Essentially I want the same behaviour as running:
log = client.log(url)
oldestEntry = log[-1]
Except without having to download the entire log. I know setting
limit=1
lets you find the newest entry. Is there any way of limiting from the reverse order?
Reverse the order of the revision_start and revision_end and set limit to 1:
import pysvn
url='http://svn.apache.org/repos/asf/httpd/httpd/trunk/README'
epoch = pysvn.Revision(pysvn.opt_revision_kind.number, 0)
head = pysvn.Revision(pysvn.opt_revision_kind.head)
client = pysvn.Client()
# Get all entries
l = client.log(url)
print len(l), l[0].revision, l[-1].revision
# Get most recent entry:
l = client.log(url, limit=1)
print len(l), l[0].revision
# Get most recent entry, again:
l = client.log(url, revision_start=head, revision_end=epoch, limit=1)
print len(l), l[0].revision
# Get least recent entry
l = client.log(url, revision_start=epoch, revision_end=head, limit=1)
print len(l), l[0].revision
The result is:
22 <Revision kind=number 1209505> <Revision kind=number 87470>
1 <Revision kind=number 1209505>
1 <Revision kind=number 1209505>
1 <Revision kind=number 87470>
Related
I write this and i don't know why product_id_list is not defined if i have defined it like 4 lines before.
Any suggestions? I thin identation is alright so I don't have any more ideas and I also searched around without luck.
Thank you!!
def make_dataSet_rowWise(reorder_product):
print('unique Product in dataset = ', len(reorder_product.product_id.unique()))
print('unique order_id in dataset = ', len(reorder_product.order_id.unique()))
product_id_list = reorder_product.product_id.unique().tolist()
product_id_list.append("order_id")
product_id_dict = {}
i = 0
for prod_id in product_id_list:
product_id_dict[prod_id] = i
i = i+1
product_id_df = pd.Dataframe(columns = product_id_list)
row_list_all = []
order_id_list = reorder_product.order_id.unique()
i = 1
for id in order_id_list:
#print(i)
i = i+1
np_zeros = np.zeros(shape = [len(product_id_list)-1])
ordered_product_list = reorder_product.loc[reorder_product.order_id == id]["product_id"].tolist()
for order_prod in ordered_product_list:
np_zeros[product_id_dict.get(order_prod)] = 1
row_list = np_zeros.tolist()
row_list.append(id)
row_list_all.append(row_list)
return (row_list_all, product_id_list)
df_row_wise = make_dataSet_rowWise(reorder_product_99Pct)
product_id_df = pd.DataFrame(df_row_wise[0], columns = df_row_wise[1])
product_id_df.head()
The error I have is this one:
NameError Traceback (most recent call last)
<ipython-input-343-07bcac1b3b48> in <module>
7 i = 0
8
----> 9 for prod_id in product_id_list:
10 product_id_dict[prod_id] = i
11 i = i+1
NameError: name 'product_id_list' is not defined
As already mentioned by the other answers, your indentation is wrong.
My recommendation is that you use a IDE like VSCode, there is also a free web version https://vscode.dev/
With such kind of IDE you can see that your indentation is wrong, check screenshot and line 27
There are also wrong indentations with the 3 for loops. The correct indentation should be as the following
I think your indentation may be wrong, the for-loops and return statement is out of the function (with your indentation) so I indented it so that it would still be part of the function...
def make_dataSet_rowWise(reorder_product):
print('unique Product in dataset = ', len(reorder_product.product_id.unique()))
print('unique order_id in dataset = ', len(reorder_product.order_id.unique()))
product_id_list = reorder_product.product_id.unique().tolist()
product_id_list.append("order_id")
product_id_dict = {}
i = 0
for prod_id in product_id_list:
product_id_dict[prod_id] = i
i = i+1
product_id_df = pd.Dataframe(columns = product_id_list)
row_list_all = []
order_id_list = reorder_product.order_id.unique()
i = 1
for id in order_id_list:
#print(i)
i = i+1
np_zeros = id.zeros(shape = [len(product_id_list)-1])
ordered_product_list = reorder_product.loc[reorder_product.order_id == id]["product_id"].tolist()
for order_prod in ordered_product_list:
np_zeros[product_id_dict.get(order_prod)] = 1
row_list = np_zeros.tolist()
row_list.append(id)
row_list_all.append(row_list)
return (row_list_all, product_id_list)
I'm new here, but i think you either need to define the variable out of the scope of
def make_dataSet_rowWise(reorder_product):
OR indent the for loops to be inside
make_dataSet_rowWise
Outcome 1 required:
The first batch of code below is in its working form.
