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import warnings
import numpy as np
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui
pg.mkQApp()
def test_bool():
truths = np.random.randint(0, 2, size=(100,)).astype(bool)
pdi = pg.PlotDataItem(truths)
xdata, ydata = pdi.getData()
assert ydata.dtype == np.uint8
def test_bound_formats():
for datatype in (bool, np.uint8, np.int16, float):
truths = np.random.randint(0, 2, size=(100,)).astype(datatype)
pdi_scatter = pg.PlotDataItem(truths, symbol='o', pen=None)
pdi_line = pg.PlotDataItem(truths)
bounds = pdi_scatter.dataBounds(1)
assert isinstance(bounds[0], float), 'bound 0 is not float for scatter plot of '+str(datatype)
assert isinstance(bounds[0], float), 'bound 1 is not float for scatter plot of '+str(datatype)
bounds = pdi_line.dataBounds(1)
assert isinstance(bounds[0], float), 'bound 0 is not float for line plot of '+str(datatype)
assert isinstance(bounds[0], float), 'bound 1 is not float for line plot of '+str(datatype)
def test_fft():
f = 20.
x = np.linspace(0, 1, 1000)
y = np.sin(2 * np.pi * f * x)
pd = pg.PlotDataItem(x, y)
pd.setFftMode(True)
x, y = pd.getData()
assert abs(x[np.argmax(y)] - f) < 0.03
x = np.linspace(0, 1, 1001)
y = np.sin(2 * np.pi * f * x)
pd.setData(x, y)
x, y = pd.getData()
assert abs(x[np.argmax(y)]- f) < 0.03
pd.setLogMode(True, False)
x, y = pd.getData()
assert abs(x[np.argmax(y)] - np.log10(f)) < 0.01
def test_setData():
pdi = pg.PlotDataItem()
#test empty data
pdi.setData([])
#test y data
y = list(np.random.normal(size=100))
pdi.setData(y)
assert len(pdi.xData) == 100
assert len(pdi.yData) == 100
#test x, y data
y += list(np.random.normal(size=50))
x = np.linspace(5, 10, 150)
pdi.setData(x, y)
assert len(pdi.xData) == 150
assert len(pdi.yData) == 150
#test clear by empty call
pdi.setData()
assert pdi.xData is None
assert pdi.yData is None
#test dict of x, y list
y += list(np.random.normal(size=50))
x = list(np.linspace(5, 10, 200))
pdi.setData({'x': x, 'y': y})
assert len(pdi.xData) == 200
assert len(pdi.yData) == 200
#test clear by zero length arrays call
pdi.setData([],[])
assert pdi.xData is None
assert pdi.yData is None
# recarray (issue #3275)
data = np.recarray((10,), dtype=[('x', float), ('y', float)])
data["x"] = np.linspace(0, 1, len(data))
data["y"] = np.linspace(10, 20, len(data))
pdi.setData(data)
assert all(pdi.xData == data["x"])
assert all(pdi.yData == data["y"])
# array with named fields
data = np.array([(1, 2), (3, 4), (5, 6)], dtype=[("x", float), ("y", float)])
pdi.setData(data)
assert all(pdi.xData == data["x"])
assert all(pdi.yData == data["y"])
def test_nonfinite():
def _assert_equal_arrays(a1, a2):
assert a1.shape == a2.shape
for ( xtest, xgood ) in zip( a1, a2 ):
assert( (xtest == xgood) or (np.isnan(xtest) and np.isnan(xgood) ) )
x = np.array([-np.inf, 0.0, 1.0, 2.0 , np.nan, 4.0 , np.inf])
y = np.array([ 1.0, 0.0,-1.0, np.inf, 2.0 , np.nan, 0.0 ])
pdi = pg.PlotDataItem(x, y)
dataset = pdi._getDisplayDataset()
_assert_equal_arrays( dataset.x, x )
_assert_equal_arrays( dataset.y, y )
with warnings.catch_warnings():
warnings.simplefilter("ignore")
x_log = np.log10(x)
y_log = np.log10(y)
x_log[ ~np.isfinite(x_log) ] = np.nan
y_log[ ~np.isfinite(y_log) ] = np.nan
pdi.setLogMode(True, True)
dataset = pdi._getDisplayDataset()
_assert_equal_arrays( dataset.x, x_log )
_assert_equal_arrays( dataset.y, y_log )
def test_opts():
# test that curve and scatter plot properties get updated from PlotDataItem methods
y = list(np.random.normal(size=100))
x = np.linspace(5, 10, 100)
pdi = pg.PlotDataItem(x, y)
pen = QtGui.QPen( QtGui.QColor('#FF0000') )
pen2 = QtGui.QPen( QtGui.QColor('#FFFF00') )
