def linear_fit():
global Plot2
valid = []
vaxes = []
for i in xrange(peak_res.size):
if not Double.isNaN(peak_res[i]):
valid.append(peak_res[i] * -1)
vaxes.append(h2k2l2[i])
if len(valid) == 0:
slog('Error: there is no available peak.')
return
ds2 = Dataset(valid, axes=[vaxes])
Plot2.set_dataset(ds2)
ds = Plot2.ds
if ds is None or len(ds) == 0:
print 'Error: no curve to fit in Plot2'
return
for d in ds:
if d.title == 'linear_fitting':
Plot2.remove_dataset(d)
d0 = ds[0]
fitting = Fitting(LINEAR_FITTING)
fitting.set_histogram(d0)
if linear_slope.value == 0:
fitting.set_param('a', 1)
else:
fitting.set_param('a', linear_slope.value)
res = fitting.fit()
res.var[:] = 0
res.title = 'linear_fitting'
Plot2.add_dataset(res)
linear_slope.value = fitting.a
print fitting.params
def getTableModel(self, book):
# print "getTableModel book",book
import java.util.Vector as Vector
import java.util.Arrays as Arrays
import java.util.Calendar as Calendar
import com.infinitekind.moneydance.model.CurrencyType as CurrencyType
import java.lang.Double as Double
ct = book.getCurrencies()
allCurrencies = ct.getAllCurrencies()
data = Vector()
today = Calendar.getInstance()
balances = self.sumBalancesByCurrency(book) # HashMap<CurrencyType, Long> contains no account info
accounts = self.getCurrencyAccounts(book) # HashMap<CurrencyType, Accounts>
self.totalBalance = 0.0
# StockGlance75.totalBalance = 0.0
for curr in allCurrencies:
if not curr.getHideInUI() and curr.getCurrencyType() == CurrencyType.Type.SECURITY:
price = self.priceOrNaN(curr, today, 0)
price1 = self.priceOrNaN(curr, today, 1)
price7 = self.priceOrNaN(curr, today, 7)
price30 = self.priceOrNaN(curr, today, 30)
price365 = self.priceOrNaN(curr, today, 365)
if not Double.isNaN(price) and (not Double.isNaN(price1) or not Double.isNaN(price7) or not Double.isNaN(price30) or not Double.isNaN(price365)):
entry = Vector(len(self.names))
bal = balances.get(curr)
balance = (0.0 if (bal == None) else curr.getDoubleValue(bal) * price)
# ? Double balance = (bal == null) ? 0.0 : curr.getDoubleValue(bal) * price;
self.totalBalance += balance
# StockGlance75.totalBalance += balance
entry.add(curr.getTickerSymbol())
entry.add(curr.getName())
entry.add(price)
entry.add(price - price1)
entry.add(balance)
entry.add((price - price1) / price1)
entry.add((price - price7) / price7)
entry.add((price - price30) / price30)
entry.add((price - price365) / price365)
entry.add(accounts.get(curr))
data.add(entry)
self.rowCurrencies.add(curr)
# StockGlance75.rowCurrencies.add(curr)
self.data = data
# StockGlance75.data = data
return self.SGTableModel(self.data, self.columnNames, self.rowCurrencies)
def createChartDataSet (self, xyPoints, name='data'):
#autoSort = 0
#allowDuplicateXValues = 0
xySeries = UnsortedXYSeries (name) #, autoSort, allowDuplicateXValues)
for i in range (len (xyPoints)):
point = xyPoints [i]
x = point.getX ()
y = point.getY ()
if (Double.isNaN (x) or Double.isNaN (y)):
continue
xySeries.add (x, y)
self.pointNames.append (point.getName ())
return XYSeriesCollection (xySeries)
def __measureVelocity(self, img, name) : measure = "velocity" dico=self.__dictCells[name] for cellname in dico.keys() : roitemp=dico[cellname].getRoi(0) xc = self.__dictMeasures[dico[cellname]]["XC"] yc = self.__dictMeasures[dico[cellname]]["YC"] d=[] for i in range(len(xc)-1) : tempseq = [ xc[i], xc[i+1], yc[i], yc[i+1] ] if not any(Double.isNaN(val)==True for val in tempseq ) : d.append(Morph.distMorph([[1, xc[i], xc[i+1]],[1, yc[i], yc[i+1]]])) else : d.append(Double.NaN) d.append(Double.NaN) self.__dictMeasures[dico[cellname]][measure]=d del(d)
def setValue(self, value):
import java.lang.Double as Double
import com.infinitekind.util.StringUtils as StringUtils
import java.awt.Color as Color
if value == None:
self.setText("")
elif Double.isNaN(float(value)):
setText("")
else:
if self.isZero(float(value)):
value = 0.0
self.setText(StringUtils.formatPercentage(float(value), self.decimalSeparator) + "%")
if float(value) < 0.0:
self.setForeground(Color.RED)
else:
self.setForeground(Color.BLACK)
def __init__(self, imp):
'''Get the metadata from the given dm3 image.
