def get_dict(*keys, **extras):
"""Returns request dict of given keys."""
_keys = ('url', 'args', 'form', 'data', 'origin', 'headers', 'files',
'json', 'method')
assert all(map(_keys.__contains__, keys))
data = request.data
form = semiflatten(request.form)
try:
_json = json.loads(data.decode('utf-8'))
except (ValueError, TypeError):
_json = None
d = dict(
url=get_url(request),
args=semiflatten(request.args),
form=form,
data=json_safe(data),
origin=request.headers.get('X-Forwarded-For', request.remote_addr),
headers=get_headers(),
files=get_files(),
json=_json,
method=request.method,
)
out_d = dict()
for key in keys:
out_d[key] = d.get(key)
out_d.update(extras)
return out_d
def get(self, product_id=None, **kwargs):
"""Return a product to edit or an empty form to create"""
template = 'admin/product/new.html'
files = get_files()
#print self.form.photo.choices
context = {
'files': files,
'form': self.form,
}
# render edit form
if product_id is not None:
product = Product.get_by_id(product_id)
if product:
self.form = ProductForm(obj=product)
self.form.tags.data = ', '.join(product.tags)
product_photo = ''
if product.photo:
product_photo = product.photo.key().id()
context.update({ 'form': self.form, 'product_photo': product_photo })
template = 'admin/product/edit.html'
else:
return redirect('/admin/shop/')
# render new form
return self.render_response(template, **context)
def prepare_patient_images(patient_id, intermediate_crop=0):
file_lst = []
prefix = str(patient_id).rjust(4, '0')
src_files = helpers.get_files(settings.BASE_PREPROCESSEDIMAGES_DIR, prefix + "*.png")
patient_dir = helpers.get_pred_patient_dir(patient_id)
helpers.create_dir_if_not_exists(patient_dir)
patient_img_dir = helpers.get_pred_patient_img_dir(patient_id)
helpers.create_dir_if_not_exists(patient_img_dir)
helpers.delete_files(patient_img_dir, "*.png")
dummy = numpy.zeros((settings.TARGET_SIZE, settings.TARGET_SIZE))
cv2.imwrite(patient_img_dir + "dummy_overlay.png", dummy)
for src_path in src_files:
file_name = ntpath.basename(src_path)
org_img = cv2.imread(src_path, cv2.IMREAD_GRAYSCALE)
cropped_img = helpers.prepare_cropped_sax_image(org_img, clahe=True, intermediate_crop=intermediate_crop, rotate=0)
if SCALE_SIZE is not None:
cropped_img = cv2.resize(cropped_img, (SCALE_SIZE, SCALE_SIZE), interpolation=cv2.INTER_AREA)
cv2.imwrite(patient_img_dir + file_name, cropped_img)
file_lst.append([file_name, "dummy_overlay.png"])
with open(patient_img_dir + "pred.lst", "wb") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(file_lst)
def test_bodies(input_files, populated_output_dir):
output_files = helpers.get_files(populated_output_dir)
for input_file in input_files:
path = os.path.join(populated_output_dir,
"post{}.html".format(input_file.file_number))
helpers.check_body_text(path, input_file.body)
def test_titles(output_dir):
files = helpers.get_files(output_dir)
potst1path = os.path.join(output_dir, files[0])
helpers.check_title(potst1path, "Post One Title")
otherpostpath = os.path.join(output_dir, files[1])
helpers.check_title(otherpostpath, "Some Other Post Title")
def test_bodies(output_dir):
files = helpers.get_files(output_dir)
potst1path = os.path.join(output_dir, files[0])
helpers.check_body_text(potst1path, "This is the body of Post One.")
otherpostpath = os.path.join(output_dir, files[1])
helpers.check_body_text(otherpostpath, "This is the body of the other post.")
def test_recursive_directory_creation():
with tempfile.TemporaryDirectory(suffix='-static') as tmpdirname:
dest = os.path.join(tmpdirname, 'subdir', 'subsubdir')
result = generate(dest)
assert result.returncode == 0
files = helpers.get_files(dest)
assert files == ['postone.html', 'some-other-post.html']
def test_body(output_dir):
files = helpers.get_files(output_dir)
post_path = os.path.join(output_dir, files[0])
helpers.check_body(
post_path,
'This is a post, with a link to <a href="https://google.com">https://google.com</a>.'
