def __call__(self, x_new):
"""Find linearly interpolated y_new = <name>(x_new).
Inputs:
x_new -- New independent variables.
Outputs:
y_new -- Linearly interpolated values corresponding to x_new.
"""
# 1. Handle values in x_new that are outside of x. Throw error,
# or return a list of mask array indicating the outofbounds values.
# The behavior is set by the bounds_error variable.
## RHC -- was x_new = atleast_1d(x_new)
x_new_1d = atleast_1d(x_new)
out_of_bounds = self._check_bounds(x_new_1d)
# 2. Find where in the orignal data, the values to interpolate
# would be inserted.
# Note: If x_new[n] = x[m], then m is returned by searchsorted.
x_new_indices = searchsorted(self.x, x_new_1d)
# 3. Clip x_new_indices so that they are within the range of
# self.x indices and at least 1. Removes mis-interpolation
# of x_new[n] = x[0]
# RHC -- changed Int to Numeric_Int to avoid name clash with numarray
x_new_indices = clip(x_new_indices, 1,
len(self.x) - 1).astype(Numeric_Int)
# 4. Calculate the slope of regions that each x_new value falls in.
lo = x_new_indices - 1
hi = x_new_indices
# !! take() should default to the last axis (IMHO) and remove
# !! the extra argument.
x_lo = take(self.x, lo, axis=self.interp_axis)
x_hi = take(self.x, hi, axis=self.interp_axis)
y_lo = take(self.y, lo, axis=self.interp_axis)
y_hi = take(self.y, hi, axis=self.interp_axis)
slope = (y_hi - y_lo) / (x_hi - x_lo)
# 5. Calculate the actual value for each entry in x_new.
y_new = slope * (x_new_1d - x_lo) + y_lo
# 6. Fill any values that were out of bounds with NaN
# !! Need to think about how to do this efficiently for
# !! mutli-dimensional Cases.
yshape = y_new.shape
y_new = y_new.flat
new_shape = list(yshape)
new_shape[self.interp_axis] = 1
sec_shape = [1] * len(new_shape)
sec_shape[self.interp_axis] = len(out_of_bounds)
out_of_bounds.shape = sec_shape
new_out = ones(new_shape) * out_of_bounds
putmask(y_new, new_out.flat, self.fill_value)
y_new.shape = yshape
# Rotate the values of y_new back so that they correspond to the
# correct x_new values.
result = swapaxes(y_new, self.interp_axis, self.axis)
try:
len(x_new)
return result
except TypeError:
return result[0]
return result
def __init__(self, x, y, axis=-1, makecopy=0, bounds_error=1,
fill_value=None):
"""Initialize a piecewise-constant interpolation class
Description:
x and y are arrays of values used to approximate some function f:
y = f(x)
This class returns a function whose call method uses piecewise-
constant interpolation to find the value of new points.
Inputs:
x -- a 1d array of monotonically increasing real values.
x cannot include duplicate values. (otherwise f is
overspecified)
y -- an nd array of real values. y's length along the
interpolation axis must be equal to the length
of x.
axis -- specifies the axis of y along which to
interpolate. Interpolation defaults to the last
axis of y. (default: -1)
makecopy -- If 1, the class makes internal copies of x and y.
If 0, references to x and y are used. The default
is to copy. (default: 0)
bounds_error -- If 1, an error is thrown any time interpolation
is attempted on a value outside of the range
of x (where extrapolation is necessary).
If 0, out of bounds values are assigned the
NaN (#INF) value. By default, an error is
raised, although this is prone to change.
(default: 1)
"""
self.datapoints = (array(x, Float), array(y, Float)) # RHC -- for access from PyDSTool
self.type = Float # RHC -- for access from PyDSTool
self.axis = axis
self.makecopy = makecopy # RHC -- renamed from copy to avoid nameclash
self.bounds_error = bounds_error
if fill_value is None:
self.fill_value = NaN # RHC -- was: array(0.0) / array(0.0)
else:
self.fill_value = fill_value
# Check that both x and y are at least 1 dimensional.
if len(shape(x)) == 0 or len(shape(y)) == 0:
raise ValueError, "x and y arrays must have at least one dimension."
