I am creating a shift table by tkcalendar.
I would like to make the selected dates show their own timetable, as illustrated in the picture.
Is it possible to achieve this by any methods provided by tkcalendar? Or the only way I can do this is created a custom calendar from zero? I went through the document, but hardly finding something useful.
NOTE: I don't want tooltip(pop-up text)
It is not possible from tkcalendar's methods to display text below the day number in the Calendar. However, you don't have to start from scratch, you can create an Agenda class inheriting from Calendar and only rewrite the methods displaying the events to put them inside the day's label instead of in a popup.
To be precise, 3 methods need to be modified: _display_days_without_othermonthdays(), _display_days_with_othermonthdays() and _show_event(), see code below. The code might seem long but I actually only modified a couple of lines in each methods compared to there original version in tkcalendar.
from tkcalendar import Calendar
class Agenda(Calendar):
def __init__(self, master=None, **kw):
Calendar.__init__(self, master, **kw)
# change a bit the options of the labels to improve display
for i, row in enumerate(self._calendar):
for j, label in enumerate(row):
self._cal_frame.rowconfigure(i + 1, uniform=1)
self._cal_frame.columnconfigure(j + 1, uniform=1)
label.configure(justify="center", anchor="n", padding=(1, 4))
def _display_days_without_othermonthdays(self):
year, month = self._date.year, self._date.month
cal = self._cal.monthdays2calendar(year, month)
while len(cal) < 6:
cal.append([(0, i) for i in range(7)])
week_days = {i: 'normal.%s.TLabel' % self._style_prefixe for i in range(7)} # style names depending on the type of day
week_days[self['weekenddays'][0] - 1] = 'we.%s.TLabel' % self._style_prefixe
week_days[self['weekenddays'][1] - 1] = 'we.%s.TLabel' % self._style_prefixe
_, week_nb, d = self._date.isocalendar()
if d == 7 and self['firstweekday'] == 'sunday':
week_nb += 1
modulo = max(week_nb, 52)
for i_week in range(6):
if i_week == 0 or cal[i_week][0][0]:
self._week_nbs[i_week].configure(text=str((week_nb + i_week - 1) % modulo + 1))
else:
self._week_nbs[i_week].configure(text='')
for i_day in range(7):
day_number, week_day = cal[i_week][i_day]
style = week_days[i_day]
label = self._calendar[i_week][i_day]
label.state(['!disabled'])
if day_number:
txt = str(day_number)
label.configure(text=txt, style=style)
date = self.date(year, month, day_number)
if date in self._calevent_dates:
ev_ids = self._calevent_dates[date]
i = len(ev_ids) - 1
while i >= 0 and not self.calevents[ev_ids[i]]['tags']:
i -= 1
if i >= 0:
tag = self.calevents[ev_ids[i]]['tags'][-1]
label.configure(style='tag_%s.%s.TLabel' % (tag, self._style_prefixe))
# modified lines:
text = '%s\n' % day_number + '\n'.join([self.calevents[ev]['text'] for ev in ev_ids])
label.configure(text=text)
else:
label.configure(text='', style=style)
def _display_days_with_othermonthdays(self):
year, month = self._date.year, self._date.month
cal = self._cal.monthdatescalendar(year, month)
next_m = month + 1
y = year
if next_m == 13:
next_m = 1
y += 1
if len(cal) < 6:
if cal[-1][-1].month == month:
i = 0
else:
i = 1
cal.append(self._cal.monthdatescalendar(y, next_m)[i])
if len(cal) < 6:
cal.append(self._cal.monthdatescalendar(y, next_m)[i + 1])
week_days = {i: 'normal' for i in range(7)} # style names depending on the type of day
week_days[self['weekenddays'][0] - 1] = 'we'
week_days[self['weekenddays'][1] - 1] = 'we'