Please assist in creating a function " def Calculations():" inclusive of all the list calculations to return the same results with the static list. With the calculations in proper functions I will be able to refine the problem and might be able to move forward ...
Outcome 2 required for those that want to go in depth...:
When I run the code on a live list that appends every x intervals it stalls the information feed. I believe it could be the creating of the appending lists in batches of increasing numbers... but I don't have a solution for it... Below is the working code...
I am getting my live data from Binance in a appending list of closes only for those who would like to test it in the live status...
The data could be coming from any source , does not need to be Binance as long as its an appending list of closes in float format...
See code below...
import itertools
l = [16.329,16.331, 16.3705, 16.3965, 16.44, 16.4227, 16.4028, 16.37, 16.3829, 16.3482, 16.3614, 16.4191, 16.4008, 16.4048, 16.4076, 16.3724, 16.3599, 16.3872, 16.3794, 16.3528, 16.3886, 16.3904, 16.3815, 16.3864, 16.4254, 16.4411, 16.4151, 16.4338, 16.4212, 16.3819, 16.2857, 16.2703, 16.2408, 16.1938, 16.2038, 16.2035, 16.217, 16.2374, 16.2414, 16.2238, 16.1787, 16.2725, 16.2964, 16.3155, 16.238, 16.2149, 16.2992, 16.3568, 16.2793, 16.2467, 16.312, 16.3117, 16.3017, 16.3465, 16.3882, 16.3698, 16.307, 16.3328, 16.3311, 16.3466, 16.3382, 16.3703, 16.3502, 16.3661, 16.38, 16.3972, 16.4141, 16.393, 16.3769, 16.3683, 16.4136, 16.3774, 16.3709, 16.3179, 16.3019, 16.3149, 16.2838, 16.2689, 16.2602, 16.2679, 16.2921, 16.312, 16.3158, 16.3198, 16.2955, 16.303, 16.327, 16.356, 16.313, 16.3, 16.2806, 16.2634, 16.2856, 16.2702, 16.2136, 16.2782, 16.276, 16.2231, 16.2255, 16.1314, 16.0796, 16.1192, 16.0977, 16.1358, 16.1408, 16.1703]
#### VARIABLES & SETTINGS ####
dataingestperiod = 17
original_list_count = len(l)
timeframed_list = l[-dataingestperiod:]
timeframed_list_count = len(timeframed_list)
def groupSequence(x):
it = iter(x)
prev, res = next(it), []
while prev is not None:
start = next(it, None)
if start and start > prev:
res.append(prev)
elif res:
yield list(res + [prev])
res = []
prev = start
def divbyZero(increasingcount,decreasingcount):
try: return increasingcount/decreasingcount
except ZeroDivisionError: return 0
def divbyZeroMain(increasingcountMain,decreasingcountMain):
try: return increasingcountMain/decreasingcountMain
except ZeroDivisionError: return 0
#### TIMEFRAMED LIST CALCULATIONS#####
listA_1 = (list(groupSequence(timeframed_list))) # obtain numbers in increasing sequence
# print(len(listA_1)) # number of increases in mixed format
listA = list(itertools.chain.from_iterable(listA_1)) # remove double brackets to enable list count
increasingcount = len(listA)
decreasingcount = timeframed_list_count - increasingcount
trend = divbyZero(increasingcount,decreasingcount)
#### MAIN APPENDING LIST CALCULATIONS #####
listMain_1 = (list(groupSequence(l)))
listMain = list(itertools.chain.from_iterable(listMain_1))
increasingcountMain = len(listMain)
decreasingcountMain = original_list_count - increasingcountMain
trendMain = divbyZeroMain(increasingcountMain,decreasingcountMain)
###Timeframed list increases-only appending to max last"dataingestperiod" perhaps problem on live feed data....###
increase_list_timeframed = []
for x in listA:
increase_list_timeframed.append(x)
### Main list increases only appending...####
increase_list_Main = []
for x in listMain:
increase_list_Main.append(x)
###Prints ON TIMEFRAMED LIST ####
print ("---------------")
print ("---------------")
print ("Timeframed list count set at max {}".format(timeframed_list_count))
print ("Count of decreases in timeframed list is {}".format(decreasingcount))
print ("Count of increases in timeframed list is {}".format(increasingcount))
print ("Current timeframed trend is {}".format(trend))
print ("---------------")
print ("---------------")
###Prints ON MAIN LIST ####
print ("Main appending list count so far is {}".format(original_list_count))
print ("Count of decreases in Main appending list is {}".format(decreasingcountMain))
print ("Count of increases in Main appending list is {}".format(increasingcountMain))
print ("Current Main trend is {}".format(trendMain))
The actual code as live to binance is listed below with the above code included. You also need to install "pip install python-binance" and "pip install websocket_client" got the binance access code from ParttimeLarry
Outcome 2 required: When run live that all calculations run uninterruptedly...
