def test_everseen(self):
"""ensure duplicate elements are ignored"""
u = mi.unique_everseen('AAAABBBBCCDAABBB')
self.assertEqual(
['A', 'B', 'C', 'D'],
list(u)
)
def wordNetNER(document):
plant_sns = (wn.synsets('plant', pos="n"))
plant = plant_sns[1] #(botany) a living organism lacking the power of locomotion #hardcoded
wordnet_names = []
#wordnet_lemmatizer = WordNetLemmatizer ##Lematizer doesn't work....
for word in document:
#word = wordnet_lemmatizer.lemmatize(word) ##Lematizer doesn't work...
mySynsets = wn.synsets(word, pos="n")
i = 0
for i in range(0, 3):
try:
given_word = mySynsets[i] #tries first 3 synsets
definition = (given_word.definition())
p1 = re.compile('plant(s?)\s')
p2 = re.compile('organism(s?)\s')
p3 = re.compile('animal(s?)\s')
match1 = p1.search(definition)
match2 = p2.search(definition)
match3 = p3.search(definition)
if match1 or match2 or match3: #if the given word has "plants" or "animals" in the def, check to see how similar it is to "plant"
similarity_score = (given_word.path_similarity(plant)) #check similarity score
if similarity_score >= 0.2:
#print(similarity_score)
#print ("The words: "+(str(given_word)) + " has a sim score of: " +str(similarity_score))
wordnet_names.append(word)
named_entities.append(word)
#hypernym = given_word.hypernyms() #hypernym is list #synset 'organism' exists #can't search in the hypernyms....hmm...
i += 1
except IndexError:
pass
wordnet_ner = (list(unique_everseen(wordnet_names)))
return wordnet_ner
def fetch_skill_api():
list_of_files = ['pre_6.txt']
#list_of_letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', ' ']
for i in list_of_files:
master_skills_list = []
i_path_join = os.path.join(os.path.abspath(os.path.dirname(__file__)), "..", "static", "data", i)
with open(i_path_join, 'r') as fp:
contents = fp.readlines()
#print contents
for line in contents:
print line
master_skills_list.extend(autosuggest_api(line))
print master_skills_list
c = list(unique_everseen(master_skills_list))
ioutput_path_join = os.path.join(os.path.abspath(os.path.dirname(__file__)), "..", "static", "data", i + 'out')
with open(ioutput_path_join, 'w') as fp:
for item in c:
fp.write("%s\n" % item)
def interpret(atom_index, grammar, user_input):
stripped_input = strip_punctuation(user_input)
base_atoms_raw = atom_index.query(stripped_input)
base_atoms = []
for a in base_atoms_raw:
base_atoms.append(a.clone_nonstopword())
stop_words = filter(lambda a: a.get_stopword(), grammar.get_atoms())
extra_atoms = []
for stop_word in stop_words:
if not stop_word in base_atoms:
extra_atoms.append(stop_word)
expanded_atoms = base_atoms + extra_atoms
result = []
for query_pattern in grammar.get_query_patterns():
phrases = generate_phrases(grammar, expanded_atoms)
queries = query_pattern.resolve(phrases)
for query in queries:
result.append(query)
result = filter(lambda q: q.get_score() > 0, result)
result = filter(lambda q: q.validate(base_atoms), result)
result = list(unique_everseen(result, lambda q: q.get_english()))
result.sort(key=lambda q: q.get_score())
result.reverse()
for q in result:
q.set_base_atoms(base_atoms)
return result
def create_a_pb_unit_cell(self,
fpb_prop,
uc_name
):
'''
fpb_prop: a tuple contains:
fuel_temps: temperature list for unique pebbles in the unit cell
a matrix of unique pebbles x n layers of fuel in a triso
coating_temps: a list that contains temp for each of the non-fuel layers in triso, e.g. 4x5
cgt: central graphite temperature
sht: shell temperature
burnups: a list of 14 burnups
uc_name: unit cell name
'''
fuel_temps, coating_temps, cgt, sht, uc_name, burnups, pb_comp_dir = fpb_prop
fpb_list = []
unique_fpb_list = {}
unique_burnups = list(unique_everseen(burnups))
unique_burnup_nb = len(unique_burnups)
assert fuel_temps.shape[0] == unique_burnup_nb, 'wrong dimension %s' %str(fuel_temps.shape)
assert coating_temps.shape[0] == unique_burnup_nb, 'wrong dimension'
# create a list of unique pebbles
for i, bu in enumerate(unique_burnups):
pb_name = 'pb%s%d' % (uc_name, bu)
unique_fpb_list[bu] = self.create_a_fuel_pebble(fuel_temps[bu-1, :],
coating_temps[unique_burnups[i]-1, :],
cgt, sht,
pb_name,
unique_burnups[i],
pb_comp_dir)
# create a list of all the 14 fuel pebbles, some of them are exactly the same
for bu in burnups:
fpb_list.append(unique_fpb_list[bu])
return fpb_list
def kth_smallest_elem(node, k):
ls = inorder_traversal(node)
# unique_everseen removes duplicates from a list in O(N) time accorinding
# to
# http://stackoverflow.com/questions/480214/how-do-you-remove-duplicates-from-a-list-in-python-whilst-preserving-order
result = list(unique_everseen(ls))
return result[k - 1]
def ref_insert_line(line, form):
"""
Inserts \ref{} functions into tex files by substituting for a regex.
:param line: a line from a tex file, in the form of a string,
which we want to process and insert references into
:param form: a regular expression specification
for a string to be replaced.
:return: a string, wherein all of the strings specified by
form are replaced by \ref{form}
"""
# lineIterator = form.search(line)
# searchAndSub = []
# lineNew = line
# while searchAndSub is not None:
# searchAndSub = form.search(line)
# lineNew = lineNew.replace()
searchresults = form.findall(line)
iterableStrings = list(unique_everseen(searchresults))
lineNew = line
for substring in iterableStrings:
lineNew = lineNew.replace(substring, r'(\ref{' + substring[1:-1] + r'})')
return lineNew
def get_tree(self, node=None, filtered_ids=[]):
"""
node - головной объект, от которого строим дерево,
если он не указан, то строим дерево из всех объектов.