prev_m = (month - 2) % 12 + 1
months = {month: '.%s.TLabel' % self._style_prefixe,
next_m: '_om.%s.TLabel' % self._style_prefixe,
prev_m: '_om.%s.TLabel' % self._style_prefixe}
week_nb = cal[0][1].isocalendar()[1]
modulo = max(week_nb, 52)
for i_week in range(6):
self._week_nbs[i_week].configure(text=str((week_nb + i_week - 1) % modulo + 1))
for i_day in range(7):
style = week_days[i_day] + months[cal[i_week][i_day].month]
label = self._calendar[i_week][i_day]
label.state(['!disabled'])
txt = str(cal[i_week][i_day].day)
label.configure(text=txt, style=style)
if cal[i_week][i_day] in self._calevent_dates:
date = cal[i_week][i_day]
ev_ids = self._calevent_dates[date]
i = len(ev_ids) - 1
while i >= 0 and not self.calevents[ev_ids[i]]['tags']:
i -= 1
if i >= 0:
tag = self.calevents[ev_ids[i]]['tags'][-1]
label.configure(style='tag_%s.%s.TLabel' % (tag, self._style_prefixe))
# modified lines:
text = '%s\n' % date.day + '\n'.join([self.calevents[ev]['text'] for ev in ev_ids])
label.configure(text=text)
def _show_event(self, date):
"""Display events on date if visible."""
w, d = self._get_day_coords(date)
if w is not None:
label = self._calendar[w][d]
if not label.cget('text'):
# this is an other month's day and showothermonth is False
return
ev_ids = self._calevent_dates[date]
i = len(ev_ids) - 1
while i >= 0 and not self.calevents[ev_ids[i]]['tags']:
i -= 1
if i >= 0:
tag = self.calevents[ev_ids[i]]['tags'][-1]
label.configure(style='tag_%s.%s.TLabel' % (tag, self._style_prefixe))
# modified lines:
text = '%s\n' % date.day + '\n'.join([self.calevents[ev]['text'] for ev in ev_ids])
label.configure(text=text)
if __name__ == '__main__':
import tkinter as tk
root = tk.Tk()
root.geometry("800x500")
agenda = Agenda(root, selectmode='none')
date = agenda.datetime.today() + agenda.timedelta(days=2)
agenda.calevent_create(date, 'Hello World', 'message')
agenda.calevent_create(date, 'Reminder 2', 'reminder')
agenda.calevent_create(date + agenda.timedelta(days=-7), 'Reminder 1', 'reminder')
agenda.calevent_create(date + agenda.timedelta(days=3), 'Message', 'message')
agenda.calevent_create(date + agenda.timedelta(days=3), 'Another message', 'message')
agenda.tag_config('reminder', background='red', foreground='yellow')
agenda.pack(fill="both", expand=True)
root.mainloop()
Related
I'm using this code to calculate pivot points.
def pivots_low(osc, LBR, LBL):
pivots = []
for i in range(len(osc) - LBR):
pivots.append(0)
pivot = True
if i > LBL:
for j in range(1, LBR + 1):
if osc[i] >= osc[i + j]:
pivot = False
for j in range(1, LBL + 1):
if osc[i] > osc[i - j]:
pivot = False
if pivot is True:
pivots[len(pivots) - 1] = osc[i]
for i in range(LBR):
pivots.append(0)
return pivots
This returns an array with 0's where there's no pivots and the value of the pivot if there is one.
When Comparing the results to TradingView (downloaded csv with pivot points), the only time it matches exactly is when lookback left and right are both 5. Otherwise it deviates in the number of total pivots and the location of some.
But using this code to calculate pivot highs:
def pivots_high(osc, LBR, LBL):
pivots = []
for i in range(len(osc)-LBR):
pivots.append(0)
pivot = True
if i > LBL:
for j in range(1,LBL + 1):
if osc[i] < osc[i-j]:
pivot = False
for j in range(1,LBR + 1):
if osc[i] <= osc[i+j]:
pivot = False
if pivot is True:
pivots[len(pivots)-1] = osc[i]
for i in range(LBR):
pivots.append(0)
return pivots
the results are perfect regardless of lookback values. But the code is almost exactly the same besides comparison.