# make a "view" of the y array that is rotated to the
# interpolation axis.
oriented_x = x
oriented_y = swapaxes(y,self.interp_axis,axis)
interp_axis = self.interp_axis
len_x,len_y = shape(oriented_x)[interp_axis], \
shape(oriented_y)[interp_axis]
if len_x != len_y:
raise ValueError, "x and y arrays must be equal in length along "\
"interpolation axis."
if len_x < 2 or len_y < 2:
raise ValueError, "x and y arrays must have more than 1 entry"
self.x = array(oriented_x,copy=self.makecopy)
self.y = array(oriented_y,copy=self.makecopy)
def __call__(self,x_new):
"""Find linearly interpolated y_new = <name>(x_new).
Inputs:
x_new -- New independent variables.
Outputs:
y_new -- Linearly interpolated values corresponding to x_new.
"""
# 1. Handle values in x_new that are outside of x. Throw error,
# or return a list of mask array indicating the outofbounds values.
# The behavior is set by the bounds_error variable.
## RHC -- was x_new = atleast_1d(x_new)
x_new_1d = atleast_1d(x_new)
out_of_bounds = self._check_bounds(x_new_1d)
# 2. Find where in the orignal data, the values to interpolate
# would be inserted.
# Note: If x_new[n] = x[m], then m is returned by searchsorted.
x_new_indices = searchsorted(self.x,x_new_1d)
# 3. Clip x_new_indices so that they are within the range of
# self.x indices and at least 1. Removes mis-interpolation
# of x_new[n] = x[0]
# RHC -- changed Int to Numeric_Int to avoid name clash with numarray
x_new_indices = clip(x_new_indices,1,len(self.x)-1).astype(Numeric_Int)
# 4. Calculate the slope of regions that each x_new value falls in.
lo = x_new_indices - 1; hi = x_new_indices
# !! take() should default to the last axis (IMHO) and remove
# !! the extra argument.
x_lo = take(self.x,lo,axis=self.interp_axis)
x_hi = take(self.x,hi,axis=self.interp_axis)
y_lo = take(self.y,lo,axis=self.interp_axis)
y_hi = take(self.y,hi,axis=self.interp_axis)
slope = (y_hi-y_lo)/(x_hi-x_lo)
# 5. Calculate the actual value for each entry in x_new.
y_new = slope*(x_new_1d-x_lo) + y_lo
# 6. Fill any values that were out of bounds with NaN
# !! Need to think about how to do this efficiently for
# !! mutli-dimensional Cases.
yshape = y_new.shape
y_new = y_new.flat
new_shape = list(yshape)
new_shape[self.interp_axis] = 1
sec_shape = [1]*len(new_shape)
sec_shape[self.interp_axis] = len(out_of_bounds)
out_of_bounds.shape = sec_shape
new_out = ones(new_shape)*out_of_bounds
putmask(y_new, new_out.flat, self.fill_value)
