def createCategoricalPatterngram(basefile, ensemble, klength, normName, mode=CHAR):
"""Creates a Categorical Patterngram as PIL Image object. It "shows
the degree of similarity that exists between one file [(the base
file)] and the rest of the ensemble of files." (Rib00)
basefile - the single file in the 'one-to-many' comparison
ensemble - the set of files used for the comparison
klength - the length of the used ngrams
normName - the name of the normalizer used to normalize the texts
mode - default: CHAR (=1); WORD (=0)
"[A] categorical patterngram [...] [is] a visualization
that displays, for each character position x in the the base
file, the number of ensemble files that contain the k-length
n-gram that begins at that position. A point is plotted at
coordinate (x,y) if the k-length n-gram beginning at character
x in the base file occurs at any location in y files of the
ensemble." (Rib00)
Rib00 - "Using Visualization to Detect Plagiarism in Computer Science Classes",
Randy L. Ribler, Marc Abrams, 2000
"""
#check preconditions
if type(basefile) != type(""):
raise NoValidArgumentError, "basefile must be of type string"
elif type(ensemble) != type([]):
raise NoValidArgumentError, "ensemble must be of type list"
elif type(klength) != type(1):
raise NoValidArgumentError, "klength must be of type integer"
elif klength<=0:
raise NoValidArgumentError, "klength must be greater zero"
elif not normName in normNames:
raise NoValidNormalizerNameError(normName)
#get Normalizer
normalize = normNames.get(normName)
#===init base file===
#normalize base file
normBase = normalize(basefile)
#create ngrams
nGramDict = createNGrams(normBase, klength, mode=mode)
#create array for base file ngrams to cnt occurencies in ensemble files
if mode == WORD:
occSize = len(normBase.split())-klength+1
else:
occSize = len(normBase)-klength+1
nGramOccurencies = [0 for x in xrange(occSize)]
#===check ensemble files===
for text in ensemble:
#===============================================================================
# for y in xrange(len(ensemble)):
# text = ensemble[y]
# if text == basefile:
# subNr = y
#===============================================================================
if text == basefile:
continue
#normalize text
normText = normalize(text)
#create ngrams
textNGrams = createNGrams(text, klength, mode=mode)
#check for common ngrams
commonNGrams = set(nGramDict.keys()) & set(textNGrams.keys())
#increase array for common ngrams
for ngram in commonNGrams:
for i in nGramDict.get(ngram):
nGramOccurencies[i] += 1
#===create CP Image===
#init
colorBG = (255, 255, 255)
colorFG = (0, 0, 0)
colorSelf = (0, 0, 200)
colorInfrequently = (200, 0, 0)
colorFrequently = (0, 200, 0)
infrequent = 3
maxNb = reduce(returnGreater, nGramOccurencies)
ydist = 10
maxY = (maxNb+2)*ydist
img = Image.new("RGB", (len(nGramOccurencies), maxY), colorBG)
draw = ImageDraw.Draw(img)
#compute max nb files
for x in xrange(len(nGramOccurencies)):
yCoord = nGramOccurencies[x]+1
y = maxY - (ydist*(yCoord))
if yCoord == 1:
color = colorSelf
elif yCoord<=infrequent:
color = colorInfrequently
else: color = colorFrequently
draw.line((x, y-(ydist-6), x, y+(ydist-6)), fill=color)
#===paste img to chart pattern===
img = createChart(img, maxNb+1, ydist, "N-gram starting character", "Nb of files containing n-gram", "Submission "+" (k="+str(klength)+")")
#clean up
del draw
#return CP chart image
return img
def createCategoricalPatterngram(basefile,
ensemble,
klength,
normName,
mode=CHAR):
"""Creates a Categorical Patterngram as PIL Image object. It "shows
the degree of similarity that exists between one file [(the base
file)] and the rest of the ensemble of files." (Rib00)
basefile - the single file in the 'one-to-many' comparison
ensemble - the set of files used for the comparison
klength - the length of the used ngrams
normName - the name of the normalizer used to normalize the texts
mode - default: CHAR (=1); WORD (=0)
"[A] categorical patterngram [...] [is] a visualization
that displays, for each character position x in the the base
file, the number of ensemble files that contain the k-length
n-gram that begins at that position. A point is plotted at
coordinate (x,y) if the k-length n-gram beginning at character
x in the base file occurs at any location in y files of the
ensemble." (Rib00)
Rib00 - "Using Visualization to Detect Plagiarism in Computer Science Classes",
Randy L. Ribler, Marc Abrams, 2000
"""
#check preconditions