What is going wrong here? This is day 3 of having this problem and I just cant fix it
To Reproduce:
Load Data:
Full_Data = pd.read_csv(file)
use this simple function to check matches between calculated pivots and TradingView pivots.
def match_pivs(data, pivs_h, pivs_l): //Data is a DataFrame loaded from tradingview csv
global lblh
global lbrh
global lbll
global lbrl
start = lbrh
if lbrl > lbrh:
start = lbrl
match_h = 0
tot_hd = 0
tot_hp = 0
match_l = 0
tot_ld = 0
tot_lp = 0
for i in range(start, len(data)):
if data['PivHigh'][i] != 0 and pivs_h[i-lbrh] != 0:
match_h += 1
if data['PivLow'][i] != 0 and pivs_l[i-lbrl] != 0:
match_l += 1
if data['PivHigh'][i] != 0:
tot_hd += 1
if data['PivLow'][i] != 0:
tot_ld += 1
if pivs_h[i] != 0:
tot_hp += 1
if pivs_l[i] != 0:
tot_lp += 1
print('PivsLow ' + str(tot_lp))
print('DataLows ' + str(tot_ld))
print('MatchesL ' + str(match_l))
print('PivsHigh ' + str(tot_hp))
print('DataHighs ' + str(tot_hd))
print('MatchesH ' + str(match_h))
and to get csv from TradingView:
//#version=5
indicator("Data Script", overlay=true, max_labels_count=500)
leftLenL = input.int(title="Pivot Low", defval=10, minval=1, inline="Pivot Low", group=lengthGroupTitle)
rightLenL = input.int(title="/", defval=10, minval=1, inline="Pivot Low", group=lengthGroupTitle)
leftLenH = input.int(title="Pivot High", defval=10, minval=1, inline="Pivot High", group=lengthGroupTitle)
rightLenH = input.int(title="/", defval=10, minval=1, inline="Pivot High", group=lengthGroupTitle)
ph = ta.pivothigh(leftLenH, rightLenH)
pl = ta.pivotlow(leftLenL, rightLenL)
if not na(ph)
plth := ph
else
plth := 0.0
if not na(pl)
pltl := pl
else
pltl := 0.0
plot(plth, 'PivHigh')
plot(pltl, 'PivLow')
then just download csv with this script loaded.
Run program with these three lines:
pl = pivots_low(Full_Data['low'], lbll, lbrl)
ph = pivots_high(Full_Data['high'], lbrh, lblh)
match_pivs(Full_Data, ph, pl)
Finally found a way.
I still have no idea why that code does not work but I've made a different way that seems to be doing the job 100% to tradingview data.
def checkhl(data_back, data_forward, hl):
if hl == 'high' or hl == 'High':
ref = data_back[len(data_back)-1]
for i in range(len(data_back)-1):
if ref < data_back[i]:
return 0
for i in range(len(data_forward)):
if ref <= data_forward[i]:
return 0
return 1
if hl == 'low' or hl == 'Low':
ref = data_back[len(data_back)-1]
for i in range(len(data_back)-1):
if ref > data_back[i]:
return 0
for i in range(len(data_forward)):
if ref >= data_forward[i]:
return 0
return 1
def pivot(osc, LBL, LBR, highlow)
left = []
right = []
for i in range(len(osc)):
pivots.append(0.0)
if i < LBL + 1:
left.append(osc[i])
if i > LBL:
right.append(osc[i])
if i > LBL + LBR:
left.append(right[0])
left.pop(0)
right.pop(0)
if checkhl(left, right, highlow):
pivots[i - LBR] = osc[i - LBR]
return pivots
then just do:
pivots_low = pivot(data, lbl, lbr, 'low')
pivots_high = pivot(data, lbl, lbr, 'high')
All the pivots will be in the actual position that they occur, not lbr bars after, otherwise the value will be 0.0
I'm not sure if this is efficient or not but it seems to work.
