def Flask_process_text(text0, module="H810", task="TMA01"):
essay = top_level_procedure(text0, None, None, None, "NVL", module, task)
# reformat n-grams into unified structure
keylemmas = essay['ke_data']['keylemmas']
bigram_keyphrases = essay['ke_data']['bigram_keyphrases']
trigram_keyphrases = essay['ke_data']['trigram_keyphrases']
quadgram_keyphrases = essay['ke_data']['quadgram_keyphrases']
myarray_ke = essay['ke_data']['myarray_ke']
scoresNfreqs = essay['ke_data']['scoresNfreqs']
# Build an associative array out of the keywords list
for (word, score, r, c) in scoresNfreqs:
__mapkeyscore[word] = score
nvl_data = {}
nvl_data['keywords'] = lemmaToJSON(keylemmas, myarray_ke)
nvl_data['bigrams'] = ngramToJSON(bigram_keyphrases, myarray_ke)
nvl_data['trigrams'] = ngramToJSON(trigram_keyphrases, myarray_ke)
nvl_data['quadgrams'] = ngramToJSON(quadgram_keyphrases, myarray_ke)
essay['nvl_data'] = nvl_data
# Get complete flat list of text's lemmas
lemmas = [
l for p in essay['se_data']['se_parasenttok'] for s in p
for l in s['lemma']
]
# build dispersion arrays for lemma
for ngram in nvl_data['keywords']:
hh = ngram['ngram'][0]
kk = [idx for idx, w in enumerate(lemmas) if w == hh]
ngram['dispersion'] = kk
h, b = np.histogram(kk, bins=10, range=(0, len(lemmas)))
ngram['trend'] = h.tolist()
# build dispersion arrays for ngrams
# TODO: dispersion does not work for key phrases; removed from data structure
#setDispersionNgram(nvl_data['bigrams'],myarray_ke,lemmas)
#setDispersionNgram(nvl_data['trigrams'],myarray_ke,lemmas)
#setDispersionNgram(nvl_data['quadgrams'],myarray_ke,lemmas)
return essay
def Flask_process_text(text0, module="H810", task="TMA01"):
essay = top_level_procedure(text0, None, None, None, "NVL", module, task)
# reformat n-grams into unified structure
keylemmas = essay["ke_data"]["keylemmas"]
bigram_keyphrases = essay["ke_data"]["bigram_keyphrases"]
trigram_keyphrases = essay["ke_data"]["trigram_keyphrases"]
quadgram_keyphrases = essay["ke_data"]["quadgram_keyphrases"]
myarray_ke = essay["ke_data"]["myarray_ke"]
scoresNfreqs = essay["ke_data"]["scoresNfreqs"]
# Build an associative array out of the keywords list
for (word, score, r, c) in scoresNfreqs:
__mapkeyscore[word] = score
nvl_data = {}
nvl_data["keywords"] = lemmaToJSON(keylemmas, myarray_ke)
nvl_data["bigrams"] = ngramToJSON(bigram_keyphrases, myarray_ke)
nvl_data["trigrams"] = ngramToJSON(trigram_keyphrases, myarray_ke)
nvl_data["quadgrams"] = ngramToJSON(quadgram_keyphrases, myarray_ke)
essay["nvl_data"] = nvl_data
# Get complete flat list of text's lemmas
lemmas = [l for p in essay["se_data"]["se_parasenttok"] for s in p for l in s["lemma"]]
# build dispersion arrays for lemma
for ngram in nvl_data["keywords"]:
hh = ngram["ngram"][0]
kk = [idx for idx, w in enumerate(lemmas) if w == hh]
ngram["dispersion"] = kk
h, b = np.histogram(kk, bins=10, range=(0, len(lemmas)))
ngram["trend"] = h.tolist()
# build dispersion arrays for ngrams
# TODO: dispersion does not work for key phrases; removed from data structure
# setDispersionNgram(nvl_data['bigrams'],myarray_ke,lemmas)
# setDispersionNgram(nvl_data['trigrams'],myarray_ke,lemmas)
# setDispersionNgram(nvl_data['quadgrams'],myarray_ke,lemmas)
return essay
def Flask_process_text(text0):
essay = top_level_procedure(text0, None, None, None, "NVL","H810","TMA01")
# reformat n-grams into unified structure
keylemmas = essay['ke_data']['keylemmas']
bigram_keyphrases = essay['ke_data']['bigram_keyphrases']
trigram_keyphrases = essay['ke_data']['trigram_keyphrases']
quadgram_keyphrases = essay['ke_data']['quadgram_keyphrases']
