def cmapRange(self):
if self._cmapRange is None: # Auto-scale mode
if self._cmapRangeCache is None:
# Build data , positive ranges
min_, minPos, max_ = minMax(self.data, minPositive=True)
maxPos = max_ if max_ > 0. else 1.
if minPos is None:
minPos = maxPos
self._cmapRangeCache = {'range': (min_, max_),
'pos': (minPos, maxPos)}
return self._cmapRangeCache['pos' if self.cmapIsLog else 'range']
else:
if not self.cmapIsLog:
return self._cmapRange # Return range as is
else:
if self._cmapRangeCache is None:
# Build a strictly positive range from cmapRange
min_, max_ = self._cmapRange
if min_ > 0. and max_ > 0.:
minPos, maxPos = min_, max_
else:
dataMin, minPos, dataMax = minMax(self.data,
minPositive=True)
if max_ > 0.:
maxPos = max_
elif dataMax > 0.:
maxPos = dataMax
else:
maxPos = 1. # Arbitrary fallback
if minPos is None:
minPos = maxPos
self._cmapRangeCache = minPos, maxPos
return self._cmapRangeCache # Strictly positive range
def _testMinMaxVsNumpy(self, data, minPos=False):
"""Single test C minMax and min positive vs Numpy min/max."""
startTime = time.time()
if minPos:
min_, minPositive, max_ = ctools.minMax(data, minPositive=True)
else:
min_, max_ = ctools.minMax(data, minPositive=False)
duration = time.time() - startTime
startTime = time.time()
try:
minNumpy, maxNumpy = np.nanmin(data), np.nanmax(data)
except ValueError:
minNumpy, maxNumpy = None, None
if minPos:
minPositiveNumpy = self._minPos(data)
durationNumpy = time.time() - startTime
self._log(data.dtype, data.size, 'duration C (s):', duration,
'duration Numpy (s):', durationNumpy)
self.assertEqual(min_, minNumpy)
if minPos:
self.assertEqual(minPositive, minPositiveNumpy)
self.assertEqual(max_, maxNumpy)
def testMinMaxNoData(self):
"""Test C minMax and min positive with no data.
"""
self._log("testMinMaxNoData")
for dtype in self.DTYPES:
# No data
data = np.array((), dtype=dtype)
with self.assertRaises(ValueError):
ctools.minMax(data, minPositive=False)
with self.assertRaises(ValueError):
ctools.minMax(data, minPositive=True)
def testMinMaxNoData(self):
"""Test C minMax and min positive with no data.
"""
self._log("testMinMaxNoData")
for dtype in self.DTYPES:
# No data
data = np.array((), dtype=dtype)
with self.assertRaises(ValueError):
ctools.minMax(data, minPositive=False)
with self.assertRaises(ValueError):
ctools.minMax(data, minPositive=True)
def testMinMaxNan(self):
"""Test C minMax and min positive with NaN.
"""
self._log("testMinMaxNan")
for dtype in self.FLOATING_DTYPES:
# All NaN
data = np.array((float("nan"), float("nan")), dtype=dtype)
min_, minPositive, max_ = ctools.minMax(data, minPositive=True)
self.assertTrue(math.isnan(min_))
self.assertEqual(minPositive, None)
self.assertTrue(math.isnan(max_))
# NaN first and positive
data = np.array((float("nan"), 1.0), dtype=dtype)
self._testMinMaxVsNumpy(data)
# NaN first and negative
data = np.array((float("nan"), -1.0), dtype=dtype)
self._testMinMaxVsNumpy(data)
# NaN last and positive
data = np.array((1.0, 2.0, float("nan")), dtype=dtype)
self._testMinMaxVsNumpy(data)
# NaN last and negative
data = np.array((-1.0, -2.0, float("nan")), dtype=dtype)
self._testMinMaxVsNumpy(data)
# Some NaN
data = np.array((1.0, float("nan"), -1.0), dtype=dtype)
self._testMinMaxVsNumpy(data)
def testMinMaxNan(self):
"""Test C minMax and min positive with NaN.
"""
self._log("testMinMaxNan")
for dtype in self.FLOATING_DTYPES:
# All NaN
data = np.array((float('nan'), float('nan')), dtype=dtype)
min_, minPositive, max_ = ctools.minMax(data, minPositive=True)
self.assertTrue(math.isnan(min_))
self.assertEqual(minPositive, None)
self.assertTrue(math.isnan(max_))
# NaN first and positive
data = np.array((float('nan'), 1.0), dtype=dtype)
self._testMinMaxVsNumpy(data)
# NaN first and negative
data = np.array((float('nan'), -1.0), dtype=dtype)
self._testMinMaxVsNumpy(data)
# NaN last and positive
data = np.array((1.0, 2.0, float('nan')), dtype=dtype)
self._testMinMaxVsNumpy(data)
# NaN last and negative
data = np.array((-1.0, -2.0, float('nan')), dtype=dtype)
self._testMinMaxVsNumpy(data)
# Some NaN
data = np.array((1.0, float('nan'), -1.0), dtype=dtype)
self._testMinMaxVsNumpy(data)
def cmapRange(self):
if self._cmapRange is None: # Auto-scale mode
if self._cmapRangeCache is None:
# Build data , positive ranges
min_, minPos, max_ = minMax(self.data, minPositive=True)
maxPos = max_ if max_ > 0. else 1.