y_new.shape = yshape
# Rotate the values of y_new back so that they correspond to the
# correct x_new values.
result = swapaxes(y_new,self.interp_axis,self.axis)
try:
len(x_new)
return result
except TypeError:
return result[0]
return result
isfb=0
for t in elements['array']:
if t[0]['name'] == 'prf':
data=[]
pts=[]
head=[]
for z in t[2]['axis']:
head.append(z[0]['name'])
pts.append(map(float,z[1].split('\n')))
for z in range(len(pts[0])):
err1=pts[head.index('RMSRES')][z]/(pts[head.index('NUM')][z])**0.5
err2=skysig/(2.)**0.5 # note sqrt(2) kluge
data.append([pts[head.index('RAD')][z],pts[head.index('INTENS')][z],1,(err1**2+err2**2)**0.5])
tmp=numarray.array(pts)
pts=numarray.swapaxes(tmp,1,0)
if sys.argv[1] == '-p': break
if t[0]['name'] == 'sfb' and sys.argv[1] != '-p':
isfb=1
data=[]
tmp=[]
head=[]
for z in t[2]['axis']:
head.append(z[0]['name'])
tmp.append(map(float,z[1].split('\n')))
for z in range(len(tmp[0])):
try: # if errorbars in sfb area
data.append([tmp[head.index('radius')][z],tmp[head.index('mu')][z], \
int(tmp[head.index('kill')][z]),tmp[head.index('error')][z]])
except:
data.append([tmp[head.index('radius')][z],tmp[head.index('mu')][z], \
def plotXYSVG(drawSpace, dataX, dataY, rank=0, dataLabel=[], plotColor = "black", axesColor="black", labelColor="black", symbolColor="red", XLabel=None, YLabel=None, title=None, fitcurve=None, connectdot=1, displayR=None, loadingPlot = 0, offset= (80, 20, 40, 60), zoom = 1, specialCases=[], showLabel = 1):
'displayR : correlation scatter plot, loadings : loading plot'
dataXRanked, dataYRanked = webqtlUtil.calRank(dataX, dataY, len(dataX))
# Switching Ranked and Unranked X and Y values if a Spearman Rank Correlation
if rank == 0:
dataXPrimary = dataX
dataYPrimary = dataY
dataXAlt = dataXRanked
dataYAlt = dataYRanked
else:
dataXPrimary = dataXRanked
dataYPrimary = dataYRanked
dataXAlt = dataX
dataYAlt = dataY
xLeftOffset, xRightOffset, yTopOffset, yBottomOffset = offset
plotWidth = drawSpace.attributes['width'] - xLeftOffset - xRightOffset
plotHeight = drawSpace.attributes['height'] - yTopOffset - yBottomOffset
if plotHeight<=0 or plotWidth<=0:
return
if len(dataXPrimary) < 1 or len(dataXPrimary) != len(dataYPrimary) or (dataLabel and len(dataXPrimary) != len(dataLabel)):
return
max_X=max(dataXPrimary)
min_X=min(dataXPrimary)
max_Y=max(dataYPrimary)
min_Y=min(dataYPrimary)
#for some reason I forgot why I need to do this
if loadingPlot:
min_X = min(-0.1,min_X)
max_X = max(0.1,max_X)
min_Y = min(-0.1,min_Y)
max_Y = max(0.1,max_Y)
xLow, xTop, stepX=detScale(min_X,max_X)
yLow, yTop, stepY=detScale(min_Y,max_Y)
xScale = plotWidth/(xTop-xLow)
yScale = plotHeight/(yTop-yLow)
#draw drawing region
r = svg.rect(xLeftOffset, yTopOffset, plotWidth, plotHeight, 'none', axesColor, 1)
drawSpace.addElement(r)
#calculate data points
data = map(lambda X, Y: (X, Y), dataXPrimary, dataYPrimary)
xCoord = map(lambda X, Y: ((X-xLow)*xScale + xLeftOffset, yTopOffset+plotHeight-(Y-yLow)*yScale), dataXPrimary, dataYPrimary)
labelFontF = "verdana"
labelFontS = 11
if loadingPlot:
xZero = -xLow*xScale+xLeftOffset
yZero = yTopOffset+plotHeight+yLow*yScale
for point in xCoord:
drawSpace.addElement(svg.line(xZero,yZero,point[0],point[1], "red", 1))
else:
if connectdot:
pass
#drawSpace.drawPolygon(xCoord,edgeColor=plotColor,closed=0)
else:
pass
for i, item in enumerate(xCoord):
if dataLabel and dataLabel[i] in specialCases:
drawSpace.addElement(svg.rect(item[0]-3, item[1]-3, 6, 6, "none", "green", 0.5))
#drawSpace.drawCross(item[0],item[1],color=pid.blue,size=5)
else:
drawSpace.addElement(svg.line(item[0],item[1]+5,item[0],item[1]-5,symbolColor,1))
drawSpace.addElement(svg.line(item[0]+5,item[1],item[0]-5,item[1],symbolColor,1))
if showLabel and dataLabel:
pass
drawSpace.addElement(svg.text(item[0], item[1]+14, dataLabel[i], labelFontS,
labelFontF, text_anchor="middle", style="stroke:blue;stroke-width:0.5;"))
#canvas.drawString(, item[0]- canvas.stringWidth(dataLabel[i],
# font=labelFont)/2, item[1]+14, font=labelFont, color=pid.blue)
#draw scale
#scaleFont=pid.Font(ttf="cour",size=14,bold=1)
x=xLow
for i in range(stepX+1):
xc=xLeftOffset+(x-xLow)*xScale
drawSpace.addElement(svg.line(xc,yTopOffset+plotHeight,xc,yTopOffset+plotHeight+5, axesColor, 1))
strX = cformat(d=x, rank=rank)
drawSpace.addElement(svg.text(xc,yTopOffset+plotHeight+20,strX,13, "courier", text_anchor="middle"))
x+= (xTop - xLow)/stepX
y=yLow
for i in range(stepY+1):
yc=yTopOffset+plotHeight-(y-yLow)*yScale
drawSpace.addElement(svg.line(xLeftOffset,yc,xLeftOffset-5,yc, axesColor, 1))
strY = cformat(d=y, rank=rank)
drawSpace.addElement(svg.text(xLeftOffset-10,yc+5,strY,13, "courier", text_anchor="end"))
y+= (yTop - yLow)/stepY
#draw label
labelFontF = "verdana"
labelFontS = 17
if XLabel:
drawSpace.addElement(svg.text(xLeftOffset+plotWidth/2.0,
yTopOffset+plotHeight+yBottomOffset-10,XLabel,
labelFontS, labelFontF, text_anchor="middle"))
if YLabel:
drawSpace.addElement(svg.text(xLeftOffset-50,
yTopOffset+plotHeight/2,YLabel,
labelFontS, labelFontF, text_anchor="middle", style="writing-mode:tb-rl", transform="rotate(270 %d %d)" % (xLeftOffset-50, yTopOffset+plotHeight/2)))
#drawSpace.drawString(YLabel, xLeftOffset-50, yTopOffset+plotHeight- (plotHeight-drawSpace.stringWidth(YLabel,font=labelFont))/2.0,
# font=labelFont,color=labelColor,angle=90)
if fitcurve:
sys.argv = [ "mod_python" ]
#from numarray import linear_algebra as la
#from numarray import ones, array, dot, swapaxes
fitYY = array(dataYPrimary)
fitXX = array([ones(len(dataXPrimary)),dataXPrimary])
AA = dot(fitXX,swapaxes(fitXX,0,1))
BB = dot(fitXX,fitYY)
bb = la.linear_least_squares(AA,BB)[0]
xc1 = xLeftOffset
yc1 = yTopOffset+plotHeight-(bb[0]+bb[1]*xLow-yLow)*yScale
if yc1 > yTopOffset+plotHeight:
yc1 = yTopOffset+plotHeight
xc1 = (yLow-bb[0])/bb[1]
xc1=(xc1-xLow)*xScale+xLeftOffset
elif yc1 < yTopOffset:
yc1 = yTopOffset
xc1 = (yTop-bb[0])/bb[1]
xc1=(xc1-xLow)*xScale+xLeftOffset
else:
pass
xc2 = xLeftOffset + plotWidth
yc2 = yTopOffset+plotHeight-(bb[0]+bb[1]*xTop-yLow)*yScale
if yc2 > yTopOffset+plotHeight:
yc2 = yTopOffset+plotHeight
xc2 = (yLow-bb[0])/bb[1]
xc2=(xc2-xLow)*xScale+xLeftOffset
elif yc2 < yTopOffset:
yc2 = yTopOffset
xc2 = (yTop-bb[0])/bb[1]
xc2=(xc2-xLow)*xScale+xLeftOffset
else:
pass
drawSpace.addElement(svg.line(xc1,yc1,xc2,yc2,"green", 1))
if displayR:
labelFontF = "trebuc"