brush = QtGui.QBrush( QtGui.QColor('#00FF00'))
brush2 = QtGui.QBrush( QtGui.QColor('#00FFFF'))
float_value = 1.0 + 20*np.random.random()
pen2.setWidth( int(float_value) )
pdi.setPen(pen)
assert pdi.curve.opts['pen'] == pen
pdi.setShadowPen(pen2)
assert pdi.curve.opts['shadowPen'] == pen2
pdi.setFillLevel( float_value )
assert pdi.curve.opts['fillLevel'] == float_value
pdi.setFillBrush(brush2)
assert pdi.curve.opts['brush'] == brush2
pdi.setSymbol('t')
assert pdi.scatter.opts['symbol'] == 't'
pdi.setSymbolPen(pen)
assert pdi.scatter.opts['pen'] == pen
pdi.setSymbolBrush(brush)
assert pdi.scatter.opts['brush'] == brush
pdi.setSymbolSize( float_value )
assert pdi.scatter.opts['size'] == float_value
def test_clear():
y = list(np.random.normal(size=100))
x = np.linspace(5, 10, 100)
pdi = pg.PlotDataItem(x, y)
pdi.clear()
assert pdi.xData is None
assert pdi.yData is None
def test_clear_in_step_mode():
w = pg.PlotWidget()
c = pg.PlotDataItem([1,4,2,3], [5,7,6], stepMode="center")
w.addItem(c)
c.clear()
def test_clipping():
y = np.random.normal(size=150)
x = np.exp2(np.linspace(5, 10, 150)) # non-uniform spacing
w = pg.PlotWidget(autoRange=True, downsample=5)
c = pg.PlotDataItem(x, y)
w.addItem(c)
c.setClipToView(True)
for x_min in range(-200, 2**10 - 100, 100):
x_max = x_min + 100
w.setXRange(x_min, x_max, padding=0)
xDisp, _ = c.getData()
# vr = c.viewRect()
if len(xDisp) > 3: # check that all points except the first and last are on screen
assert( xDisp[ 1] >= x_min and xDisp[ 1] <= x_max )
assert( xDisp[-2] >= x_min and xDisp[-2] <= x_max )
c.setDownsampling(ds=1) # disable downsampling
for x_min in range(-200, 2**10 - 100, 100):
x_max = x_min + 100
w.setXRange(x_min, x_max, padding=0)
xDisp, _ = c.getData()
# vr = c.viewRect() # this tends to be out of data, so we check against the range that we set
if len(xDisp) > 3: # check that all points except the first and last are on screen
assert( xDisp[ 0] == x[ 0] or xDisp[ 0] < x_min ) # first point should be unchanged, or off-screen
assert( xDisp[ 1] >= x_min and xDisp[ 1] <= x_max )
assert( xDisp[-2] >= x_min and xDisp[-2] <= x_max )
assert( xDisp[-1] == x[-1] or xDisp[-1] > x_max ) # last point should be unchanged, or off-screen
c.setData(x=np.zeros_like(y), y=y) # test zero width data set:
# test center and expected number of remaining data points
for center, num in ((-100.,1), (100.,1), (0.,len(y)) ):
# when all elements are off-screen, only one will be kept
# when all elements are on-screen, all should be kept
# and the code should not crash for zero separation
w.setXRange( center-50, center+50, padding=0 )
xDisp, yDisp = c.getData()
assert len(xDisp) == num
assert len(yDisp) == num
w.close()
def test_downsampling_with_connect():
# Test that down sampling and view clipping works correctly when using the connect vector
x = np.linspace(0.0, 7.0, num=1000)
x = np.concatenate((x[:300], x[700:]))
y = np.sin(x)
connect = np.ones(len(x), dtype=bool)
connect[299] = False
nc = (x[~connect].item(), y[~connect].item())
w = pg.PlotWidget()
c = pg.PlotDataItem(x, y, connect=connect)
w.addItem(c)
c.setClipToView(True)
w.setXRange(1.0, 6.0)
# verify that the connect vector is clipped correctly
xs, ys = c.getData()
cs = c.curve.opts['connect']
assert len(xs) == len(cs)
assert nc == (xs[~cs].item(), ys[~cs].item())
c.setClipToView(False)
w.setXRange(0.0, 7.0)
for method in ['subsample', 'mean', 'peak']:
c.setDownsampling(5, method=method)
# verify that the connect vector is downsampled to the same size
xs, _ = c.getData()
cs = c.curve.opts['connect']
assert len(xs) == len(cs)
w.close()
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