"""
def get_descendants(node):
descendents = []
children = node.get_children()
children.filter(pk__in=filtered_ids)
for n in children:
n_descendents = get_descendants(n)
n_descendents = [n for n in n_descendents if n.pk in filtered_ids]
descendents += n_descendents
return [node] + descendents
if node:
tree = get_descendants(node)
else:
tree = []
lev1_pages = self.filter(level=1)
if filtered_ids:
lev1_pages = self.filter(pk__in=filtered_ids)
for node in lev1_pages:
tree += get_descendants(node)
from more_itertools import unique_everseen
tree = list(unique_everseen(tree))
return tree
def main():
names = []
types = ['Grass', 'Poison', 'Fire', 'Dragon', 'Flying', 'Water', 'Bug', 'Normal', 'Electric', 'Ground', 'Fairy', 'Fighting', 'Psychic', 'Rock', 'Steel', 'Ice', 'Ghost', 'Dark']
scrapeNames(names, types)
pokemon = {
'images': {},
'heights': {},
'weights': {},
'powers': {},
'names': []
}
scrapePowers(pokemon, names)
scrapeHeightsWeights(pokemon, names)
names = list(unique_everseen(names))
pokemon['names'] = names
scrapeImages(pokemon, names)
fileName = 'stats/stats.json'
file = open(fileName, 'w')
json.dump(pokemon, file, indent = 2)
file.close()
def _countEntities(cols):
for i in range(len(cols)):
entries = cols[i]["entries"]
entityCount = 0
multipleEnts = False
for entry in entries:
entrySoup = BeautifulSoup(entry, "lxml")
links = entrySoup.findAll("a")
linksHref = [aTag["href"] for aTag in links]
linksHref = list(unique_everseen(linksHref))
linksCount = len(links)
for link in links:
# Image werden nicht gezählt
if link.find("img") != None:
linksCount -= 1
# Ebenso dürfen es nur Wikipedia-interne Links sein
elif link["href"][0:5] != "/wiki":
linksCount -= 1
# Manche Tabellen setzen doppelte Verlinkunden (e.g. TableID = 513, List of Olympic medalists in basketball)
elif link["href"] in linksHref:
linksHref.remove(link["href"]) # Beim ersten Mal löschen
elif link["href"] not in linksHref:
linksCount -= 1 # Bei jedem weiteren Mal, die Anzahl korrigieren
if linksCount > 0:
entityCount += 1
if linksCount > 1:
multipleEnts = True
# Bewertung: Maximal 50 Punkte möglich (100% sind Entitäten)
cols[i]["rating"] = int(math.floor(MAX_ENTITIES_POINTS * (entityCount / len(entries))))
cols[i]["entityCount"] = entityCount
cols[i]["multipleEntities"] = multipleEnts
def keys(self, pattern):
"""Aggregated keys method."""
def _keys(node, pattern):
for result in node.keys(pattern):
self._output_queue.put(result)
results = self._runner(_keys, pattern)
# return list(OrderedDict.fromkeys(results))
return sorted(list(unique_everseen(results)))
def clash_table(self,i,j): units = []; units.extend(self.row(i)); units.extend(self.col(j)); units.extend(self.box(which_box(i,j))); units = list(unique_everseen(units)); values = list(chain(*[ list(unit.available_values) for unit in units ])); return(table(values));
def __init__(self, data=np.asarray([[0, 0]]), cls_label=np.asarray([0]),
ses_label=np.asarray([0]), buff_size=BUFF_SIZE,
n_components=(K_CLS, K_SES, K_RES), beta=BETA,
NMF_updates='beta', n_iter=N_ITER, lambdas=[0, 0, 0],
normalize=False, fixed_factors=None, verbose=0,
dist_mode='segment',Wn=None):
self.data_shape = data.shape
self.buff_size = np.min((buff_size, data.shape[0]))
self.n_components = np.asarray(n_components, dtype='int32')
self.beta = theano.shared(np.asarray(beta, theano.config.floatX),
name="beta")
self.verbose = verbose
self.normalize = normalize
self.lambdas = np.asarray(lambdas, dtype=theano.config.floatX)
self.n_iter = n_iter
self.NMF_updates = NMF_updates
self.iters = {}
self.scores = []
self.dist_mode = dist_mode
if fixed_factors is None:
fixed_factors = []
self.fixed_factors = fixed_factors
fact_ = np.asarray([base.nnrandn((self.data_shape[1],
np.sum(self.n_components)))
for i in more_itertools.unique_everseen(itertools.izip(cls_label,
ses_label))])
self.W = theano.shared(fact_.astype(theano.config.floatX), name="W",
borrow=True, allow_downcast=True)
fact_ = np.asarray(base.nnrandn((self.data_shape[0],
np.sum(self.n_components))))
self.H = theano.shared(fact_.astype(theano.config.floatX), name="H",
borrow=True, allow_downcast=True)
self.factors_ = [self.H, self.W]
if Wn is not None:
self.Wn = Wn
self.X_buff = theano.shared(np.zeros((self.buff_size,
self.data_shape[1])).astype(theano.config.floatX),
name="X_buff")
if (self.NMF_updates == 'groupNMF') & (self.dist_mode == 'iter'):
self.cls_sums = theano.shared(np.zeros((np.max(cls_label)+1,
self.data_shape[1],
self.n_components[0])
).astype(theano.config.floatX),
name="cls_sums",
borrow=True,
allow_downcast=True)
self.ses_sums = theano.shared(np.zeros((np.max(ses_label)+1,
self.data_shape[1],
self.n_components[1])
).astype(theano.config.floatX),
name="ses_sums",
borrow=True,
allow_downcast=True)
self.get_sum_function()
self.get_updates_functions()
self.get_norm_function()
self.get_div_function()
def getLessonList(courselink, quality):
global session
coursehtml = (session.get(courselink)).text
lessonLinkRegex = re.compile('https?://www.cybrary.it/video/\w+(?:-[\w]+)*/')
matchedLessonLink = list(unique_everseen(lessonLinkRegex.findall(coursehtml)))
for link in matchedLessonLink:
print "Downloading "+link
downloadVideos(getVideoLink(link), quality)
def reorder_cls_ses(data, cls, ses, with_index=False):
"""reorder the data such that there is only
one continuous bloc for each pair class/session
Parameters
----------
data : array
the data
cls : array
the class labels for the data
ses : array
the session label for the data
with_index : Boolean (default False)
if True, the function returns the reordered indexes together
with data and labels
Returns
-------
data : array with the same shape as data
reordered data
cls : array with the same shape as cls
reordered class labels
ses : array with the same shape as ses
reordered session labels
ind : array with the same shape as data.shape[1]
reordered indexes (only if with_index==True)
"""
data_ordered = np.zeros((data.shape))
cls_ordered = np.zeros((cls.shape))
ses_ordered = np.zeros((ses.shape))
if with_index:
index = np.arange((data.shape[1],))
index_ordered = np.zeros((index.shape))
data_fill = 0
for i in more_itertools.unique_everseen(itertools.izip(cls, ses)):
ind = np.where((cls == i[0]) & (ses == i[1]))[0]
bloc_length = data[(cls == i[0]) & (ses == i[1]), :].shape[0]
data_ordered[data_fill:data_fill+bloc_length, ] = data[ind, :]
cls_ordered[data_fill:data_fill+bloc_length] = cls[ind]
ses_ordered[data_fill:data_fill+bloc_length] = ses[ind]
if with_index:
index_ordered[data_fill:data_fill+bloc_length] = index[ind]
data_fill += bloc_length
if with_index:
return {
'data': data_ordered,
'cls': cls_ordered,
'ses': ses_ordered,
'ind': index_ordered}
else:
return {
'data': data_ordered,
'cls': cls_ordered,
'ses': ses_ordered}
def onlymonths(wordfile,months):
if len(wordfile) == months:
return map(lambda x:x.split("\n")[0],wordfile)
splt = map(lambda x: x.split(","),wordfile)
startdate = map(lambda x: x[0],splt)
times = map(lambda x: int(x[1]),splt)
times = mergedate(startdate,times)
startdate = list(unique_everseen(startdate))
wordfile = map(lambda x:x[0]+ "," + str(x[1]),zip(startdate,times))
return wordfile
def bootstrap(region, environment, domain, instance_type, coreos_channel,
coreos_version, available, flotilla_container):
coreos = CoreOsAmiIndex()
cloudformation = FlotillaCloudFormation(environment, domain, coreos)
region_meta = RegionMetadata(environment)
regions = [r for r in unique_everseen(region)]
cloudformation.tables(regions)
region_params = region_meta.store_regions(regions, available, instance_type,
coreos_channel, coreos_version,
flotilla_container)
cloudformation.schedulers(region_params)
logger.info('Bootstrap complete')
def group_runs(data_dict):
"""Find unique model/physics groups"""
all_info = data_dict.keys()
model_physics_list = []
for key, group in groupby(all_info, lambda x: x[0:2]):
model_physics_list.append(key)
family_list = list(unique_everseen(model_physics_list))
return family_list
def matchOrganisms2(bigrams):
latin_names = []
for grams in bigrams:
(w1, w2) = grams.split(" ")
p1 = re.compile('[a-z]{2,}[b-df-hj-np-tv-z]{1,}(i$|is$|ia$|ae$|um$|us$|es$|arum$)') #wont pick up ones that match in 'a'; too many false positives
match1 = p1.search(w1)
p2 = re.compile('[a-z]{2,}[b-df-hj-np-tv-z]{1,}(a$|i$|is$|ia$|ae$|um$|us$|es$|arum$)') #picks up the word 'genes'
match2 = p2.search(w2)
if match1 and match2:
latin_names.append(grams)
named_entities.append(grams)
return (list(unique_everseen(latin_names)))
def filters_mapping_content(exact_results: List, similarity_results: List, threshold: float) -> Tuple:
"""Parses compiled mapping results, when results exist, to determine a final aggregated mapping result.
Args:
exact_results: A nested list containing 3 sub-lists where sub-list[0] contains exact match uris, sub-list[1]
contains exact match labels, and sub-list[2] contains exact match evidence.
similarity_results: A nested list containing 3 sub-lists where sub-list[0] contains similarity uris,
sub-list[1] contains similarity labels, and sub-list[2] contains similarity evidence.
threshold: A float that specifies a cut-off for filtering cosine similarity results.
Returns:
A tuple of lists containing mapping results for a given row. The first tuple contains exact mapping results
and the second contains similarity results. Both lists contains 3 items: uris, labels, and evidence.
"""
exact_uri, exact_label, exact_evid = exact_results
sim_uri, sim_label, sim_evid = similarity_results
exact_result: Optional[List[Any]] = [None, None, None]
sim_result: Optional[List[Any]] = [None, None, None]
# format results
if exact_uri:
exact_result = [list(unique_everseen(exact_uri)),
list(unique_everseen(exact_label)),
' | '.join(exact_evid)]
if sim_uri:
if any(x for x in sim_evid[0].split(' | ') if float(x.split('_')[-1]) == 1.0):
evid_list = sim_evid[0].split(' | ')
sim_keep = [evid_list.index(x) for x in evid_list if float(x.split('_')[-1]) == 1.0]
uris, labels = [sim_uri[x] for x in sim_keep], [sim_label[x] for x in sim_keep]
sim_result = [uris, labels, ' | '.join([evid_list[x] for x in sim_keep])]
elif any(x for x in sim_evid[0].split(' | ') if float(x.split('_')[-1]) >= threshold):
evid_list = sim_evid[0].split(' | ')
sim_keep = [evid_list.index(x) for x in evid_list if float(x.split('_')[-1]) >= threshold]
uris, labels = [sim_uri[x] for x in sim_keep], [sim_label[x] for x in sim_keep]
sim_result = [uris, labels, ' | '.join([evid_list[x] for x in sim_keep])]
else:
sim_result = [sim_uri, sim_label, ' | '.join(sim_evid)]
return exact_result, sim_result
def evidence_writer(evidences, sentence_id, data_source, resource_v,
rule_predicates, rule_type):
item_set = OrderedSet()
entity_set = []
# print rule_predicates
for evidence in evidences:
# print evidence[1]
if evidence[1] in rule_predicates:
if evidence[0] == resource_v[0] and evidence[2] == resource_v[
1] and evidence[1] == data_source:
pass
elif evidence[0] == resource_v[1] and evidence[2] == resource_v[
0] and evidence[1] in ["keyPerson", "capital"]:
pass
else:
try:
if '"' not in evidence[0] and '"' not in evidence[2]:
if ':' not in evidence[0] and ':' not in evidence[2]:
if '#' not in evidence[0] and '#' not in evidence[
2]:
if '&' not in evidence[
0] and '&' not in evidence[2]:
if '=' not in evidence[
0] and '=' not in evidence[2]:
if ' ' not in evidence[
0] and ' ' not in evidence[2]:
entity_1 = '"' + evidence[0] + '"'
entity_2 = '"' + evidence[2] + '"'
item_set.add(evidence[1] + '(' +
entity_1 + ',' +
entity_2 + ').')
except:
pass
else:
pass
# print "here"
# print item_set
with open(evidence_path + str(sentence_id) + '_.txt', 'wb') as csvfile:
for i in item_set:
if '*' not in i:
try:
# print i
csvfile.write(i.encode('utf-8') + '\n')
# csvfile.write(i+'\n')
except:
pass
with open(evidence_path + str(sentence_id) + '_.txt', 'r') as f, \
open(evidence_path + str(sentence_id) + '_unique.txt', 'wb') as out_file:
out_file.writelines(unique_everseen(f))
remove_file = evidence_path + str(sentence_id) + '_.txt'
os.remove(remove_file)
return item_set, entity_set
def main(ifilename):
file_name = ifilename
file_name_wo_end = file_name[:-4]
f = open(file_name)
file_content = f.read()
m3 = re.findall('xmlUrl=\"(.+?)\"', file_content)
m4 = re.findall('text=\"(.+?)\"', file_content)
m4.pop(0)
result = []
for i in range(len(m4)):
print(m4[i], m3[i])
result.append((m4[i], m3[i]))
rss_dict = {}
for name, url in tqdm(result):
try:
r = requests.get(
url,
headers={
'User-Agent':
'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:58.0) Gecko/20100101 Firefox/58.0'
},
timeout=15)
except:
print("failed: " + url)
rss_dict[name] = r.text
tqdm.write('Downloaded ' + url + '\n')
json_filename = file_name_wo_end + ".json"
with open(json_filename, 'w') as outfile:
json.dump(rss_dict, outfile)
all_urls_3 = []
for content in rss_dict.values():
m = re.findall('\"(http\S+?\.(?:mp3|mp4))[\"\?]', content,
re.IGNORECASE)
if m:
for item in m:
all_urls_3.append(item)
txt_filename = file_name_wo_end + ".txt"
f = open(txt_filename, 'w')
unique_urls = list(unique_everseen(all_urls_3))
for url in unique_urls:
f.write(url)
f.write("\n")
f.close()
def get_most_informative_features(self, n=50):
# Determine the most relevant features, and display them.
cpdist = self.classifier._feature_probdist
Feats = []
for (fname, fval) in self.classifier.most_informative_features(n+5):
def labelprob(l):
return cpdist[l, fname].prob(fval)
labels = sorted(
[l for l in self.classifier._labels if fval in cpdist[l, fname].samples()],
key=labelprob,
)
if len(labels) == 1:
continue
l0 = labels[0]
l1 = labels[-1]
Feats.append((fname, fval, l1, l0))
#return Feats
def format_double_feat(feature):
if feature.startswith("("):
feature = re.findall(r"\(u?[\"\'](.+)[\"\'], u?[\"\'](.+)[\"\']\)",feature)[0]
if feature[0]=="<START>":
return "<span class='innerFeature'>%s</span> at the beginning" % feature[1]
elif feature[1]=="<END>":
return "<span class='innerFeature'>%s</span> at the end" % feature[0]
else:
feature = "<span class='innerFeature'>%s</span> followed by <span class='innerFeature'>%s</span>" % (feature[0],feature[1])
return str(feature)
return "<span class='innerFeature'>%s</span>" % feature
def format_feat(fname,fval,l1,l0):
if l1=="good":
if fval==None:
return "do not contain %s" % (format_double_feat(fname))
else:
return "contain %s" % (format_double_feat(fname))
elif l1=="bad":
if fval==None:
return "contains %s" % (format_double_feat(fname))
else:
return "do not contain %s" % (format_double_feat(fname))
formatted_features = [format_feat(*f) for f in Feats]
return list(more_itertools.unique_everseen(formatted_features))[:20]
def stim_select_callback(attr, old, new, kwargs=plot_kwargs):
# Values from widget are not exact stimuli names
stim = get_filename(META, stim_select.value)
image_cds.data = get_img(stim).data
print('got_here')
# remove current gaze plot
remove_glyphs(image_plot, GAZE_COLORS)
x_dim = int(META[stim]['x_dim'])
y_dim = int(META[stim]['y_dim'])
station_count = META[stim]['station_count']
city = META[stim]['txt_name']
# Grab old title, set new text
t = matrix_plot.title
t.text = stim_select.value + ' - (' + str(station_count) + ' stations)'
# Retaining other settings
color = color_select.value
metric = METRICS.get(metric_select.value)
matrix_cds.data = get_matrix_cds(stim, USERS, DF, color, metric).data
# Yields unique 'xname's, preserving order
if PRESENTING:
order = list(unique_everseen(matrix_cds.data['xname']))
matrix_plot.x_range.factors = order
matrix_plot.y_range.factors = list(reversed(order))
image_plot.x_range.start = 0
image_plot.y_range.start = 0
image_plot.x_range.end = x_dim
image_plot.y_range.end = y_dim
plot_w = x_dim + kwargs['min_border_left'] + kwargs['min_border_right']
plot_h = y_dim + kwargs['min_border_top'] + kwargs['min_border_bottom']
X = [
item for sublist in matrix_cds.data['MappedFixationPointX']
for item in sublist
]
Y = [
item for sublist in matrix_cds.data['MappedFixationPointY']
for item in sublist
]
duration = [
item for sublist in matrix_cds.data['FixationDuration']
for item in sublist
]
fixation_cds.data = get_fixation_points(X, Y, duration).data
class JusteatSpider(scrapy.Spider):
name = "justeat"
allowed_domains = ["just-eat.co.uk"]
# download the restuarant_urls.txt from justeat1 spider output
with open("restuarant_urls.txt") as f:
restuarant_urls = [x.strip('\n') for x in f.readlines()]
restuarant_urls = list(unique_everseen(restuarant_urls))
# we need to run this in stages, about 7000 restuarants at a time max.
print("total number of restaurants: " + str(len(restuarant_urls)))
print("first record: " + str(restuarant_urls[0]))
print("8000th record: " + str(restuarant_urls[7999]))
print("last record: " + str(restuarant_urls[len(restuarant_urls) - 1]))
while True:
to_do = input("Enter Stage 1 to X (max 6 for now) ")
if to_do == "1" or to_do == "2" or to_do == "3" or to_do == "4" or to_do == "5" or to_do == "6":
break
# build a list of urls to scrap
start_urls = []
startrange = 7000 * (int(to_do) - 1)
endrange = 7000 * (int(to_do))
if endrange > len(restuarant_urls):
endrange = len(restuarant_urls)
for a in range(startrange, endrange):
start_urls.append("https://www.just-eat.co.uk" +
str(restuarant_urls[a]))
def parse(self, response):
for sel in response.xpath('//div[@class="restaurantParts"]'):
item = JusteatItem()
item['name'] = sel.xpath('//*[@itemprop="name"]/text()').extract()
item['address'] = sel.xpath(
'//*[@itemprop="address"]/span/text()').extract()
item['cuisine'] = sel.xpath(
'.//div/p/span[@itemprop="servesCuisine"]/text()').extract()
item['ratingvalue'] = sel.xpath(
'.//div/p[@itemprop="aggregateRating"]/meta[@itemprop="ratingValue"]/@content'
).extract()
item['ratingcount'] = sel.xpath(
'.//div/p[@itemprop="aggregateRating"]/meta[@itemprop="ratingCount"]/@content'
).extract()
item['ratingbest'] = sel.xpath(
'.//div/p[@itemprop="aggregateRating"]/meta[@itemprop="bestRating"]/@content'
).extract()
item['ratingworst'] = sel.xpath(
'.//div/p[@itemprop="aggregateRating"]/meta[@itemprop="worstRating"]/@content'
).extract()
item['name_id'] = sel.xpath('.//@data-restaurant-id').extract()
yield item
def polygon_2_vtx(starting_vertex, edges_to_visit):
from more_itertools import unique_everseen
if not edges_to_visit:
return
closed = False
print("Edges to visit:", edges_to_visit)
subpolygon = []
found_vertex = starting_vertex
while not closed:
for index, edge in enumerate(edges_to_visit.copy()):
visiting_vertex = found_vertex
#if visiting_vertex not in set(edge) and index==len(edges_to_visit.copy()):
# Tracer()()
#print("Not found in list of edges")
#closed=True
#break
if visiting_vertex not in set(edge):
continue
subpolygon.append(visiting_vertex)
print("Visiting vertex", visiting_vertex)
found_starting_vtx = False
subpolygon.append(found_vertex)
print(visiting_vertex, " in ", edge)
for index in set(edge):
if visiting_vertex != index:
found_vertex = index
print("Found vertex:", found_vertex)
subpolygon.append(found_vertex)
print("Removing edge", edge)
edges_to_visit.discard(edge)
print(edges_to_visit)
if found_vertex == starting_vertex:
subpolygon = list(unique_everseen(subpolygon))
print("Back to starting vertex")
closed = True
break
if len(subpolygon) <= 3:
return
else:
return subpolygon
def get_level_order(bms_table: dict) -> List[str]:
"""
表データからレベル順を得る。
:param bms_table: 表データ
:return: level_order
"""
# ひとまずデータ部に出現するレベルを出現順に列挙
level_order_from_data = list(unique_everseen([chart["level"] for chart in bms_table["data"]]))
# ヘッダ部にlevel_orderの指定がなかったら、データ部から得たレベル順をそのまま使う (仕様)
if "level_order" not in bms_table["header"]:
return level_order_from_data
# ヘッダ部にlevel_orderの指定があったらそれを使う (仕様)
level_order = bms_table["header"]["level_order"]
# のだが、データ部との不整合が起こらないように以下のような処理を行っておく (独自解釈)
level_order = map(str, level_order) # データ部に合わせてstrにしておく (なぜか仕様がUnion[str, int]である)
level_order = list(unique_everseen(level_order)) # 重複があったら除去しておく
omissions = [level for level in level_order if level not in level_order_from_data] # 抜けがあったら……
return level_order + omissions # 後ろにつけておく
def list_load_balancers_external_subnets(self):
client = self.session.client('elbv2')
lb_list_external = []
for lb in client.describe_load_balancers()['LoadBalancers']:
if lb['Scheme'] == 'internet-facing':
lb_list_external.append(lb)
if not lb_list_external:
print(colored("[WARNING] No Public Load Balancers detected", "red"))
sub_list = []
for i in lb_list_external:
for k in i['AvailabilityZones']:
sub_list.append(k['SubnetId'])
return list(unique_everseen(sub_list))
def working(self):
"""A list of the current tests with warning or errors. A
device is working if the list is empty.
This property returns, for the last test performed of each type,
the one with the worst ``severity`` of them, or ``None`` if no
test has been executed.
"""
from ereuse_devicehub.resources.event.models import Test
current_tests = unique_everseen(
(e for e in reversed(self.events) if isinstance(e, Test)),
key=attrgetter('type')) # last test of each type
return self._warning_events(current_tests)
def add_to_udl(self, new_udl):
"""
Adds more dictionaries to the udl. Also sorts them and removes double
entries. Doesn't add dictionaries with confirmed wrong urls or levels.
"""
new_udl = [ud for ud in new_udl if ud["URL"] not in self.wrong_urls]
if self.confirmed_level is not None:
new_udl = [
ud for ud in new_udl if ud["Level"] == self.confirmed_level
]
self.udl = new_udl + self.udl
self.udl = list(unique_everseen(self.udl))
self.udl = sorted(self.udl, key=lambda k: k['Score'], reverse=True)
def getPods():
Initconnection.Initconnection.loadConfig()
apiV1 = client.CoreV1Api()
Logging.Logging.log('Retrieving pods')
ret = apiV1.list_pod_for_all_namespaces(watch=False)
output = []
for pod in ret.items:
if pod is None:
Logging.Logging.log('Pods were not found')
else:
output.append(pod.metadata.name)
return list(unique_everseen(output))
def weighted_cosine_match(a, b):
"""Cosine match w/ weights based on order """
a_list = list(unique_everseen(a))
b_list = list(unique_everseen(b))
domain = list(unique_everseen(a_list + b_list))
a_vec = [
_weight_of(word, a_list, "descending") if word in a_list else 0
for word in domain
]
b_vec = [
_weight_of(word, b_list, "ascending") if word in b_list else 0
for word in domain
]
product = sum(x * y for x, y in zip(a_vec, b_vec))
a_mag = math.sqrt(sum(x * x for x in a_vec))
b_mag = math.sqrt(sum(x * x for x in b_vec))
return product / (a_mag * b_mag)
def parse1(self, a_pbed, input_file, type):
if type == 's':
str_list = []
str_list.append(
'%%---Pebble unit cell with position from input file\n' +
'pbed %d %d "%s"\n' %
(self.univ.id, a_pbed.coolant.gen.univ.id, input_file))
str_list.append('%%---Coolant in the unit cell\n' +
a_pbed.coolant.generate_output())
str_list.append('%%---Pebbles in the unit cell(pbed)\n')
for pb in list(unique_everseen(a_pbed.pb_list)):
str_list.append(pb.generate_output())
return ''.join(str_list)
def get_groups_by_types(
self,
types: List[str]) -> Union[AlignmentGroup, List[AlignmentGroup]]:
"""Return AlignmentGroups by fragment type.
:param types: list of types
:return:
"""
groups = self.groups_by_type()
if isinstance(types, str):
return groups[types]
else:
return list(unique_everseen(flatten([groups[t] for t in types])))
def evaluate(predicts, real_labels):
error = 1 - accuracy_score(predicts, real_labels) # Compute error
accuracy_score_of_all = accuracy_score(predicts, real_labels)
print(f"\nTotal accuracy of predictions: {accuracy_score_of_all}")
cnf_mat = confusion_matrix(real_labels, predicts, list(unique_everseen(real_labels))) # Create confusion matrix
print(cnf_mat)
df_cm = pd.DataFrame(cnf_mat, index=list(unique_everseen(real_labels)), columns=list(unique_everseen(real_labels)),)
col_sum = df_cm.sum(axis=1)
df_cm = df_cm.div(col_sum,axis = 0)
plt.figure(figsize=(20,20))
heatmap = sns.heatmap(df_cm, annot=True)
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=8)
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=8)
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.title("Confusion Matrix, Normalized")
plt.show()
return error,cnf_mat
def names(vma):
""" Obtain names of all coordinates defined in the V-Matrix.
:param vma: V-Matrix
:type vma: automol V-Matrix data structure
:rtype: tuple(str)
"""
name_mat = name_matrix(vma)
_names = filter(lambda x: x is not None,
numpy.ravel(numpy.transpose(name_mat)))
return tuple(more_itertools.unique_everseen(_names))
def getNodes():
Initconnection.Initconnection.loadConfig()
Logging.Logging.log('Retrieving Nodes')
apiV1 = client.CoreV1Api()
nodes = apiV1.list_node()
endpoints = []
for node in nodes.items:
if node is None:
Logging.Logging.log('No nodes found')
else:
endpoints.append(f'{node.metadata.name}')
return list(unique_everseen(endpoints))
def bootstrap(region, environment, domain, instance_type, coreos_channel,
coreos_version, available, flotilla_container):
coreos = CoreOsAmiIndex()
cloudformation = FlotillaCloudFormation(environment, domain, coreos)
region_meta = RegionMetadata(environment)
regions = [r for r in unique_everseen(region)]
cloudformation.tables(regions)
region_params = region_meta.store_regions(regions, available,
instance_type, coreos_channel,
coreos_version,
flotilla_container)
cloudformation.schedulers(region_params)
logger.info('Bootstrap complete')
def find_topics(self,test):
topics = []
tp_num = []
for quest in test.sscquestions_set.all():
tp_number = quest.topic_category
try:
tp_name = changeIndividualNames(tp_number,quest.section_category)
except:
tp_name = 'Topics'
topics.append(tp_name)
topics = list(unique_everseen(topics))
num_questions = len(test.sscquestions_set.all())
return topics,num_questions
def step(context, modes):
context.execute_steps(
u"""then wait for element {element} identified by css_selector""".
format(element=DETECTMODE))
labels_list = context.browser.find_elements_by_css_selector(DETECTMODE)
modes_list = modes.split(",")
label_obtained_elements = []
for element in labels_list:
if element.text != "":
label_obtained_elements.append(element.text)
label_obtained_elements = list(unique_everseen(label_obtained_elements))
assert modes_list == label_obtained_elements, "Error: the list of options is not the expected. Expected {expected}, obtained {obtained}".format(
expected=modes_list, obtained=label_obtained_elements)
def getServices():
Initconnection.Initconnection.loadConfig()
Logging.Logging.log('Retrieving Services')
apiV1 = client.CoreV1Api()
services = apiV1.list_service_for_all_namespaces()
output = []
for service in services.items:
if service is None:
Logging.Logging.log('No services found')
else:
output.append(f'{service.metadata.name}')
return list(unique_everseen(output))
def _filter_unique_results(results):
return list(
unique_everseen(
results,
key=lambda x: (
x["query"]["sequence_id"],
x["query"]["start"],
x["query"]["end"],
x["subject"]["sequence_id"],
x["subject"]["start"],
x["subject"]["end"],
),
))
def df_to_mct(df):
full_array = np.array(df)
loadcases = np.array(df.columns.values)[1:]
combinations = full_array[2:, 0]
load_types = full_array[1, 1:]
factors = full_array[2:, 1:]
mct_string = '*LOADCOMB ; Combinations\n; NAME=NAME, KIND, ACTIVE, bES, iTYPE, DESC, iSERV-TYPE, nLCOMTYPE, nSEISTYPE ; line 1\n; ANAL1, LCNAME1, FACT1, ... ; from line 2'
#Creates load combinations
for row_index, row in enumerate(factors):
combination = combinations[row_index]
mct_string = mct_string + '\n' + ' NAME=' + str(
combination) + ', GEN, ACTIVE, 0, 0, , 0, 0, 0\n'
for factor_index, factor in enumerate(row):
load_type = load_types[factor_index]
loadcase = loadcases[factor_index]
if factor_index != 0:
mct_string = mct_string + ', '
mct_string = mct_string + str(load_type) + ', ' + str(
loadcase) + ', ' + str(factor)
#Creates combination envelopes
combination_envelopes = []
for comb in combinations:
combination_envelopes.append(comb.split('_')[0])
combination_envelopes.append(comb.split('|')[0])
combination_envelopes = list(unique_everseen(combination_envelopes))
combination_groups = []
for envelope in combination_envelopes:
group = []
for comb in combinations:
if envelope in comb:
group.append(comb)
combination_groups.append(group)
for group_index, group in enumerate(combination_groups):
mct_string = mct_string + '\n' + ' NAME=' + str(
combination_envelopes[group_index]
) + ', GEN, ACTIVE, 0, 1, , 0, 0, 0\n'
for comb_index, comb in enumerate(group):
if comb_index != 0:
mct_string = mct_string + ', '
mct_string = mct_string + 'CB' + ', ' + str(comb) + ', ' + '1.0'
return mct_string
def gets_entity_ancestors(graph: Graph,
uris: List[Union[URIRef, str]],
rel: Union[URIRef, str] = RDFS.subClassOf,
cls_lst: Optional[List] = None) -> List:
"""A method that recursively searches an ontology hierarchy to pull all ancestor concepts for an input entity.
Args:
graph: An RDFLib graph object assumed to contain ontology data.
uris: A list of at least one ontology RDFLib URIRef object or string.
rel: A string or RDFLib URI object containing a predicate.
cls_lst: A list of URIs representing the ancestor classes found for the input class_uris.
Returns:
An ordered (desc; root to leaf) list of ontology objects containing the input uris ancestor hierarchy. Example:
input: [URIRef('http://purl.obolibrary.org/NCBITaxon_11157')]
output: ['http://purl.obolibrary.org/NCBITaxon_10239', 'http://purl.obolibrary.org/NCBITaxon_2559587',
'http://purl.obolibrary.org/NCBITaxon_2497569', 'http://purl.obolibrary.org/NCBITaxon_11157']
"""
prop = rel if isinstance(rel, URIRef) else URIRef(rel)
cls_lst = [] if cls_lst is None else cls_lst
cls_lst = list(
unique_everseen([
x if isinstance(x, URIRef) else URIRef(obo + x) for x in cls_lst
]))
uris = list(
unique_everseen(
[x if isinstance(x, URIRef) else URIRef(obo + x) for x in uris]))
ancs = list(
unique_everseen(
[j for k in [graph.objects(x, prop) for x in uris] for j in k]))
if len(ancs) == 0 or len(set(ancs).difference(set(cls_lst))) == 0:
return list(unique_everseen([str(x) for x in cls_lst]))
else:
uris = [x for x in ancs if x not in cls_lst]
for i in uris:
cls_lst.insert(0, i)
return gets_entity_ancestors(graph, uris, prop, cls_lst)
def monte_carlo(forward_,reverse_):
forward=[]
reverse=[]
fasta_sequences = SeqIO.parse(open(forward_),'fasta')
for fasta in fasta_sequences:
name, sequence = fasta.id, fasta.seq
forward.append(str(sequence).replace('\n', ''))
fasta_sequences = SeqIO.parse(open(reverse_),'fasta')
for fasta in fasta_sequences:
name, sequence = fasta.id, fasta.seq
reverse.append(str(sequence).replace('\n', ''))
seed=1000 # Iterations
matches=[]
count=0
cytosine=0
fh=open('forward_d/monte.carlo', 'w+')
match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)
while seed != 0:
sequence=forward[int(random.random()*(len(forward)-1))] # get a random sequence from [ 0, N ] contigs
width= int(random.random()*(len(sequence)-1) *0.20+13) # Take lower 13+[0-20] % of sequence length
first=int(random.random()*(len(sequence)-1-width))
subsequence=sequence[first:first+width]
for x in reverse:
index=x.find(subsequence)
if index != -1 :
count_=ccount(reverse,reverse.index(x))+index
fh.write(str(count_)+"\t"+str(eval(str(count_+width)))+"\n")
for i in range(0,width):
matches.append(count_+i)
count+=1
else:
count_=ccount(reverse,reverse.index(x))+index
alignment = sw.align(x,subsequence)
cytosine+=alignment.dump()
seed-=1
fh.close()
forward_length=ccount(forward,"1",True)
xlim=ccount(reverse,"1",True)
plt.hlines(1,1,xlim) # Draw a horizontal line
plt.xlim(0,xlim)
plt.ylim(0.5,1.5)
matches=list(unique_everseen(matches))
y = np.ones(np.shape(matches)) # Make all y values the same
plt.plot(matches,y,'|',ms = 40) # Plot a line at each location specified in `matches`
plt.axis('off')
plt.show()
print "[",count,"/ ",forward_length,"] hits, [",cytosine,"]"
def gen_Ck(k, L):
flatten = [item for subtuple in L
for item in subtuple] #flattens all candidates
uniqueflatten = list(
unique_everseen(flatten)) #gets out duplicate candidates
Ck = list(combinations(
uniqueflatten,
k)) #creates list of all possible combinations of k length
for c in Ck:
if has_infeq_subsets(k, L, c):
Ck.remove(c)
else:
pass
return Ck
def get_image_url_list(category, data_type=None):
"""
For a given data type, returns a list of filenames.
Args:
category (string): Category name as a string (from list_image_sets())
data_type (string, optional): "train" or "val"
Returns:
list of strings: list of all filenames for that particular category
"""
df = _load_data(category, data_type=data_type)
image_url_list = list(unique_everseen(list(img_dir + df['fname'])))
return image_url_list
def unique_mail(emails: Iterator[Email]) -> Iterator[Email]:
# remove duplicates (from a file being
# in multiple boxes and the 'default' inbox)
# some formats won't have a message id,
# but hopefully the date/subject creates a unique
# key in that case
yield from unique_everseen(
emails,
key=lambda m: (
m.subject_json,
m.message_id_json,
m.dt,
),
)
def get_int_mapping(dataframe, column):
""" Returns index, reverse_index, and list of unique items in a pandas datframe """
# Convert series to list
column_to_list = dataframe[column].tolist()
# Find set of unique items and convert to a list
unique_items_list = list(unique_everseen(column_to_list))
# Create indexes for each item
item_index = {item: idx for idx, item in enumerate(unique_items_list)}
index_item = {idx: item for item, idx in item_index.items()}
return item_index, index_item, unique_items_list
def get_image_url_list(category, data_type=None):
"""
For a given data type, returns a list of filenames.
Args:
category (string): Category name as a string (from list_image_sets())
data_type (string, optional): "train" or "val"
Returns:
list of strings: list of all filenames for that particular category
"""
df = _load_data(category, data_type=data_type)
image_url_list = list(
unique_everseen(list(img_dir + df['fname'])))
return image_url_list
def _get_header_add(table):
"""Finds and returns the header of a table.
:param table: A basketball-reference stats table.
:returns: Header of the table as a list of strings.
"""
def replace_titles(title):
return multiple_replace(
title,
{'%': 'P', '3': 'T', '+/-': 'PlusMinus'})
titles = (
replace_titles(str(th.get_text()))
for th in table.find_all('th')
)
return list(unique_everseen(titles))
def _merged_column_names_from(self, dataset_list):
elements = []
for idx_dataset, dataset in enumerate(dataset_list):
# require getting the code from the dataset object always
code = self.__dataset_objects__()[idx_dataset].code
for index, column_name in enumerate(dataset.column_names):
# only include column names that are not filtered out
# by specification of the column_indexes list
if self._include_column(dataset, index):
# first index is the date, don't modify the date name
if index > 0:
elements.append(self._rename_columns(code, column_name))
else:
elements.append(column_name)
return list(unique_everseen(elements))
def check_segments_length(self, data, cls_label, ses_label):
cls = []
cls_ind = []
for i in more_itertools.unique_everseen(itertools.izip(cls_label, ses_label)):
cls.append(i)
start = np.where((cls_label == i[0]) & (ses_label == i[1]))[0][0]
stop = np.where((cls_label == i[0]) & (ses_label == i[1]))[0][-1]+1
cls_ind.append([start, np.min([start+self.buff_size, stop])])
if data[(cls_label == i[0]) & (ses_label == i[1]), :].shape[0] > self.buff_size:
ind = np.where((cls_label == i[0]) & (ses_label == i[1]))[0]
if self.verbose > 0:
print "segment {0} to {1} is too long (length={2}, buffer={3})"\
"\n please increase buffer size or segment will be truncated"\
.format(ind[0], ind[-1], ind[-1]-ind[0]+1, self.buff_size)
return np.asarray(cls), np.asarray(cls_ind)
def writeStory():
markovText = ""
text = ""
topic = sys.argv[1]
post_limit = 5
if sys.argv[2] == "twitter":
# Get topic to search
tweets = searchTweets(topic)
# Parse Tweets
for tweet in tweets:
if len(tweet.strip()) > 5:
# Remove social crap from tweets (credit: https://stackoverflow.com/a/8377440)
stripped = lambda tweet: re.compile('\#').sub('', re.compile('RT @').sub('@', tweet, count=1))
text += ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",stripped(tweet)).split()) + '. '
text = re.sub("( s )","\'s ",text)
text = re.sub("( ll )","\'ll ",text)
text = re.sub("( t )","\'t ",text)
text = re.sub("( ve )","\'ve ",text)
text = re.sub("(https|http)"," ",text)
text = re.sub("( m )","\'m ",text)
text = re.sub("(y all)","y\'all",text)
text = re.sub("( amp )"," & ",text)
text = re.sub("( re )","\'r ",text)
text = re.sub("( d )","\'d ",text)
text = re.sub("(NoneNone)"," ",text)
# Generate markov
splitText = generateMarkov(text).rsplit(' ', 1)[0].split(".")
for tweet in list(unique_everseen(splitText)):
markovText+=tweet+'. '
elif sys.argv[2] == "reddit":
r = praw.Reddit(user_agent='storytime script: amorgan.me/storytime')
r.login('storytime_bot', 'Jnw5v9pgbHW6qXXrNb24EJkGeE8KwZ', disable_warning=True)
submissions = r.get_subreddit(topic).get_new(limit=10)
# Get comments
for x in xrange(0,post_limit):
submission = next(submissions)
#comments = praw.helpers.flatten_tree(submission.comments)
# Parse comments
print (submission)
#for comment in submission.comments:
#markovText+=comment.body
# print(comment.body)
print (markovText)
def parse1(self, a_pbed, input_file, type):
if type == 's':
str_list = []
str_list.append(
'%%---Pebble unit cell with position from input file\n' +
'pbed %d %d "%s"\n' %
(self.univ.id, a_pbed.coolant.gen.univ.id,
input_file))
str_list.append(
'%%---Coolant in the unit cell\n' +
a_pbed.coolant.generate_output())
str_list.append(
'%%---Pebbles in the unit cell(pbed)\n')
for pb in list(unique_everseen(a_pbed.pb_list)):
str_list.append(pb.generate_output())
return ''.join(str_list)
def get_num_concepts(self): """Calculates the oncepts in the text """ # Inititialize empty list concepts = [] pairs = self.word_pairs for pair in pairs: first = pair[0] second = pair[1] concepts.append(first) concepts.append(second) num_concepts = len(list(unique_everseen(concepts))) return num_concepts
def __init__(self):
#init DB connection
from Database_Connection import Database_Connection
database_connection = Database_Connection()
#get list of words from the database
self.list_of_words = database_connection.get_entries_as_list()
words_from_database = database_connection.get_entries_as_list()
#remove quotes because it breaks SQLite
for i in range(0, len(words_from_database)):
words_from_database[i] = words_from_database[i].encode('ascii','ignore')
words_from_database[i] = words_from_database[i].replace("'", "") #todo - fix this shit
words_from_database[i] += " "
#add the words that were just downloaded from Facebook
self.list_of_words.extend(self.load_words_from_json())
words_from_json = self.load_words_from_json()
#remove quotes because it breaks SQLite
for i in range(0, len(words_from_json)):
words_from_json[i] = words_from_json[i].encode('ascii','ignore')
words_from_json[i] = words_from_json[i].replace("'", "") #todo - fix this shit
words_from_json[i] += " "
#remove duplicates
from more_itertools import unique_everseen
mega_list = self.list_of_words
#normailze megalist for deleting duplicates
mega_list = [string.strip() for string in mega_list]
pruned_list = list(unique_everseen(mega_list))
print "mega list - {0}".format(len(mega_list))
print "pruned list - {0}".format(len(pruned_list))
#remove quotes because it breaks SQLite
for i in range(0, len(pruned_list)):
pruned_list[i] = pruned_list[i].encode('ascii','ignore')
pruned_list[i] = pruned_list[i].replace("'", "") #todo - fix this shit
pruned_list[i] += " "
database_connection.wipe_database()
database_connection.add_list_to_database(pruned_list)
def get_urls(self, soup):
urls = []
tags = soup.find_all(href=True) + soup.find_all(src=True)
for tag in tags:
if tag.get('href') is not None:
if self.url not in tag.get('href'):
urls.append(urljoin(self.url, tag.get('href')))
else:
urls.append(tag.get('href'))
if tag.get('src') is not None:
if self.url not in tag.get('src'):
urls.append(urljoin(self.url, tag.get('src')))
else:
urls.append(tag.get('src'))
return unique_everseen(urls)
def reorder_by_paths(x, paths):
"""
Reorder the columns activity matrix according to a set of
the most probable paths through a network. Useful for visualizing repeated sequences.
:param x: activity matrix (rows are timepoints, cols are nodes)
:param paths: list of tuple paths
:return: x with optimally permuted columns
"""
# find all the nodes that appear in probable paths, ordered by those paths
ordering = list(unique_everseen(chain.from_iterable(paths)))
# add in the rest of the nodes randomly
for node in range(x.shape[1]):
if node not in ordering:
ordering.append(node)
# reorder the columns
return x[:, np.array(ordering)], ordering
def harvestobsids():
baseurl = "http://waarnemingen.be/soort/view/%s?from=1900-01-01&to=2100-01-01&rows=100&page=%s"
conn = sqlite3.connect("pissebed.db")
c = conn.cursor()
ids = c.execute("select id from species")
ids = list(itertools.chain.from_iterable(ids))
for id in ids:
page = 1
previous = []
while True:
try:
url = baseurl % (id, page)
res = br.open(url).read()
obsids = list(unique_everseen(extractobsids(res)))
print "Species " + str(id) + ", page " + str(page) + ": found " + str(len(obsids)) + " observations"
if cmp(previous, obsids) == 0:
break
previous = obsids
for obsid in obsids:
records = c.execute("select * from observations where id = ?", (obsid,)).fetchall()
if len(records) == 0:
print "New observation: %s" % obsid
c.execute("insert into observations (id, species_id) values (?, ?)", (obsid, id))
conn.commit()
else:
print "Observation %s already exists" % obsid
except Exception, e:
print "Error: " + str(e)
page = page + 1
if page > maxpages:
break
time.sleep(random.randint(mindelay, maxdelay))