def resultsToTorc(resultList, colored=False):
"""Takes the result and returns an image showing a Torc indicating the
similarity relations of the compared texts in the results.
A Torc is a kind of overview which allows the user to recognize the similarity
relations between different texts. Therefore all texts are arranged on a circle.
For each relation of similarity between two texts a connecting line is drawn.
"""
#check preconditions
if type(resultList) != type([]):
raise NoValidArgumentError, 'Input must be of type list'
elif len(resultList) == 0:
return None
else:
for result in resultList:
if type(result) != type(PlagResult()):
raise NoValidArgumentError, 'Input list should only contain values of type PlagResult.'
#1. get all identifiers of the results
idSet = set()
for result in resultList:
for id in result.getIdentifier():
idSet.add(id)
idSet = list(idSet)
idSet.sort()
#2. create a circle with a size depending on the number of identifier
font = ImageFont.load_default()
freespace = computeMaxIdLength(idSet, font)
margin = 10
radius = computeRadius(
len(idSet)) # computes radius depending on number of ids
xM = freespace + radius + margin #middle x pos of circle
yM = freespace + radius + margin #middle y pos of circle
img = Image.new('RGB', (2 * xM, 2 * yM), (255, 255, 255))
draw = ImageDraw.Draw(img)
draw.arc((freespace + margin, freespace + margin, freespace + margin +
(2 * radius), freespace + margin + (2 * radius)),
0,
360,
fill=(150, 150, 150))
#3. arrange the ids along the circle and save the coordinates for each id
distToNextId = 360 / len(idSet)
angles = range(0, 360, distToNextId)
idPosDict = {}
for idNr in xrange(0, len(idSet)):
# x = xM + r * cos phi und y = yM + r * sin phi
pos = (xM + (radius * cos(radians(angles[idNr]))),
yM + (radius * sin(radians(angles[idNr]))))
idPosDict.setdefault(idSet[idNr], pos)
# use a truetype font and draw the id names
for id in idPosDict:
draw.text(computeFontPos(font, draw, str(id), idPosDict.get(id), xM,
yM),
str(id),
font=font,
fill=(0, 0, 0))
#4. walk through the results and plot the similarity relations as lines between the Ids
if colored:
#TODO: Params von aussen eingeben?
clusters = getClusters(resultList,
onlyPositives=False,
onlyNonZeroSimilarities=False)
for result in resultList:
if result.isSuspectPlagiarism():
ids = result.getIdentifier()
if colored:
color = getColorForScope(
getClusterNr(ids[0], ids[1], clusters),
range(len(clusters)))
else:
color = (0, 0, 0)
draw.line([idPosDict.get(ids[0]),
idPosDict.get(ids[1])],
fill=color)
del draw #free draw instance
#5. return the image
return img
def createHeatmapChart(matrix, xLabels, yLabels, scopes = [0.2, 0.4, 0.6, 0.8, 1],
recSize = 20, colorBG = (255, 255, 255), colorFG = (0, 0, 0),
colorChartBG = (230, 230, 230), colorGrid = (100, 100, 100),
font = ImageFont.load_default()):
"""Creates a heatmap chart for the given values in the matrix labeled with the labels
given in xDict and yDict TODO:
options:
- scopes - list of values that describe the scopes for the matrix values,
e.g. scopes = [0.2, 0.4, 0.6, 0.8, 1] (default)
- font - font used
- recSize - size of a single indicator rectangle in the heatmap
- colorBG - background color of the chart as rgb value (default: (255, 255, 255))
- colorFG - foreground color of the chart as rgb value (default: (0, 0, 0))
- colorChartBG - background color of the chart as rgb value (default: (230, 230, 230))
- colorGrid - color of the grid as rgb value (dafault: (100, 100, 100)); if set to
"None" no grid will be drawn
"""
#===init===
#margin to left and bottom
margin = 5
#maximal identifier length
maxIdLength = computeMaxIdLength(set(xLabels+yLabels), font)
#size of the img
maxX = margin + maxIdLength + ((len(xLabels)+2) * recSize)
maxY = margin + maxIdLength + (len(yLabels)+2) * recSize
#lower left corner fo the chart
offset = (margin+maxIdLength+4, maxY-maxIdLength-margin)
#===create img===
img = Image.new("RGB", (maxX, maxY), colorBG)
draw = ImageDraw.Draw(img)
#draw chart background
draw.rectangle((offset, (offset[0]+((len(xLabels)+1)*recSize), offset[1]-((len(yLabels)+1)*recSize))), fill=colorChartBG)