import itertools
import copy
import websocket, json, pprint, talib, numpy
from binance.client import Client
from binance.enums import *
#DATA FROM WEBSOCKETS########
SOCKET = "wss://stream.binance.com:9443/ws/linkusdt#kline_1m"
API_KEY = 'yourAPI_KEY'
API_SECRET ='yourAPI_Secret'
closes = [] # created for RSI indicator only using closes
in_position = False
client = Client(API_KEY, API_SECRET) # tld='us'
def order(side, quantity, symbol,order_type=ORDER_TYPE_MARKET):
try:
print("sending order")
order = client.create_order(symbol=symbol, side=side, type=order_type, quantity=quantity)
print(order)
except Exception as e:
print("an exception occured - {}".format(e))
return False
return True
def on_open(ws):
print('opened connection')
# start_time = datetime.datetime.now().time().strftime('%H:%M:%S')
# try:
# file = open("C:/GITPROJECTS/binance-bot/csvstats.txt","+a")
# file.write("New Session Open Connection Start at time {}\n".format(datetime.datetime.now())))
# finally:
# file.close()
def on_close(ws):
print('closed connection')
def on_message(ws, message):
global closes, in_position
print('received message')
json_message = json.loads(message)
pprint.pprint(json_message)
candle = json_message['k']
is_candle_closed = candle['x']
close = candle['c']
if is_candle_closed:
print("candle closed at {}".format(close))
closes.append(float(close))
print("closes")
print(closes)
####################################################################################
########CALCULATIONS ON INDICATORS #################################################
# dataingestperiod = 5
l = copy.deepcopy(closes)
maincountofcloses = len(l)
print ("Total count of closes so far {}".format(maincountofcloses))
#### VARIABLES & SETTINGS ####
l = copy.deepcopy(closes)
dataingestperiod = 3
original_list_count = len(l)
#print ("Main list count so far is {}".format(original_list_count))
timeframed_list = l[-dataingestperiod:]
timeframed_list_count = len(timeframed_list)
#print ("Timeframed list count set at max {}".format(timeframed_list_count))
def groupSequence(x):
it = iter(x)
prev, res = next(it), []
while prev is not None:
start = next(it, None)
if start and start > prev:
res.append(prev)
elif res:
yield list(res + [prev])
res = []
prev = start
def divbyZero(increasingcount,decreasingcount):
try: return increasingcount/decreasingcount
except ZeroDivisionError: return 0
def divbyZeroMain(increasingcountMain,decreasingcountMain):
try: return increasingcountMain/decreasingcountMain
except ZeroDivisionError: return 0
#### TIMEFRAMED LIST CALCULATIONS#####
listA_1 = (list(groupSequence(timeframed_list))) # obtain numbers in increasing sequence
# print(len(listA_1)) # number of increases in mixed format
listA = list(itertools.chain.from_iterable(listA_1)) # remove double brackets to enable list count
increasingcount = len(listA)
decreasingcount = timeframed_list_count - increasingcount
trend = divbyZero(increasingcount,decreasingcount)
#### MAIN APPENDING LIST CALCULATIONS #####
listMain_1 = (list(groupSequence(l)))
listMain = list(itertools.chain.from_iterable(listMain_1))
increasingcountMain = len(listMain)
decreasingcountMain = original_list_count - increasingcountMain
trendMain = divbyZeroMain(increasingcountMain,decreasingcountMain)
increase_list_timeframed = []
for x in listA:
increase_list_timeframed.append(x)
increase_list_Main = []
for x in listMain:
increase_list_Main.append(x)
###Prints ON TIMEFRAMED LIST ####
print ("---------------")
print ("---------------")
print ("Timeframed list count set at max {}".format(timeframed_list_count))
print ("Count of decreases in timeframed list is {}".format(decreasingcount))
print ("Count of increases in timeframed list is {}".format(increasingcount))
print ("Current timeframed trend is {}".format(trend))
print ("---------------")
print ("---------------")
###Prints ON MAIN LIST ####
print ("Main appending list count so far is {}".format(original_list_count))
print ("Count of decreases in Main appending list is {}".format(decreasingcountMain))
print ("Count of increases in Main appending list is {}".format(increasingcountMain))
print ("Current Main trend is {}".format(trendMain))
# threading.Timer(10.0, divbyZeroMain).start()
# threading.Timer(10.0, divbyZero).start()
# ws = websocket.WebSocketApp(SOCKET, on_open=on_open, on_close=on_close, on_message=on_message)
# ws.run_forever()
ws = websocket.WebSocketApp(SOCKET, on_open=on_open, on_close=on_close, on_message=on_message)
ws.run_forever()
I want to do a system using a Autorules fuzzy controller where i create rules from process real data.