)
def view_post():
return jsonify(
args=convert_to_normal_dict(request.args),
data=json.dumps(request.json),
files=get_files(),
form=request.form,
headers=dict(request.headers),
json=request.json,
origin=request.remote_addr,
url=request.url)
def view_anything():
headers = dict(request.headers)
headers['Referer'] = request.referrer
return jsonify(
args=convert_to_normal_dict(request.args),
data=json.dumps(request.json),
files=get_files(),
form=request.form,
headers=headers,
json=request.json,
method=request.method,
origin=request.remote_addr,
url=request.url)
def test_body(output_dir):
files = helpers.get_files(output_dir)
post_path = os.path.join(output_dir, files[0])
body = helpers.get_tag(post_path, 'body')
# get all child tags
# https://www.crummy.com/software/BeautifulSoup/bs4/doc/#true
paragraphs = body.find_all(True)
assert len(paragraphs) == 3
for paragraph in paragraphs:
assert paragraph.name == 'p'
contents = [p.string.strip() for p in paragraphs]
assert contents[0] == "This is the first paragraph. It has multiple sentences."
assert contents[1] == "Then there's another paragraph."
assert contents[2] == "And another."
def get(self, offer_id=None, **kwargs):
"""Return an offer to edit or an empty form to create"""
template = 'admin/daily/new.html'
files = get_files()
context = {
'form': self.form,
'files': files,
}
# render edit
if offer_id is not None:
offer = Offer.get_by_id(offer_id)
offer_photo = ''
if offer.photo:
offer_photo = offer.photo.key().id()
if offer:
self.form = OfferForm(obj=offer)
context.update({ 'form': self.form, 'offer_photo': offer_photo })
template = 'admin/daily/edit.html'
else:
return redirect('admin/daily/')
# render new
return self.render_response(template, **context)
def main():
files = get_files()
print(files)
key = generate_sym_key()
encrypt_files(files, key)
return
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from helpers import get_files, apply_tweet_tokenizer
if __name__ == "__main__":
tokenizer_path = '/home/robin/Documents/software/ark-tweet-nlp-0.3.2'
txt_path = '/home/robin/Documents/phd/tweet-stance-classification/corpus_txt'
output_path = '/home/robin/Documents/phd/tweet-stance-classification/corpus_tokenized'
files = get_files(txt_path)
apply_tweet_tokenizer(files, tokenizer_path, output_path)
def main():
files = get_files()
key = generate_sym_key()
decrypt_files(files, key)
return
def disambiguate_word(disambiguated_word, context, verbose=False):
# form the files that are to be searched
files = helpers.get_files(ngram_freq_folder)
first_two_letters = disambiguated_word[:2] # apparently every pair of letters exists
# search only the relevant files
# from 'googlebooks-eng-all-2gram-20120701-do.gz_1' to 'do'
files = [file for file in files if file.split('-')[-1].split('.')[
0] == first_two_letters] # from 'googlebooks-eng-all-2gram-20120701-do.gz_1' to do
# print('Files to be processed:', files)
# Sense with the highest score is considered best.
scores = []
# get every word sense from WordNet
for sense in wn.synsets(disambiguated_word):
#print(' ')
#print(sense)
examples = []
for example in sense.examples():
examples += tokenizer.tokenize(example)
# print(sense.definition())
definition = tokenizer.tokenize(sense.definition())
# get hyponyms and hypernyms for the sense to get larger overlap
hypernyms = []
for hypernym in sense.hypernyms():
hypernyms += tokenizer.tokenize(hypernym.definition())
for example in hypernym.examples():
hypernyms += tokenizer.tokenize(example)
#print('hypernyms: ', hypernyms)
hyponyms = []
for hyponym in sense.hyponyms():
hyponyms += tokenizer.tokenize(hyponym.definition())
for example in hyponym.examples():
hyponyms += tokenizer.tokenize(example)
#print('hyponyms: ', hyponyms)
# just concatenate the definition and all the examples
signature = definition + examples + hypernyms + hyponyms
#print('signature: ', signature)
# retain the information of the original word in stemming
# signature format: [(stem1, word1), (stem2, word2)...]
# stem the words in signature
signature = [(stemmer.stem(word), word) for word in signature]
#for index, pair in signature:
# for w in stop:
# if w == pair[0]:
# continue
# signature[index] = pair
# remove defined stopwords based on the stemmed word
without_stopwords = [] # helper variable
for pair in signature:
if pair[0] in stop:
continue
else:
without_stopwords.append(pair)
signature = without_stopwords
#print('signature: ', signature)
# form overlap between the context and the current sense
overlap = []
for w1 in context:
for w2 in signature:
# TODO handle
if w1[0] == w2[0]:
# Append the original non-stemmed word. Words are not stemmed in ngram-coocurence data.
overlap.append(w1[1]) # Use context base-word on default.