if minPos is None:
minPos = maxPos
self._cmapRangeCache = {
'range': (min_, max_),
'pos': (minPos, maxPos)
}
return self._cmapRangeCache['pos' if self.cmapIsLog else 'range']
else:
if not self.cmapIsLog:
return self._cmapRange # Return range as is
else:
if self._cmapRangeCache is None:
# Build a strictly positive range from cmapRange
min_, max_ = self._cmapRange
if min_ > 0. and max_ > 0.:
minPos, maxPos = min_, max_
else:
dataMin, minPos, dataMax = minMax(self.data,
minPositive=True)
if max_ > 0.:
maxPos = max_
elif dataMax > 0.:
maxPos = dataMax
else:
maxPos = 1. # Arbitrary fallback
if minPos is None:
minPos = maxPos
self._cmapRangeCache = minPos, maxPos
return self._cmapRangeCache # Strictly positive range
def _testMinMaxVsNumpy(self, data, minPos=False):
"""Single test C minMax and min positive vs Numpy min/max."""
startTime = time.time()
if minPos:
min_, minPositive, max_ = ctools.minMax(data, minPositive=True)
else:
min_, max_ = ctools.minMax(data, minPositive=False)
duration = time.time() - startTime
startTime = time.time()
try:
minNumpy, maxNumpy = np.nanmin(data), np.nanmax(data)
except ValueError:
minNumpy, maxNumpy = None, None
if minPos:
minPositiveNumpy = self._minPos(data)
durationNumpy = time.time() - startTime
self._log(data.dtype, data.size, "duration C (s):", duration, "duration Numpy (s):", durationNumpy)
self.assertEqual(min_, minNumpy)
if minPos:
self.assertEqual(minPositive, minPositiveNumpy)
self.assertEqual(max_, maxNumpy)
def __init__(self, xData, yData, colorData=None,
xError=None, yError=None,
lineStyle=None, lineColor=None,
lineWidth=None, lineDashPeriod=None,
marker=None, markerColor=None, markerSize=None,
fillColor=None):
self._isXLog = False
self._isYLog = False
self.xData, self.yData, self.colorData = xData, yData, colorData
if fillColor is not None:
self.fill = _Fill2D(color=fillColor)
else:
self.fill = None
# Compute x bounds
if xError is None:
self.xMin, self.xMinPos, self.xMax = minMax(xData,
minPositive=True)
else:
# Takes the error into account
if hasattr(xError, 'shape') and len(xError.shape) == 2:
xErrorPlus, xErrorMinus = xError[0], xError[1]
else:
xErrorPlus, xErrorMinus = xError, xError
self.xMin, self.xMinPos, _ = minMax(xData - xErrorMinus,
minPositive=True)
self.xMax = (xData + xErrorPlus).max()
# Compute y bounds
if yError is None:
self.yMin, self.yMinPos, self.yMax = minMax(yData,
minPositive=True)
else:
# Takes the error into account
if hasattr(yError, 'shape') and len(yError.shape) == 2:
yErrorPlus, yErrorMinus = yError[0], yError[1]
else:
yErrorPlus, yErrorMinus = yError, yError
self.yMin, self.yMinPos, _ = minMax(yData - yErrorMinus,
minPositive=True)
self.yMax = (yData + yErrorPlus).max()
self._errorBars = _ErrorBars(xData, yData, xError, yError,
self.xMin, self.yMin)
kwargs = {'style': lineStyle}
if lineColor is not None:
kwargs['color'] = lineColor
if lineWidth is not None:
kwargs['width'] = lineWidth
if lineDashPeriod is not None:
kwargs['dashPeriod'] = lineDashPeriod
self.lines = _Lines2D(**kwargs)
kwargs = {'marker': marker}
if markerColor is not None:
kwargs['color'] = markerColor
if markerSize is not None:
kwargs['size'] = markerSize
self.points = _Points2D(**kwargs)
def __init__(self,
xData,
yData,
colorData=None,
xError=None,
yError=None,
lineStyle=None,
lineColor=None,
lineWidth=None,
lineDashPeriod=None,
marker=None,
markerColor=None,
markerSize=None,
fillColor=None):
self._isXLog = False
self._isYLog = False
self.xData, self.yData, self.colorData = xData, yData, colorData
if fillColor is not None:
self.fill = _Fill2D(color=fillColor)
else:
self.fill = None
# Compute x bounds
if xError is None:
self.xMin, self.xMinPos, self.xMax = minMax(xData,
minPositive=True)
else:
# Takes the error into account
if hasattr(xError, 'shape') and len(xError.shape) == 2:
xErrorPlus, xErrorMinus = xError[0], xError[1]
else:
xErrorPlus, xErrorMinus = xError, xError
self.xMin, self.xMinPos, _ = minMax(xData - xErrorMinus,
minPositive=True)
self.xMax = (xData + xErrorPlus).max()
# Compute y bounds
if yError is None:
self.yMin, self.yMinPos, self.yMax = minMax(yData,
minPositive=True)
else:
# Takes the error into account
if hasattr(yError, 'shape') and len(yError.shape) == 2:
yErrorPlus, yErrorMinus = yError[0], yError[1]
else:
yErrorPlus, yErrorMinus = yError, yError
self.yMin, self.yMinPos, _ = minMax(yData - yErrorMinus,
minPositive=True)
self.yMax = (yData + yErrorPlus).max()
self._errorBars = _ErrorBars(xData, yData, xError, yError, self.xMin,
self.yMin)
kwargs = {'style': lineStyle}
if lineColor is not None:
kwargs['color'] = lineColor
if lineWidth is not None:
kwargs['width'] = lineWidth
if lineDashPeriod is not None:
kwargs['dashPeriod'] = lineDashPeriod
self.lines = _Lines2D(**kwargs)
kwargs = {'marker': marker}
if markerColor is not None:
kwargs['color'] = markerColor
if markerSize is not None:
kwargs['size'] = markerSize
self.points = _Points2D(**kwargs)
