def get_timetrace_static(self, detect, duration=1, acc=None):
"""gets the timetrace data from the adwin with the duration in seconds
and the accuracy in seconds"""
if not type(detect) == type([]):
detect = list(detect)
self.logger = logging.getLogger(get_all_caller())
if acc != None:
self.adw.Set_Processdelay(self.proc_num, m.floor(acc / 25e-9))
delay = self.adw.Get_Processdelay(self.proc_num)
self.logger.info(
'Making static timetrace with %s for %ss and precision of %ss' %
(', '.join([i.properties['Name'] for i in detect]), duration, acc))
num_ticks = int(duration / (delay * 25e-9))
#self.set_par(par.properties['Dev_type'],int(detect.properties['Type'][:5],36))
#self.set_par(par.properties['Port'],detect.properties['Input']['Hardware']['PortID'])
dev_params = np.array([])
for i in detect:
dev_params = np.append(dev_params, [
int(i.properties['Type'][:5], 36),
i.properties['Input']['Hardware']['PortID']
])
self.set_datalong(dev_params, data.properties['dev_params'])
self.set_par(par.properties['Num_devs'], len(detect))
self.set_par(par.properties['Num_ticks'], num_ticks)
self.set_par(par.properties['Case'], 3)
self.start()
self.wait()
array = np.array(list(self.get_fifo(fifo.properties['Scan_data'])))
split_data = []
for i in range(len(detect)):
split_data.append(array[i::len(detect)])
index = np.arange(num_ticks) * (delay * 25e-9)
return split_data, index
def abort(filename):
logger = logging.getLogger(get_all_caller())
logger.critical('You quit!')
header = "type,x-pos,y-pos,z-pos,first scan time" + ",time_%s"*(len(data[0,:])-5) %tuple(range((len(data[0,:])-5)))
np.savetxt("%s%s_abort.txt" %(savedir,filename), data,fmt='%s', delimiter=",", header=header)
logger.info('Aborted file saved as %s%s_abort.txt' %(savedir,filename))
sys.exit(0)
def focus_full(self, detect, devs, center, dims_default, accuracy_default, rate=1, steps=3, speed=50):
self.logger = logging.getLogger(get_all_caller())
devs = np.array(devs)
center = np.array(center)
dims = copy.copy(dims_default)
accuracy = copy.copy(accuracy_default)
for i in range(len(devs)):
self.set_device_value(devs[i],center[i])
if len(devs)==len(center)==len(dims)==len(accuracy):
self.logger.info('Focus on a particle with detector %s'%detect.properties['Name'])
self.logger.info('At the position %s' %center)
for i in range(steps):
for j in range(len(devs)):
self.scan_static(detect,[devs[j]],[center[j]],[dims[j]],[accuracy[j]],speed)
center[j] = center[j] - dims[j]/2 + (self.scan_image[0][1:].argmax()+1)*accuracy[j]
self.set_device_value(devs[j],center[j])
dims[j] /= rate
accuracy[j] /= rate
self.logger.info('At the position %s' %center)
values = []
for i in range(len(devs)):
values.append(self.dev_value[devs[i].properties['Name']])
return np.array(values)
else:
self.logger.error('Dimensions of the arrays do not match')
return np.array([])
def load(self, process=None):
""" Loads the processes. """
if process == None:
process = self.proc_num
self.adw.Load_Process(self.proc)
self.logger = logging.getLogger(get_all_caller())
self.logger.info("Loaded process %s" % process)
def stop(self, process=None):
""" Stops the process"""
if process == None:
process = self.proc_num
self.adw.Stop_Process(process)
self.logger = logging.getLogger(get_all_caller())
self.logger.info("Stopped process %s" % process)
def set_par(self,index,value):
"""sets a parameter value"""
if 0<index<=80:
self.logger = logging.getLogger(get_all_caller())
self.logger.debug("Setting Par %s to %s" %(int(index),int(value)))
self.adw.Set_Par(int(index),int(value))
else:
self.logger.error('The Parameter number %s is out of range(0,81)' %index)
def get_data(self,number,length,start=1):
"""gets a array from adwin"""
if 0<number<=200:
self.logger = logging.getLogger(get_all_caller())
self.logger.debug("Getting Data %s from index %s to %s" %(number,start,start+length))
return self.adw.GetData_Long(number,start,length)
else:
self.logger.error('The array number %s is out of range(0,201)' %(number))
def get_par(self,index):
"""gets a parameter from adwin"""
if 0<index<=80:
self.logger = logging.getLogger(get_all_caller())
self.logger.debug("Getting Par %s" %int(index))
return self.adw.Get_Par(int(index))
else:
self.logger.error('The Parameter number %s is out of range(0,81)' %index)
return -1
def clear_digout(self, port):
""" Sets the digout port to 0"""
self.logger = logging.getLogger(get_all_caller())
if 0 <= port < 16:
self.logger.debug('Setting digital port %s to 0' % port)
self.set_par(par.properties['Case'], 7)
self.set_par(par.properties['Port'], port)
self.start()
self.wait()
else:
self.logger.error('The port %s is out of range(0,16)' % port)
def go_to_position(self, devs, center):
self.logger = logging.getLogger(get_all_caller())
devs = np.array(devs)
center = np.array(center)
if len(devs)==len(center):
for i in range(len(devs)):
self.logger.info('Go to the specified position')
self.logger.info('Device %s to %s' %(devs[i].properties['Name'],center[i]))
self.set_device_value(devs[i],center[i])
else:
self.logger.error('Dimensions of the arrays do not match')
def set_datalong(self,array,arr_num,start_index=1):
"""sets a data array"""
if 0<arr_num<=200:
c_array=(ctypes.c_long * len(array))(0)
array=array.astype('int')
for i in range(len(array)):
c_array[i]=array[i]
self.logger = logging.getLogger(get_all_caller())
self.logger.debug("Set data array %s with length %s" %(arr_num,len(array)))
self.logger.debug("Set data array %s to %s" %(arr_num,array))
self.adw.SetData_Long(c_array, int(arr_num), int(start_index), len(array))
else:
self.logger.error('The array number %s is out of range(0,201)' %(arr_num))
def __init__(self, process):
DEVICENUMBER = 1 # By default this is the number
RAISE_EXCEPTIONS = 1
self.adw = ADwin(DEVICENUMBER, RAISE_EXCEPTIONS)
self.proc = process
self.proc_num = int(process[-1])
if self.proc_num == 0:
self.proc_num = 10
self.scan_settings = dict()
self.dev_value = dict()
self.running = False
self.logger = logging.getLogger(get_all_caller())
self.logger.info('Init the class with process %s' % process)
def get_digin(self, port):
""" Gets the value of the digin port"""
self.logger = logging.getLogger(get_all_caller())
if 0 <= port < 16:
self.logger.info('Getting data from digital port %s' % port)
self.set_par(par.properties['Case'], 5)
self.start()
self.wait()
digin_data = self.get_par(par.properties['Output_value'])
digin_data = bin(digin_data)[2:]
digin_data = '0' * (16 - len(digin_data)) + digin_data
return int(digin_data[-port - 1])
else:
self.logger.error('The port %s is out of range(0,16)' % port)
def adc(self,channel,gain=1):
"""uses adc to convert a voltage into a value
0 = -10V/gain, 32768= 0V and 65536 = 9.999695V/gain """
self.logger = logging.getLogger(get_all_caller())
if 0<=channel<=15:
self.logger.debug('Converting analog signal from channel %s'%channel)
self.set_par(par.properties['Port'],channel)
self.set_par(par.properties['Input_value'],gain)
self.set_par(par.properties['Case'],2)
self.start()
self.wait()
return self.get_par(par.properties['Output_value'])
else:
self.logger.error('The channel %s is out of range(0,16)' %channel)
def get_timetrace_dynamic(self, detect, duration=1, acc=0.01):
"""gets the timetrace data from the adwin with the duration in seconds
and the accuracy in seconds"""
if not type(detect) == type([]):
detect = list(detect)
self.logger = logging.getLogger(get_all_caller())
if not self.running:
self.logger.info('Making dynamic timetrace with %s' %