#overlap = [word for word in context if word in signature]
if verbose:
print('Sense: ', sense, 'Overlap: ', overlap)
# form score from the overlapped words
score = 0
for word in overlap:
# get occurrences for every word with the disambiguated_word
# divided by len(signature) not to give more weight because of a lengthy definition
score = score + get_word_occurrences(word, disambiguated_word, files) / len(signature)
scores.append([score, sense])
# print(scores)
# loop through the scores to get the maximum
predicted_sense = [0, 'Initial string here']
for i in scores:
if i[0] > predicted_sense[0]:
predicted_sense = i
#print('Final sense: ', predicted_sense)
return predicted_sense
def visualize():
if request.method == 'POST':
data_form = request.form.get('data')
if data_form:
data = json.loads(data_form)
data = refine_data(data)
infected_areas = []
infected_issues = []
for value in data:
infected_areas.append(
(value[0], disease_map[value[0]], value[1]))
infected_issues.append(
(value[0], organ_map[value[0]], value[1]))
infected_organs = []
infected_organs.append(get_most_infected_organs(infected_areas[:]))
infected_organs.append(get_secondary_system())
infected_organs.append(get_circulatory_system())
infected_organs.append(get_primary_endocrine())
infected_organs.append(get_digestive_system())
infected_systems = get_ranked_systems(infected_areas[:])
infected_issues = get_files(infected_issues[:])
return render_template('organs.html',
infected_areas=infected_areas,
infected_issues=infected_issues,
infected_systems=infected_systems,
infected_organs=infected_organs,
details=details)
else:
return render_template('home.html')
else:
infected_areas = [
('Dopamine:Dopamine', '', 20),
('Adrenals:Adrenal_Hypo-function', 'ADRENALS', 20),
('Adrenals:Adrenal_Hyper-function', 'ADRENALS', 20),
('Parasympathetic_Nervous_System:Parasympathetic_Nervous_System',
'', 18), ('Thyroid:Hypothyroidism', 'THYROID', 17),
('Liver:Liver', 'LIVER', 16),
('Parathyroid:Parathyroid', 'PARATHYROID', 14),
('Lungs:Lungs', 'LUNGS', 14),
('Stomach:Hyperacidity_Dyspepsia', 'STOMACH', 13),
('Gaba_Glutamate:Gaba_Glutamate', '', 13),
('Kidneys:Kidneys', 'KIDNEYS', 12),
('Sympathetic_Nervous_System:Sympathetic_Nervous_System', '', 11),
('Small_Intestine:Ilium', 'SMALL_INTESTINE', 11),
('Glycemia:Glycemia-Hyper', '', 11),
('Thyroid:Hyperthyroidism', 'THYROID', 10),
('Small_Intestine:Jejunum', 'SMALL_INTESTINE', 9),
('Testes:Testes', 'TESTES', 7),
('Pituitary:Pituitary', 'PITUITARY', 7),
('Stomach:Hypoacidity', 'STOMACH', 6),
('Small_Intestine:Duodenum', 'SMALL_INTESTINE', 5),
('Serotonin:Serotonin', '', 3), ('Heart:Heart', 'HEART', 3),
('Hypothalamus:Hypothalamus', 'HYPOTHALAMUS', 2),
('Glycemia:Glycemia-Hypo', '', 0)
]
infected_issues = [
('Dopamine:Dopamine', 'Dopamine', 20),
('Adrenals:Adrenal_Hypo-function', 'ADRENALS', 20),
('Adrenals:Adrenal_Hyper-function', 'ADRENALS', 20),
('Parasympathetic_Nervous_System:Parasympathetic_Nervous_System',
'Parasympathetic_Nervous_System', 18),
('Thyroid:Hypothyroidism', 'THYROID', 17),
('Liver:Liver', 'LIVER', 16),
('Parathyroid:Parathyroid', 'PARATHYROID', 14),
('Lungs:Lungs', 'LUNGS', 14),
('Stomach:Hyperacidity_Dyspepsia', 'STOMACH', 13),
('Gaba_Glutamate:Gaba_Glutamate', 'Gaba_Glutamate', 13),
('Kidneys:Kidneys', 'KIDNEYS', 12),
('Sympathetic_Nervous_System:Sympathetic_Nervous_System',
'Sympathetic_Nervous_System', 11),
('Small_Intestine:Ilium', 'SMALL_INTESTINE', 11),
('Glycemia:Glycemia-Hyper', 'E', 11),
('Thyroid:Hyperthyroidism', 'THYROID', 10),
('Small_Intestine:Jejunum', 'SMALL_INTESTINE', 9),
('Testes:Testes', 'TESTES', 7),
('Pituitary:Pituitary', 'PITUITARY', 7),
('Stomach:Hypoacidity', 'STOMACH', 6),
('Small_Intestine:Duodenum', 'SMALL_INTESTINE', 5),
('Serotonin:Serotonin', 'Serotonin', 3),
('Heart:Heart', 'HEART', 3),