myarray_ke = essay['ke_data']['myarray_ke']
scoresNfreqs = essay['ke_data']['scoresNfreqs']
# Build an associative array out of the keywords list
for (word,score,r,c) in scoresNfreqs:
__mapkeyscore[word] = score
nvl_data = {}
nvl_data['keywords'] = lemmaToJSON(keylemmas,myarray_ke)
nvl_data['bigrams'] = ngramToJSON(bigram_keyphrases,myarray_ke)
nvl_data['trigrams'] = ngramToJSON(trigram_keyphrases,myarray_ke)
nvl_data['quadgrams'] = ngramToJSON(quadgram_keyphrases,myarray_ke)
essay['nvl_data'] = nvl_data
return essay
essay_fname, 'r',
encoding='utf-8') # Open current essay file for reading
essay_txt = f.read() # Read in the essay and set to var 'essay_txt'
f.close() # Close the essay file
string = essay_fname[:-4] + '_results' + '.txt'
newfilename = os.path.join(tempdir1, string)
nf = codecs.open(
newfilename, 'w', encoding='utf-8'
) # Open 'newfilename' (for writing to) and set open file to var 'nf'
#if dev == 'DGF':
#nf2.write('\n') # Add blank lines to the essay results file
nf2.write(str(
essay_fname)) # Write the new file name to the essay results file
nf2.write('; ')
essay = top_level_procedure(essay_txt, essay_fname, nf, nf2, dev,
"H810", "TMA01")
## #############################
## #############################
## ### This section is for drawing figures. Comment it in, and comment out previous line. Also change 'return' line in 'top_level_procedure'.
## #############################
## #############################
## essay, gr_se_sample, gr_ke_sample = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01")
## string = essay_fname[:-4] + '_gr_se_sample_nodes' + '.png'
## figurefilename = os.path.join(tempdir1, string)
## #pos=nx.circular_layout(gr_se_sample)
## #pos=nx.graphviz_layout(gr_se_sample,prog="neato")
## plt.figure(1, figsize=(8,8))
## x = gr_se_sample.nodes()
## #plt.title(essay_fname)
## nx.draw(gr_se_sample, font_size=0, font_color='c', font_weight='normal',\
) # Open 'newfilename' (for writing to) and set open file to var 'nf'
#if dev == 'DGF':
#nf2.write('\n') # Add blank lines to the essay results file
nf2.write(str(
essay_fname)) # Write the new file name to the essay results file
nf2.write('; ')
#essay = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01")
#############################
#############################
### This section is for drawing diagrams. Comment it in, and comment out previous line. Also change 'return' line in 'top_level_procedure'.
#############################
#############################
# essay, gr_se_sample, gr_ke_sample = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01") # xxxx 'essay' argument not needed here, only running EssayAnalyser not OpenEssayist, next line down is normal version
gr_se_sample, gr_ke_sample = top_level_procedure(
essay_txt, essay_fname, nf, nf2, dev, "H810", "TMA01")
#print 'Key sentence subgraph for rainbow diagram'
#print(gr_se_sample.adj) # This is how you print a networkx graph
string = essay_fname[:-4] + '_gr_se_sample_nodes' + '.png'
figurefilename = os.path.join(tempdir1, string)
#pos=nx.circular_layout(gr_se_sample)
#pos=nx.graphviz_layout(gr_se_sample,prog="neato")
plt.figure(1, figsize=(8, 8))
x = gr_se_sample.nodes()
print '\nNumber of nodes in key sentence subgraph for rainbow diagram = ', len(
x)
#print gr_se_sample.nodes(data = True)
plt.title(essay_fname)
nx.draw(gr_se_sample, font_size=0, font_color='c', font_weight='normal',\
#nx.draw(gr_se_sample, font_size=5, font_color='b', font_weight='normal',\
node_size=500,
for essay_fname in filelist: # For each file in the current directory...