I am trying to calculate RSI using simple functions.
The general formula for it is:
RSI = 100/(1+RS), where RS = Exponential Moving Average of gains / -||- of losses.
Here is what I am getting:
enter image description here
Here it is how should it look like:
enter image description here
I have everything double checked or even triple checked, but I can't find any mistake.
Thus I need your help, I know that the question is very simple though I need some help, I have no idea where I have made the mistake.
The general idea of RSI is that it should be low where the price is "low" and high, where the price is high, and generally no matter what I try I have it upside down.
def EMA(close_price_arr, n):
a = (2/n + 1)
EMA_n = np.empty((1, len(close_price_arr)))
for i in range(len(close_price_arr)):
if i < n:
# creating NaN values where it is impossible to calculate EMA to drop it later after connecting the whole database
EMA_n[0, i] = 'NaN'
if i >= n:
# Calaculating nominator and denominator of EMA
for j in range(n):
nominator_ema += close_price_arr[i - j] * a**(j)
denominator_ema += a**(j)
EMA_n[0, i] = nominator_ema / denominator_ema
nominator_ema = 0
denominator_ema = 0
return EMA_n
def gains(close_price_arr):
gain_arr = np.empty((len(close_price_arr) - 1))
for i in range(len(close_price_arr)):
if i == 0:
pass
if i >= 1:
if close_price_arr[i] > close_price_arr[i - 1]:
gain_arr[i - 1] = (close_price_arr[i] - close_price_arr[i-1])
else:
gain_arr[i - 1] = 0
return gain_arr
def losses(close_price_arr):
loss_arr = np.empty((len(close_price_arr) - 1))
for i in range(len(close_price_arr)):
if i == 0:
pass
if i >= 1:
if close_price_arr[i] < close_price_arr[i - 1]:
loss_arr[i - 1] = abs(close_price_arr[i] - close_price_arr[i - 1])
else:
loss_arr[i - 1] = 0
return loss_arr
def RSI(gain_arr, loss_arr, n):
EMA_u = EMA(gain_arr, n)
EMA_d = EMA(loss_arr, n)
EMA_diff = EMA_u / EMA_d
x,y = EMA_diff.shape
print(x, y)
RSI_n = np.empty((1, y))
for i in range(y):
if EMA_diff[0, i] == 'NaN':
RSI_n[0, i] = 'NaN'
print(i)
else:
RSI_n[0, i] = 100 / (1 + EMA_diff[0, i])
return RSI_n
#contextmanager
def show_complete_array():
oldoptions = np.get_printoptions()
np.set_printoptions(threshold=np.inf)
try:
yield
finally:
np.set_printoptions(**oldoptions)
np.set_printoptions(linewidth=3000)
pd.set_option('display.max_columns', None)
# Specyfying root folder, file folder and file
FILE = 'TVC_SILVER, 5.csv'
FOLDER = 'src'
PROJECT_ROOT_DIR = '.'
