=[
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dict(name='Red', type='bool', value=True),
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dict(name='Green', type='bool', value=True),
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dict(name='Blue', type='bool', value=True),
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dict(name='Alpha', type='bool', value=True),
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]),
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dict(name='Enabled', type='bool', value=True),
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dict(name='Default', type='color'),
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])
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def map(self, data):
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data = data[self.fieldName]
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colors = np.empty((len(data), 4))
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default = np.array(fn.colorTuple(self['Default'])) / 255.
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colors[:] = default
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for v in self.param('Values'):
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mask = data == v.maskValue
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c = np.array(fn.colorTuple(v.value())) / 255.
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colors[mask] = c
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#scaled = np.clip((data-self['Min']) / (self['Max']-self['Min']), 0, 1)
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#cmap = self.value()
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#colors = cmap.map(scaled, mode='float')
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#mask = np.isnan(data) | np.isinf(data)
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#nanColor = self['NaN']
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#nanColor = (nanColor.red()/255., nanColor.green()/255., nanColor.blue()/255., nanColor.alpha()/255.)
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#colors[mask] = nanColor
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return colors
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