def updateFullFrameImage(self, parameters):
p = parameters
if p["imageCategory"] == "ROI":
p["fullFrame"] = apogee.singleImage(p["filenames"][0], self.maxImageSize)
x, y = shape(p["fullFrame"])
p["part"] = array([0, x, 0, y])
else:
p["fullFrame"] = apogee.odTriple(p["filenames"], self.maxImageSize, p["partLumCorr"], p["partAtomCount"])
self.updateADwinID(parameters)
def analyseShot(shotPath, returnTemplate, image=[], filenames=['li_roi.fit', 'rb_roi.fit'], insituSizeLi=40e-6, insituSizeRb=40e-6):
"""
returns measurment data of a single shot. Posible values for returnTemplate:
* ID: ADwin ID
* ADwin varname: ADwin code value
* N_Li/N_Rb: atomnumber Li/Rb
* e.g. fwhmH_Li: cloud width h/v
* T_Li/T_Rb: temperature
* etc...
"""
import os
import adwin
import apogee
if image != []:
image_Li = image
image_Rb = image
adwinFilename = shotPath
else:
if 'N_Li' in returnTemplate or 'fwhmH_Li' in returnTemplate or 'fwhmV_Li' in returnTemplate or 'T_Li' in returnTemplate:
image_Li = apogee.singleImage(os.path.join(shotPath, filenames[0]))
if 'N_Rb' in returnTemplate or 'fwhmH_Rb' in returnTemplate or 'fwhmV_Rb' in returnTemplate or 'T_Rb' in returnTemplate:
image_Rb = apogee.singleImage(os.path.join(shotPath, filenames[1]))
adwinFilename = os.path.join(shotPath, 'adwin_code.acm')
if 'AmpH_Li' in returnTemplate or 'OffH_Li' in returnTemplate or 'PosH_Li' in returnTemplate or 'fwhmH_Li' in returnTemplate or 'N_Li_Gauss' in returnTemplate or 'T_Li' in returnTemplate:
gaussfitCoffLiH = gaussfit(image_Li)
if 'AmpV_Li' in returnTemplate or 'OffV_Li' in returnTemplate or 'PosV_Li' in returnTemplate or 'fwhmV_Li' in returnTemplate or 'N_Li_Gauss' in returnTemplate or 'T_Li' in returnTemplate:
gaussfitCoffLiV = gaussfit(image_Li, dir = 1)
if 'AmpH_Rb' in returnTemplate or 'OffH_Rb' in returnTemplate or 'PosH_Rb' in returnTemplate or 'fwhmH_Rb' in returnTemplate or 'N_Rb_Gauss' in returnTemplate or 'T_Rb' in returnTemplate:
gaussfitCoffRbH = gaussfit(image_Rb)
if 'AmpV_Rb' in returnTemplate or 'OffV_Rb' in returnTemplate or 'PosV_Rb' in returnTemplate or 'fwhmV_Rb' in returnTemplate or 'N_Rb_Gauss' in returnTemplate or 'T_Rb' in returnTemplate:
gaussfitCoffRbV = gaussfit(image_Rb, dir = 1)
returnList = empty([0, 0], dtype=object)
for request in returnTemplate:
if request == 'N_Li':
returnList = append(returnList, atomCount(image_Li))
elif request == 'N_Rb':
returnList = append(returnList, atomCount(image_Rb))
elif request == 'ID':
returnList = append(returnList, os.path.split(shotPath)[-1])
elif request == 'N_Rb_Gauss':
returnList = append(returnList, atomCountGaussfit(gaussfitCoffRbH, gaussfitCoffRbV))
elif request == 'N_Li_Gauss':
returnList = append(returnList, atomCountGaussfit(gaussfitCoffLiH, gaussfitCoffLiV))
elif request == 'fwhmH_Li':
returnList = append(returnList, fwhm(gaussfitCoffLiH[1][2]))
elif request == 'fwhmV_Li':
returnList = append(returnList, fwhm(gaussfitCoffLiV[1][2]))
elif request == 'fwhmH_Rb':
returnList = append(returnList, fwhm(gaussfitCoffRbH[1][2]))
elif request == 'fwhmV_Rb':
returnList = append(returnList, fwhm(gaussfitCoffRbV[1][2]))
elif request == 'OffH_Li':
returnList = append(returnList, gaussfitCoffLiH[1][3])
elif request == 'OffV_Li':
returnList = append(returnList, gaussfitCoffLiV[1][3])
elif request == 'OffH_Rb':
returnList = append(returnList, gaussfitCoffRbH[1][3])
elif request == 'OffV_Rb':
returnList = append(returnList, gaussfitCoffRbV[1][3])
elif request == 'PosH_Li':
returnList = append(returnList, gaussfitCoffLiH[1][1])
elif request == 'PosV_Li':
returnList = append(returnList, gaussfitCoffLiV[1][1])
elif request == 'PosH_Rb':
returnList = append(returnList, gaussfitCoffRbH[1][1])
elif request == 'PosV_Rb':
returnList = append(returnList, gaussfitCoffRbV[1][1])
elif request == 'AmpH_Li':
returnList = append(returnList, gaussfitCoffLiH[1][0])
elif request == 'AmpV_Li':
returnList = append(returnList, gaussfitCoffLiV[1][0])
elif request == 'AmpH_Rb':
returnList = append(returnList, gaussfitCoffRbH[1][0])
elif request == 'AmpV_Rb':
returnList = append(returnList, gaussfitCoffRbV[1][0])
elif request == 'T_Li':
if abs(gaussfitCoffLiH[1][2] - gaussfitCoffLiV[1][2]) > (gaussfitCoffLiH[1][2]+gaussfitCoffLiV[1][2])/4:
sigma = nan #max([gaussfitCoffLiV[1][2], gaussfitCoffLiH[1][2]])
print 'T_Li not well determined!'
else:
sigma = (gaussfitCoffLiH[1][2]+gaussfitCoffLiV[1][2]) / 2
returnList = append(returnList, temperatur(adwin.GetValue('ABB_TOF_LI', adwinFilename)*1e-6, sigma, 7, insituSizeLi))
elif request == 'T_Rb':
if abs(gaussfitCoffRbH[1][2] - gaussfitCoffRbV[1][2]) > (gaussfitCoffRbH[1][2]+gaussfitCoffRbV[1][2])/4:
sigma = nan #max([gaussfitCoffRbV[1][2], gaussfitCoffRbH[1][2]])
print 'T_Rb not well determined!'
else:
sigma = (gaussfitCoffRbH[1][2]+gaussfitCoffRbV[1][2]) / 2
returnList = append(returnList, temperatur(adwin.GetValue('ABB_TOF_RB', adwinFilename)*1e-6, sigma, 87, insituSizeRb))
else:
returnList = append(returnList, adwin.GetValue(request, adwinFilename))
return returnList
def drawImageArea(self, parameters):
p = parameters
ImageScale = array(p["part"]) * p["pixelSize"] * p["axScale"]
data = pd.Series(zeros(len(p["values2calc"])), index=p["indexLst"], dtype=float)
# image field
if p["imageCategory"] == "opt. Dichte" or p["imageCategory"] == "ROI":
image = p["fullFrame"][p["part"][0] : p["part"][1], p["part"][2] : p["part"][3]]
if (p["part"][1] - p["part"][0] < 800 and p["part"][3] - p["part"][2] < 800) or p["imageCategory"] == "ROI":
data = pd.Series(
imageAnalyse.analyseShot(p["adwinfile"], p["values2calc"], image), index=p["indexLst"], dtype=float
)
imageAtomCount = p["fullFrame"][
p["partAtomCount"][0] : p["partAtomCount"][1], p["partAtomCount"][2] : p["partAtomCount"][3]
]
data["n"] = imageAnalyse.analyseShot(p["adwinfile"], [p["values2calc"][0]], imageAtomCount)
if p["ODmaxAuto"].get_active():
p["ODmax"].set_value(imageAtomCount.max())
if data["n"] < 1e6:
title1 = p["imageAx"].set_title(
(r"N(" + p["species"] + r") = ${:,.2f}\,\times\,10^3$").format(data["n"] / 1e3)
)
if (
self.MessageType == "N_Li"
and p["species"] == "Li"
or self.MessageType == "N_Rb"
and p["species"] == "Rb"
):
self.windowMessage.set_markup(
u'<span font="120">N('
+ p["species"]
+ r")= "
+ u"%.2f\u00d710\u00b3</span>" % (data["n"] / 1e3)
)
else:
title1 = p["imageAx"].set_title(
(r"N(" + p["species"] + r") = ${:,.2f}\,\times\,10^6$").format(data["n"] / 1e6)
)
if (
self.MessageType == "N_Li"
and p["species"] == "Li"
or self.MessageType == "N_Rb"
and p["species"] == "Rb"
):
self.windowMessage.set_markup(
u'<span font="120">N('
+ p["species"]
+ r")= "
+ u"%.2f\u00d710\u2076</span>" % (data["n"] / 1e6)
)
else:
if p["imageCategory"] == "Atomrohbild":
image = apogee.singleImage(p["filenames"][0], p["part"])
elif p["imageCategory"] == "Hellrohbild":
image = apogee.singleImage(p["filenames"][1], p["part"])
elif p["imageCategory"] == "Dunkelrohbild":
image = apogee.singleImage(p["filenames"][2], p["part"])
title1 = p["imageAx"].set_title(p["species"] + r": " + p["imageCategory"])
if p["ODmaxAuto"].get_active():
p["ODmax"].set_value(image.max())
p["imageObj"].set_data(image)
p["imageObj"].set_extent([ImageScale[2], ImageScale[3], ImageScale[1], ImageScale[0]])
p["imageObj"].set_clim(0, p["ODmax"].get_value())
title1.set_fontsize(32)
title1.set_y(1.04)
if not data["t"] == 0 and not math.isnan(float(data["t"] * 1e6)):
self.drawCrosshair(
p["lineFitH"],
p["lineFitV"],
[
data["posh"] * p["pixelSize"] * p["axScale"] + ImageScale[2],
data["posv"] * p["pixelSize"] * p["axScale"] + ImageScale[0],
],
)
else:
self.drawCrosshair(p["lineFitH"], p["lineFitV"], [-1e5, -1e5])
# linescan gaussian fit
p["linescanAx"].cla()
p["linescanAx"].plot(linspace(ImageScale[2], ImageScale[3], size(image, 1)), imageAnalyse.linescan(image))
p["linescanAx"].plot(
linspace(ImageScale[2], ImageScale[3], size(image, 0)), imageAnalyse.linescan(image, dir=1), color="0.85"
)
if math.isnan(float(data["t"] * 1e6)):
title3 = p["linescanAx"].set_title(u"T(" + p["species"] + r") uneindeutig")
p["linescanAx"].text(
0.02, 0.05, u"TOF = {:.2f}ms".format(data["abb_tof"] / 1e3), transform=p["linescanAx"].transAxes
)
elif data["t"] == 0:
title3 = p["linescanAx"].set_title("")
else:
title3 = p["linescanAx"].set_title(u"T(" + p["species"] + u") = {:.2f}\u00b5K".format(data["t"] * 1e6))
p["linescanAx"].text(
0.02,
0.85,
u"H-Pos: {:.2f}\u00b5m".format(data["posh"] * p["pixelSize"] * p["axScale"] + ImageScale[2]),
transform=p["linescanAx"].transAxes,
)
p["linescanAx"].text(
0.02,
0.7,
u"H-FWHM: {:.2f}\u00b5m".format(data["fwhmh"] * p["axScale"]),
transform=p["linescanAx"].transAxes,
)
p["linescanAx"].text(
0.98,
0.85,
u"V-Pos: {:.2f}\u00b5m".format(data["posv"] * p["pixelSize"] * p["axScale"] + ImageScale[0]),
transform=p["linescanAx"].transAxes,
color="0.7",
horizontalalignment="right",
)
p["linescanAx"].text(
0.98,
0.7,
u"V-FWHM: {:.2f}\u00b5m".format(data["fwhmv"] * p["axScale"]),
transform=p["linescanAx"].transAxes,
color="0.7",
horizontalalignment="right",
)
p["linescanAx"].plot(
linspace(ImageScale[2], ImageScale[3], size(image, 1)),
imageAnalyse.gauss(
range(0, size(image.conj().transpose(), 0)),
data["amph"],
data["posh"],
imageAnalyse.fwhm(data["fwhmh"], reverse=1),
data["offh"],
),
"--",
)
p["linescanAx"].plot(
linspace(ImageScale[2], ImageScale[3], size(image, 0)),
imageAnalyse.gauss(