My problem is when i use the new data to simulate the fuzzy controller with the rules extracted of the older data, i get a error about the crisp output that cannot be calculated because do not exist rules enough, what is totally normal because my fuzzy system needs more rules and that's my point! I want to implement a new routine that analyses the crisp input/output and create new rules from this data, after that, i want to go back in my code and simulate again.
Is there a function that blocks the AssertionError from stop the code and redirect to another def or to a previously code line?
I tried to find some lib with a function that allows me to redirect the error steady to stop the code but no sucess. I think i will have to change the skfuzzy defuzz def code to allow to make it.
Thank you very much.
''' python
step = stp_ini
k = 0
delay = stp_ini
end = stp_fim - stp_ini
Tout_sim = pd.DataFrame(columns=['val'])
Vent_sim = pd.DataFrame(columns=['val'])
start = timeit.default_timer()
for step in range(end-k):
clear_output(wait=True)
simulation.input['PCA1'] = comp1n[step+delay]
simulation.input['PCA2'] = comp2n[step+delay]
simulation.input['PCA3'] = comp3n[step+delay]
simulation.input['PCA4'] = comp4n[step+delay]
simulation.input['Vent'] = dataoutf.NumVentOn[step+delay]
simulation.compute()
Tout_sim = Tout_sim.append({'val':simulation.output['Tout']},ignore_index=True)
stop = timeit.default_timer()
if ((step/(stp_fim-k-1))*100) < 5:
expected_time = "Calculating..."
else:
time_perc = timeit.default_timer()
expected_time = np.round( ( (time_perc-start)/(step/(end-k-1)) )/60,2)
'''
~\AppData\Local\Continuum\anaconda3\lib\site-packages\skfuzzy\control\controlsystem.py in defuzz(self)
587 self.var.defuzzify_method)
588 except AssertionError:
--> 589 raise ValueError("Crisp output cannot be calculated, likely "
590 "because the system is too sparse. Check to "
591 "make sure this set of input values will "
ValueError: Crisp output cannot be calculated, likely because the system is too sparse. Check to make sure this set of input values will activate at least one connected Term in each Antecedent via the current set of Rules.
edit:
I try to wrap the line code ValueError by try but the ValueError is activated yet
def defuzz(self):
"""Derive crisp value based on membership of adjective(s)."""
if not self.sim._array_inputs:
ups_universe, output_mf, cut_mfs = self.find_memberships()
if len(cut_mfs) == 0:
raise ValueError("No terms have memberships. Make sure you "
"have at least one rule connected to this "
"variable and have run the rules calculation.")
