def __init__(self, manager, config, name):
try:
import MMCorePy
except ImportError:
if sys.platform != 'win32':
raise
# MM does not install itself to standard path. User should take care of this,
# but we can make a guess..
path = config.get('path', 'C:\\Program Files\\Micro-Manager-1.4')
sys.path.append(path)
os.environ['PATH'] = os.environ['PATH'] + ';' + path
try:
import MMCorePy
finally:
sys.path.pop()
self.camName = str(name) # we will use this name as the handle to the MM camera
self.mmc = MMCorePy.CMMCore()
self._triggerProp = None # the name of the property for setting trigger mode
self._triggerModes = ({}, {}) # forward and reverse mappings for the names of trigger modes
self._binningMode = None # 'x' or 'xy' for binning strings like '1' and '1x1', respectively
self.camLock = Mutex(Mutex.Recursive) ## Lock to protect access to camera
self._config = config
Camera.__init__(self, manager, config, name) ## superclass will call setupCamera when it is ready.
self.acqBuffer = None
self.frameId = 0
self.lastFrameTime = None
def __init__(self, *args, **kargs):
self.camLock = Mutex(Mutex.Recursive) ## Lock to protect access to camera
self.ringSize = 50
Camera.__init__(self, *args, **kargs) ## superclass will call setupCamera when it is ready.
self.acqBuffer = None
self.frameId = 0
self.lastIndex = None
self.lastFrameTime = None
self.stopOk = False
def __init__(self, *args, **kargs):
self.camLock = Mutex(Mutex.Recursive) ## Lock to protect access to camera
self.ringSize = 100
Camera.__init__(self, *args, **kargs) ## superclass will call setupCamera when it is ready.
self.acqBuffer = None
self.frameId = 0
self.lastIndex = None
self.lastFrameTime = None
self.stopOk = False
def __init__(self, manager, config, name):
self.camName = str(
name) # we will use this name as the handle to the MM camera
mmpath = config.get('path')
self.mmc = micromanager.getMMCorePy(mmpath)
self._triggerProp = None # the name of the property for setting trigger mode
self._triggerModes = (
{}, {}
) # forward and reverse mappings for the names of trigger modes
self._binningMode = None # 'x' or 'xy' for binning strings like '1' and '1x1', respectively
self.camLock = Mutex(
Mutex.Recursive) ## Lock to protect access to camera
self._config = config
Camera.__init__(
self, manager, config,
name) ## superclass will call setupCamera when it is ready.
self.acqBuffer = None
self.frameId = 0
self.lastFrameTime = None
def start(self, block=True):
#print "PVCam: start"
if not self.isRunning():
self.lastIndex = None
#print " not running already; start camera"
Camera.start(self, block) ## Start the acquisition thread
self.startTime = ptime.time()
## pvcams can take a long time
if block:
tm = self.getParam('triggerMode')
if tm != 'Normal':
#print " waiting for trigger to arm"
waitTime = 0.3 ## trigger needs about 300ms to prepare (?)
else:
waitTime = 0
sleepTime = (self.startTime + waitTime) - ptime.time()
if sleepTime > 0:
#print " sleep for", sleepTime
time.sleep(sleepTime)
def start(self, block=True):
#print "PVCam: start"
if not self.isRunning():
self.lastIndex = None
#print " not running already; start camera"
Camera.start(self, block) ## Start the acquisition thread
self.startTime = ptime.time()
## pvcams can take a long time
if block:
tm = self.getParam('triggerMode')
if tm != 'Normal':
#print " waiting for trigger to arm"
waitTime = 0.3 ## trigger needs about 300ms to prepare (?)
else:
waitTime = 0
sleepTime = (self.startTime + waitTime) - ptime.time()
if sleepTime > 0:
#print " sleep for", sleepTime
time.sleep(sleepTime)
def __init__(self, manager, config, name):
self.camLock = Mutex(
Mutex.Recursive) ## Lock to protect access to camera
self.ringSize = 100
self.frameId = 0
self.noise = np.random.normal(
size=10000000, loc=100,
scale=10) ## pre-generate noise for use in images
if 'images' in config:
self.bgData = {}
self.bgInfo = {}
for obj, filename in config['images'].items():
file = manager.fileHandle(filename)
ma = file.read()
self.bgData[obj] = ma.asarray()
self.bgInfo[obj] = file.info().deepcopy()
self.bgInfo[obj]['depths'] = ma.xvals(0)
else:
self.bgData = mandelbrot(w=4000, maxIter=60).astype(np.float32)
self.bgInfo = None
self.background = None
self.params = OrderedDict([
('triggerMode', 'Normal'),
('exposure', 0.001),
#('binning', (1,1)),
#('region', (0, 0, 512, 512)),
('binningX', 1),
('binningY', 1),
('regionX', 0),
('regionY', 0),
('regionW', 512),
('regionH', 512),
('gain', 1.0),
('sensorSize', (512, 512)),
('bitDepth', 16),
])
self.paramRanges = OrderedDict([
('triggerMode', (['Normal', 'TriggerStart'], True, True, [])),
('exposure', ((0.001, 10.), True, True, [])),
#('binning', ([range(1,10), range(1,10)], True, True, [])),
#('region', ([(0, 511), (0, 511), (1, 512), (1, 512)], True, True, [])),
('binningX', (range(1, 10), True, True, [])),
('binningY', (range(1, 10), True, True, [])),
('regionX', ((0, 511), True, True, ['regionW'])),
('regionY', ((0, 511), True, True, ['regionH'])),
('regionW', ((1, 512), True, True, ['regionX'])),
('regionH', ((1, 512), True, True, ['regionY'])),
('gain', ((0.1, 10.0), True, True, [])),
('sensorSize', (None, False, True, [])),
('bitDepth', (None, False, True, [])),
])
self.groupParams = {
'binning': ('binningX', 'binningY'),
'region': ('regionX', 'regionY', 'regionW', 'regionH')
}
sig = np.random.normal(size=(512, 512), loc=1.0, scale=0.3)
sig = scipy.ndimage.gaussian_filter(sig, (3, 3))
sig[20:40, 20:40] += 1
sig[sig < 0] = 0
self.signal = sig
Camera.__init__(
self, manager, config,
name) ## superclass will call setupCamera when it is ready.