labelFontS = 14
NNN = len(dataX)
corr = webqtlUtil.calCorrelation(dataXPrimary,dataYPrimary,NNN)[0]
if NNN < 3:
corrPValue = 1.0
else:
if abs(corr) >= 1.0:
corrPValue = 0.0
else:
ZValue = 0.5*log((1.0+corr)/(1.0-corr))
ZValue = ZValue*sqrt(NNN-3)
corrPValue = 2.0*(1.0 - reaper.normp(abs(ZValue)))
NStr = "N of Cases=%d" % NNN
if rank == 1:
corrStr = "Spearman's r=%1.3f P=%3.2E" % (corr, corrPValue)
else:
corrStr = "Pearson's r=%1.3f P=%3.2E" % (corr, corrPValue)
drawSpace.addElement(svg.text(xLeftOffset,yTopOffset-10,NStr,
labelFontS, labelFontF, text_anchor="start"))
drawSpace.addElement(svg.text(xLeftOffset+plotWidth,yTopOffset-25,corrStr,
labelFontS, labelFontF, text_anchor="end"))
"""
"""
return
def plotXY(canvas, dataX, dataY, rank=0, dataLabel=[], plotColor = pid.black, axesColor=pid.black, labelColor=pid.black, lineSize="thin", lineColor=pid.grey, idFont="arial", idColor=pid.blue, idSize="14", symbolColor=pid.black, symbolType="circle", filled="yes", symbolSize="tiny", XLabel=None, YLabel=None, title=None, fitcurve=None, connectdot=1, displayR=None, loadingPlot = 0, offset= (80, 20, 40, 60), zoom = 1, specialCases=[], showLabel = 1, bufferSpace = 15):
'displayR : correlation scatter plot, loadings : loading plot'
dataXRanked, dataYRanked = webqtlUtil.calRank(dataX, dataY, len(dataX))
#get ID font size
idFontSize = int(idSize)
#If filled is yes, set fill color
if filled == "yes":
fillColor = symbolColor
else:
fillColor = None
if symbolSize == "large":
sizeModifier = 7
fontModifier = 12
elif symbolSize == "medium":
sizeModifier = 5
fontModifier = 8
elif symbolSize == "small":
sizeModifier = 3
fontModifier = 3
else:
sizeModifier = 1
fontModifier = -1
if rank == 0: # Pearson correlation
bufferSpace = 0
dataXPrimary = dataX
dataYPrimary = dataY
dataXAlt = dataXRanked #Values used just for printing the other corr type to the graph image
dataYAlt = dataYRanked #Values used just for printing the other corr type to the graph image
else: # Spearman correlation: Switching Ranked and Unranked X and Y values
dataXPrimary = dataXRanked
dataYPrimary = dataYRanked
dataXAlt = dataX #Values used just for printing the other corr type to the graph image
dataYAlt = dataY #Values used just for printing the other corr type to the graph image
xLeftOffset, xRightOffset, yTopOffset, yBottomOffset = offset
plotWidth = canvas.size[0] - xLeftOffset - xRightOffset
plotHeight = canvas.size[1] - yTopOffset - yBottomOffset
if plotHeight<=0 or plotWidth<=0:
return
if len(dataXPrimary) < 1 or len(dataXPrimary) != len(dataYPrimary) or (dataLabel and len(dataXPrimary) != len(dataLabel)):
return
max_X=max(dataXPrimary)
min_X=min(dataXPrimary)
max_Y=max(dataYPrimary)
min_Y=min(dataYPrimary)
#for some reason I forgot why I need to do this
if loadingPlot:
min_X = min(-0.1,min_X)
max_X = max(0.1,max_X)
min_Y = min(-0.1,min_Y)
max_Y = max(0.1,max_Y)
xLow, xTop, stepX=detScale(min_X,max_X)
yLow, yTop, stepY=detScale(min_Y,max_Y)
xScale = plotWidth/(xTop-xLow)
yScale = plotHeight/(yTop-yLow)
#draw drawing region
canvas.drawRect(xLeftOffset-bufferSpace, yTopOffset, xLeftOffset+plotWidth, yTopOffset+plotHeight+bufferSpace)
canvas.drawRect(xLeftOffset-bufferSpace+1, yTopOffset, xLeftOffset+plotWidth, yTopOffset+plotHeight+bufferSpace-1)
#calculate data points
data = map(lambda X, Y: (X, Y), dataXPrimary, dataYPrimary)
xCoord = map(lambda X, Y: ((X-xLow)*xScale + xLeftOffset, yTopOffset+plotHeight-(Y-yLow)*yScale), dataXPrimary, dataYPrimary)
labelFont=pid.Font(ttf=idFont,size=idFontSize,bold=0)
if loadingPlot:
xZero = -xLow*xScale+xLeftOffset
yZero = yTopOffset+plotHeight+yLow*yScale
for point in xCoord:
canvas.drawLine(xZero,yZero,point[0],point[1],color=pid.red)
else:
if connectdot:
canvas.drawPolygon(xCoord,edgeColor=plotColor,closed=0)
else:
pass
symbolFont = pid.Font(ttf="fnt_bs", size=12+fontModifier,bold=0)
for i, item in enumerate(xCoord):
if dataLabel and dataLabel[i] in specialCases:
canvas.drawRect(item[0]-3, item[1]-3, item[0]+3, item[1]+3, edgeColor=pid.green)
#canvas.drawCross(item[0],item[1],color=pid.blue,size=5)
else:
if symbolType == "vertRect":
canvas.drawRect(x1=item[0]-sizeModifier+2,y1=item[1]-sizeModifier-2, x2=item[0]+sizeModifier-1,y2=item[1]+sizeModifier+2, edgeColor=symbolColor, edgeWidth=1, fillColor=fillColor)
elif (symbolType == "circle" and filled != "yes"):
canvas.drawString(":", item[0]-canvas.stringWidth(":",font=symbolFont)/2+1,item[1]+2,color=symbolColor, font=symbolFont)
elif (symbolType == "circle" and filled == "yes"):
canvas.drawString("5", item[0]-canvas.stringWidth("5",font=symbolFont)/2+1,item[1]+2,color=symbolColor, font=symbolFont)
elif symbolType == "horiRect":
canvas.drawRect(x1=item[0]-sizeModifier-1,y1=item[1]-sizeModifier+3, x2=item[0]+sizeModifier+3,y2=item[1]+sizeModifier-2, edgeColor=symbolColor, edgeWidth=1, fillColor=fillColor)
elif (symbolType == "square"):
canvas.drawRect(x1=item[0]-sizeModifier+1,y1=item[1]-sizeModifier-4, x2=item[0]+sizeModifier+2,y2=item[1]+sizeModifier-3, edgeColor=symbolColor, edgeWidth=1, fillColor=fillColor)
elif (symbolType == "diamond" and filled != "yes"):
canvas.drawString(",", item[0]-canvas.stringWidth(",",font=symbolFont)/2+2, item[1]+6, font=symbolFont, color=symbolColor)
elif (symbolType == "diamond" and filled == "yes"):
canvas.drawString("D", item[0]-canvas.stringWidth("D",font=symbolFont)/2+2, item[1]+6, font=symbolFont, color=symbolColor)
elif symbolType == "4-star":
canvas.drawString("l", item[0]-canvas.stringWidth("l",font=symbolFont)/2+1, item[1]+3, font=symbolFont, color=symbolColor)
elif symbolType == "3-star":
canvas.drawString("k", item[0]-canvas.stringWidth("k",font=symbolFont)/2+1, item[1]+3, font=symbolFont, color=symbolColor)
else:
canvas.drawCross(item[0],item[1]-2,color=symbolColor, size=sizeModifier+2)
if showLabel and dataLabel:
if (symbolType == "vertRect" or symbolType == "diamond"):
labelGap = 15
elif (symbolType == "4-star" or symbolType == "3-star"):
labelGap = 12
else:
labelGap = 11
canvas.drawString(dataLabel[i], item[0]- canvas.stringWidth(dataLabel[i],
font=labelFont)/2 + 1, item[1]+(labelGap+sizeModifier+(idFontSize-12)), font=labelFont, color=idColor)
#draw scale
scaleFont=pid.Font(ttf="cour",size=16,bold=1)
x=xLow
for i in range(stepX+1):
xc=xLeftOffset+(x-xLow)*xScale
if ((x == 0) & (rank == 1)):
pass
else:
