def list_symbols(self, date_range):
libraries = self._get_libraries(date_range)
return sorted(
list(
set(
itertools.chain(
*[l.library.list_symbols() for l in libraries]))))
def neighbors(self, node):
assert node.startswith(u"n_")
edges = self.bipartite_graph.neighbors(node)
neighbors = map(lambda edge: self.bipartite_graph.neighbors(edge),
edges)
return list(set(itertools.chain(*neighbors)) - set([node]))
def student_notes(request, userid):
try:
student = Person.objects.get(find_userid_or_emplid(userid))
except Person.DoesNotExist:
student = get_object_or_404(NonStudent, slug=userid)
if request.POST and 'note_id' in request.POST:
# the "hide note" box was checked: process
note = get_object_or_404(AdvisorNote,
pk=request.POST['note_id'],
unit__in=request.units)
note.hidden = request.POST['hide'] == "yes"
note.save()
if isinstance(student, Person):
notes = AdvisorNote.objects.filter(
student=student, unit__in=request.units).order_by("-created_at")
form_subs = FormSubmission.objects.filter(
initiator__sfuFormFiller=student,
form__unit__in=Unit.sub_units(request.units),
form__advisor_visible=True)
visits = AdvisorVisit.objects.filter(
student=student, unit__in=request.units).order_by('-created_at')
# decorate with .entry_type (and .created_at if not present so we can sort nicely)
for n in notes:
n.entry_type = 'NOTE'
for fs in form_subs:
fs.entry_type = 'FORM'
fs.created_at = fs.last_sheet_completion()
items = list(itertools.chain(notes, form_subs))
items.sort(key=lambda x: x.created_at, reverse=True)
nonstudent = False
else:
notes = AdvisorNote.objects.filter(
nonstudent=student, unit__in=request.units).order_by("-created_at")
visits = AdvisorVisit.objects.filter(
nonstudent=student, unit__in=request.units).order_by('-created_at')
for n in notes:
n.entry_type = 'NOTE'
items = notes
nonstudent = True
show_transcript = False
# For demo purposes only.
# if 'UNIV' in [u.label for u in request.units]:
# show_transcript = True
template = 'advisornotes/student_notes.html'
context = {
'items': items,
'student': student,
'userid': userid,
'nonstudent': nonstudent,
'show_transcript': show_transcript,
'units': request.units,
'visits': visits
}
return render(request, template, context)
def real():
import pandas as pd
df = pd.read_csv('one_gallon_on_burner.log')
df.columns = ['time', 'T']
df['time'] /= 1000.
df = df.iloc[df['time'].values >= 50.]
df = df.iloc[df['time'].values <= 100.]
deg_inf = 25
sig_T = 0.02
t_0 = 30
s_0 = 0
s_max = 1
observations = zip(df['time'].values, df['T'].values)
x0 = np.array([30., 0.15, np.log(0.2), np.log(0.05)])
ret = list(run_filter(x0, observations, t_0=t_0, s_0=s_0, s_max=s_max,
deg_inf=deg_inf, sig_T=sig_T))
ts = df['time'].values
def extend(cnt):
t, x, P = ret[-1]
dt = 1.
for i in range(cnt):
t += dt
x_new, F, Q = process_model(x, dt, t, t_0=t_0, s_0=s_0, s_max=s_max,
deg_inf=deg_inf, sig_T=sig_T)
x, P = kalman_predict(x, P, (lambda xx: x_new, F), Q)
x[2] = min(x[2], np.log(2))
x[3] = min(x[3], np.log(0.01))
yield t, x, P
ret = itertools.chain(ret, list(extend(10)))
ts, mus, Ps = zip(*list(ret))
mus = np.array(mus)
vars = np.array([np.diag(P) for P in Ps])
fig, axes = plt.subplots(2, 2, sharex=True)
for ax, mu, var, vname in zip(axes.reshape(-1), mus.T,
vars.T, ['T', 'm', 'H_max', 'k']):
ax.set_title(vname)
ax.plot(ts, mu, color='steelblue', lw=2)
ax.fill_between(ts, mu + np.sqrt(var), mu - np.sqrt(var), alpha=0.8, color='steelblue')
delta = np.max(mu) - np.min(mu)
lim = [np.min(mu) - 0.3 * delta, np.max(mu) + 0.3 * delta]
ax.set_ylim(lim)
plt.show()
def student_notes(request, userid):
try:
student = Person.objects.get(find_userid_or_emplid(userid))
except Person.DoesNotExist:
student = get_object_or_404(NonStudent, slug=userid)
if request.POST and 'note_id' in request.POST:
# the "hide note" box was checked: process
note = get_object_or_404(AdvisorNote, pk=request.POST['note_id'], unit__in=request.units)
note.hidden = request.POST['hide'] == "yes"
note.save()
if isinstance(student, Person):
notes = AdvisorNote.objects.filter(student=student, unit__in=request.units).order_by("-created_at")
alerts = Alert.objects.filter(person=student, alerttype__unit__in=request.units, hidden=False).order_by("-created_at")
form_subs = FormSubmission.objects.filter(initiator__sfuFormFiller=student, form__unit__in=Unit.sub_units(request.units),
form__advisor_visible=True)
# decorate with .entry_type (and .created_at if not present so we can sort nicely)
for n in notes:
n.entry_type = 'NOTE'
for a in alerts:
a.entry_type = 'ALERT'
for fs in form_subs:
fs.entry_type = 'FORM'
fs.created_at = fs.last_sheet_completion()
items = list(itertools.chain(notes, alerts, form_subs))
items.sort(key=lambda x: x.created_at, reverse=True)
nonstudent = False
else:
notes = AdvisorNote.objects.filter(nonstudent=student, unit__in=request.units).order_by("-created_at")
for n in notes:
n.entry_type = 'NOTE'
items = notes
nonstudent = True
show_transcript = False
if 'UNIV' in [u.label for u in request.units]:
show_transcript = True
template = 'advisornotes/student_notes.html'
if 'compact' in request.GET:
template = 'advisornotes/student_notes_compact.html'
context = {'items': items, 'student': student, 'userid': userid, 'nonstudent': nonstudent,
'show_transcript': show_transcript, 'units': request.units}
return render(request, template, context)
def quality(sketch_matrix, train_sketch, test_sketch, train_targets,
test_targets):
k = sketch_matrix.k
L = sketch_matrix.L
train_cols_count = np.shape(train_sketch)[1]
if multilabel:
test_targets_pred = []
else:
test_targets_proba = np.empty(len(test_targets))
for i in range(np.shape(test_sketch)[1]):
col_i = test_sketch[:, i:i + 1]
similar_cols = SketchMatrix._get_similar_columns(
col_i, train_sketch, k, L, train_cols_count)
similar_targets = itertools.chain(
*map(lambda c: train_targets[c], similar_cols))
# similar_targets = list(itertools.chain(*map(lambda c: train_targets[c], similar_cols)))
if multilabel:
target_proportions = statistics.get_multilabel_target_proportions(
similar_targets,
np.shape(similar_cols)[0])
targets_pred = filter(
lambda target: target_proportions[target] >
multilabel_prediction_threshold, target_proportions)
test_targets_pred.append(targets_pred)
# print "Col:", i, ", Target:", test_targets[i], ", Est. target: ", targets_pred
# print "Similar cols:", similar_cols
# print "Similar targets:", similar_targets
# print "--------------------------------------"
# _acc, _prec, _recall, _f1 = statistics.multi_label_scores([test_targets[i]], [targets_pred])
# fp = open(output_dir + "classification_sketch", "a")
# fp.write("Col: {0}, Target: {1}, Est. target: {2}\n".format(i, test_targets[i], targets_pred))
# fp.write("Accuracy: {0}, Precision: {1}, Recall: {2}, F1: {3}\n".format(_acc, _prec, _recall, _f1))
# fp.write("Similar cols: {0}\n".format(list(similar_cols)))
# fp.write("Similar targets: {0}\n".format(similar_targets))
# fp.write("--------------------------------------\n")
# fp.close()
else:
estimated_target_proba_i = statistics.predict_binary_target_proba(
similar_targets)
test_targets_proba[i] = estimated_target_proba_i
if multilabel:
# TODO: compute also AUC?
acc, prec, recall, f1 = statistics.multi_label_scores(
test_targets, test_targets_pred)
return -1., acc, prec, recall, f1
else:
return all_scores(test_targets, test_targets_proba)
def get(self):
if(not isUserAdmin(self)):
self.session[LOGIN_NEXT_PAGE_KEY] = self.URL
self.redirect("/")
return
today = datetime.datetime.now(timeZone)
twoWeeksAgo = today + datetime.timedelta(days = - 14)
unseenUsers = User.all().filter("lastOrder < ", twoWeeksAgo)
unseenUsersOrdered = sorted(unseenUsers, key=getLastOrderDate)
taskedUsers = User.all().filter("taskList >= ", None)
template_values={
'users': itertools.chain(taskedUsers, unseenUsersOrdered),
}
template = jinja_environment.get_template('templates/crm/crmTaskList.html')
self.printPage("Felhasználók", template.render(template_values), False, False)
def quality(sketch_matrix, train_sketch, test_sketch, train_targets, test_targets):
k = sketch_matrix.k
L = sketch_matrix.L
train_cols_count = np.shape(train_sketch)[1]
if multilabel:
test_targets_pred = []
else:
test_targets_proba = np.empty(len(test_targets))
for i in range(np.shape(test_sketch)[1]):
col_i = test_sketch[:, i : i + 1]
similar_cols = SketchMatrix._get_similar_columns(col_i, train_sketch, k, L, train_cols_count)
similar_targets = itertools.chain(*map(lambda c: train_targets[c], similar_cols))
# similar_targets = list(itertools.chain(*map(lambda c: train_targets[c], similar_cols)))
if multilabel:
target_proportions = statistics.get_multilabel_target_proportions(similar_targets, np.shape(similar_cols)[0])
targets_pred = filter(lambda target: target_proportions[target] > multilabel_prediction_threshold, target_proportions)
test_targets_pred.append(targets_pred)
# print "Col:", i, ", Target:", test_targets[i], ", Est. target: ", targets_pred
# print "Similar cols:", similar_cols
# print "Similar targets:", similar_targets
# print "--------------------------------------"
# _acc, _prec, _recall, _f1 = statistics.multi_label_scores([test_targets[i]], [targets_pred])
# fp = open(output_dir + "classification_sketch", "a")
# fp.write("Col: {0}, Target: {1}, Est. target: {2}\n".format(i, test_targets[i], targets_pred))
# fp.write("Accuracy: {0}, Precision: {1}, Recall: {2}, F1: {3}\n".format(_acc, _prec, _recall, _f1))
# fp.write("Similar cols: {0}\n".format(list(similar_cols)))
# fp.write("Similar targets: {0}\n".format(similar_targets))
# fp.write("--------------------------------------\n")
# fp.close()
else:
estimated_target_proba_i = statistics.predict_binary_target_proba(similar_targets)
test_targets_proba[i] = estimated_target_proba_i
if multilabel:
# TODO: compute also AUC?
acc, prec, recall, f1 = statistics.multi_label_scores(test_targets, test_targets_pred)
return -1., acc, prec, recall, f1
else:
return all_scores(test_targets, test_targets_proba)
def get_successful_analyze_task_results(group_result, chunked = False):
'''
Get results for successful tasks from `group_result` (meaning their results) and ignore revoked ones.
Parameters
----------
group_result : GroupResult
chunked : bool, optional (default is False)
If work has been divided into chunks.
Returns
-------
list< tuple<id, gridfs (bool)>>
'''
results = []
def check_n_add(res):
''' Check if `res` is ready and has not been revoked etc. '''
try:
if res is not None:
result = res.get(propagate = False)
# if chunked, result is list of multiple tasks -> unpack results
if chunked:
result = Util.flatten(result)
# no result available if e.g. exception raised
if result is not None:
results.append(result)
# TaskRevokedError
except Exception:
pass
for res in group_result:
check_n_add(res)
# single result is of type list< tuple<id, gridfs (bool)>>
# so flatten the result!
return list(itertools.chain(*results))
def list_symbols(self, date_range):
libraries = self._get_libraries(date_range)
return sorted(list(set(itertools.chain(*[l.library.list_symbols() for l in libraries]))))
def main():
global opts, args
parser = OptionParser( version="%prog 2.0", usage='Usage: %prog [options] <dirs>...' )
parser.set_defaults( **{
'verbose': False,
'dir_mode': False
} )
parser.add_option( '-d', '--dir-mode', action='store_true' )
parser.add_option( '-v', '--verbose', action='store_true' )
opts, args = parser.parse_args()
dirs = args
if not dirs:
dirs.append( '*' )
hive = {}
if opts.verbose:
print( "SCAN PHASE..." )
if opts.dir_mode:
files = set( x for x in itertools.chain( *( iglob( x ) for x in dirs ) ) if os.path.isdir( x ) )
else:
files = set( x for x in itertools.chain( *( iglob( x ) for x in dirs ) ) if os.path.isfile( x ) )
if opts.verbose:
print( "FOUND:", len( files ) )
for path in files:
entry = make_entry( os.path.basename( path ) )
if not entry:
continue
entry.add_path( path )
ean = entry.get_ean()
id_date = entry.get_id_date()
id_vol = entry.get_id_vol()
new_entry = reduce( lambda x, y: x.absorb( y ), set( filter( None, [ ean in hive and hive[ean], id_date in hive and hive[id_date], id_vol in hive and hive[id_vol], entry] ) ) )
info = {}
if new_entry.get_ean():
info[new_entry.get_ean()] = new_entry
if new_entry.get_id_date():
info[new_entry.get_id_date()] = new_entry
if new_entry.get_id_vol():
info[new_entry.get_id_vol()] = new_entry
hive.update( info )
if opts.verbose:
print( 'FILTER & SORT PHASE...' )
for entry in set( x for x in hive.values() if len( x.paths ) > 1 ):
paths = sorted( entry.paths, key=( opts.dir_mode and dirsize or ( lambda x: os.stat( x ).st_size ) ), reverse=True )
retain = paths.pop( 0 )
if opts.verbose:
print( '----:', retain )
for path in paths:
if opts.verbose:
print( ' -:', path )
reclaim( path )
else:
if opts.verbose:
print()
updated_path = reduce( lambda x, y: y.sub( '', x ), [make_entry.re_isbn, make_entry.re_maz], os.path.basename( retain ) )
if entry.ean:
updated_path = '[#{}]{}'.format( entry.ean, updated_path )
if entry.id:
updated_path = '[@{} {}#{:03d}]{}'.format( entry.id, entry.date or '0000-00', int( entry.vol or 0 ), updated_path )
updated_path = os.path.join( os.path.dirname( retain ), updated_path )
if retain != updated_path:
try:
os.renames( retain, updated_path )
except:
pass
for size, car in data:
print car, size
test = "google"
compress_ex(test)
ret = compress(test)
r1, r2 = itertools.tee(ret)
r3, r4 = itertools.tee(r1)
print list(r2)
print ''.join(decompress(r3))
decompress_ex(r4)
a = 'google'
b = 'yahoo'
iterm = itertools.chain(a, b)
for i in iterm:
print i
iterm = itertools.count(11)
for i in iterm:
if i == 21:
break
print i
iterm = itertools.cycle(a)
for i, n in enumerate(iterm):
if i == 21:
break
print n
L = [
"Marc Bolan", "David Bowie", "Mick Ronson", "Ian Hunter", "Morgan Fisher",
import itertools
'''
Method 2
'''
list1=['a','b','c']
list2=[1,2]
zip(x,list2) for x in itertools.permutations(list1,len(list2))]
def sumOfTwo(a, b, v):
'''
Method 3
'''
combined = [zip(i,a) for i in itertools.permutations(b,len(a))]
cc = map(sum,itertools.chain(*combined))
if v in cc:
return True
else:
return False
def sumOfTwo(a, b, v):
'''
Method 4
'''
#if len(a) == 0 or len(b) == 0:
# return False
s = map(sum,itertools.product(a, b))
#s = [sum(x) for x in list(itertools.product(a, b))]
print s
def neighbors(self, node):
assert node.startswith(u"n_")
edges = self.bipartite_graph.neighbors(node)
neighbors = map(lambda edge: self.bipartite_graph.neighbors(edge), edges)
return list(set(itertools.chain(*neighbors)) - set([node]))