self.acqBuffer = None
self.frameId = 0
self.lastIndex = None
self.lastFrameTime = None
self.stopOk = False
self.sigGlobalTransformChanged.connect(self.globalTransformChanged)
## generate list of mock cells
cells = np.zeros(20,
dtype=[('x', float), ('y', float), ('size', float),
('value', float), ('rate', float),
('intensity', float), ('decayTau', float)])
cells['x'] = np.random.normal(size=cells.shape,
scale=100e-6,
loc=-1.5e-3)
cells['y'] = np.random.normal(size=cells.shape,
scale=100e-6,
loc=4.4e-3)
cells['size'] = np.random.normal(size=cells.shape,
scale=2e-6,
loc=10e-6)
cells['rate'] = np.random.lognormal(size=cells.shape, mean=0,
sigma=1) * 1.0
cells['intensity'] = np.random.uniform(size=cells.shape,
low=1000,
high=10000)
cells['decayTau'] = np.random.uniform(size=cells.shape,
low=15e-3,
high=500e-3)
self.cells = cells
def quit(self):
#print "quit() called from QCamDevice."
Camera.quit(self)
self.qcd.quit()
def __init__(self, *args, **kargs):
self.camLock = Mutex(Mutex.Recursive) ## Lock to protect access to camera
Camera.__init__(self, *args, **kargs) ## superclass will call setupCamera when it is ready.
def quit(self):
Camera.quit(self)
time.sleep(2)
self.cam.close()
def quit(self):
#print "quit() called from QCamDevice."
Camera.quit(self)
self.qcd.quit()
def __init__(self, *args, **kargs):
self.camLock = Mutex(
Mutex.Recursive) ## Lock to protect access to camera
Camera.__init__(
self, *args,
**kargs) ## superclass will call setupCamera when it is ready.
def __init__(self, *args, **kargs):
self.camLock = Mutex(Mutex.Recursive) ## Lock to protect access to camera
self.ringSize = 100
self.frameId = 0
self.noise = np.random.normal(size=10000000, loc=100, scale=50) ## pre-generate noise for use in images
self.bgData = mandelbrot(w=1000, maxIter=60).astype(np.float32)
self.background = None
self.params = OrderedDict([
('triggerMode', 'Normal'),
('exposure', 0.001),
#('binning', (1,1)),
#('region', (0, 0, 512, 512)),
('binningX', 1),
('binningY', 1),
('regionX', 0),
('regionY', 0),
('regionW', 512),
('regionH', 512),
('gain', 1.0),
('sensorSize', (512, 512)),
('bitDepth', 16),
])
self.paramRanges = OrderedDict([
('triggerMode', (['Normal'], True, True, [])),
('exposure', ((0.001, 10.), True, True, [])),
#('binning', ([range(1,10), range(1,10)], True, True, [])),
#('region', ([(0, 511), (0, 511), (1, 512), (1, 512)], True, True, [])),
('binningX', (range(1,10), True, True, [])),
('binningY', (range(1,10), True, True, [])),
('regionX', ((0, 511), True, True, ['regionW'])),
('regionY', ((0, 511), True, True, ['regionH'])),
('regionW', ((1, 512), True, True, ['regionX'])),
('regionH', ((1, 512), True, True, ['regionY'])),
('gain', ((0.1, 10.0), True, True, [])),
('sensorSize', (None, False, True, [])),
('bitDepth', (None, False, True, [])),
])
self.groupParams = {
'binning': ('binningX', 'binningY'),
'region': ('regionX', 'regionY', 'regionW', 'regionH')
}
sig = np.random.normal(size=(512, 512), loc=1.0, scale=0.3)
sig = scipy.ndimage.gaussian_filter(sig, (3, 3))
sig[20:40, 20:40] += 1
sig[sig<0] = 0
self.signal = sig
Camera.__init__(self, *args, **kargs) ## superclass will call setupCamera when it is ready.
self.acqBuffer = None
self.frameId = 0
self.lastIndex = None
self.lastFrameTime = None
self.stopOk = False
self.sigGlobalTransformChanged.connect(self.globalTransformChanged)
## generate list of mock cells
cells = np.zeros(20, dtype=[('x', float), ('y', float), ('size', float), ('value', float), ('rate', float), ('intensity', float), ('decayTau', float)])
cells['x'] = np.random.normal(size=cells.shape, scale=100e-6, loc=100e-6)
cells['y'] = np.random.normal(size=cells.shape, scale=100e-6)
cells['size'] = np.random.normal(size=cells.shape, scale=2e-6, loc=10e-6)
cells['rate'] = np.random.lognormal(size=cells.shape, mean=0, sigma=1) * .3
cells['intensity'] = np.random.uniform(size=cells.shape, low=1000, high=10000)
cells['decayTau'] = np.random.uniform(size=cells.shape, low=15e-3, high=500e-3)
self.cells = cells
def quit(self):
Camera.quit(self)
self.pvc.quit()