#startfiletime = time() # Set current time to a variable for later calculations
if essay_fname[-3:] == 'txt': # If a file name ends in 'txt'...
print '\n', essay_fname # Print to shell to monitor progress
f = codecs.open(essay_fname, 'r',encoding='utf-8') # Open current essay file for reading
essay_txt = f.read() # Read in the essay and set to var 'essay_txt'
f.close() # Close the essay file
string = essay_fname[:-4] + '_results' + '.txt'
newfilename = os.path.join(tempdir1, string)
nf = codecs.open(newfilename, 'w',encoding='utf-8') # Open 'newfilename' (for writing to) and set open file to var 'nf'
#if dev == 'DGF':
#nf2.write('\n') # Add blank lines to the essay results file
nf2.write(str(essay_fname)) # Write the new file name to the essay results file
nf2.write('; ')
essay = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01")
## #############################
## #############################
## ### This section is for drawing figures. Comment it in, and comment out previous line. Also change 'return' line in 'top_level_procedure'.
## #############################
## #############################
## essay, gr_se_sample, gr_ke_sample = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01")
## string = essay_fname[:-4] + '_gr_se_sample_nodes' + '.png'
## figurefilename = os.path.join(tempdir1, string)
## #pos=nx.circular_layout(gr_se_sample)
## #pos=nx.graphviz_layout(gr_se_sample,prog="neato")
## plt.figure(1, figsize=(8,8))
## x = gr_se_sample.nodes()
## #plt.title(essay_fname)
## nx.draw(gr_se_sample, font_size=0, font_color='c', font_weight='normal',\
newfilename = os.path.join(tempdir1, string)
nf = codecs.open(newfilename, 'w',encoding='utf-8') # Open 'newfilename' (for writing to) and set open file to var 'nf'
#if dev == 'DGF':
#nf2.write('\n') # Add blank lines to the essay results file
nf2.write(str(essay_fname)) # Write the new file name to the essay results file
nf2.write('; ')
#essay = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01")
#############################
#############################
### This section is for drawing diagrams. Comment it in, and comment out previous line. Also change 'return' line in 'top_level_procedure'.
#############################
#############################
# essay, gr_se_sample, gr_ke_sample = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01") # xxxx 'essay' argument not needed here, only running EssayAnalyser not OpenEssayist, next line down is normal version
gr_se_sample, gr_ke_sample = top_level_procedure(essay_txt,essay_fname,nf,nf2,dev,"H810","TMA01")
#print 'Key sentence subgraph for rainbow diagram'
#print(gr_se_sample.adj) # This is how you print a networkx graph
string = essay_fname[:-4] + '_gr_se_sample_nodes' + '.png'
figurefilename = os.path.join(tempdir1, string)
#pos=nx.circular_layout(gr_se_sample)
#pos=nx.graphviz_layout(gr_se_sample,prog="neato")
plt.figure(1, figsize=(8,8))
x = gr_se_sample.nodes()
print '\nNumber of nodes in key sentence subgraph for rainbow diagram = ', len(x)
#print gr_se_sample.nodes(data = True)
plt.title(essay_fname)
nx.draw(gr_se_sample, font_size=0, font_color='c', font_weight='normal',\
#nx.draw(gr_se_sample, font_size=5, font_color='b', font_weight='normal',\
node_size=500,
#stretch_factor=100,