if type(basefile) != type(""):
raise NoValidArgumentError, "basefile must be of type string"
elif type(ensemble) != type([]):
raise NoValidArgumentError, "ensemble must be of type list"
elif type(klength) != type(1):
raise NoValidArgumentError, "klength must be of type integer"
elif klength <= 0:
raise NoValidArgumentError, "klength must be greater zero"
elif not normName in normNames:
raise NoValidNormalizerNameError(normName)
#get Normalizer
normalize = normNames.get(normName)
#===init base file===
#normalize base file
normBase = normalize(basefile)
#create ngrams
nGramDict = createNGrams(normBase, klength, mode=mode)
#create array for base file ngrams to cnt occurencies in ensemble files
if mode == WORD:
occSize = len(normBase.split()) - klength + 1
else:
occSize = len(normBase) - klength + 1
nGramOccurencies = [0 for x in xrange(occSize)]
#===check ensemble files===
for text in ensemble:
#===============================================================================
# for y in xrange(len(ensemble)):
# text = ensemble[y]
# if text == basefile:
# subNr = y
#===============================================================================
if text == basefile:
continue
#normalize text
normText = normalize(text)
#create ngrams
textNGrams = createNGrams(text, klength, mode=mode)
#check for common ngrams
commonNGrams = set(nGramDict.keys()) & set(textNGrams.keys())
#increase array for common ngrams
for ngram in commonNGrams:
for i in nGramDict.get(ngram):
nGramOccurencies[i] += 1
#===create CP Image===
#init
colorBG = (255, 255, 255)
colorFG = (0, 0, 0)
colorSelf = (0, 0, 200)
colorInfrequently = (200, 0, 0)
colorFrequently = (0, 200, 0)
infrequent = 3
maxNb = reduce(returnGreater, nGramOccurencies)
ydist = 10
maxY = (maxNb + 2) * ydist
img = Image.new("RGB", (len(nGramOccurencies), maxY), colorBG)
draw = ImageDraw.Draw(img)
#compute max nb files
for x in xrange(len(nGramOccurencies)):
yCoord = nGramOccurencies[x] + 1
y = maxY - (ydist * (yCoord))
if yCoord == 1:
color = colorSelf
elif yCoord <= infrequent:
color = colorInfrequently
else:
color = colorFrequently
draw.line((x, y - (ydist - 6), x, y + (ydist - 6)), fill=color)
#===paste img to chart pattern===
img = createChart(img, maxNb + 1, ydist, "N-gram starting character",
"Nb of files containing n-gram",
"Submission " + " (k=" + str(klength) + ")")
#clean up
del draw
#return CP chart image
return img
def createCompositeCategoricalPatterngram(basefile, ensemble, klength, normName, mode=CHAR):
"""Creates a Composite Categorical Patterngram as PIL Image object. It "shows
which particular files are similar." (Rib00)
basefile - the single file in the 'one-to-many' comparison
ensemble - the set of files used for the comparison
klength - the length of the used ngrams
normName - the name of the normalizer used to normalize the texts
mode - default: CHAR (=1); WORD (=0)
A composite categorical patterngram is a visualization that displays,
for each character position x in the the base file, which file of the
ensemble files also contain the k-length n-gram that begins at that
position. "A point is plotted at (x,y) if the k-length n-gram beginning
at x in the base file occurs one or more times in file y." (Rib00)
Rib00 - "Using Visualization to Detect Plagiarism in Computer Science Classes",
Randy L. Ribler, Marc Abrams, 2000
"""
#check preconditions
if type(basefile) != type(""):
raise NoValidArgumentError, "basefile must be of type string"
elif type(ensemble) != type([]):
raise NoValidArgumentError, "ensemble must be of type list"
elif type(klength) != type(1):
raise NoValidArgumentError, "klength must be of type integer"
elif klength<=0:
raise NoValidArgumentError, "klength must be greater zero"
elif not normName in normNames:
raise NoValidNormalizerNameError(normName, normNames)
#get Normalizer
normalize = normNames.get(normName)
#===init base file===
#normalize base file
normBase = normalize(basefile)
#create ngrams
nGramDict = createNGrams(normBase, klength, mode)
#===create CPP Image===
#init
colorBG = (255, 255, 255)
colorFG = (0, 0, 0)
colorSelf = (0, 0, 200)
colorInfrequently = (200, 0, 0)
colorFrequently = (0, 200, 0)
infrequent = 2
ydist = 10
if mode == WORD:
maxX = len(normBase.split())-klength+1
else:
maxX = len(normBase)-klength+1
maxY = (len(ensemble)+1)*ydist
img = Image.new("RGB", (maxX, maxY), colorBG)
draw = ImageDraw.Draw(img)
subNr = ''
#===check ensemble files===
for y in xrange(len(ensemble)): # +2?