csv_path = os.path.join(PROJECT_ROOT_DIR, FOLDER, FILE)
# reading csv
price_data = pd.read_csv(csv_path, delimiter=',')
price_data_copy = price_data.copy()
price_data_nodate = price_data.copy().drop('time', axis=1)
price_data_np = price_data_nodate.to_numpy(dtype='float32')
close_price = price_data_np[:, 3]
EMA15 = EMA(close_price_arr=close_price, n=15)
EMA55 = EMA(close_price_arr=close_price, n=55)
gain = gains(close_price_arr=close_price)
loss = losses(close_price_arr=close_price)
RSI14 = RSI(gain_arr=gain, loss_arr=loss, n=14)
Try this:
"""dataset is a dataframe"""
def RSI(dataset, n=14):
delta = dataset.diff()
dUp, dDown = delta.copy(), delta.copy()
dUp[dUp < 0] = 0
dDown[dDown > 0] = 0
RolUp = pd.Series(dUp).rolling(window=n).mean()
RolDown = pd.Series(dDown).rolling(window=n).mean().abs()
RS = RolUp / RolDown
rsi= 100.0 - (100.0 / (1.0 + RS))
return rsi
Fairly new to Python. I'm parsing an XML file and the following code returns the undesired results. I can understand why I'm getting my results - there are two escalations in the XML for this deal and I'm getting results for each set. I'm need help updating my code to only return the monthly rent for each escalation in the XML:
<RentEscalations>
<RentEscalation ID="354781">
<BeginIn>7</BeginIn>
<Escalation>3.8</Escalation>
<RecurrenceInterval>12</RecurrenceInterval>
<EscalationType>bump</EscalationType>
</RentEscalation>
<RentEscalation ID="354782">
<BeginIn>61</BeginIn>
<Escalation>1.0</Escalation>
<RecurrenceInterval>12</RecurrenceInterval>
<EscalationType>bump</EscalationType>
</RentEscalation>
</RentEscalations>
The rent starts at $3.00/sqft for the first 6 months. This XML block shows that, for each 12 months (RecurrenceInterval), the rent will be $6.80/sqft ($3.00 base + $3.80 escalation). The following twelve months will be $10.60 ($6.80 + 3.80). Each year, the amount per square foot will increase by $3.80 until the 61st month in the term. At that point, the rent will increase by $1.00/sqft for the remainder of the term. The entire term of the lease is 120 months.
My results include 114 results based on the first escalation (3.80/sqft) followed by 114 rows showing as if the rent starts at $3.00/sqft incrementing by $1.00/sqft each year.
Any help is appreciated!
import xml.etree.ElementTree as ET
import pyodbc
import dateutil.relativedelta as rd
import datetime as dt
tree = ET.parse('C:\\FileLocation\\DealData.xml')
root = tree.getroot()
for deal in root.findall("Deals"):
for dl in deal.findall("Deal"):
dealid = dl.get("DealID")
for dts in dl.findall("DealTerms/DealTerm"):
dtid = dts.get("ID")
darea = float(dts.find("RentableArea").text)
dterm = int(dts.find("LeaseTerm").text)
for brrent in dts.findall("BaseRents/BaseRent"):
brid = brrent.get("ID")
rent = float(brrent.find("Rent").text)
darea = float(dts.find("RentableArea").text)
per = brrent.find("Period").text
dtstart = dts.find("CommencementDate").text
startyr = int(dtstart[0:4])
startmo = int(dtstart[5:7])
startday = int(dtstart[8:])
start = dt.date(startyr, startmo, startday)
end = start + rd.relativedelta(months=dterm)
if brrent.find("Duration").text is None:
duration = 0
else:
duration = int(brrent.find("Duration").text)
termbal = dterm - duration
for resc in dts.findall("RentEscalations/RentEscalation"):
rescid = resc.get("ID")
esctype = resc.find("EscalationType").text
begmo = int(resc.find("BeginIn").text)
esc = float(resc.find("Escalation").text)
intrvl = int(resc.find("RecurrenceInterval").text)
if intrvl != 0:
pers = termbal / intrvl
else:
pers = 0
escst = start + rd.relativedelta(months=begmo - 1)
i = 0
x = begmo
newrate = rent
while i < termbal:
billdt = escst + rd.relativedelta(months=i)
if per == "rsf/year":
monthlyamt = (newrate + esc) * darea / 12.0
if per == "month":
monthlyamt = newrate + esc
if per == "year":
monthlyamt = (newrate + esc) / 12.0
if per == "rsf/month":
monthlyamt = (newrate + esc) * darea
try:
if i % intrvl == 0:
level = x + 1
newrent = monthlyamt
x += 1
newrate += esc
else:
level = x
except ZeroDivisionError:
break
i += 1
if dealid == "1254278":
print(dealid, dtid, rescid, dterm, darea, escst, rent, intrvl, esctype, termbal, \
monthlyamt, billdt, pers, level, newrate, newrent)
parts_list = imp_parts_df['Parts'].tolist()
sub_week_list = ['2016-12-11', '2016-12-04', '2016-11-27', '2016-11-20', '2016-11-13']
i = 0
start = DT.datetime.now()
for p in parts_list:
for thisdate in sub_week_list:
thisweek_start = pd.to_datetime(thisdate, format='%Y-%m-%d') #'2016/12/11'
thisweek_end = thisweek_start + DT.timedelta(days=7) # add 7 days to the week date
val_shipped = len(shipment_df[(shipment_df['loc'] == 'USW1') & (shipment_df['part'] == str(p)) & (shipment_df['shipped_date'] >= thisweek_start) & (shipment_df['shipped_date'] < thisweek_end)])
print(DT.datetime.now() - start).total_seconds()
shipment_df has around 35000 records
partlist has 436 parts
sub_week_list has 5 dates in it
it took overall 438.13 secs to run this code
Is there any faster way to do it?
