def __next__(self):
# nlive_points has to tell how many points were used when
# the last sample was returned
self.nlive_points = len(self.live_points)
# select worst point
self.live_points.sort(key=itemgetter(0))
Li, ui, xi = self.live_points.pop()
Lsecond = self.live_points[-1][0]
#if (Lsecond - Li) < (self.Lmax - Li) * self.expected_steps:
# current iteration of nested integrator
# it = self.samples - self.nlive_points
r = (1 - exp(Lsecond - Li)) / (1 - exp(self.Lmax - Li)) / (1 - exp(-1./self.nlive_points))
if r < 1e-3:
# there is a very flat curve
# do not add a point (contract)
print('Adaption: contracting')
pass
elif r > 1:
# currently have a steep curve and maximum not exceeded
# then fill up (expand)
# this step can be parallelized
print('Adaption: expanding')
while len(self.live_points) < self.nlive_points_max:
self.add_point(Lmin=Li)
else:
# reasonably flat, keep steady state
if len(self.live_points) < self.nlive_points_max:
self.add_point(Lmin=Li)
# make sure we have at least the minimum number of points
while len(self.live_points) < self.nlive_points_min:
self.add_point(Lmin=Li)
return ui, xi, Li
def __next__(self):
# nlive_points has to tell how many points were used when
# the last sample was returned
self.nlive_points = len(self.live_points)
# select worst point
self.live_points.sort(key=itemgetter(0))
Li, ui, xi = self.live_points.pop()
Lsecond = self.live_points[-1][0]
#if (Lsecond - Li) < (self.Lmax - Li) * self.expected_steps:
# current iteration of nested integrator
# it = self.samples - self.nlive_points
r = (1 - exp(Lsecond - Li)) / (1 - exp(self.Lmax - Li)) / (1 - exp(-1./self.nlive_points))
if r < 1e-3:
# there is a very flat curve
# do not add a point (contract)
print('Adaption: contracting')
pass
elif r > 1:
# currently have a steep curve and maximum not exceeded
# then fill up (expand)
# this step can be parallelized
print('Adaption: expanding')
while len(self.live_points) < self.nlive_points_max:
self.add_point(Lmin=Li)
else:
# reasonably flat, keep steady state
if len(self.live_points) < self.nlive_points_max:
self.add_point(Lmin=Li)
# make sure we have at least the minimum number of points
while len(self.live_points) < self.nlive_points_min:
self.add_point(Lmin=Li)
return ui, xi, Li
books = [b.replace('-', ' ') for b in books]
books = [b.replace('_', ' ') for b in books]
books = [b.replace(' ', '+') for b in books]
# Getting exact title and rating of books from Goodreads
def book_search(q, page=1, search_field='all'):
auth_client = client.GoodreadsClient(key, secret)
books_list = auth_client.search_books(str(q), page, search_field)
return books_list[0].title, books_list[0].average_rating
ratings = []
for book in books:
try:
print(book)
ratings.append(book_search(book))
print(book_search(book))
print('-----')
except:
print('error!')
continue
# Sorting books by rating
ratings.sort(key=itemgetter(1), reverse=True)
# Exporting sorted list in a CSV file
with open('output.csv', 'w') as f:
wr = csv.writer(f)
wr.writerows(ratings)
print(ratings)
def schema_extract(x, **types_dict):
# each kwarg is a a key, value pair
# the key is the name of the attribute and the field in the database
# value is the type to be used internally in the database
return {key : value(itemgetter(key)(x)) for key, value in types_dict}