'''
extractor = GatanMetadataExtractor(imp)
self.exposure = extractor.getExposure()
self.magnification = extractor.getMagnification()
self.mag_factor = extractor.getActualMagnification() / self.magnification
self.mag_unit = 'x'
if not Double.isNaN(extractor.getEnergyloss()):
self.energyloss = extractor.getEnergyloss()
else:
self.energyloss = 0
self.date = extractor.getDateAndTime()
date_formater = SimpleDateFormat('yyyyMMdd')
self.date_string = date_formater.format(self.date)
self.name = extractor.getName()
self.prop_dict = {}
def __measurecumulDist(self, img, name) : measure = "cumulatedDist" dico=self.__dictCells[name] for cellname in dico.keys() : roitemp=dico[cellname].getRoi(0) xc = self.__dictMeasures[dico[cellname]]["XC"] yc = self.__dictMeasures[dico[cellname]]["YC"] d=[] d.append(0) for i in range(1,len(xc)-1) : tempseq = [ xc[i], xc[i+1], yc[i], yc[i+1] ] if not any(Double.isNaN(val)==True for val in tempseq ) : lastdist = Morph.distMorph([[1, xc[i], xc[i+1]],[1, yc[i], yc[i+1]]]) d.append(d[-1]+lastdist) else : d.append(Double.NaN) d.append(Double.NaN) self.__dictMeasures[dico[cellname]][measure]=d del(d)
def setValue(self, value):
import java.lang.Double as Double
import java.awt.Color as Color
if value == None:
self.setText("")
elif Double.isNaN(float(value)):
self.setText("")
else:
if self.isZero(float(value)):
value = 0.0
if self.noDecimals:
# MD format functions can't print comma-separated values without a decimal point so
# we have to do it ourselves
scaledValue = float(value) * self.relativeTo.getUserRate();
self.setText(self.relativeTo.getPrefix() + " " + self.noDecimalFormatter.format(scaledValue) + self.relativeTo.getSuffix())
else:
scaledValue = self.relativeTo.convertValue(self.relativeTo.getLongValue(float(value)))
self.setText(self.relativeTo.formatFancy(scaledValue, self.decimalSeparator))
if float(value) < 0.0:
self.setForeground(Color.RED)
else:
self.setForeground(Color.BLACK)
def getTemperatureHumiditySensor(self):
result = grovepi.get(lambda: self.__temperatureHumiditySensor.get())
if Double.isNaN(result.temperature) or Double.isNaN(result.humidity):
# Read error.
return None
return result
def graphbuttonPressed(event) :
colors=[]
#img=IJ.getImage()
#nbslices=self.__img.getImageStackSize()
nbslices=self.__maxLife
acell=dico.values()[0]
if self.__useTime :
x = acell.getListTimes()
namex="Time sec"
else :
x = range(1,nbslices+1)
namex = "Frame"
maxx=max(x)
minx=min(x)
#x=[i for i in range(1,nbslices+1)]
font=Font("new", Font.BOLD, 14)
tempname = WindowManager.getUniqueName(self.__img.getShortTitle())
for i in range(len(self.__measures)) :
#print "i", i, self.__measures[i]
yarray=[]
flag=True
miny=10000000000
maxy=-1000000000
#we find the min and max values in order to set the scale.
