def get_other(obj, objects):
"""Return another object in the Iterable besides the given one."""
if len(objects) < 2: return obj # make sure there *is* another object we can choose
other = rchoice(objects)
while other == obj:
other = rchoice(objects)
return other
def __init__(self, state=None, height=4, width=4, obstacle_chance=.3):
self.time_step = 0
self.resources = {}
if state: # prefab level
self.state = np.array(state)
self.height, self.width = self.state.shape
# Place the agent and the goal
self.goal_pos = np.argwhere(self.state == self.input_chars['goal'])[0]
self.agent_pos = np.argwhere(self.state == self.input_chars['agent'])[0]
for pos in (self.agent_pos, self.goal_pos):
self.state[tuple(pos)] = self.chars['empty']
else: # initialize a random VaseWorld
self.height, self.width = height, width
if not 0 <= obstacle_chance <= 1:
raise Exception('Chance of a square containing an obstacle must be in [0, 1].')
self.obstacle_chance = obstacle_chance # how likely any given square is to contain an obstacle
self.state = np.random.choice([self.chars['empty'], *self.obstacles], size=(height, width),
p=[1 - self.obstacle_chance, *[obstacle_chance / len(self.obstacles)
for _ in self.obstacles]])
# Place the agent and the goal
empty = np.argwhere(self.state == self.chars['empty'])
self.agent_pos = rchoice(empty)
self.goal_pos = rchoice(empty)
self.original_agent_pos = self.agent_pos.copy()
self.original_state = self.state.copy()
self.num_squares = self.width * self.height
self.clearable = self.check_clearable()
def tournament_selection(self, k=0.75):
pOne, pTwo = rchoice(self.population), rchoice(self.population)
# modified to avoid using __cmp__ method of chromosome
if urand() < k:
return pOne if pOne > pTwo else pTwo
else:
return pOne if pOne < pTwo else pTwo
def generateImg(data, pos1, pos2):
background = Image.open("bg.png")
background.convert('RGBA')
bg = Image.new("RGBA", background.size, (255,255,255,255))
bg.paste(background, (0, 0))
def drawRoundPic(im, xy):
im.convert('RGBA')
mask = Image.open('mask.png').convert('L')
nim = ImageOps.fit(im, mask.size, centering=(0.5, 0.5))
nim.putalpha(mask)
bg.paste(nim, xy, nim)
def drawCenterText(msg, xy, wh=(300,50)):
draw.textsize(msg, font=font2)
fsize = font2.getsize( msg )
draw.text(((wh[0] - fsize[0])/2 + xy[0],(wh[1]-fsize[1])/2 + xy[1]), msg, font=font2, fill="black")
im = Image.open("images/"+str(pos1+1)+".jpg")
drawRoundPic(im, (89, 171))
im = Image.open("images/"+str(pos2+1)+".jpg")
drawRoundPic(im, (609, 171))
draw = ImageDraw.Draw( bg )
deepBlue = (0,62,142)
wordCho1=rchoice(words)
wordCho2=rchoice(words2)
draw.text( (430, 282), wordCho1, font=font, fill=deepBlue )
draw.text( (928, 282), wordCho2, font=font, fill=deepBlue )
drawCenterText(data[pos1]['name'], (85,480))
drawCenterText(data[pos2]['name'], (608,480))
return bg
def fill_random_cell(self): empty_cells = self.get_empty_cells() if len(empty_cells) > 0: (row_index, col_index)= rchoice(empty_cells) cell_value = rchoice(self.new_cell_distribution) self.board[row_index][col_index] = cell_value return (row_index, col_index)
def run(url, depth, i):
global BASE_URL
global DONE
global COLORMAP
global currColorNum
if i > depth:
print(clr.Style.BRIGHT + clr.Back.WHITE + clr.Fore.BLACK +
"You rode the wiki!" + clr.Style.RESET_ALL)
else:
DONE.append(url)
POSSIBLE_URLS = list()
HTML = bs(req.get(url).content, 'lxml')
PAGE_TITLE = HTML.find('h1', id="firstHeading").text
nextColor = COLORMAP[currColorNum]
currColorNum = (currColorNum + 1) % 6
print(clr.Style.BRIGHT + clr.Fore.WHITE + ("-" * (i + 1)) +
PAGE_TITLE + " - " + nextColor + url + clr.Style.RESET_ALL)
for a in HTML.find_all('a', href=HREFREGEX):
if a.text and validUrl(a['href']) and a['href'] not in url:
for doneURL in DONE:
if a['href'] not in doneURL and DONE != list():
POSSIBLE_URLS.append(a['href'])
if POSSIBLE_URLS == list():
run("", depth, depth)
else:
SEC_URL = rchoice(POSSIBLE_URLS)
while SEC_URL == url:
SEC_URL = rchoice(POSSIBLE_URLS)
URL = BASE_URL + SEC_URL
run(URL, depth, i + 1)
def pareto_tournament_selection(self, k=0.75):
'''Psuedo-Pareto tournament selection for multi-objective offspring. Offspring that are
better in a majority (2/3) of the fitness terms win the tournaments.'''