canvas.drawLine(xc,yTopOffset+plotHeight + bufferSpace,xc,yTopOffset+plotHeight+5 + bufferSpace, color=axesColor)
strX = cformat(d=x, rank=rank)
if ((strX == "0") & (rank == 1)):
pass
else:
canvas.drawString(strX,xc-canvas.stringWidth(strX,font=scaleFont)/2,yTopOffset+plotHeight+20 + bufferSpace,font=scaleFont)
x+= (xTop - xLow)/stepX
y=yLow
for i in range(stepY+1):
yc=yTopOffset+plotHeight-(y-yLow)*yScale
if ((y == 0) & (rank == 1)):
pass
else:
canvas.drawLine(xLeftOffset - bufferSpace,yc,xLeftOffset-5 - bufferSpace,yc, color=axesColor)
strY = cformat(d=y, rank=rank)
if ((strY == "0") & (rank == 1)):
pass
else:
canvas.drawString(strY,xLeftOffset-canvas.stringWidth(strY,font=scaleFont)- 10 - bufferSpace,yc+4,font=scaleFont)
y+= (yTop - yLow)/stepY
#draw label
labelFont=pid.Font(ttf="verdana",size=canvas.size[0]/45,bold=0)
titleFont=pid.Font(ttf="verdana",size=canvas.size[0]/40,bold=0)
if (rank == 1 and not title):
canvas.drawString("Spearman Rank Correlation", xLeftOffset-canvas.size[0]*.025+(plotWidth-canvas.stringWidth("Spearman Rank Correlation",font=titleFont))/2.0,
25,font=titleFont,color=labelColor)
elif (rank == 0 and not title):
canvas.drawString("Pearson Correlation", xLeftOffset-canvas.size[0]*.025+(plotWidth-canvas.stringWidth("Pearson Correlation",font=titleFont))/2.0,
25,font=titleFont,color=labelColor)
if XLabel:
canvas.drawString(XLabel,xLeftOffset+(plotWidth-canvas.stringWidth(XLabel,font=labelFont))/2.0,
yTopOffset+plotHeight+yBottomOffset-25,font=labelFont,color=labelColor)
if YLabel:
canvas.drawString(YLabel, xLeftOffset-65, yTopOffset+plotHeight- (plotHeight-canvas.stringWidth(YLabel,font=labelFont))/2.0,
font=labelFont,color=labelColor,angle=90)
labelFont=pid.Font(ttf="verdana",size=20,bold=0)
if title:
canvas.drawString(title,xLeftOffset+(plotWidth-canvas.stringWidth(title,font=labelFont))/2.0,
20,font=labelFont,color=labelColor)
if fitcurve:
import sys
sys.argv = [ "mod_python" ]
#from numarray import linear_algebra as la
#from numarray import ones, array, dot, swapaxes
fitYY = array(dataYPrimary)
fitXX = array([ones(len(dataXPrimary)),dataXPrimary])
AA = dot(fitXX,swapaxes(fitXX,0,1))
BB = dot(fitXX,fitYY)
bb = la.linear_least_squares(AA,BB)[0]
xc1 = xLeftOffset
yc1 = yTopOffset+plotHeight-(bb[0]+bb[1]*xLow-yLow)*yScale
if yc1 > yTopOffset+plotHeight:
yc1 = yTopOffset+plotHeight
xc1 = (yLow-bb[0])/bb[1]
xc1=(xc1-xLow)*xScale+xLeftOffset
elif yc1 < yTopOffset:
yc1 = yTopOffset
xc1 = (yTop-bb[0])/bb[1]
xc1=(xc1-xLow)*xScale+xLeftOffset
else:
pass
xc2 = xLeftOffset + plotWidth
yc2 = yTopOffset+plotHeight-(bb[0]+bb[1]*xTop-yLow)*yScale
if yc2 > yTopOffset+plotHeight:
yc2 = yTopOffset+plotHeight
xc2 = (yLow-bb[0])/bb[1]
xc2=(xc2-xLow)*xScale+xLeftOffset
elif yc2 < yTopOffset:
yc2 = yTopOffset
xc2 = (yTop-bb[0])/bb[1]
xc2=(xc2-xLow)*xScale+xLeftOffset
else:
pass
canvas.drawLine(xc1 - bufferSpace,yc1 + bufferSpace,xc2,yc2,color=lineColor)
if lineSize == "medium":
canvas.drawLine(xc1 - bufferSpace,yc1 + bufferSpace+1,xc2,yc2+1,color=lineColor)
if lineSize == "thick":
canvas.drawLine(xc1 - bufferSpace,yc1 + bufferSpace+1,xc2,yc2+1,color=lineColor)
canvas.drawLine(xc1 - bufferSpace,yc1 + bufferSpace-1,xc2,yc2-1,color=lineColor)
if displayR:
labelFont=pid.Font(ttf="trebuc",size=canvas.size[0]/60,bold=0)
NNN = len(dataX)
corr = webqtlUtil.calCorrelation(dataXPrimary,dataYPrimary,NNN)[0]
if NNN < 3:
corrPValue = 1.0
else:
if abs(corr) >= 1.0:
corrPValue = 0.0
else:
ZValue = 0.5*log((1.0+corr)/(1.0-corr))
ZValue = ZValue*sqrt(NNN-3)
corrPValue = 2.0*(1.0 - reaper.normp(abs(ZValue)))
NStr = "N = %d" % NNN
strLenN = canvas.stringWidth(NStr,font=labelFont)
if rank == 1:
if corrPValue < 0.0000000000000001:
corrStr = "Rho = %1.3f P < 1.00 E-16" % (corr)
else:
corrStr = "Rho = %1.3f P = %3.2E" % (corr, corrPValue)
else:
if corrPValue < 0.0000000000000001:
corrStr = "r = %1.3f P < 1.00 E-16" % (corr)
else:
corrStr = "r = %1.3f P = %3.2E" % (corr, corrPValue)
strLen = canvas.stringWidth(corrStr,font=labelFont)
canvas.drawString(NStr,xLeftOffset,yTopOffset-10,font=labelFont,color=labelColor)
canvas.drawString(corrStr,xLeftOffset+plotWidth-strLen,yTopOffset-10,font=labelFont,color=labelColor)
return xCoord
def __init__(self,
x,
y,
axis=-1,
makecopy=0,
bounds_error=1,
fill_value=None):
"""Initialize a piecewise-constant interpolation class
Description:
x and y are arrays of values used to approximate some function f:
y = f(x)
This class returns a function whose call method uses piecewise-
constant interpolation to find the value of new points.
Inputs:
x -- a 1d array of monotonically increasing real values.
x cannot include duplicate values. (otherwise f is
overspecified)
y -- an nd array of real values. y's length along the
interpolation axis must be equal to the length
of x.
axis -- specifies the axis of y along which to
interpolate. Interpolation defaults to the last
axis of y. (default: -1)
makecopy -- If 1, the class makes internal copies of x and y.
If 0, references to x and y are used. The default
is to copy. (default: 0)
bounds_error -- If 1, an error is thrown any time interpolation
is attempted on a value outside of the range
of x (where extrapolation is necessary).
If 0, out of bounds values are assigned the
NaN (#INF) value. By default, an error is
raised, although this is prone to change.
(default: 1)
"""
self.datapoints = (array(x, Float), array(y, Float)
) # RHC -- for access from PyDSTool
self.type = Float # RHC -- for access from PyDSTool
self.axis = axis
self.makecopy = makecopy # RHC -- renamed from copy to avoid nameclash
self.bounds_error = bounds_error
if fill_value is None:
self.fill_value = NaN # RHC -- was: array(0.0) / array(0.0)
else:
self.fill_value = fill_value
# Check that both x and y are at least 1 dimensional.
if len(shape(x)) == 0 or len(shape(y)) == 0:
raise ValueError, "x and y arrays must have at least one dimension."
# make a "view" of the y array that is rotated to the
# interpolation axis.
oriented_x = x
oriented_y = swapaxes(y, self.interp_axis, axis)
interp_axis = self.interp_axis
len_x,len_y = shape(oriented_x)[interp_axis], \
shape(oriented_y)[interp_axis]
if len_x != len_y:
raise ValueError, "x and y arrays must be equal in length along "\
"interpolation axis."
if len_x < 2 or len_y < 2:
raise ValueError, "x and y arrays must have more than 1 entry"
self.x = array(oriented_x, copy=self.makecopy)
self.y = array(oriented_y, copy=self.makecopy)