range(0, size(image.conj().transpose(), 1)),
data["ampv"],
data["posv"],
imageAnalyse.fwhm(data["fwhmv"], reverse=1),
data["offv"],
),
"--",
color="0.85",
)
p["linescanAx"].plot(
[
data["posh"] * p["pixelSize"] * p["axScale"] + ImageScale[2],
data["posh"] * p["pixelSize"] * p["axScale"] + ImageScale[2],
],
p["linescanAx"].get_ylim(),
"k:",
)
posVrelAxH = (data["posv"] * p["pixelSize"] * p["axScale"]) / (ImageScale[1] - ImageScale[0]) * (
ImageScale[3] - ImageScale[2]
) + ImageScale[2]
p["linescanAx"].plot([posVrelAxH, posVrelAxH], p["linescanAx"].get_ylim(), ":", color="0.5")
p["linescanAx"].plot([p["Marker1Pos"][0], p["Marker1Pos"][0]], p["linescanAx"].get_ylim(), "k-")
posVrelAxHMarker1 = (p["Marker1Pos"][1] - ImageScale[0]) / (ImageScale[1] - ImageScale[0]) * (
ImageScale[3] - ImageScale[2]
) + ImageScale[2]
p["linescanAx"].plot([posVrelAxHMarker1, posVrelAxHMarker1], p["linescanAx"].get_ylim(), "-", color="0.85")
p["linescanAx"].text(
0.02, 0.05, u"TOF = {:.2f}ms".format(data["abb_tof"] / 1e3), transform=p["linescanAx"].transAxes
)
if not data["automatisierung"] == 0:
p["linescanAx"].text(
0.98,
0.05,
u"auto: {:.0f}/{:.0f} #{:.0f}".format(
data["auto_step_pos"], abs(data["auto_break"]), data["auto_n_pos"]
),
transform=p["linescanAx"].transAxes,
horizontalalignment="right",
)
p["linescanAx"].set_xlim((ImageScale[2], ImageScale[3]))
p["linescanAx"].grid(True, color="0.5")
p["linescanAx"].set_xlabel(u"\u00b5m")
p["linescanAx"].ticklabel_format(style="sci", axis="y", scilimits=(0, 3))
title3.set_fontsize(18)
title3.set_y(1.06)
p["canvasObj"].draw_idle()
return data
def analyseShot(shotPath,
returnTemplate,
image=[],
filenames=['li_roi.fit', 'rb_roi.fit'],
insituSizeLi=40e-6,
insituSizeRb=40e-6):
"""
returns measurment data of a single shot. Posible values for returnTemplate:
* ID: ADwin ID
* ADwin varname: ADwin code value
* N_Li/N_Rb: atomnumber Li/Rb
* e.g. fwhmH_Li: cloud width h/v
* T_Li/T_Rb: temperature
* etc...
"""
import os
import adwin
import apogee
if image != []:
image_Li = image
image_Rb = image
adwinFilename = shotPath
else:
if 'N_Li' in returnTemplate or 'fwhmH_Li' in returnTemplate or 'fwhmV_Li' in returnTemplate or 'T_Li' in returnTemplate:
image_Li = apogee.singleImage(os.path.join(shotPath, filenames[0]))
if 'N_Rb' in returnTemplate or 'fwhmH_Rb' in returnTemplate or 'fwhmV_Rb' in returnTemplate or 'T_Rb' in returnTemplate:
image_Rb = apogee.singleImage(os.path.join(shotPath, filenames[1]))
adwinFilename = os.path.join(shotPath, 'adwin_code.acm')
if 'AmpH_Li' in returnTemplate or 'OffH_Li' in returnTemplate or 'PosH_Li' in returnTemplate or 'fwhmH_Li' in returnTemplate or 'N_Li_Gauss' in returnTemplate or 'T_Li' in returnTemplate:
gaussfitCoffLiH = gaussfit(image_Li)
if 'AmpV_Li' in returnTemplate or 'OffV_Li' in returnTemplate or 'PosV_Li' in returnTemplate or 'fwhmV_Li' in returnTemplate or 'N_Li_Gauss' in returnTemplate or 'T_Li' in returnTemplate:
gaussfitCoffLiV = gaussfit(image_Li, dir=1)
if 'AmpH_Rb' in returnTemplate or 'OffH_Rb' in returnTemplate or 'PosH_Rb' in returnTemplate or 'fwhmH_Rb' in returnTemplate or 'N_Rb_Gauss' in returnTemplate or 'T_Rb' in returnTemplate:
gaussfitCoffRbH = gaussfit(image_Rb)
if 'AmpV_Rb' in returnTemplate or 'OffV_Rb' in returnTemplate or 'PosV_Rb' in returnTemplate or 'fwhmV_Rb' in returnTemplate or 'N_Rb_Gauss' in returnTemplate or 'T_Rb' in returnTemplate:
gaussfitCoffRbV = gaussfit(image_Rb, dir=1)
returnList = empty([0, 0], dtype=object)
for request in returnTemplate:
if request == 'N_Li':
returnList = append(returnList, atomCount(image_Li))
elif request == 'N_Rb':
returnList = append(returnList, atomCount(image_Rb))
elif request == 'ID':
returnList = append(returnList, os.path.split(shotPath)[-1])
elif request == 'N_Rb_Gauss':
returnList = append(
returnList, atomCountGaussfit(gaussfitCoffRbH,
gaussfitCoffRbV))
elif request == 'N_Li_Gauss':
returnList = append(
returnList, atomCountGaussfit(gaussfitCoffLiH,
gaussfitCoffLiV))
elif request == 'fwhmH_Li':
returnList = append(returnList, fwhm(gaussfitCoffLiH[1][2]))
elif request == 'fwhmV_Li':
returnList = append(returnList, fwhm(gaussfitCoffLiV[1][2]))
elif request == 'fwhmH_Rb':
returnList = append(returnList, fwhm(gaussfitCoffRbH[1][2]))
elif request == 'fwhmV_Rb':
returnList = append(returnList, fwhm(gaussfitCoffRbV[1][2]))
elif request == 'OffH_Li':
returnList = append(returnList, gaussfitCoffLiH[1][3])
elif request == 'OffV_Li':
returnList = append(returnList, gaussfitCoffLiV[1][3])
elif request == 'OffH_Rb':
returnList = append(returnList, gaussfitCoffRbH[1][3])
elif request == 'OffV_Rb':
returnList = append(returnList, gaussfitCoffRbV[1][3])
elif request == 'PosH_Li':
returnList = append(returnList, gaussfitCoffLiH[1][1])
elif request == 'PosV_Li':
returnList = append(returnList, gaussfitCoffLiV[1][1])
elif request == 'PosH_Rb':
returnList = append(returnList, gaussfitCoffRbH[1][1])
elif request == 'PosV_Rb':
returnList = append(returnList, gaussfitCoffRbV[1][1])
elif request == 'AmpH_Li':
returnList = append(returnList, gaussfitCoffLiH[1][0])
elif request == 'AmpV_Li':
returnList = append(returnList, gaussfitCoffLiV[1][0])
elif request == 'AmpH_Rb':
returnList = append(returnList, gaussfitCoffRbH[1][0])
elif request == 'AmpV_Rb':
returnList = append(returnList, gaussfitCoffRbV[1][0])
elif request == 'T_Li':
if abs(gaussfitCoffLiH[1][2] - gaussfitCoffLiV[1][2]) > (
gaussfitCoffLiH[1][2] + gaussfitCoffLiV[1][2]) / 4:
sigma = nan #max([gaussfitCoffLiV[1][2], gaussfitCoffLiH[1][2]])
print 'T_Li not well determined!'
else:
sigma = (gaussfitCoffLiH[1][2] + gaussfitCoffLiV[1][2]) / 2
returnList = append(
returnList,
temperatur(
adwin.GetValue('ABB_TOF_LI', adwinFilename) * 1e-6, sigma,
7, insituSizeLi))
elif request == 'T_Rb':
if abs(gaussfitCoffRbH[1][2] - gaussfitCoffRbV[1][2]) > (
gaussfitCoffRbH[1][2] + gaussfitCoffRbV[1][2]) / 4:
sigma = nan #max([gaussfitCoffRbV[1][2], gaussfitCoffRbH[1][2]])
print 'T_Rb not well determined!'
else:
sigma = (gaussfitCoffRbH[1][2] + gaussfitCoffRbV[1][2]) / 2
returnList = append(
returnList,
temperatur(
adwin.GetValue('ABB_TOF_RB', adwinFilename) * 1e-6, sigma,
87, insituSizeRb))
else:
returnList = append(returnList,
adwin.GetValue(request, adwinFilename))
return returnList