parts_list = imp_parts_df['Parts'].astype(str).tolist()
i = 0
start = DT.datetime.now()
for p in parts_list:
q = 'loc == "xxx" & part == #p & "2016-11-20" <= shipped_date < "2016-11-27"'
val_shipped = len(shipment_df.query(q))
print (DT.datetime.now() - start).total_seconds()
After solving a naive datetime problem I am facing a new problem on a view to generate graphs. Now I get mktime argument out of range.
I have no idea how to solve it. I didn't write the code, I am using it from a colleague of mine and I can't seem o understand why it fails. I think it has to do with a function that runs overtime and the error pops out.
#login_required(login_url='/accounts/login/')
def loggedin(request):
data = []
data2 = []
data3 = []
dicdata2 = {}
dicdata3 = {}
datainterior = []
today = timezone.localtime(timezone.now()+timedelta(hours=1)).date()
tomorrow = today + timedelta(1)
semana= today - timedelta(7)
today = today - timedelta(1)
semana_start = datetime.combine(today, time())
semana_start = timezone.make_aware(semana_start, timezone.utc)
today_start = datetime.combine(today, time())
today_start = timezone.make_aware(today_start, timezone.utc)
today_end = datetime.combine(tomorrow, time())
today_end = timezone.make_aware(today_end, timezone.utc)
for modulo in Repository.objects.values("des_especialidade").distinct():
dic = {}
mod = str(modulo['des_especialidade'])
dic["label"] = str(mod)
dic["value"] = Repository.objects.filter(des_especialidade__iexact=mod).count()
data.append(dic)
for modulo in Repository.objects.values("modulo").distinct():
dic = {}
mod = str(modulo['modulo'])
dic["label"] = str(mod)
dic["value"] = Repository.objects.filter(modulo__iexact=mod, dt_diag__gte=semana_start).count()
datainterior.append(dic)
# print mod, Repository.objects.filter(modulo__iexact=mod).count()
# data[mod] = Repository.objects.filter(modulo__iexact=mod).count()
dicdata2['values'] = datainterior
dicdata2['key'] = "Cumulative Return"
dicdata3['values'] = data
dicdata3['color'] = "#d67777"
dicdata3['key'] = "Diagnosticos Identificados"
data3.append(dicdata3)
data2.append(dicdata2)
#-------sunburst
databurst = []
dictburst = {}
dictburst['name'] = "CHP"
childrenmodulo = []
for modulo in Repository.objects.values("modulo").distinct():
childrenmodulodic = {}
mod = str(modulo['modulo'])
childrenmodulodic['name'] = mod
childrenesp = []
for especialidade in Repository.objects.filter(modulo__iexact=mod).values("des_especialidade").distinct():
childrenespdic = {}
esp = str(especialidade['des_especialidade'])
childrenespdic['name'] = esp
childrencode = []
for code in Repository.objects.filter(modulo__iexact=mod,des_especialidade__iexact=esp).values("cod_diagnosis").distinct():
childrencodedic = {}
codee= str(code['cod_diagnosis'])
childrencodedic['name'] = 'ICD9 - '+codee
childrencodedic['size'] = Repository.objects.filter(modulo__iexact=mod,des_especialidade__iexact=esp,cod_diagnosis__iexact=codee).count()
childrencode.append(childrencodedic)
childrenespdic['children'] = childrencode
#childrenespdic['size'] = Repository.objects.filter(des_especialidade__iexact=esp).count()
childrenesp.append(childrenespdic)
