def example_FSA_StepNoisy():
'''
step function
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("a noisy Step")
PySpectra.setComment("Input for Utils->FSA/SSA")
PySpectra.setWsViewport("DINA5")
g = utils.createStep(name="step",
xMin=-10.,
xMax=10.,
nPts=101,
lineColor='red',
amplitude=3.)
g.y += np.random.random_sample((len(g.x), )) * 0.5
PySpectra.display()
return
def testImageMB2( self):
print "testGraphics.testImageMB2"
PySpectra.setWsViewport( 'DINA5S')
PySpectra.cls()
PySpectra.delete()
(xmin, xmax) = (-2., 1)
(ymin, ymax) = (-1.5, 1.5)
(width, height) = (300, 300)
maxiter = 20
#
# create the image by supplying the limits
#
m = PySpectra.Image( name = "MandelbrotSet2",
xMin = xmin, xMax = xmax, width = width,
yMin = ymin, yMax = ymax, height = height,
xLabel = "eh_mot01", yLabel = "eh_mot02")
r1 = np.linspace(xmin, xmax, width)
r2 = np.linspace(ymin, ymax, height)
for i in range(width):
for j in range(height):
res = self.mandelbrot(r1[i] + 1j*r2[j],maxiter)
m.setPixelWorld( x = r1[i], y = r2[j], value = res)
PySpectra.cls()
PySpectra.display()
PySpectra.processEventsLoop(1)
self.assertEqual( m.xMin, xmin)
self.assertEqual( m.xMax, xmax)
self.assertEqual( m.yMin, ymin)
self.assertEqual( m.yMax, ymax)
self.assertEqual( m.height, height)
self.assertEqual( m.width, width)
#
# to understand '+ 1'consider : x [-2, 1], width 100
# if x == 1 -> ix == 100, so we need '+ 1'
#
self.assertEqual( m.data.shape[0], width)
self.assertEqual( m.data.shape[1], height)
return
def testImageMB1( self):
print "testGraphics.testImageMB1"
PySpectra.setWsViewport( 'DINA5S')
PySpectra.cls()
PySpectra.delete()
(xmin, xmax) = (-2., 1)
(ymin, ymax) = (-1.5, 1.5)
(width, height) = (500, 500)
maxiter = 20
r1 = np.linspace(xmin, xmax, width)
r2 = np.linspace(ymin, ymax, height)
n3 = np.empty((width, height))
for i in range(width):
for j in range(height):
n3[i,j] = self.mandelbrot(r1[i] + 1j*r2[j],maxiter)
#
# create the image by supplying data
#
m = PySpectra.Image( name = "MandelbrotSet1", data = n3,
xMin = xmin, xMax = xmax, width = width,
yMin = ymin, yMax = ymax, height = height,
xLabel = "eh_mot01", yLabel = "eh_mot02")
PySpectra.display()
self.assertEqual( m.xMin, xmin)
self.assertEqual( m.xMax, xmax)
self.assertEqual( m.yMin, ymin)
self.assertEqual( m.yMax, ymax)
self.assertEqual( m.height, height)
self.assertEqual( m.width, width)
#
# to understand '+ 1'consider : x [-2, 1], width 100
# if x == 1 -> ix == 100, so we need '+ 1'
#
self.assertEqual( m.data.shape[0], width)
self.assertEqual( m.data.shape[1], height)
PySpectra.processEventsLoop( 2)
def example_Gauss2():
'''
2 gauss plot
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("Two Gauss curves")
PySpectra.setComment(
"To demonstrate how SSA limits can be defined with VLines")
PySpectra.setWsViewport("DINA5")
g = PySpectra.Scan(name="gauss", xMin=-10., xMax=10., nPts=101)
mu1 = 0.
sigma1 = 1.
mu2 = 6.5
sigma2 = 1.2
g.y = 1./(sigma1*np.sqrt(2.*np.pi))*np.exp( -(g.y-mu1)**2/(2*sigma1**2)) + \
2./(sigma2*np.sqrt(2.*np.pi))*np.exp( -(g.y-mu2)**2/(2*sigma2**2))
PySpectra.display()
return
def example_Gauss():
'''
gauss plot
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("A simple Gauss curve")
PySpectra.setComment("Can be used with SSA, calculating derivative and so")
PySpectra.setWsViewport("DINA5")
g = utils.createGauss(name="gauss",
xMin=-5.,
xMax=5.,
nPts=101,
lineColor='red',
x0=0.,
sigma=1.,
amplitude=1.)
PySpectra.display()
return
def testWsViewport( self):
'''
'''
print "testGraphics.testWsViewport"
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle( "go through the viewports")
sinus = PySpectra.Scan( name = 'sinus',
xMin = 0., xMax = 6.0, nPts = 101, lineColor = 'red', doty = True)
sinus.y = np.sin( sinus.y)
for elm in [ 'DINA4', 'DINA4P', 'DINA4S', 'DINA5', 'DINA5P', 'DINA5S',
'DINA6', 'DINA6P', 'DINA6S']:
PySpectra.setWsViewport( elm)
PySpectra.display()
PySpectra.processEventsLoop( 1)
print "testGraphics.testWsViewport, DONE"
def example_ScanningAutoscaleX():
'''
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("scanning, x-axis is re-scaled")
PySpectra.setWsViewport("DINA5")
sinus = PySpectra.Scan(name='sinus',
xMin=0.,
xMax=6.0,
nPts=101,
autoscaleX=True,
lineColor='red')
for i in range(sinus.nPts):
sinus.setX(i, i / 10. + 0.01)
sinus.setY(i, math.sin(i / 10.))