for cellname in self.__listcellname :
colors.append(dico[cellname].getColor())
yarray.append(self.__dictMeasures[dico[cellname]][self.__measures[i]])
#for meas in self.__dictMeasures[dico[cellname]][self.__measures[i]] :
for meas in yarray[-1] :
if (meas<miny) and (Double.isNaN(meas)==False) :
miny=meas
if max(yarray[-1])>maxy : maxy=max(yarray[-1])
miny-=0.1*miny
maxy+=0.1*maxy
count=0.05
for j in range(len(yarray)) :
if j==0 :
if len(self.__measures)>1 :
plot=Plot("Plots-"+str(self.__measures[i]),namex,str(self.__measures[i]),x,yarray[j])
else :
plot=Plot("Plot-"+tempname,namex,str(self.__measures[i]),x,yarray[j])
plot.setLimits(minx,maxx,miny,maxy)
plot.setColor(colors[j])
plot.changeFont(font)
plot.addLabel(0.05, count, self.__listcellname[j])
else :
plot.setColor(colors[j])
plot.setLineWidth(3)
plot.addPoints(x,yarray[j],Plot.LINE)
plot.addLabel(0.05, count, self.__listcellname[j])
count+=0.05
plot.setColor(colors[0])
plot.show()
if len(self.__measures)>1 :
IJ.run("Images to Stack", "name="+tempname+"-plots title=Plots- use")
def nonlinear_fit():
global Plot3
valid = []
vaxes = []
for i in xrange(peak_res.size):
if not Double.isNaN(peak_res[i]):
valid.append(peak_res[i] * -1)
vaxes.append(twod[i])
if len(valid) == 0:
slog('Error: there is not any available peak.')
return
ds3 = Dataset(valid, axes=[vaxes])
Plot3.set_dataset(ds3)
ds = Plot3.ds
if len(ds) == 0:
print 'Error: no curve to fit in Plot3'
return
for d in ds:
if d.title == 'nonlinear_fit':
Plot3.remove_dataset(d)
d3 = ds[0]
cur = d3 * math.pi / 180
function = '2*asin(k1/x[0])+k2'
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(cur)
m = 1.67492861e-027
h = 6.626068e-034
eV = 1.60217646e-019
pl = 1.0e10
l = h * pl * 100 / math.sqrt(linear_slope.value * eV * 20 * m)
# if lambda_fit.value == 0 and s2_offset.value == 0:
# fitting.set_param('k1', 0)
## fitting.set_param('k2', 0)
# else:
# fitting.set_param('k1', lambda_fit.value)
# fitting.set_param('k2', s2_offset.value)
try:
v_lmd = lmd.value
except:
v_lmd = 0
fitting.set_param('k1', v_lmd)
fitting.set_param('k2', 0)
fitting.fitter.setResolutionMultiple(10)
# fitting.fitter.getRawFitter().fitParameterSettings("k1").setStepSize(0.00001);
# fitting.fitter.getRawFitter().fitParameterSettings("k2").setStepSize(0.00001);
for i in xrange(5):
res = fitting.fit()
lambda_fit.value = fitting.k1
s2_offset.value = fitting.k2 * 180 / math.pi
fit_quality.value = fitting.fitter.getQuality()
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(cur)
fitting.set_param('k1', lambda_fit.value)
res.var[:] = 0
res.title = 'nonlinear_fit'
res = res * 180 / math.pi
Plot3.add_dataset(res)
# lambda_fit.value = fitting.k1
# s2_offset.value = fitting.k2 * 180 / math.pi
# fit_quality.value = fitting.fitter.getQuality()
m1_new.value = math.asin(lambda_fit.value / 2 / D_space) * 180 / math.pi
m2_new.value = 2 * m1_new.value
slog('Chi2 = ' + str(fit_quality.value))
slog('lambda = ' + str(lambda_fit.value))
slog('meV = ' + str((h * pl / lambda_fit.value / m) ** 2 * m * 500 / eV))
slog('two-theta_offset = ' + str(s2_offset.value))
def spireMonoBeamSrc(freqx,beamRad,beamProfs,beamConst,effFreq,gamma,srcProf,array):
"""
========================================================================
Implements the full beam model, convolved with a source profile,
to generate the monochromatic beam profile and corresponding monochromatic
beam solid angle at a given frequency.