text = ensemble[y]
yPos = maxY - (ydist*(y+1))
#if text is basefile mark with colorSelf in img
if text == basefile:
subNr = y+1
for x in xrange(maxX):
draw.line((x, yPos-(ydist-6), x, yPos+(ydist-6)), fill=colorSelf)
#else
#for each text compute ngrams
#compute common ngrams
#draw corresponding lines for each common ngram colored aufter its
#frequency
else:
#normalize text
normText = normalize(text)
#create ngrams
textNGrams = createNGrams(normText, klength, mode)
#check for common ngrams
commonNGrams = set(nGramDict.keys()) & set(textNGrams.keys())
#draw for each common ngram the appropriate marking
for ngram in commonNGrams:
#for each position of the common ngram draw right marking
for i in nGramDict.get(ngram):
#get occurencies of the ngram in the text
cntNgram = len(textNGrams.get(ngram))
#choose color for occurency
if cntNgram <= infrequent:
color = colorInfrequently
else:
color = colorFrequently
#draw line
draw.line((i, yPos-(ydist-6), i, yPos+(ydist-6)), fill=color)
#===paste img to chart pattern===
img = createChart(img, len(ensemble), ydist, "N-gram starting character", "File number", "Submission nb: "+str(subNr)+" (k="+str(klength)+")")
return img
def createCompositeCategoricalPatterngram(basefile,
ensemble,
klength,
normName,
mode=CHAR):
"""Creates a Composite Categorical Patterngram as PIL Image object. It "shows
which particular files are similar." (Rib00)
basefile - the single file in the 'one-to-many' comparison
ensemble - the set of files used for the comparison
klength - the length of the used ngrams
normName - the name of the normalizer used to normalize the texts
mode - default: CHAR (=1); WORD (=0)
A composite categorical patterngram is a visualization that displays,
for each character position x in the the base file, which file of the
ensemble files also contain the k-length n-gram that begins at that
position. "A point is plotted at (x,y) if the k-length n-gram beginning
at x in the base file occurs one or more times in file y." (Rib00)
Rib00 - "Using Visualization to Detect Plagiarism in Computer Science Classes",
Randy L. Ribler, Marc Abrams, 2000
"""
#check preconditions
if type(basefile) != type(""):
raise NoValidArgumentError, "basefile must be of type string"
elif type(ensemble) != type([]):
raise NoValidArgumentError, "ensemble must be of type list"
elif type(klength) != type(1):
raise NoValidArgumentError, "klength must be of type integer"
elif klength <= 0:
raise NoValidArgumentError, "klength must be greater zero"
elif not normName in normNames:
raise NoValidNormalizerNameError(normName, normNames)
#get Normalizer
normalize = normNames.get(normName)
#===init base file===
#normalize base file
normBase = normalize(basefile)
#create ngrams
nGramDict = createNGrams(normBase, klength, mode)
#===create CPP Image===
#init
colorBG = (255, 255, 255)
colorFG = (0, 0, 0)
colorSelf = (0, 0, 200)
colorInfrequently = (200, 0, 0)
colorFrequently = (0, 200, 0)
infrequent = 2
ydist = 10
if mode == WORD:
maxX = len(normBase.split()) - klength + 1
else:
maxX = len(normBase) - klength + 1
maxY = (len(ensemble) + 1) * ydist
img = Image.new("RGB", (maxX, maxY), colorBG)
draw = ImageDraw.Draw(img)
subNr = ''
#===check ensemble files===
for y in xrange(len(ensemble)): # +2?
text = ensemble[y]
yPos = maxY - (ydist * (y + 1))
#if text is basefile mark with colorSelf in img
if text == basefile:
subNr = y + 1
for x in xrange(maxX):
draw.line((x, yPos - (ydist - 6), x, yPos + (ydist - 6)),
fill=colorSelf)
#else
#for each text compute ngrams
#compute common ngrams
#draw corresponding lines for each common ngram colored aufter its
#frequency
else:
#normalize text
normText = normalize(text)
#create ngrams
textNGrams = createNGrams(normText, klength, mode)
#check for common ngrams
commonNGrams = set(nGramDict.keys()) & set(textNGrams.keys())
#draw for each common ngram the appropriate marking
for ngram in commonNGrams:
#for each position of the common ngram draw right marking
for i in nGramDict.get(ngram):
#get occurencies of the ngram in the text
cntNgram = len(textNGrams.get(ngram))
#choose color for occurency
if cntNgram <= infrequent:
color = colorInfrequently
else:
color = colorFrequently
#draw line
draw.line((i, yPos - (ydist - 6), i, yPos + (ydist - 6)),
fill=color)
#===paste img to chart pattern===
img = createChart(
img, len(ensemble), ydist, "N-gram starting character", "File number",
"Submission nb: " + str(subNr) + " (k=" + str(klength) + ")")
return img