', '.join([i.properties['Name'] for i in detect]))
self.logger.info('for %ss and precision of %ss' % (duration, acc))
self.adw.Set_Processdelay(self.proc_num, m.floor(acc / 25e-9))
num_ticks = int(duration / (acc))
#self.set_par(par.properties['Dev_type'],int(detect.properties['Type'][:5],36))
#self.set_par(par.properties['Port'],detect.properties['Input']['Hardware']['PortID'])
dev_params = np.array([])
for i in detect:
dev_params = np.append(dev_params, [
int(i.properties['Type'][:5], 36),
i.properties['Input']['Hardware']['PortID']
])
self.set_datalong(dev_params, data.properties['dev_params'])
self.set_par(par.properties['Num_devs'], len(detect))
self.set_par(par.properties['Num_ticks'], num_ticks)
self.set_par(par.properties['Case'], 3)
self.start()
self.running = bool(self.adw.Process_Status(self.proc_num))
self.array = np.array(
list(self.get_fifo(fifo.properties['Scan_data'])))
split_data = []
for i in range(len(detect)):
length = np.floor(len(self.array) / len(detect)) * len(detect)
split_data.append(self.array[i:length:len(detect)])
self.excess_data = self.array[length:] or np.array([])
return split_data
elif self.running:
self.logger.debug('Getting data from danamic timetrace')
split_data = []
self.array = np.array(
list(self.get_fifo(fifo.properties['Scan_data'])))
try:
image = np.append(self.excess_data, image)
except:
pass
for i in range(len(detect)):
length = np.floor(len(self.array) / len(detect)) * len(detect)
split_data.append(self.array[i:length:len(detect)])
self.excess_data = self.array[length:] or np.array([])
return split_data
def set_device_value(self,dev,value):
"""sets a value to the device"""
self.logger = logging.getLogger(get_all_caller())
self.dev_value[dev.properties['Name']] = value
calibration=dev.properties['Output']['Calibration']
port = dev.properties['Output']['Hardware']['PortID']
value = int((value-calibration['Offset'])/calibration['Slope'])
max = dev.properties['Output']['Limits']['Max']
min = dev.properties['Output']['Limits']['Min']
if (min < value < max):
self.logger.debug('Setting value to device %s' %dev.properties['Name'])
self.dac(port,value)
else:
self.logger.error('Value exceeded boundaries of device %s' %dev.properties['Name'])
self.logger.error('Value %s is out of range(%s,%s)' %(value,min,max))
def __init__(self,plot_parti,plot_backg,particles=None):
self.logger = logging.getLogger(get_all_caller())
self.logger.info('Starting intecactive add/remove particles')
self.plot_parti=plot_parti
self.plot_backg=plot_backg
try:
self.particles_x=particles[0,:]
self.particles_y=particles[1,:]
except:
if particles!=None:
self.logger.warning("input incorrect no particles plotted")
self.particles_x = np.array([])
self.particles_y = np.array([])
self.back_x = np.array([])
self.back_y = np.array([])
self.cid = plot_parti.figure.canvas.mpl_connect('button_press_event', self)
def dac(self,channel,value,unit=None):
"""uses dac to convert the value into a voltage
0 = -10V, 32768= 0V and 65536 = 9.999695V"""
self.logger = logging.getLogger(get_all_caller())
if not unit==None:
value = int((value-3278)/6553.6)
if 0<=value<=65536 and 0<=channel<=15:
self.logger.debug('Dac of %s to %sV' %(value,(value-3278)/6553.6))
self.set_par(par.properties['Port'],channel)
self.set_par(par.properties['Input_value'],value)
self.set_par(par.properties['Case'],1)
self.start()
self.wait()
elif 0<=channel<=15:
self.logger.error('The value %s is out of range(0,65537)'%value)
else:
self.logger.error('The channel %s us out of range(0,16)'%channel)
def get_fifo(self,number,length='all'):
"""gets a fifo array from adwin"""
self.logger = logging.getLogger(get_all_caller())
if 0<number<=200:
if length=='all':
length = self.adw.Fifo_Full(number)
if 0<self.adw.Fifo_Full(number) >= length:
cdata = self.adw.GetFifo_Long(number,int(length))
data=[]
for i in range(len(cdata)):
data.append(cdata[i])
self.logger.debug("Getting FIFO %s with length %s" %(number,length))
return np.array(data)
else:
self.logger.warning('not enough elements in the fifo array')
return np.array([])
else:
self.logger.error('The fifo number %s is out of range(0,201)' %(number))
def __init__(self,
name=None,
type='Adwin',
filename='config/config_devices.xml'):
self.logger = logging.getLogger(get_all_caller())
tree = ET.ElementTree(file=filename)
root = tree.getroot()
if root.find(".//*[@Name='%s']" % name) != None:
self.logger.info('Loaded the data for %s in %s' % (name, filename))
self.properties = xmltodict(
root.find(".%s//*[@Name='%s']" % (type, name)))
elif name == None:
self.properties = []
self.logger.info('Loaded all the data from %s' % (filename))
for tags in root.find(".%s" % type):
name = tags.get('Name')
self.properties.append(name)
else:
self.logger.error("Name of Device is not in XML-file")
def get_par_all(self):
"""gets all the parameter from adwin"""
self.logger = logging.getLogger(get_all_caller())
self.logger.debug("Getting all Pars")
return self.adw.Get_Par_All()
def wait(self, ref_time=0.1):
""" Waits until the process is finished"""
self.logger = logging.getLogger(get_all_caller())
self.logger.info("Waiting")
while self.adw.Process_Status(self.proc_num):
time.sleep(ref_time)
def find(self,
image,
fwhm,
hmin=None,
nsigma=1.5,
roundlim=[-1., 1.],
sharplim=[0.2, 1.]):
"""Identifies stars in an image
ASTROLIB-routine
Returns a list [x, y, flux, sharpness, roundness].
INPUTS:
image -- 2D array containing the image
hmin -- Minimum threshold for detection. Should be 3-4 sigma above background RMS.
fwhm -- FWHM to be used for the convolution filter. Should be the same as the PSF FWHM.
nsigma --: radius of the convolution kernel. (default: 1.5)
roundlim -- Threshold for the roundness criterion. (default: [-1,1])
sharplim -- Threshold for the sharpness criterion. (default: [0.2,1])
OUTPUT:
x -- vector of x positions of maxima in image
y -- vector of y positions of maxima in image
flux -- vector of flux of maxima in image
sharpness -- vector of sharpness of maxima in image
roundness -- vector of roundness of maxima in image
EXAMPLE:
>>> import pyfits
>>> import sp.find as find
>>> image = pyfits.getdata('test.fits')
>>> dim_y, dim_x = image.shape
>>> [x, y, flux, sharpness, roundness] = find(image, 15, 5.)
"""
###
# Setting the convolution kernel
###
self.logger = logging.getLogger(get_all_caller())
if hmin == None:
std = np.std(image)
median = np.median(image)
hmin = median + 3 * std
sigmatofwhm = 2 * np.sqrt(2 * np.log(2))
radius = nsigma * fwhm / sigmatofwhm # Radius is 1.5 sigma
if radius < 1.0:
radius = 1.0
fwhm = sigmatofwhm / nsigma
self.logger.warning("Radius of convolution box smaller than one.")
self.logger.warning("Setting the 'fwhm' to minimum value %f" %
fwhm)
sigsq = (fwhm / sigmatofwhm)**2 # sigma squared
nhalf = int(radius) # Center of the kernel
nbox = 2 * nhalf + 1 # Number of pixels inside of convolution box
middle = nhalf # Index of central pixel
self.logger.info('Attempt to find particles')
self.logger.info('With fwhm = %s, hmin = %s, nsigma = %s,' %
(fwhm, hmin, nsigma))
self.logger.info('roundlim = %s, sharplim = %s' % (roundlim, sharplim))
kern_y, kern_x = np.ix_(
np.arange(nbox), np.arange(nbox)) # x,y coordinates of the kernel
g = (kern_x - nhalf)**2 + (
kern_y -
nhalf)**2 # Compute the square of the distance to the center
mask = g <= radius**2 # We make a mask to select the inner circle of radius "radius"
nmask = mask.sum(
) # The number of pixels in the mask within the inner circle.
g = np.exp(-0.5 * g / sigsq) # We make the 2D gaussian profile
###
# Convolving the image with a kernel representing a gaussian (which is assumed to be the psf)
###
c = g * mask # For the kernel, values further than "radius" are equal to zero
c[mask] = (c[mask] - c[mask].mean()) / (
c[mask].var() * nmask) # We normalize the gaussian kernel
c1 = g[nhalf] # c1 will be used to the test the roundness
c1 = (c1 - c1.mean()) / ((c1**2).sum() - c1.mean())
h = scipy.ndimage.convolve(image, c, mode='constant',
cval=0.0) # Convolve image with kernel "c"
h[:
nhalf, :] = 0 # Set the sides to zero in order to avoid border effects
h[-nhalf:, :] = 0
h[:, :nhalf] = 0
h[:, -nhalf:] = 0
mask[middle,
middle] = False # From now on we exclude the central pixel
nmask = mask.sum() # so the number of valid pixels is reduced by 1
goody, goodx = mask.nonzero(
) # "good" identifies position of valid pixels
###
# Identifying the point source candidates that stand above the background
###
indy, indx = (h >= hmin).nonzero(
) # we identify point that are above the threshold, image coordinate
nfound = indx.size # nfound is the number of candidates
if nfound <= 0:
self.logger.error(
"There is no source meeting the 'hmin' criterion.")
self.logger.error("Aborting the 'find' function.")
return None
offsetsx = np.resize(
goodx - middle, (nfound, nmask)
) # a (nfound, nmask) array of good positions in the mask, mask coordinate
offsetsx = indx + offsetsx.T # a (nmask, nfound) array of positions in the mask for each candidate, image coordinate
offsetsy = np.resize(
goody - middle, (nfound, nmask)
) # a (nfound, nmask) array of good positions in the mask, mask coordinate
offsetsy = indy + offsetsy.T # a (nmask, nfound) array of positions in the mask for each candidate, image coordinate
offsets_vals = h[
offsetsy,
offsetsx] # a (nmask, nfound) array of mask values roundness each candidate
vals = h[indy,
indx] # a (nfound) array of the intensity of each candidate
###
# Identifying the candidates that are local maxima
###
ind_goodcandidates = ((vals - offsets_vals) > 0).all(
axis=0
) # a (nfound) array identifying the candidates whose values are above the mask (i.e. neighboring) pixels, candidate coordinate
nfound = ind_goodcandidates.sum() # update the number of candidates
if nfound <= 0:
self.logger.error(
"There is no source meeting the 'hmin' criterion that is a local maximum."
)
self.logger.error("Aborting the 'find' function.")
return None
indx = indx[
ind_goodcandidates] # a (nfound) array of x indices of good candidates, image coordinate
indy = indy[
ind_goodcandidates] # a (nfound) array of y indices of good candidates, image coordinate
###
# Identifying the candidates that meet the sharpness criterion
###
d = h[indy,
indx] # a (nfound) array of the intensity of good candidates
d_image = image[
indy,
indx] # a (nfound) array of the intensity of good candidates in the original image (before convolution)
offsetsx = offsetsx[:,
ind_goodcandidates] # a (nmask, nfound) array of positions in the mask for each candidate, image coordinate
offsetsy = offsetsy[:,
ind_goodcandidates] # a (nmask, nfound) array of positions in the mask for each candidate, image coordinate
offsets_vals = image[offsetsy, offsetsx]
sharpness = (d_image - offsets_vals.sum(0) / nmask) / d
ind_goodcandidates = (sharpness > sharplim[0]) * (
sharpness < sharplim[1]
) # a (nfound) array of candidates that meet the sharpness criterion
nfound = ind_goodcandidates.sum() # update the number of candidates
if nfound <= 0:
self.logger.error(
"There is no source meeting the 'sharpness' criterion.")
self.logger.error("Aborting the 'find' function.")
return None
indx = indx[
ind_goodcandidates] # a (nfound) array of x indices of good candidates, image coordinate
indy = indy[
ind_goodcandidates] # a (nfound) array of y indices of good candidates, image coordinate
sharpness = sharpness[
ind_goodcandidates] # update sharpness with the good candidates
###
# Identifying the candidates that meet the roundness criterion
###
temp = np.arange(nbox) - middle # make 1D indices of the kernel box
temp = np.resize(
temp,
(nbox, nbox)) # make 2D indices of the kernel box (for x or y)
offsetsx = np.resize(
temp, (nfound, nbox, nbox)
) # make 2D indices of the kernel box for x, repeated nfound times
offsetsy = np.resize(
temp.T, (nfound, nbox, nbox)
) # make 2D indices of the kernel box for y, repeated nfound times
offsetsx = (indx + offsetsx.swapaxes(0, -1)).swapaxes(
0, -1) # make it relative to image coordinate
offsetsy = (indy + offsetsy.swapaxes(0, -1)).swapaxes(
0, -1) # make it relative to image coordinate
offsets_vals = image[
offsetsy,
offsetsx] # a (nfound, nbox, nbox) array of values (i.e. the kernel box values for each nfound candidate)
dx = (offsets_vals.sum(2) * c1).sum(1)
dy = (offsets_vals.sum(1) * c1).sum(1)
roundness = 2 * (dx - dy) / (dx + dy)
ind_goodcandidates = (roundness > roundlim[0]) * (
roundness < roundlim[1]
) * (dx >= 0.) * (
dy >= 0.
) # a (nfound) array of candidates that meet the roundness criterion
nfound = ind_goodcandidates.sum() # update the number of candidates
if nfound <= 0:
self.logger.error(
"There is no source meeting the 'roundness' criterion.")
self.logger.error("Aborting the 'find' function.")
return None
indx = indx[
ind_goodcandidates] # a (nfound) array of x indices of good candidates, image coordinate
indy = indy[
ind_goodcandidates] # a (nfound) array of y indices of good candidates, image coordinate
sharpness = sharpness[
ind_goodcandidates] # update sharpness with the good candidates
roundness = roundness[
ind_goodcandidates] # update roundness with the good candidates
offsets_vals = offsets_vals[
ind_goodcandidates] # update offsets_vals with good candidates
offsetsx = offsetsx[ind_goodcandidates]
offsetsy = offsetsy[ind_goodcandidates]
###
# Recenter the source position and compute the approximate flux
###
c = np.empty((nfound, 2), dtype=float)
for i in range(nfound):
c[i] = scipy.ndimage.center_of_mass(offsets_vals[i])
x = c[:, 1] + indx - middle
y = c[:, 0] + indy - middle
flux = h[indy, indx]
return np.array([x, y, flux, sharpness, roundness])
def scan_dynamic(self, detect, devs, center, dims, accuracy, speed=10):
"""a function that does a scan. The number of axes you can choose yourself.
detect: a device(s) that does the detection of your signal
devs: array devices which you are using,
center: array of starting piont of the center (unit of the device),
dims: array of the dimensions (unit of the device),
accuracy: array of accuracy value (unit of the device),
speed: the duration of one pixel (ms)"""
self.logger = logging.getLogger(get_all_caller())
if not self.running:
devs = np.array(devs)
center = np.array(center)
dims = np.array(dims)
accuracy = np.array(accuracy)
if not type(detect) == type([]):
detect = [detect]
if len(devs) == len(center) == len(dims) == len(accuracy) <= 3:
self.logger.info('Making a dynamic scan')
port = np.zeros(3)
pix = (np.array(dims) / np.array(accuracy)).astype('int')
increment = np.zeros(3)
start = -np.ones(3)
for i in range(len(devs)):
port[i] = devs[i].properties['Output']['Hardware'][
'PortID']
calibration = devs[i].properties['Output']['Calibration']
start[i] = (center[i] - calibration['Offset'] -
dims[i] / 2) / calibration['Slope']
increment[i] = int(accuracy[i] / calibration['Slope'])
min = devs[i].properties['Output']['Limits']['Min']
max = devs[i].properties['Output']['Limits']['Max']
if (start[i] < min
or max < start[i] + pix[i] * increment[i]):
self.logger.error(
'Error boundaries of device %s exceeded' %
devs[i].properties['Name'])
self.logger.error('Value %s is out of range(%s,%s)' %
(start[i], min, max))
return False
self.logger.info('Range(%s,%s) for device %s' %
(center[i] - dims[i] / 2, center[i] +
dims[i] / 2, devs[i].properties['Name']))
self.scan_settings[devs[i].properties['Name'] +
'_start'] = center[i] - dims[i] / 2
self.scan_settings[devs[i].properties['Name'] +
'_accuracy'] = accuracy[i]
#self.set_par(par.properties['Port'],detect.properties['Input']['Hardware']['PortID'])
#self.set_par(par.properties['Dev_type'],int(detect.properties['Type'][:5],36))
dev_params = np.array([])
for i in detect:
dev_params = np.append(dev_params, [
int(i.properties['Type'][:5], 36),
i.properties['Input']['Hardware']['PortID']
])
self.set_datalong(dev_params, data.properties['dev_params'])
self.set_par(par.properties['Num_devs'], len(detect))
self.pix = np.append(pix, np.ones(3 - len(pix)))
self.set_datalong(
np.append((port, start, self.pix), increment),
data.properties['Scan_params'])
total = int(np.prod(self.pix))
self.set_par(par.properties['Case'], 4)
self.adw.Set_Processdelay(self.proc_num,
int(speed * 1e-3 / 25e-9))
self.start()
time.sleep(0.1)
self.running = bool(self.adw.Process_Status(self.proc_num))
temp = np.zeros(total)
temp[:] = np.nan
image = self.get_fifo(fifo.properties['Scan_data'])
self.scan_image = []
for i in range(len(detect)):
index = np.isnan(temp).argmax()
length = np.min(
(len(temp[index:]) * len(detect),
np.floor(len(image) / len(detect)) * len(detect)))
temp[index:index +
length / len(detect)] = image[i:length:len(detect)]
self.scan_image.append(
np.squeeze(temp.reshape((self.pix[::-1]))))
temp = np.zeros(total)
temp[:] = np.nan
self.excess_data = image[length:] or np.array([])
return self.scan_image
else:
self.logger.error(
"Not all input arrays have the same length or are longer the 3"
)
return False
elif self.running:
self.logger.debug('Getting data from danamic scan')
image = self.get_fifo(fifo.properties['Scan_data'])
try:
image = np.append(self.excess_data, image)
except:
pass
for i in range(len(detect)):
temp = self.scan_image[i].flatten()
index = np.isnan(temp).argmax()
length = np.min(
(len(temp[index:]) * len(detect),
np.floor(len(image) / len(detect)) * len(detect)))
temp[index:index +
length / len(detect)] = image[i:length:len(detect)]
self.scan_image[i] = np.squeeze(temp.reshape((self.pix[::-1])))
self.excess_data = image[length:]
return self.scan_image
def boot(self):
""" Boots the ADwin. """
self.logger = logging.getLogger(get_all_caller())
self.logger.info('Booted the Adwin')
self.adw.Boot('c:\\adwin\\ADwin9.btl')
def scan_static(self, detect, devs, center, dims, accuracy, speed=10):
""" A function that does a scan. The number of axes you can choose yourself.
detect: a device that does the detection of your signal
devs: array devices which you are using,
center: array of starting piont of the center (unit of the device),
dims: array of the dimensions (unit of the device),
accuracy: array of accuracy value (unit of the device),
speed: the duration of one pixel (ms)
"""
self.logger = logging.getLogger(get_all_caller())
devs = np.array(devs)
center = np.array(center)
dims = np.array(dims)
accuracy = np.array(accuracy)
if not type(detect) == type([]):
detect = [detect]
if len(devs) == len(center) == len(dims) == len(accuracy) <= 3:
port = np.zeros(3)
pix = (np.array(dims) / np.array(accuracy)).astype('int')
increment = np.zeros(3)
start = -np.ones(3)
self.logger.info('Making a static scan')
for i in range(len(devs)):
port[i] = devs[i].properties['Output']['Hardware']['PortID']
calibration = devs[i].properties['Output']['Calibration']
start[i] = (center[i] - calibration['Offset'] -
dims[i] / 2) / calibration['Slope']
increment[i] = accuracy[i] / calibration['Slope']
min = devs[i].properties['Output']['Limits']['Min']
max = devs[i].properties['Output']['Limits']['Max']
if (start[i] < min or max < start[i] + pix[i] * increment[i]):
self.logger.error(
'Error boundaries of device %s exceeded' %
devs[i].properties['Name'])
self.logger.error('Value %s is out of range(%s,%s)' %
(start[i], min, max))
raise ValueError('Boundaries exceded')
self.logger.info('Range(%s,%s) for device %s' %
(center[i] - dims[i] / 2,
center[i] + dims[i] / 2, devs[i]))
self.scan_settings[devs[i].properties['Name'] +
'_start'] = center[i] - dims[i] / 2
self.scan_settings[devs[i].properties['Name'] +
'_accuracy'] = accuracy[i]
#self.set_par(par.properties['Port'],detect.properties['Input']['Hardware']['PortID'])
#self.set_par(par.properties['Dev_type'],int(detect.properties['Type'][:5],36))
dev_params = np.array([])
for i in detect:
dev_params = np.append(dev_params, [
int(i.properties['Type'][:5], 36),
i.properties['Input']['Hardware']['PortID']
])
self.set_datalong(dev_params, data.properties['dev_params'])
self.set_par(par.properties['Num_devs'], len(detect))
pix = np.append(pix, np.ones(3 - len(pix)))
self.set_datalong(np.append((port, start, pix), increment),
data.properties['Scan_params'])
total = int(np.prod(pix))
self.set_par(par.properties['Case'], 4)
self.adw.Set_Processdelay(self.proc_num, int(speed * 1e-3 / 25e-9))
self.start()
while self.adw.Process_Status(self.proc_num):
number = self.get_par(par.properties['Pix_done'])
perc = int(number / total * 100)
stdout.write("\r{0:d}%".format(perc))
stdout.flush()
time.sleep(0.5)
print("")
temp = np.array(
list(self.get_fifo(fifo.properties['Scan_data'], total)))
self.scan_image = []
for i in range(len(detect)):
self.scan_image.append(
np.squeeze(temp[i::len(detect)].reshape(
(pix[::-1])) / (speed * 1e-3)))
return self.scan_image
else:
self.logger.error(
"Not all input arrays have the same length or are longer the 3"
)
raise InputError(
"Not all input arrays have the same length or are longer the 3"
)
return False
from __future__ import division
import numpy as np
from ADwin import ADwin
import time
import scipy.ndimage
import matplotlib.pyplot as plt
import math as m
from sys import stdout
import ctypes
import psutil
from lib.xml2dict import device, variables
import logging
from lib.logger import get_all_caller
import copy
logger = logging.getLogger(get_all_caller())
class adq(ADwin):
def __init__(self, process):
DEVICENUMBER = 1 # By default this is the number
RAISE_EXCEPTIONS = 1
self.adw = ADwin(DEVICENUMBER, RAISE_EXCEPTIONS)
self.proc = process
self.proc_num = int(process[-1])
if self.proc_num == 0:
self.proc_num = 10
self.scan_settings = dict()
self.dev_value = dict()
self.running = False
self.logger = logging.getLogger(get_all_caller())