Inputs:
freqx: (float) frequency [Hz] for which monochromatic beam
profile and area should be calculated
beamRad: (array float) radius vector from the input beam profiles
beamProfs: (dataset) PhotRadialCorrBeam object
[used to retrieve core beam profile]
beamConst: (array float) constant part of beam profile for "array"
[passed to prevent repeated calls]
effFreq: (float) effective frequency [Hz] of array
gamma: (float) Exponent of powerlaw describing FWHM dependence
on frequency
srcProf: (array float) source density profile, corresponding to
radius column in beamProfs
array: (string) spire array ('Psw'|'Pmw'|'Plw')
Outputs: (list of objects)
(float) Beam area [arcsec^2] at frequency freqx
Calculation:
Scales the core beam profile width as (freqx/effFreq)^gamma.
Queries the calibration file to generate new core beam profile.
Uses constant beam profile where it is larger than core profile.
Multiplies by a source profile
Integrates over radius to calculate beam area.
Dependencies:
herschel.ia.numeric.toolbox.interp.LinearInterpolator
herschel.ia.numeric.toolbox.integr.TrapezoidalIntegrator
2013/12/19 C. North initial version
"""
#calculate the "scaled" radius, as nu^-gamma
radNew=beamRad*(freqx/effFreq)**-gamma
maxRad=max(beamRad)
nRad=len(beamRad)
#ensure it doesn't go out of range
radNew[radNew.where(radNew > maxRad)]=maxRad
#get the corresponding beam profiles
beamNew=Double1d(nRad)
for r in range(nRad):
beamNew[r]=beamProfs.getCoreCorrection(radNew[r],array)
#apply the "constant" beam where appropriate
#beamConst=beamProfs.getConstantCorrectionTable().getColumn(array).data
isConst=beamNew.where(beamNew < beamConst)
beamNew[isConst]=beamConst[isConst]
#copy source Profile and multiply by beam
beamNew = srcProf.copy().mult(beamNew,key='Src x Beam')
beamMonoArea = beamNew.calcArea()
if beamMonoArea==0:
print 'calculated area=0'
#try just the source
srcArea=srcProf.calcArea(forceAnalytical=True)
if Double.isNaN(srcArea):
#no analytical source
print 'Source is very small. No analytical area for source'
else:
print 'Source is very small. Replacing area with analytical area of source'
beamMonoArea=srcArea
## THIS IS ONLY VALID FOR AREA
## FULL PROFILE OF CONVOLUTION REQUIRES PROPER CONVOLUTION
#integrate to get solid angle (in arcsec^2)
#beamNew=beamNew * srcProf.profile
#beamInterp=LinearInterpolator(beamRad,beamNew * 2. * PI * beamRad)
#integrator=TrapezoidalIntegrator(0,maxRad)
#beamMonoArea=integrator.integrate(beamInterp)
return (beamMonoArea,beamNew.profile)