#draw heatmap
for row in xrange(len(matrix)):
for col in xrange(len(matrix[0])):
pos = (offset[0]+col*recSize, offset[1]-row*recSize, offset[0]+(col+1)*recSize, offset[1]-(row+1)*recSize)
fill = getColorForScope(matrix[row][col], scopes, colorChartBG)
draw.rectangle(pos, fill=fill)
#draw grid
#x-axis
for i in xrange(len(yLabels)+1):
yPos = offset[1]-((i+1)*recSize)
draw.line(((offset[0], yPos), (offset[0]+recSize*(len(xLabels)+1), yPos)), fill=colorGrid)
#y-axis
for i in xrange(len(xLabels)+1):
draw.line(((offset[0]+(i+1)*recSize, offset[1]), (offset[0]+(i+1)*recSize, offset[1]-recSize*(len(yLabels)+1))), fill=colorGrid)
#draw axes
#x-axis
draw.line((offset, (offset[0]+recSize*(len(xLabels)+1), offset[1])), fill=colorFG)
for i in xrange(len(xLabels)):
draw.line(((offset[0]+recSize/2+i*recSize, offset[1]-2),(offset[0]+recSize/2+i*recSize, offset[1]+2)), fill=colorFG)
#y-axis
draw.line((offset, (offset[0], offset[1]-recSize*(len(yLabels)+1))), fill=colorFG)
for i in xrange(len(yLabels)):
draw.line(((offset[0]-2, offset[1]-(recSize/2+i*recSize)),(offset[0]+2, offset[1]-(recSize/2+i*recSize))), fill=colorFG)
#draw ids
for i in xrange(len(yLabels)):
name = yLabels[i]
pos = i
draw.text((margin, offset[1]-((pos+1)*recSize)), name, font=font, fill=colorFG)
for i in xrange(len(xLabels)):
name = xLabels[i]
pos = i
img.paste(rotText(name, font=font, colorBG=colorBG, colorFG=colorFG), (offset[0]+(recSize/5)+((pos)*recSize), offset[1]+2))
#clean up
del draw
#return heatmap chart img
return img
def resultsToTorc(resultList, colored=False):
"""Takes the result and returns an image showing a Torc indicating the
similarity relations of the compared texts in the results.
A Torc is a kind of overview which allows the user to recognize the similarity
relations between different texts. Therefore all texts are arranged on a circle.
For each relation of similarity between two texts a connecting line is drawn.
"""
#check preconditions
if type(resultList) != type([]):
raise NoValidArgumentError, 'Input must be of type list'
elif len(resultList) == 0:
return None
else:
for result in resultList:
if type(result) != type(PlagResult()):
raise NoValidArgumentError, 'Input list should only contain values of type PlagResult.'
#1. get all identifiers of the results
idSet = set()
for result in resultList:
for id in result.getIdentifier():
idSet.add(id)
idSet = list(idSet)
idSet.sort()
#2. create a circle with a size depending on the number of identifier
font = ImageFont.load_default()
freespace = computeMaxIdLength(idSet, font)
margin = 10
radius = computeRadius(len(idSet)) # computes radius depending on number of ids
xM = freespace + radius + margin #middle x pos of circle
yM = freespace + radius + margin #middle y pos of circle
img = Image.new('RGB', (2*xM, 2*yM), (255, 255, 255))
draw = ImageDraw.Draw(img)
draw.arc((freespace+margin, freespace+margin, freespace+margin+(2*radius), freespace+margin+(2*radius)), 0, 360, fill = (150, 150, 150))
#3. arrange the ids along the circle and save the coordinates for each id
distToNextId = 360 / len(idSet)
angles = range(0, 360, distToNextId)
idPosDict = {}
for idNr in xrange(0, len(idSet)):
# x = xM + r * cos phi und y = yM + r * sin phi
pos = (xM + (radius * cos(radians(angles[idNr]))),
yM + (radius * sin(radians(angles[idNr]))))
idPosDict.setdefault(idSet[idNr], pos)
# use a truetype font and draw the id names
for id in idPosDict:
draw.text(computeFontPos(font, draw, str(id), idPosDict.get(id), xM, yM),
str(id),
font=font,
fill = (0, 0, 0))
#4. walk through the results and plot the similarity relations as lines between the Ids
if colored:
#TODO: Params von aussen eingeben?
clusters = getClusters(resultList, onlyPositives=False, onlyNonZeroSimilarities=False)
for result in resultList:
if result.isSuspectPlagiarism():
ids = result.getIdentifier()
if colored:
color = getColorForScope(getClusterNr(ids[0], ids[1], clusters), range(len(clusters)))
else:
color = (0,0,0)
draw.line([idPosDict.get(ids[0]), idPosDict.get(ids[1])], fill = color)
del draw #free draw instance
#5. return the image
return img