try:
return defuzz(ups_universe, output_mf,
self.var.defuzzify_method)
except AssertionError:
try:
new_c1 = []
new_c2 = []
new_c3 = []
new_c4 = []
new_vent = []
new_tout = []
newcondition1 = []
newcondition2 = []
newcondition3 = []
newcondition4 = []
newcondition5 = []
newcondition6 = []
#input
n = 0
for n in range(len(namespca)):
new_c1.append(fuzz.interp_membership(PCA1.universe, PCA1[namespcapd.name.loc[n]].mf, comp1n[step]))
new_c2.append(fuzz.interp_membership(PCA2.universe, PCA2[namespcapd.name.loc[n]].mf, comp2n[step]))
new_c3.append(fuzz.interp_membership(PCA3.universe, PCA3[namespcapd.name.loc[n]].mf, comp3n[step]))
new_c4.append(fuzz.interp_membership(PCA4.universe, PCA4[namespcapd.name.loc[n]].mf, comp4n[step]))
n = 0
for n in range(len(namesvent)):
new_vent.append(fuzz.interp_membership(Vent.universe, Vent[namesventpd.name.loc[n]].mf, dataoutf.NumVentOn[step]))
#output
n = 0
for n in range(len(namestemp)):
new_tout.append(fuzz.interp_membership(Tout.universe, Tout[namestemppd.name.loc[n]].mf, dataoutf.TsaidaHT[step]))
#new_c1 = np.transpose(new_c1)
new_c1_conv = pd.DataFrame(new_c1)
#new_c2 = np.transpose(new_c2)
new_c2_conv = pd.DataFrame(new_c2)
#new_c3 = np.transpose(new_c3)
new_c3_conv = pd.DataFrame(new_c3)
#new_c4 = np.transpose(new_c4)
new_c4_conv = pd.DataFrame(new_c4)
#new_vent = np.transpose(new_vent)
new_vent_conv = pd.DataFrame(new_vent)
#new_tout = np.transpose(new_tout)
new_tout_conv = pd.DataFrame(new_tout)
i=0
for i in range(pcamf):
newcondition1.append([new_c1_conv.idxmax(axis=0) == i])
newcondition2.append([new_c2_conv.idxmax(axis=0) == i])
newcondition3.append([new_c3_conv.idxmax(axis=0) == i])
newcondition4.append([new_c4_conv.idxmax(axis=0) == i])
i=0
for i in range(ventmf):
newcondition5.append([new_vent_conv.idxmax(axis=0) == i])
i=0
for i in range(tempmf):
newcondition6.append([new_tout_conv.idxmax(axis=0) == i])
choicelistpca = namespca
choicelistvent = namesvent
choicelisttout = namestemp
new_c1_rules = np.select(newcondition1, choicelistpca)
new_c2_rules = np.select(newcondition2, choicelistpca)
new_c3_rules = np.select(newcondition3, choicelistpca)
new_c4_rules = np.select(newcondition4, choicelistpca)
new_vent_rules = np.select(newcondition5, choicelistvent)
new_tout_rules = np.select(newcondition6, choicelisttout)
new_rules = np.vstack([new_c1_rules,new_c2_rules,new_c3_rules,new_c4_rules,new_vent_rules,new_tout_rules])
new_rules = new_rules.T
new_rulespd = pd.DataFrame(new_rules,columns=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'])
#Checar se a nova regra está dentro do conjunto de regras fuzzy atual
if pd.merge(new_rulespd,AutoRules, on=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'],how='inner').empty:
print('Nova regra não encontrada no conjunto atual de regras fuzzy!')
else:
pd.merge(new_rulespd,AutoRules, on=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'],how='inner')
"""except AssertionError:
raise ValueError("Crisp output cannot be calculated, likely "
"because the system is too sparse. Check to "
"make sure this set of input values will "
"activate at least one connected Term in each "
"Antecedent via the current set of Rules.")"""
else:
# Calculate using array-aware version, one cut at a time.
output = np.zeros(self.sim._array_shape, dtype=np.float64)
it = np.nditer(output, ['multi_index'], [['writeonly', 'allocate']])
for out in it:
universe, mf = self.find_memberships_nd(it.multi_index)
out[...] = defuzz(universe, mf, self.var.defuzzify_method)
return output
Wrap the line of code that raises ValueError in a try. And decide what to do in its except ValueError: clause. Perhaps continue-ing on to the next iteration might be reasonable.
Query
Balance.objects.filter(~Q(fax_date=F('paused_date')))
returns empty qs even though I do have objects that fit the condition "fax date field not equal to paused date". Is it possible to use ~Q and F together like that?
ran a test like this:
deals = Deal.objects.all()
balance_pre = Balance.objects.filter(~Q(fax_date=F('paused_date')), fax_date__isnull=False, reserved=False)
agr_nums = list(deals.filter(agr_name__isnull=False).values_list('agr_name', flat=True).distinct())
agrs_with_fax = 0
for agr_num in agr_nums:
try:
balance_agr = Balance.objects.get(number__icontains=agr_num)
if balance_agr.fax_date is not None and balance_agr.fax_date != balance_agr.paused_date and not balance_agr.reserved:
agrs_with_fax += 1
except Balance.DoesNotExist:
pass
agrs_with_fax2 = 0
for agr_num in agr_nums:
try:
balance_pre.get(number__icontains=agr_num)
agrs_with_fax2 += 1
except Balance.DoesNotExist:
pass
r = [agrs_with_fax, agrs_with_fax2, balance_agr.fax_date, balance_agr.paused_date, balance_agr.reserved]
r returned is
[55, 0, datetime.date(2018, 7, 11), None, False]
I don't see my error, both cycles should return same result.