pOne, pTwo = rchoice(self.population), rchoice(self.population)
f1 = MATH.array([pOne.fitness,pOne.parsimony,pOne.finitewts])
f2 = MATH.array([pTwo.fitness,pTwo.parsimony,pTwo.finitewts])
if urand() < k:
return pOne if MATH.sign((f1-f2)).sum() > 0 else pTwo
else:
return pOne if MATH.sign((f1-f2)).sum() < 0 else pTwo
def _set_destination(self):
"""Randomly choose next url to travel"""
if not self.possible_urls:
self.depth_counter = self.depth
else:
next_url_tail = rchoice(self.possible_urls)
while next_url_tail in self.next_url:
next_url_tail = rchoice(self.possible_urls)
self.depth_counter += 1
self.next_url = self.base_url + next_url_tail
def id_gen(length, base=BASE[16]):
# """You can add a function description here to be accessed by help()"""
# (remove the #)
# if it is a simple problem, you can probably do it in a short list comprehension
# it is even more readable (sometimes):
# key is a string made of random choices from a base repeated length times
key = ''.join([rchoice(base) for i in range(length)])
# numpy.random counterpart for this method:
# ''.join(numpy.random.choice(list(base), length))
# if you decide to use numpy you can avoid creating the lists here
# by defining the base as a list before the calling the function
# (i.e. in the environment variables)
# (useful if you have to repeat this many times over)
# the method you had before is a bit inhumane:
# key is a string made of a random selections between zero and
# the length of elements from a base repeated length times
# key = ''.join([base[random.randint(0, length(base))] for i in range(length)])
return key
def get_choice_egreedy(explore, num_choices, models, features):
#e-greedy
if random.random() < explore:
return rchoice(range(num_choices))
else:
probs = [model.probs_single(features)[1] for model in models]
return np.argmax(probs)
async def stats(self, ctx):
"""A few stats."""
# get_owner = bot.get_user_info(config['ownerid'])
statInfo = await ctx.bot.application_info()
statEmbed = discord.Embed(
title='Stats',
description='This bot is'
' powered by [lolbot](https://github.com/tilda/lolbot), a fast and powerful '
'Python bot.',
colour=0x690E8)
statEmbed.add_field(name='Owner',
value=statInfo.owner.mention + '(' +
str(statInfo.owner) + ' - ID: ' +
str(statInfo.owner.id) + ')')
statEmbed.add_field(name='Python', value=sys.version)
statEmbed.add_field(name='discord.py', value=discord.__version__)
statEmbed.add_field(name='Servers', value=len(self.bot.guilds))
statPool = [
'What have you done now?', 'Why should I do this again?', 'Oh..',
'Where did the RAM go?', 'grumble grumble', 'Please hold.',
'No, just, no.', 'Have you tried rebooting?',
'memework makes the dreamwork!'
]
statEmbed.set_footer(text=rchoice(statPool))
try:
await ctx.send(embed=statEmbed)
except:
await ctx.send('Sorry, I can\'t send the Embed.')
await ctx.send('Maybe I don\'t have Embed Links permission?')
else:
pass
async def stats(self, ctx):
"""A few stats."""
statInfo = await ctx.bot.application_info()
statEmbed = discord.Embed(
title='Stats',
description='This bot is'
' powered by [lolbot](https://github.com/tilda/lolbot), a fast and powerful '
'Python bot.',
colour=0x690E8)
statEmbed.add_field(name='Owner',
value=statInfo.owner.mention + '(' +
str(statInfo.owner) + ' - ID: ' +
str(statInfo.owner.id) + ')')
statEmbed.add_field(name='Python', value=get_python_version())
statEmbed.add_field(name='discord.py', value=discord.__version__)
statEmbed.add_field(name='Servers', value=f'{len(self.bot.guilds)}')
statEmbed.add_field(name='Uptime',
value=bot_uptime(self.bot.init_time))
statPool = [
'What have you done now?', 'Why should I do this again?', 'Oh..',
'Where did the RAM go?', 'grumble grumble', 'Please hold.',
'No, just, no.', 'Have you tried rebooting?',
'memework makes the dreamwork!', 'cool and good'
]
statEmbed.set_footer(text=rchoice(statPool))
await ctx.send(embed=statEmbed)
def rcontract_min_cut(self):
"""
David Karger's '90s Random Contraction Algorithm
- While there are more than 2 vertices:
i. Pick a remaining edge (u,v) uniformly at random.
ii. Merge (or “contract”) u and v into a single vertex.
iii. Remove self-loops
- Return cut represented by final 2 vertices.
"""
min_cut = 0
tmp_graph = deepcopy(self)
total_of_nodes = len(tmp_graph.nodes())
# edge cases:
if total_of_nodes in (0, 1):
return min_cut
# contraction loop (need to contract len-2 times to reach 2 nodes)
for nodes_count in reversed(range(3, total_of_nodes + 1)):
# i. Pick a remaining edge (u,v) uniformly at random.
v, u = rchoice(tmp_graph.edges())
# ii. Merge (or “contract”) u and v into a single vertex.
# iii. Remove self-loops
tmp_graph.merge_nodes(v, u)
# result passing
total_of_nodes = len(tmp_graph.nodes())
if total_of_nodes is 2:
min_cut = len(tmp_graph.edges())
return min_cut
else:
print("Error in rcontract_min_cut")
return min_cut
return min_cut
def rcontract_min_cut(self):
"""
David Karger's '90s Random Contraction Algorithm
- While there are more than 2 vertices:
i. Pick a remaining edge (u,v) uniformly at random.
ii. Merge (or “contract”) u and v into a single vertex.
iii. Remove self-loops
- Return cut represented by final 2 vertices.
"""
min_cut = 0
tmp_graph = deepcopy(self)
total_of_nodes = len(tmp_graph.nodes())
# edge cases:
if total_of_nodes in (0, 1):
return min_cut
# contraction loop (need to contract len-2 times to reach 2 nodes)
for nodes_count in reversed(range(3, total_of_nodes+1)):
# i. Pick a remaining edge (u,v) uniformly at random.
v, u = rchoice(tmp_graph.edges())
# ii. Merge (or “contract”) u and v into a single vertex.
# iii. Remove self-loops
tmp_graph.merge_nodes(v, u)
# result passing
total_of_nodes = len(tmp_graph.nodes())
if total_of_nodes is 2:
min_cut = len(tmp_graph.edges())
return min_cut
else:
print("Error in rcontract_min_cut")
return min_cut
return min_cut
def _newPrimaryKey(self):
'''Return a new random string for use as a primary key.'''
N = 20
chars = string.ascii_uppercase + string.digits
pk = [rchoice(chars) for _ in range(N)]
pk = ''.join(pk)
return pk
def genrand():
n = random()
if n <= 0.1:
return wildcard
elif n <= 0.1025:
return dunce
else:
return rchoice(normtiles)
def symcolrhexcodes(symbols):
"""
returns dict of color codes mapped with alphabets symbol in symbols
"""
return {
symbol: '#' + ''.join([rchoice("8A6C2B590D1F4E37") for _ in "FFFFFF"])
for symbol in symbols
}
def _select_topic(self, mode):
if mode == "old":
tid = rchoice(list(self.settings["topics"].keys()))
elif mode == "new":
tid = self.settings["topic_queue"].pop()
dataIO.save_json(self.path, self.settings)
else:
tid = mode
return self.settings["topics"][tid]["content"]
def _select_topic(self, mode):
if mode == "old":
tid = rchoice(list(self.settings["topics"].keys()))
elif mode == "new":
tid = self.settings["topic_queue"].pop()
dataIO.save_json(self.path, self.settings)
else:
tid = mode
return self.settings["topics"][tid]["content"]
def handle_turn_ai(player):
number_list = list(range(9))
valid = True
while valid:
choice_ai = rchoice(number_list)
number_list.remove(choice_ai)
if board[choice_ai] == "-":
valid = False
board[choice_ai] = player
def insert_blanks(s, min_chars=32, max_chars=128):
"""
Inserts random zero width characters in the given string
This is useful for avoiding filters
"""
for _ in range(1, randint(min_chars, max_chars)):
i = randint(0, len(s))
s = s[:i] + rchoice(blanks) + s[i:]
return s
def give_name(self):
"""
Randomizer function to give an entity a random name. Arbitrary
amounts of names can be added to these arrays.
"""
if self.gender == "male":
self.name = rchoice([
"albert",
"brutus",
"chris",
])
if self.gender == "female":
self.name = rchoice([
"ally",
"beatrix",
"chloe",
])
def anonymize(masking):
i = 0
for rec in SeqIO.parse(sys.stdin, "fastq"):
rec.id, rec.description, i = make_id(i)
if masking:
rec.seq = Seq.Seq(len(rec) * 'N')
else:
rec.seq = Seq.Seq(''.join(
[rchoice(['A', 'C', 'T', 'G']) for i in range(len(rec))]))
print(rec.format("fastq"))
def get_random_movie():
addr = r"https://www.themoviedb.org/movie"
resp = requests.get(addr)
soup = BeautifulSoup(resp.content, "html.parser")
titles = []
for header in soup.find_all("h2"):
for link in header.find_all("a"):
titles.append(link.get("title"))
return rchoice(titles)
def get_choice(self, features):
if random.random() < self.options['explore']:
return rchoice(range(self.num_choices))
else:
feats = np.array(features).reshape(1, 2)
probs = [
model.predict_proba(feats)[0] for model in self.choice_models
]
probs = [prob[1] if len(prob) == 2 else prob[0] for prob in probs]
return np.argmax(probs)
def _into_sentence(s: Sentence, prefix: list[int],
conn_dict: dict[int,
tp.Iterable[str]], variables: list[str]):
l = prefix[0]
if l == 0:
s.append(rchoice(variables))
else:
possible_main = conn_dict[l]
main = s.generate(rchoice(possible_main))
if l == 2:
# INFIX
s.append('(')
_into_sentence(s, prefix[1:], conn_dict, variables)
s.append(main)
_into_sentence(s, prefix[1:], conn_dict, variables)
s.append(')')
else:
s.append(main)
for _ in range(l):
_into_sentence(s, prefix[1:], conn_dict, variables)
def get_profile_image(name):
default = {
'title': name,
'source': default_media,
}
profile = next((i for i in user_media if i['title'] == name), default)
medID = rchoice(random_ids)
random_ids.remove(medID)
profile['mediaId'] = str(medID)
profile['type'] = "profile-image"
requests.post(local_url + 'media', json=json.dumps(profile))
return profile
def advance(self):
"""Step forward one step in time."""
# log data BEFORE manipulating the population
if MATH.remainder(MATH.int(self.time),self.sampGen) == 0:
self.notify(time=self.time)
# first select a round of parents
parents = [self.select_parent(method=self.selectionMethod) for x in xrange(self.forestSize)]
offspring = list()
if not self.elitism:
for i in xrange(0, self.forestSize):
offspring.append(self.mate(rchoice(parents),rchoice(parents)))
else:
fitvals = MATH.asarray([x.fitness for x in self.population])
toCopy = fitvals.argsort()[-self.eliteN:]
offspring += [self.population[x].copy() for x in toCopy]
# now mate to create the remaining population
for i in xrange(0, self.forestSize-self.eliteN):
offspring.append(self.mate(rchoice(parents), rchoice(parents)))
# overwrite current forest
self.population = offspring
# advance time
self.time += 1
def get_trajectories():
test = read_from_src_edgelist_to_idvalue()
unprocessed = set([i + 1 for i in range(len(test))])
final = []
while len(unprocessed) > 0:
current = traverse(rchoice(list(unprocessed)), test)
unprocessed.difference_update(current)
# print(unprocessed)
final.append(list(current))
#print("final =", final)
return final
def readFBDataSet():
fileNames = [
os.path.join('datasets', 'messages', f, 'message.json')
for f in os.listdir(os.path.join('datasets', 'messages'))
]
messages = ""
while len(messages) < MAX_CHAT_LENGTH:
messages += loadSimpleMessageSet(loadSimpleJson(rchoice(fileNames)))
print(len(messages), ' chars loaded for trainings')
return messages
def select_parent(self,method,**kwargs):
"""A dispatcher that implements a variety of selection methods and returns a single parent
to place in the pool for the next generation.
Current allowed methods (all strings):
-'tournament' : requires a parameter k. two individuals are chosen at random; if
rand < k, the fitter individual is selected. if rand > k, the less fit one is.
Regardless of selected method, the parent is returned as a CGAChromosome object."""
mstring = method + '_selection'
if hasattr(self, mstring):
parent = getattr(self,mstring)(**kwargs)
else:
parent = rchoice(self.population) # pick at random if there's a problem
return parent
def random(session): # useful only when you have a lot of stories (obviously)
days = range((datetime.now() - session.birthday).days + 1)
for i in range(25): # try 25 times
_story_exists, date = session.find_stories(session.birthday +
timedelta(rchoice(days)))
if not date:
break
story = Story(session, date)
if story.get_path():
return story.view()
print ERROR, "Looks like you don't have much stories in the given location!"
def main(argv):
if len(argv)==2:
global K = int(argv[0])
global COEFFICIENT = float(argv[1])
result_f = codecs.open('data/result.txt', 'w+','utf-8')
start = time.clock()
i = 1
with codecs.open('data/queries.txt', 'r','utf-8') as f:
for line in f:
res = line.split('\t')
category = res[6]
user = res[1]
query = res[2]+ '\t'+ res[3]+ '\t'+ res[4]+ '\t'+ res[5]
if not category in data:
data[category] = [K, [], []] # [MAX_QUERIES, USERS, QUERIES]
curcat = data[category]
curcat[1].append(user)
curcat[2].append(query)
if len(curcat[1])>=curcat[0]: #Category full
query_choice = rchoice(curcat[2])
if curcat[1].count(query_choice[0]) >= curcat[0]: # All users are the same
curcat[0] = curcat[0]*COEFFICIENT
else:
user_choice = rchoice(curcat[1])
while user_choice==query_choice[0]: # Find a different user
user_choice = rchoice(curcat[1])
result_f.write(query_choice[2] + '\t'+user_choice+'\t'+query_choice[1]+'\n')
curcat[1].remove(user_choice)
curcat[2].remove(query_choice)
i+=1
mem = asizeof.asizeof(data)
total=time.clock()-start
result_f.close()
def makeSimilarStatus(status, generator):
choices = [generator() for i in range(250)]
status = set(letters_and_whitespace(status).lower().split())
common = set(['a', 'in', 'the', 'and', 'to', 'do', 'are', 'you', 'so', 'is', 'of'])
status = status - common
best_choice = rchoice(choices)
best = 0
for choice in choices:
choice = choice.split('.')[0]
match = set(letters_and_whitespace(choice.lower()).split()) & status
if len(match) > best:
best = len(match)
best_choice = choice
print(best_choice, match)
return best_choice
def traverse(offset_start, source_graph):
current_trajectory = set()
next_index = offset_start - 1
#print(f"next_number = {next_index + 1}")
options = source_graph[next_index][2]
#print(f"options = {options}")
while not set(options).issubset(current_trajectory):
current_trajectory.add(next_index + 1)
next_index = rchoice(options) - 1
#print(f"next_number = {next_index + 1}")
options = source_graph[next_index][2]
#print(f"options = {options}")
if len(current_trajectory) < 3:
current_trajectory = traverse(next_index, source_graph)
return current_trajectory
def get_choice(self, request_method, url, **kwargs):
cho = []
while True:
try:
cho = rchoice(self.new_address)
choice = {'https': cho}
print('Proxy currently being tested: {}'.format(choice))
response = requests.request(request_method,
url,
proxies=choice,
timeout=10,
**kwargs)
if response.status_code == 200:
print('Current proxy is functional.')
break
except:
print('Error: Could not connect, looking for another proxy.')
self.new_address.remove(cho)
pass
def __init__(self):
"""
Initialize all essential values here, make sure that
more complex values are set to some undifined state,
then call a function that sets them.
"""
# Basic assignments here
self.gender = rchoice(["male", "female"])
self.name = "undefined"
self.id = gen_uuid4()
self.skills = {"undefined": True}
self.vitals = {"undefined": True}
self.stats = {"undefined": True}
self.position = {"x": 0, "y": 0, "z": 0}
# List of functions to set more complex characteristics
self.give_name()
self.initialize_skills()
self.initialize_stats()
self.initialize_vitals()
def index(request):
'''
index page view
'''
possibleWords = [ # these are that get chosen on the home page
" smarter",
" better",
" brilliant",
" genius",
"n amazing",
" superior",
]
# checking if word has already been chosen
if request.session.get("homepageWord") in [None, "", "None"]:
# choosing word and setting session var
request.session["homepageWord"] = rchoice(possibleWords)
# rendering index page
request.session['page'] = 'Home'
return render(request, 'index.html')
def download_stocks(symbol: str = None,
dl_path: str = None,
proxies_path: str = None,
interval_arg: str = None):
"""Fetches stocks csv's on yahoo finance.
Args:
symbol: Symbol of the stock to be downloaded
dl_path: Filepath to indicate where to store the downloaded stocks.
proxies_path: Filepath to indicate the proxies to take
interval_arg: Interval of the stock to be downloaded
"""
stock_path = f'{dl_path}/{symbol}_{interval_arg}.csv'
if os.path.isfile(stock_path) == True:
return stock_path
if proxies_path is not None:
proxies = _get_proxies(proxies_path)
stock_name = os.path.join(dl_path, symbol + '_' + interval_arg + '.csv')
if proxies_path is not None:
proxy = rchoice(proxies)
print(f'Downloading {symbol} on proxy {proxy} ...')
stock = yf.download(symbol,
proxy=proxy,
period='max',
interval=interval_arg)
else:
print(f'Downloading {symbol} on proxy 0.0.0.0 ...')
stock = yf.download(symbol, period='max', interval=interval_arg)
if stock.shape[0] > 2:
if list(("1wk", "1mo", "3mo")).count(interval_arg) > 0:
stock = stock[0:len(stock) - 1]
stock.to_csv(stock_name)
print(f'Successfully Downloaded stock {symbol} !')
return stock_path
else:
print(f'Couldn\'t download stock {symbol}')
return None
def mate(self, parentOne, parentTwo):
"""Accepts two parents and returns ONE offspring; if the offspring is unchanged from one of
the parents, the fitness values are not re-evaluated, just copied. Only one category of
mutation is performed on a single offspring."""
