So I have an Article class that models the articles in a store. When I create a new article, I want it to have an EAN 13 code. So I initialize the article with a 12 digits code and use the check_ean13() funtion to retrieve the control digit. It works but seems like in any moment, when the object is created, rewrite the ean13 attribute and replaces it for None. Any ideas?
Main
if __name__ == "__main__":
# create article
art1 = Article("123456789087", "Article 1", 145.6, 200.0)
print art1
print art1.get_ean13()
class Article
class Article:
def __init__(self, cod, art_name, cost, listprice):
self.ean13 = self.set_ean13(cod)
self.art_name = art_name
self.cost = cost
self.listprice = listprice
self.commission = None
self.promotion=[]
def get_ean13(self):
return self.ean13
def set_ean13(self,cod):
cd = self.check_ean13(cod)
ean13 = cod + str(cd)
self.ean13=ean13
def check_ean13(self, code):
checksum = 0
for i, digit in enumerate(reversed(code)):
checksum += int(digit) * 3 if (i % 2 == 0) else int(digit)
return (10 - (checksum % 10)) % 10
output:
None - Article 1 list price: 400.0
None
self.ean13 = self.set_ean13(cod)
set_ean13 doesn't return anything, so you're effectively doing self.ean13 = None here. Just call the method without assigning the result.
self.set_ean13(cod)
Related
I have a thread that is running inside a route where the thread job is to do some expensive work, store variables and than I'll need to use these variables in another flask route.
When I am using the session variable (Redis) as a parameter in the thread function in order to add the data and extract it later it does not find the variables that I have stored in it.
In contrast, when I declare a global_dict and pass it to the thread function instead of session, the code works great.
As the thread function can be used by multiple users simultaneously, storing it in a global_dict is not a good practice.
Why using session in my code does not work?
In the following code, if I replace global_dict with session I won't be able to access it in the /result route.
Per doc:
"Redis can handle up to 2^32 keys, and was tested in practice to handle at least 250 million keys per instance.
Every hash, list, set, and sorted set, can hold 2^32 elements.
In other words your limit is likely the available memory in your system."
P.S Sorry for the long code blocks.
#app.route("/build",methods=["GET", "POST"])
#login_required
def build():
if request.method == "POST":
global th
global finished
finished= False
#copy_current_request_context
def operation(global_dict):
global finished
symbols = request.form.get("symbols")
mc.set("symbols", symbols)
if contains_multiple_words(symbols) == False:
flash("The app purpose is to optimize a portfolio given a list of stocks. Please enter a list of stocks seperated by a new row.")
return redirect("/build")
Build(session["user_id"], symbols.upper(), request.form.get("start"), request.form.get("end"), request.form.get("funds"), request.form.get("short"), request.form.get("volatility"), request.form.get("gamma"), request.form.get("return"))
db.session.commit()
try:
df = yf.download(symbols, start=request.form.get("start"), end=request.form.get("end"), auto_adjust = False, prepost = False, threads = True, proxy = None)["Adj Close"].dropna(axis=1, how='all')
failed=(list(shared._ERRORS.keys()))
df = df.replace(0, np.nan)
try:
global_dict['listofna']=df.columns[df.isna().iloc[-2]].tolist()+failed
except IndexError:
flash("Please enter valid stocks from Yahoo Finance.")
return redirect("/build")
df = df.loc[:,df.iloc[-2,:].notna()]
except ValueError:
flash("Please enter a valid symbols (taken from Yahoo Finance)")
return redirect("/build")
def enter_sql_data(app, df, nasdaq_exchange_info, Stocks):
for ticker in df.columns:
ticker=ticker.upper()
if any(sublist[1]==ticker in sublist for sublist in nasdaq_exchange_info) is False:
ticker_ln = yf.Ticker(ticker).stats()["price"].get('longName')
if not ticker_ln:
ticker_ln = ticker
ticker_list=[ticker_ln, ticker]
with app.app_context():
new_stock=Stocks(ticker, ticker_ln)
db.session.add(new_stock)
db.session.commit()
nasdaq_exchange_info.extend([ticker_list])
global nasdaq_exchange_info
app1 = app._get_current_object()
p1 = Process(target=enter_sql_data, args=[app1, df, nasdaq_exchange_info, Stocks])
p1.start()
prices = df.copy()
fig = px.line(prices, x=prices.index, y=prices.columns, title='Price Graph')
fig = fig.update_xaxes(rangeslider_visible=True)
fig.update_layout(width=1350, height=900)
global_dict['plot_json'] = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
exp_cov = risk_models.exp_cov(prices, frequency=252)
#plotting the covariance matrix
heat = go.Heatmap(
z = risk_models.cov_to_corr(exp_cov),
x = exp_cov.columns.values,
y = exp_cov.columns.values,
zmin = 0, # Sets the lower bound of the color domain
zmax = 1,
xgap = 1, # Sets the horizontal gap (in pixels) between bricks
ygap = 1,
colorscale = 'RdBu'
)
title = 'Covariance matrix'
layout = go.Layout(
title_text=title,
title_x=0.5,
width=800,
height=800,
xaxis_showgrid=False,
yaxis_showgrid=False,
yaxis_autorange='reversed'
)
fig1=go.Figure(data=[heat], layout=layout)
fig1.update_layout(width=500, height=500)
global_dict['plot_json1'] = json.dumps(fig1, cls=plotly.utils.PlotlyJSONEncoder)
S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
heat = go.Heatmap(
z = risk_models.cov_to_corr(S),
x = S.columns.values,
y = S.columns.values,
zmin = 0, # Sets the lower bound of the color domain
zmax = 1,
xgap = 1, # Sets the horizontal gap (in pixels) between bricks
ygap = 1,
colorscale = 'RdBu'
)
title = 'Ledoit-Wolf shrinkage'
layout = go.Layout(
title_text=title,
title_x=0.5,
width=800,
height=800,
xaxis_showgrid=False,
yaxis_showgrid=False,
yaxis_autorange='reversed'
)
fig2=go.Figure(data=[heat], layout=layout)
fig2.update_layout(width=500, height=500)
global_dict['plot_json2'] = json.dumps(fig2, cls=plotly.utils.PlotlyJSONEncoder)
#Section 2 -Return estimation
#it is often a bad idea to provide returns using a simple estimate like the mean of past returns. Research suggests that better off not providing expected returns – you can then just find the min_volatility() portfolio or use HRP.
mu = pypfopt.expected_returns.capm_return(prices)
fig3 = px.bar(mu, orientation='h')
fig3.update_layout(width=700, height=500)
global_dict['plot_json3'] = json.dumps(fig3, cls=plotly.utils.PlotlyJSONEncoder)
#using risk models optimized for the Efficient frontier to reduce to min volitility, good for crypto currencies - not implemented in the website now.
ef = EfficientFrontier(None, S)
try:
ef.min_volatility()
weights = ef.clean_weights()
nu = pd.Series(weights)
fig4 = px.bar(nu, orientation='h')
fig4.update_layout(width=700, height=500)
global_dict['plot_json4'] = json.dumps(fig4, cls=plotly.utils.PlotlyJSONEncoder)
av=ef.portfolio_performance()[1]
global_dict['av']=round(av, 3)*1
#if we want to buy the portfolio mentioned above
df = df.iloc[[-1]]
for col in df.columns:
if col.endswith(".L"):
df.loc[:,col] = df.loc[:,col]*GBPtoUSD()
try:
latest_prices = df.iloc[-1]
except IndexError:
flash("There is an issue with Yahoo API please try again later")
return redirect("/")
# prices as of the day you are allocating
if float(request.form.get("funds")) <= 0 or float(request.form.get("funds")) == " ":
flash("Amount need to be a positive number")
return redirect("/build")
if float(request.form.get("funds")) < float(latest_prices.min()):
flash("Amount is not high enough to cover the lowest priced stock")
return redirect("/build")
try:
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=float(request.form.get("funds")))
except TypeError:
delisted=df.columns[df.isna().any()].tolist()
delisted= ", ".join(delisted)
flash("Can't get latest prices for the following stock/s, please remove to contiue : %s" % delisted)
return redirect("/build")
alloc, global_dict['leftover'] = da.lp_portfolio()
global_dict['alloc']=alloc
global_dict['latest_prices']=latest_prices
except ValueError:
pass
#Maximise return for a given risk, with L2 regularisation
try:
ef = EfficientFrontier(mu, S)
ef.add_objective(objective_functions.L2_reg, gamma=(float(request.form.get("gamma")))) # gamme is the tuning parameter
ef.efficient_risk(int(request.form.get("volatility"))/100)
weights = ef.clean_weights()
su = pd.DataFrame([weights])
fig5 = px.pie(su, values=weights.values(), names=su.columns)
fig5.update_traces(textposition='inside')
fig5.update_layout(width=500, height=500, uniformtext_minsize=12, uniformtext_mode='hide')
global_dict['plot_json5'] = json.dumps(fig5, cls=plotly.utils.PlotlyJSONEncoder)
global_dict['perf'] =ef.portfolio_performance()
except Exception as e:
flash(str(e))
return redirect("/build")
#if we want to buy the portfolio mentioned above
for col in df.columns:
if col.endswith(".L"):
df.loc[:,col] = df.loc[:,col]*GBPtoUSD()
latest_prices1 = df.iloc[-1] # prices as of the day you are allocating
if float(request.form.get("funds")) <= 0 or float(request.form.get("funds")) == " ":
flash("Amount need to be a positive number")
return redirect("/build")
if float(request.form.get("funds")) < float(latest_prices.min()):
flash("Amount is not high enough to cover the lowest priced stock")
return redirect("/build")
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=float(request.form.get("funds")))
alloc1, global_dict['leftover1'] = da.lp_portfolio()
global_dict['alloc1']=alloc1
global_dict['latest_prices1']=latest_prices1
#Efficient semi-variance optimization
returns = pypfopt.expected_returns.returns_from_prices(prices)
returns = returns.dropna()
es = EfficientSemivariance(mu, returns)
try:
es.efficient_return(float(request.form.get("return"))/100)
except ValueError as e:
flash(str(e))
return redirect("/build")
global_dict['perf2']=es.portfolio_performance()
weights = es.clean_weights()
#if we want to buy the portfolio mentioned above
for col in df.columns:
if col.endswith(".L"):
df.loc[:,col] = df.loc[:,col]*GBPtoUSD()
latest_prices2 = df.iloc[-1] # prices as of the day you are allocating
if float(request.form.get("funds")) <= 0 or float(request.form.get("funds")) == " ":
flash("Amount need to be a positive number")
return redirect("/build")
if float(request.form.get("funds")) < float(latest_prices.min()):
flash("Amount is not high enough to cover the lowest priced stock")
return redirect("/build")
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=float(request.form.get("funds")))
alloc2, global_dict['leftover2'] = da.lp_portfolio()
global_dict['alloc2']=alloc2
global_dict['latest_prices2']=latest_prices2
mc.delete("symbols")
global_dict['ret']=float(request.form.get("return"))
global_dict['gamma']=request.form.get("gamma")
global_dict['volatility']=request.form.get("volatility")
finished = True
global global_dict
th = Thread(target=operation, args=[global_dict])
th.start()
return render_template("loading.html")
else:
if mc.get("symbols"):
cached_symbols=mc.get("symbols")
else:
cached_symbols=''
availableCash=db.session.query(Users.cash).filter_by(id=session["user_id"]).first().cash
return render_template("build.html", availableCash=round(availableCash, 4), GBP=GBPtoUSD(), nasdaq_exchange_info=nasdaq_exchange_info, cached_symbols=cached_symbols, top_50_crypto=top_50_crypto, top_world_stocks=top_world_stocks, top_US_stocks=top_US_stocks, top_div=top_div)
app.route('/result')
def result():
return render_template("built.html",av=global_dict['av'], leftover=global_dict['leftover'], alloc=global_dict['alloc'], ret=global_dict['ret'],gamma=global_dict['gamma'],volatility=global_dict['volatility'],perf=global_dict['perf'], perf2=global_dict['perf2'], alloc1=global_dict['alloc1'], alloc2=global_dict['alloc2'], plot_json=global_dict['plot_json'], plot_json1=global_dict['plot_json1'], plot_json2=global_dict['plot_json2'], plot_json3=global_dict['plot_json3'], plot_json4=global_dict['plot_json4'], plot_json5=global_dict['plot_json5'], leftover1=global_dict['leftover1'], leftover2=global_dict['leftover2'],listofna=(', '.join(global_dict['listofna'])))
I am trying to validate the findings of a paper by testing it on the same model architecture as well as the same dataset reported by the paper. I have been using the imagenet script provided in the official pytorch repository's examples section to do the same.
class AverageMeter(object):
"""Computes and stores the average and current value
Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
def init(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the precision#k for the specified values of k"""
maxk = max(topk)
batchsize = target.size(0)
, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0)
res.append(correctk.mul(100.0 / batch_size))
return res
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
# measure data loading time
print(f"Processing {batch_idx+1}/{len(test_loader)}")
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
# compute output
outputs = model(inputs)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
print(prec1,prec5)
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
print(top1)
print(top5)
However the top 5 error which I am getting by using this script is not matching with the one in the paper. Can anyone tell me what is wrong in this particular snippet?
hey guys:)im sorry for all the code but i feel its necessary for u to see everything..
i tried everything... i hidden prints in the code, debugging for ten times, triple checked the built in methods, and still, the .crawl() method dosnt remove any object from the final_list.
the object of my assignment is to built two classes:
Web_page : holds data of a web page.(the pages come in the form of html files saved in a folder on my desktop. Crawler: compare between pages and hold a list of the uniqe pages---> final_list
import re
import os
def remove_html_tags(s):
tag = False
quote = False
out = ""
for c in s:
if c == '<' and not quote:
tag = True
elif c == '>' and not quote:
tag = False
elif (c == '"' or c == "'") and tag:
quote = not quote
elif not tag:
out = out + c
return out
def lev(s1, s2):
return lev_iter(s1, s2, dict())
def lev_iter(s1, s2, mem):
(i,j) = (len(s1), len(s2))
if (i,j) in mem:
return mem[(i,j)]
s1_low = s1.lower()
s2_low = s2.lower()
if len(s1_low) == 0 or len(s2_low) == 0:
return max(len(s1_low), len(s2_low))
d1 = lev_iter(s1_low[:-1], s2_low, mem) + 1
d2 = lev_iter(s1_low, s2_low[:-1], mem) + 1
last = 0 if s1_low[-1] == s2_low[-1] else 1
d3 = lev_iter(s1_low[:-1], s2_low[:-1], mem) + last
result = min(d1, d2, d3)
mem[(i,j)] = result
return result
def merge_spaces(content):
return re.sub('\s+', ' ', content).strip()
""" A Class that holds data on a Web page """
class WebPage:
def __init__(self, filename):
self.filename = filename
def process(self):
f = open(self.filename,'r')
LINE_lst = f.readlines()
self.info = {}
for i in range(len(LINE_lst)):
LINE_lst[i] = LINE_lst[i].strip(' \n\t')
LINE_lst[i] = remove_html_tags(LINE_lst[i])
lines = LINE_lst[:]
for line in lines:
if len(line) == 0:
LINE_lst.remove(line)
self.body = ' '.join(LINE_lst[1:])
self.title = LINE_lst[0]
f.close()
def __str__(self):
return self.title + '\n' + self.body
def __repr__(self):
return self.title
def __eq__(self,other):
n = lev(self.body,other.body)
k = len(self.body)
m = len(other.body)
return float(n)/max(k,m) <= 0.15
def __lt__(self,other):
return self.title < other.title
""" A Class that crawls the web """
class Crawler:
def __init__(self, directory):
self.folder = directory
def crawl(self):
pages = [f for f in os.listdir(self.folder) if f.endswith('.html')]
final_list = []
for i in range(len(pages)):
pages[i] = WebPage(self.folder + '\\' + pages[i])
pages[i].process()
for k in range(len(final_list)+1):
if k == len(final_list):
final_list.append(pages[i])
elif pages[i] == final_list[k]:
if pages[i] < final_list[k]:
final_list.append(pages[i])
final_list.remove(final_list[k])
break
print final_list
self.pages = final_list
everything works fine besides this freaking line final_list.remove(final_list[k]). help please? whats wrong here?
I'm not sure why your code doesn't work, it's difficult to test it because I don't know what kind of input should end up calling remove().
I suggest following these steps:
Make sure that remove() is called at some point.
remove() relies on your __eq__() method to find the item to remove, so make sure that __eq__() isn't the culprit.
As a side note, you will probably want to replace this:
self.folder + '\\' + pages[i]
with:
import os.path
# ...
os.path.join(self.folder, page[i])
This simple change should make your script work on all operating systems, rather than on Windows only. (GNU/Linux, Mac OS and other Unix-like OS use “/” as path separator.)
Please also consider replacing loops of this form:
for i in range(len(sequence)):
# Do something with sequence[i]
with:
for item in sequence:
# Do something with item
If you need the item index, use enumerate():
for i, item in enumerate(sequence):
models.py
class Restaurant(models.Model)
food_rating = RatingField(range=2, weight=5,can_change_vote = True,allow_delete = True,allow_anonymous = True)
service_rating = RatingField(range=2, weight=5,can_change_vote = True,allow_delete = True,allow_anonymous = True)
ambience_ratiing = RatingField(range=2, weight=5,can_change_vote = True,allow_delete = True,allow_anonymous = True)
view.py code
r = Restaurant.objects.get(pk=1)
r.food_rating.add(score = -1 , user = request.user , ip_address =request.META.get('HTTP_REFERER'))
print r.food_rating.score
error
djangoratings.exceptions.InvalidRating: -1 is not a valid choice for food_rating
doubt
my food_rating field is eligible to take two scores , how am i supposed to change the score so that i can implement vote up and vote down feature , on vote up , i should be able to add 1 to the existing score and on vote down i should be able to subtract a vote , please help , thanks in advance
The problem comes from this script:
if score < 0 or score > self.field.range:
raise InvalidRating("%s is not a valid choice for %s" % (score, self.field.name))
Short answer: convert the [-x:y] interval you want to use for display, into [-x+x:y+x] in your code to avoid this problem. If you wanted [-5:5], then use [-5+5:5+5] which is [0:10]. If you wanted [-50:100] then use [-50+50:100+50] = [0:150] and so on ... It's a simple formula, that shouldn't be a problem for a programer ;)
Long answer: either you fork djangoratings, either you open an issue asking to add a setting enabling negative ratings ... and probably he'll reject it, because of the simple interval conversion workaround, here's some more concrete examples:
class Restaurant(models.Model):
# blabla :)
ambience_rating = RatingField(range=5, weight=5,can_change_vote = True,allow_delete = True,allow_anonymous = True)
def get_adjusted_ambiance_rating(self):
return self.ambience_rating - 3
So, if ambience_rating is "1" (the lowest score), get_adjusted_ambiance_rating() will return -2.
If ambience_rating is "5" (the highest score), get_ambiance_rating_with_negative() will return 2.
Adapt this example/trick to your needs.
You should probably make a single method for all ratings:
def get_adjusted_rating(self, which):
return getattr(self, '%s_rating' % which) - 3
Callable as such:
restaurant.get_adjusted_rating('ambiance')
restaurant.get_adjusted_rating('food')
# etc ...
And maybe a template filter:
#register.filter
def get_adjusted_rating(restaurant, which):
return restaurant.get_adjusted_rating(which)
Usable as such:
{{ restaurant|get_adjusted_rating:"ambiance" }}
{{ restaurant|get_adjusted_rating:"food" }}
{# etc, etc #}
More details about template filters.
i have a function that give me the result that im expecting in console mode, but if i try to use the function with Django, the page never load and just have a loop calculating and never end.
Any idea ?
*sorry with my english
Console function (WORK GREAT):
def sum_digitos(n):
sum = 0;
while n != 0:
sum += n % 10
n /= 10
if sum > 9:
x = str(sum)
y =list(x)
sum = int(y[0]) + int(y[1])
return sum
print sum_digitos(2461978)
Django views:
def Calcular(request):
if request.method == 'POST':
form = NumerologiaForm(request.POST)
if form.is_valid():
sum = 0;
ano = str(request.POST['fecha_year'])
mes = str(request.POST['fecha_month'])
dia = str(request.POST['fecha_day'])
data = dia + mes + ano
fecha = int(data)
while fecha != 0:
f = fecha
sum += f % 10
f /= 10
if sum > 9:
x = str(sum)
y =list(x)
sum = int(y[0]) + int(y[1])
resultado = get_object_or_404(Numero,numero = sum)
return HttpResponseRedirect(resultado.get_absolute_url())
else:
form = NumerologiaForm()
return render_to_response('numerologiaForm.html',{'form':form})
Try:
f = fecha
while f!= 0:
sum += f % 10
f /= 10
if sum > 9:
x = str(sum)
y =list(x)
sum = int(y[0]) + int(y[1])
It seems you were changing f, but checking fecha for the looping.
Sanjay's answer is the correct one, and I recommend it. I just wanted to ask why you didn't just do:
from numerology import sum_digitos
def Calcular(request):
# In your code, you return HttpResponseRedirect using a nonexistent
# "resultado" variable if the form is not valid. This will raise an
# exception. I think you meant to indent "return Http..." one step more.
if request.method == 'POST':
form = NumerologiaForm(request.POST)
else:
form = NumerologiaForm()
# "or..." part of next line not needed if form.is_valid() returns
# False for a blank form.
if not form.is_valid() or form == NumerologiaForm():
return render_to_response('numerologiaForm.html', {'form': form})
ano = str(request.POST['fecha_year'])
mes = str(request.POST['fecha_month'])
dia = str(request.POST['fecha_day'])
resultado = get_object_or_404(Numero,
numero=sum_digitos(int(dia + mes + ano)))
return HttpResponseRedirect(resultado.get_absolute_url())
You had a working function in Python already... why not just import it and use it?
There's no need to go to all that work to sum the digits in that number, because the sum of the digits is num % 9. If num % 9 is zero, then the actual sum of digits is 9.
By changing your method to
def sum_digitos(n):
sum_ = n % 9
return sum_ if sum_ != 0 else 9
You will completely avoid whatever issue was happening inside your original method.
You don't say what the rest of your environment is like, but you should be using f //= 10 to ensure that you're performing integer division.