childrenmodulodic['children'] = childrenesp
childrenmodulo.append(childrenmodulodic)
dictburst['children'] = childrenmodulo
databurst.append(dictburst)
# print databurst
# --------stacked area chart
datastack = []
for modulo in Repository.objects.values("modulo").distinct():
datastackdic = {}
mod = str(modulo['modulo'])
datastackdic['key'] = mod
monthsarray = []
year = timezone.localtime(timezone.now()+timedelta(hours=1)).year
month = timezone.localtime(timezone.now()+timedelta(hours=1)).month
last = timezone.localtime(timezone.now()+timedelta(hours=1)) - relativedelta(years=1)
lastyear = int(last.year)
lastmonth = int(last.month)
#i = 1
while lastmonth <= int(month) or lastyear<int(year):
date = str(lastmonth) + '/' + str(lastyear)
if (lastmonth < 12):
datef = str(lastmonth + 1) + '/' + str(lastyear)
else:
lastmonth = 01
lastyear = int(lastyear)+1
datef = str(lastmonth)+'/'+ str(lastyear)
lastmonth = 0
datainicial = datetime.strptime(date, '%m/%Y')
datainicial = timezone.make_aware(datainicial, timezone.utc)
datafinal = datetime.strptime(datef, '%m/%Y')
datafinal = timezone.make_aware(datafinal, timezone.utc)
#print "lastmonth",lastmonth,"lastyear", lastyear
#print "datainicial:",datainicial,"datafinal: ",datafinal
filtro = Repository.objects.filter(modulo__iexact=mod)
count = filtro.filter(dt_diag__gte=datainicial, dt_diag__lt=datafinal).count()
conv = datetime.strptime(date, '%m/%Y')
ms = datetime_to_ms_str(conv)
monthsarray.append([ms, count])
#i += 1
lastmonth += 1
datastackdic['values'] = monthsarray
datastack.append(datastackdic)
#print datastack
if request.user.last_login is not None:
#print(request.user.last_login)
contador_novas = Repository.objects.filter(dt_diag__lte=today_end, dt_diag__gte=today_start).count()
return render_to_response('loggedin.html',
{'user': request.user.username, 'contador': contador_novas, 'data': data, 'data2': data2,
'data3': data3,
'databurst': databurst, 'datastack':datastack})
def datetime_to_ms_str(dt):
return str(1000 * mktime(dt.timetuple()))
I think the problem is with this condition.
while lastmonth <= int(month) or lastyear<int(year):
During December, month=12, so lastmonth <= int(month) will always be True. So the loop whill always return True, even once lastyear is more that the current year.
You want to loop if the loop is in the previous year, or if the loop is in the current year and the month is not in the future. Therefore, I think you want to change it to the following:
while lastyear < year or (lastyear == year and lastmonth <= month):
To be sure that the code is working and to understand it, you need to add lots of print statements to the loops, see how lastmonth and lastyear change, and check that the loop exits when you expect it to. You also need to test it for other values of year and month so that it doesn't break next month. Ideally you want to extract this bit of the code into a separate function. It would be easier to understand the loop if it only returned a list of (month, year) integers, instead of doing lots of date formatting at the same time. Then it would be easier to add unit tests.