# one parent will form the basis for the new offspring; the other is just there for
# potential crossover (by default the mother will become the offspring)
mother, father = parentOne.copy(), parentTwo.copy()
# fitEval will be set to True if any mutations occur that make the offspring different from
# its copied parent; this way we avoid unnecessary fitness evaluations
fitEval = False
# only one category of mutation is allowed per mating event
r = urand()
if r < self.pC: # parental crossover
fitEval = True
mNode = rchoice(mother.tree.getNodes())
fNode = rchoice(father.tree.getNodes())
CGAGenerator.single_crossover(mNode,fNode)
elif r < self.pC + self.pHC: # headless chicken crossover
fitEval = True
mNode = rchoice(mother.tree.getNodes())
rNode = rchoice(self.initialize_tree().getNodes())
CGAGenerator.single_crossover(mNode,rNode)
elif r < self.pC + self.pHC + self.pM: # point mutation (uses pM/node for mutation prob.)
fitEval = True
for n in mother.tree.getNodes():
if urand() < self.pM:
CGAGenerator.point_mutate(mother.tree, n)
fitEval = True
elif r < self.pC + self.pHC + self.pM + self.pP: # pruning - guaranteed to do one pruning operation
fitEval = True
mNode = rchoice(mother.tree.getNodes())
CGAGenerator.prune(mother.tree,mNode)
elif r < self.pC + self.pHC + self.pM + self.pP + self.pG: # growth - guaranteed to do one growth op.
fitEval = True
mTerm = rchoice(mother.tree.getTermini())
CGAGenerator.grow(mother.tree,mTerm)
else: # offspring will just be a copy
pass
if fitEval:
mother.fitness,mother.parsimony,mother.finitewts = self.evaluate_fitness(mother.tree)
return mother
deepBlue = (0,62,142)
wordCho1=rchoice(words)
wordCho2=rchoice(words2)
draw.text( (430, 282), wordCho1, font=font, fill=deepBlue )
draw.text( (928, 282), wordCho2, font=font, fill=deepBlue )
drawCenterText(data[pos1]['name'], (85,480))
drawCenterText(data[pos2]['name'], (608,480))
return bg
jsonData=u'[{"name":"鰹魚調味料","promo":"該品牌單筆指定品滿150送15現金紅利","url":"http://www.savesafe.com.tw/Products/ProductView.aspx?s_id=f9897fe9bacfb5f718bf711e7a1d738b"},{"name":"靠得住","promo":"大促銷$149元","url":"http://www.savesafe.com.tw/Products/ProductView.aspx?s_id=bf19a2a50bb1208b257ac4c6fd5c91c0"},{"name":"100%純橄欖油","promo":"","url":"http://www.savesafe.com.tw/Products/ProductView.aspx?t_s_id=43013&s_id=717123634b662e150798d4667d3c6047"},{"name":"青蔥蘇打餅乾","promo":"","url":"http://www.savesafe.com.tw/Products/ProductView.aspx?t_s_id=41592&s_id=4266242f39cfc67bda72e62f7e050b92"},{"name":"堅果山核桃","promo":"熱賣商品","url":""},{"name":"鮮奶油","promo":"熱賣商品","url":""},{"name":"紅燒鰻","promo":"","url":"http://www.savesafe.com.tw/Products/ProductView.aspx?s_id=9684a1353c4b44f0342ac73cf2da1d46"}]'
words = [u'加上', u'混合', u'搭配']
words2 = [u'好棒棒', u'好厲害', u'猴塞雷', u'好有事', u'搖一搖', u'好壞壞', u'真好人', u'好勵志', u'就甘心']
data=json.loads(jsonData)
rset=range(0, len(data))
mylist = range(len(data))
res=0
while mylist:
choice1 = rchoice(mylist)
mylist.remove(choice1)
if mylist:
choice2 = rchoice(mylist)
mylist.remove(choice2)
img = generateImg(data, choice1, choice2)
res+=1
img.save("result/r"+str(res)+".jpg")
