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 createClusterHeatmap(resultList, recSize = 20, onlyPositiveResults=True):
"""Creates a heatmap chart showing the clusters found in the given results.
"""
#===create cluster matrix===
if onlyPositiveResults:
#get all postive Results
results = getPositiveResults(resultList)
else:
results = resultList
if not results:
return None
#extract ids and clusters
ids = getIdentifier(results)
ids.sort()
clusters = getClusters(results)
print str(clusters)
#create matrix
matrix = [[0 for x in ids] for y in ids]
for y in xrange(len(ids)):
for x in xrange(len(ids)):
xL = ids[x]
yL = ids[y]
if x!=y:
val = getClusterNr(xL, yL, clusters)
else: val = None
matrix[y][x] = val
#===init===
font = ImageFont.load_default()
scopes = [(i+0.5) for i in xrange(len(clusters)+1)]
print scopes
#color init
colorImgFG = (0,0,0)
colorImgBG = (255,255,255)
colorChartBG = (230, 230, 230)
colorGrid = (100,100,100)
#create heatmap img
img = createHeatmapChart(matrix,
ids,
ids,
scopes = scopes,
recSize = recSize,
font = font)
maxX = img.size[0]
maxY = img.size[1]
#add legend
#===============================================================================
# legendimg = createLegend(scopes, maxY, font, colorImgBG, colorImgFG)
# newImg = Image.new("RGB", (maxX+legendimg.size[0], maxY))
# newImg.paste(img, (0,0))
# newImg.paste(legendimg, (img.size[0]+1, 0))
#===============================================================================
#clean up
del font
#return img
# return newImg
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