('Hypothalamus:Hypothalamus', 'HYPOTHALAMUS', 2),
('Glycemia:Glycemia-Hypo', 'F', 0)
]
infected_organs = []
infected_organs.append(get_most_infected_organs(infected_areas[:]))
infected_organs.append(get_secondary_system())
infected_organs.append(get_circulatory_system())
infected_organs.append(get_primary_endocrine())
infected_organs.append(get_digestive_system())
infected_systems = get_ranked_systems(infected_areas[:])
infected_issues = get_files(infected_issues[:])
return render_template('organs.html',
infected_areas=infected_areas,
infected_issues=infected_issues,
infected_systems=infected_systems,
infected_organs=infected_organs,
details=details)
return render_template('home.html')
def test_files(output_dir):
files = helpers.get_files(output_dir)
assert files == ['post.html']
def test_files(output_dir):
files = helpers.get_files(output_dir)
assert files == ['postone.html', 'some-other-post.html']
def load_train_data(data_dir,
id_regex=r'\d{6}_\d{6}_\d{1,3}',
x_regex=r'\d{6}_\d{6}_\d{1,3}_image.tif',
y_regex=r'\d{6}_\d{6}_\d{1,3}_labels.tif'):
'''
Load train data from a directory according to a naming convention
Parameters
----------
data_dir: str
Directory containing data
id_regex: r string
regex that will be used to extract IDs that will
be used to label network output
x_regex: r string
regex that represents image file names, complete with
extension (tiff please)
y_regex: r string
regex that represents training label files, complete
with extension (tiff please)
Returns
-------
xs: list of torch.Tensor
List of images for training
ys: list of torch.Tensor
List of affinities for training
ids: list of str
ID strings by which each image and label are named.
Eventually used for correctly labeling network output
'''
# Get file names for images and training labels
x_paths, y_paths = get_files(data_dir, x_regex=x_regex, y_regex=x_regex)
# Get IDs
id_pattern = re.compile(id_regex)
ids = []
x_paths.sort()
y_paths.sort()
for i in range(len(x_paths)):
xn = Path(x_paths[i]).stem # this could have been avoided
yn = Path(y_paths[i]).stem # why would I bother now though?!
# assumes there will be a match for each
xid = id_pattern.search(xn)[0]
yid = id_pattern.search(yn)[0]
m = 'There is a mismatch in image and label IDs'
assert xid == yid, m
ids.append(xid)
# Get images and training labels in tensor form
xs = []
ys = []
for i in range(len(x_paths)):
xp = x_paths[i]
yp = y_paths[i]
x = imread(xp)
x = normalise_data(x)
y = imread(yp)
xs.append(torch.from_numpy(x))
ys.append(torch.from_numpy(y))
# returns objects in the same manner as get_train_data()
print('------------------------------------------------------------')
print(f'Loaded {len(xs)} sets of training data')
print_labels_info(ys[0].shape)
return xs, ys, ids
def test_files(input_files, populated_output_dir):
input_filenames = [
"post{}.html".format(f.file_number) for f in input_files
]
output_filenames = helpers.get_files(populated_output_dir)
assert output_filenames == input_filenames
def sum_occurencies(input_folder, files, output_folder='./output/'):
''' Find all the same entries in the files and sum their occurrencies.'''
for file in files:
with open(input_folder + file, 'r', encoding='utf-8',
errors='ignore') as input_file:
if not os.path.exists(os.path.dirname(output_folder + file)):
os.makedirs(os.path.dirname(output_folder + file))
with open(output_folder + file, 'w',
encoding='utf-8') as output_file:
print(file)
for line in input_file.readlines():
tokens = line.rstrip().split('\t')
output_line = ''
for word in tokens:
output_line += (word + '\t')
output_line = output_line[:-2] # remove the extra \t
output_line += '\n'
output_file.write(output_line)
if __name__ == "__main__":
files = helpers.get_files(input_folder)
remove_pos_tag(input_folder,
files,
output_folder='D:/data/letters_pos_removed/')