PySpectra.display(['sinus'])
time.sleep(0.01)
return
def example_FSA_GaussVeryNoisy():
'''
gauss plot
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("a very noisy Gauss at 0.12345")
PySpectra.setComment("Input for Util->FSA/SSA")
PySpectra.setWsViewport("DINA5")
g = utils.createGauss(name="gauss",
xMin=-5.,
xMax=5.,
nPts=101,
lineColor='red',
x0=0.12345,
sigma=1.,
amplitude=1.)
g.y += np.random.random_sample((len(g.x), )) * 0.3
PySpectra.display()
return
def example_Lissajous():
'''
plots and updates a Lissajous figure
'''
PySpectra.setWsViewport("DINA5S")
PySpectra.cls()
PySpectra.delete()
scan = PySpectra.Scan(name='Lissajous', nPts=1000, xMin=-1., xMax=1.)
x = np.linspace(0., 6.5, 1000)
y = np.linspace(0., 6.5, 1000)
scan.x = np.cos(x)
scan.y = np.sin(y)
PySpectra.display()
for i in range(500):
x = x + 0.005
scan.smartUpdateDataAndDisplay(np.cos(x), np.sin(y))
def example_ScanningReverseAutoscaleX():
'''
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle(
"scanning in reverse direction, the x-axis is re-scaled")
PySpectra.setWsViewport("DINA5")
sinus = PySpectra.Scan(name='sinus',
xMin=0.,
xMax=6.0,
nPts=101,
autoscaleX=True,
lineColor='red')
for i in range(sinus.nPts):
x = 10. - i / 10.
sinus.x[i] = x
sinus.y[i] = math.sin(i / 10.)
sinus.currentIndex = i
PySpectra.display(['sinus'])
time.sleep(0.02)
def example_LogPlotWithText():
'''
create 1 scan, y-log scale, one text
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setWsViewport("DINA5")
t1 = PySpectra.Scan(name="t1",
xMin=0.01,
xMax=10.,
nPts=101,
lineColor='blue',
xLabel='Position',
yLabel='signal',
yLog=True)
t1.addText(text="a left/center aligned text, should be in the center",
x=0.05,
y=0.5,
hAlign='left',
vAlign='center')
PySpectra.display()
def example_ScanningReverse():
'''
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("scanning in reverse direction, x-axis is fixed")
PySpectra.setWsViewport("DINA5")
sinus = PySpectra.Scan(name='sinus',
xMin=0.,
xMax=6.0,
nPts=101,
autoscaleX=False,
lineColor='red')
sinus.xMax = 10.
for i in range(sinus.nPts):
x = 10. - i / 10.
sinus.setX(i, x)
sinus.setY(i, math.sin(i / 10.))
PySpectra.display(['sinus'])
time.sleep(0.05)
return
def example_Create5Plots():
'''
create 5 scans, different colors, demonstrate overlay feature
'''
PySpectra.cls()
PySpectra.delete()
PySpectra.setTitle("5 Scans, t5 is overlaid to t3")
PySpectra.setComment("Notice that sin has autoscalY == False")
PySpectra.setWsViewport("DINA5")
t1 = PySpectra.Scan(name="t1",
lineColor='blue',
yLabel='sin',
yMin=-2,
yMax=2,
autoscaleY=False)
t1.y = np.sin(t1.x)
t2 = PySpectra.Scan("t2", xLabel='Position', yLabel='cos', symbol='+')
t2.y = np.cos(t2.x)
t3 = PySpectra.Scan(name="t3",
lineColor='green',
xLabel='Position',
yLabel='tan')
t3.y = np.tan(t3.x)
t4 = PySpectra.Scan(name="t4",
lineColor='NONE',
xLabel='Position',
yLabel='random',
symbol='+',
symbolColor='CYAN')
t4.y = np.random.random_sample((len(t4.y), ))
t5 = PySpectra.Scan(name="t5",
lineColor='magenta',
xLabel='Position',
yLabel='x**2')
t5.y = t5.x * t5.x
PySpectra.overlay('t5', 't3')
PySpectra.display()
def exampleImageMBVia_toPyspLocal():
'''
this examples simulates the toPyspMonitor() interface
replace PySpectra.toPyspLocal() with HasyUtils.toPyspMonitor() to connect to pyspMonitor.py
'''
PySpectra.setWsViewport('DINA5S')
PySpectra.cls()
PySpectra.delete()
(xmin, xmax) = (-2., -0.5)
(ymin, ymax) = (0, 1.5)
(width, height) = (10, 10)
maxiter = 25
#
# do the clean-up before we start
#
hsh = PySpectra.toPyspLocal(
{'command': ['delete', 'setWsViewport DINA5S', 'cls']})
if hsh['result'] != "done":
print("error from ['delete', 'setWsViewport DINA5S', 'cls']")
return
#
# create the image
#
hsh = {
'Image': {
'name': "Mandelbrot",
'xMin': xmin,
'xMax': xmax,
'width': width,
'yMin': ymin,
'yMax': ymax,
'height': height
}
}
hsh = PySpectra.toPyspLocal(hsh)
if hsh['result'] != "done":
print("error from putData")
return
#
# fill the image, pixel by pixel
#
r1 = np.linspace(xmin, xmax, width)
r2 = np.linspace(ymin, ymax, height)
for i in range(width):
for j in range(height):
res = mandelbrot(r1[i] + 1j * r2[j], maxiter)
hsh = {
'command': ['setPixelImage Mandelbrot %d %d %g' % (i, j, res)]
}
hsh = PySpectra.toPyspLocal(hsh)
if hsh['result'] != "done":
print("error from setPixel")
return
PySpectra.cls()
PySpectra.display()
return