I created a Balance model in a fresh project just to test that print(qs.query) will show you the generated query(not in all cases) in this case. I used also exclude as #daniel-roseman suggested to prove that they were equivalent. I hope this help you.
>>> from django.db.models import F, Q
>>> qs = Balance.objects.filter(~Q(fax_date=F('paused_date')))
>>> print(qs.query)
SELECT "so_balance"."id", "so_balance"."fax_date", "so_balance"."paused_date"
FROM "so_balance" WHERE NOT ("so_balance"."fax_date" =
("so_balance"."paused_date"))
>>> qs = Balance.objects.exclude(fax_date=F('paused_date'))
>>> print(qs.query)
SELECT "so_balance"."id", "so_balance"."fax_date", "so_balance"."paused_date"
FROM "so_balance" WHERE NOT ("so_balance"."fax_date" =
("so_balance"."paused_date"))
Using Python 2.7....
The print thread["Subject"] should return:
please ignore - test2
and
Please ignore - test
It does successfully with this:
def thread_generator(threads):
tz = pytz.timezone('America/Chicago')
POSTS_SINCE_HOURS = 24
now_date = datetime.datetime.now(tz)
time_stamp = now_date - datetime.timedelta(hours=POSTS_SINCE_HOURS)
for thread in threads:
last_post_time = convert_time(thread["LatestPostDate"])
if last_post_time > time_stamp:
print thread["Subject"]
row = {
"url": thread["Url"],
"author_username": thread["Author"]["Username"],
"author_name": thread["Author"]["DisplayName"],
"thread_id": thread["Id"],
"forum_id": thread["ForumId"],
"subject": thread["Subject"],
"created_date": thread["Content"]["CreatedDate"],
"reply_count": thread["ReplyCount"],
"latest_post_date": thread["LatestPostDate"],
"latest_reply_author": thread["LatestForumReplyAuthorId"] }
But, when adding the yield row the print thread["Subject"] does not show Please ignore - test as it should.
def thread_generator(threads):
tz = pytz.timezone('America/Chicago')
POSTS_SINCE_HOURS = 24
now_date = datetime.datetime.now(tz)
time_stamp = now_date - datetime.timedelta(hours=POSTS_SINCE_HOURS)
for thread in threads:
last_post_time = convert_time(thread["LatestPostDate"])
if last_post_time > time_stamp:
print thread["Subject"]
row = {
"url": thread["Url"],
"author_username": thread["Author"]["Username"],
"author_name": thread["Author"]["DisplayName"],
"thread_id": thread["Id"],
"forum_id": thread["ForumId"],
"subject": thread["Subject"],
"created_date": thread["Content"]["CreatedDate"],
"reply_count": thread["ReplyCount"],
"latest_post_date": thread["LatestPostDate"],
"latest_reply_author": thread["LatestForumReplyAuthorId"] }
yield row
Why is this? Please ignore - test should still show with print thread["Subject"]. Makes no sense to me.
UPDATE: How the generators is called
def sanitize_threads(threads):
for thread in thread_generator(threads):
do stuff
thread_batch.append(thread)
return thread_batch
def get_unanswered_threads():
slug = 'forums/threads/unanswered.json?PageSize=100'
status_code, threads = do_request(slug)
if status_code == 200:
threads = threads["Threads"]
thread_batch = sanitize_threads(threads)
database_update(thread_batch)
Have you tried actually calling next() on the resultant generator? If you call the function with yield the same way you call the function without, in the yield case you'll get a generator object as a result. A generator doesn't evaluate what's inside it until you actually require a value of it, which can be done with next(generator).
For example:
>>> def nogen():
... '''Create a list of values 0-3 and print each one as you build the list.'''
... r = []
... for i in range(3):
... print(i)
... r.append(i)
... return r
...
>>> def firstgen():
... '''Create an iterator of values 0-3 and print each one as you yield the value.'''
... for i in range(3):
... print(i)
... yield i
...
>>> a = nogen() # This function is eager and does everything at once.
0
1
2
>>> a
[0, 1, 2]
>>> b = firstgen() # Note there's no output immediately after this line.
>>> list(b) # The output happens as the list coercion causes the generator to execute.
0
1
2
[0, 1, 2]
>>> c = firstgen() # You can also see this a step at a time using `next`
>>> next(c)
0
0
>>> next(c)
1
1
>>> next(c)
2
2
>>> next(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration