I have an array in python called dates with this content:
[datetime.datetime(2012, 1, 11, 17, 24, 12, 676000), datetime.datetime(2012, 2, 3, 11, 25, 17, 73000), datetime.datetime(2012, 2, 3, 14, 9, 23, 699000), datetime.datetime(2012, 2, 4, 9, 15, 26, 644000), datetime.datetime(2012, 2, 4, 17, 14, 36, 65000), datetime.datetime(2012, 2, 5, 6, 18, 31, 139000), datetime.datetime(2012, 2, 5, 14, 55, 28, 62000), datetime.datetime(2012, 2, 5, 18, 28, 59, 379000), datetime.datetime(2012, 2, 6, 12, 24, 21, 768000), datetime.datetime(2012, 2, 6, 17, 32, 46, 675000), datetime.datetime(2012, 2, 14, 11, 33, 6, 74000), datetime.datetime(2012, 2, 14, 11, 36, 48, 11000), datetime.datetime(2012, 2, 16, 8, 54, 14, 175000), datetime.datetime(2012, 2, 16, 18, 33, 9, 200000), datetime.datetime(2012, 2, 20, 8, 41, 2, 550000), datetime.datetime(2012, 2, 20, 9, 4, 37, 446000), datetime.datetime(2012, 2, 20, 10, 10, 42, 950000), datetime.datetime(2012, 2, 20, 21, 21, 21, 986000), datetime.datetime(2012, 2, 21, 9, 1, 8, 429000), datetime.datetime(2012, 2, 21, 12, 5, 20, 475000), datetime.datetime(2012, 2, 21, 13, 23, 25, 281000), datetime.datetime(2012, 2, 21, 15, 4, 29, 366000), datetime.datetime(2012, 2, 21, 15, 12, 21, 729000), datetime.datetime(2012, 2, 21, 15, 29, 10, 723000), datetime.datetime(2012, 2, 21, 18, 10, 24, 822000), datetime.datetime(2012, 2, 22, 10, 42, 11, 689000), datetime.datetime(2012, 2, 22, 13, 10, 1, 309000), datetime.datetime(2012, 2, 22, 20, 28, 34, 260000), datetime.datetime(2012, 2, 27, 17, 53, 19, 225000), datetime.datetime(2012, 2, 28, 8, 13, 57, 139000), datetime.datetime(2012, 3, 2, 7, 55, 11, 505000), datetime.datetime(2012, 3, 2, 21, 6, 35, 270000), datetime.datetime(2012, 3, 5, 8, 10, 47, 76000), datetime.datetime(2012, 3, 5, 9, 15, 15, 448000), datetime.datetime(2012, 3, 7, 18, 15, 35, 401000), datetime.datetime(2012, 3, 15, 8, 6, 56, 968000), datetime.datetime(2012, 3, 16, 15, 34, 10, 59000), datetime.datetime(2012, 3, 20, 18, 19, 13, 687000), datetime.datetime(2012, 3, 22, 8, 50, 28, 983000), datetime.datetime(2012, 3, 23, 8, 26, 5, 468000), datetime.datetime(2012, 3, 27, 7, 50, 14, 474000), datetime.datetime(2012, 3, 27, 15, 14, 35, 59000), datetime.datetime(2012, 4, 5, 7, 23, 1, 374000), datetime.datetime(2012, 4, 6, 13, 8, 59, 578000), datetime.datetime(2012, 4, 6, 13, 34, 24, 843000), datetime.datetime(2012, 4, 6, 15, 35, 40, 538000), datetime.datetime(2012, 4, 10, 7, 0, 37, 455000), datetime.datetime(2012, 4, 10, 7, 12, 37, 199000), datetime.datetime(2012, 4, 10, 7, 39, 16, 366000), datetime.datetime(2012, 4, 10, 7, 55, 51, 228000), datetime.datetime(2012, 4, 11, 7, 53, 31, 699000), datetime.datetime(2012, 4, 11, 15, 32, 21, 582000), datetime.datetime(2012, 4, 13, 10, 22, 4, 673000), datetime.datetime(2012, 4, 16, 7, 17, 20, 578000), datetime.datetime(2012, 11, 29, 16, 5, 21, 53000), datetime.datetime(2012, 11, 29, 16, 6, 15, 244000), datetime.datetime(2013, 1, 25, 9, 45, 48, 921000), datetime.datetime(2013, 2, 4, 18, 1, 1, 418000), datetime.datetime(2013, 2, 5, 6, 14, 55, 728000), datetime.datetime(2013, 2, 5, 17, 2, 11, 959000), datetime.datetime(2013, 2, 7, 6, 4, 8, 629000), datetime.datetime(2013, 2, 7, 18, 6, 47, 247000), datetime.datetime(2013, 2, 8, 5, 36, 55, 702000), datetime.datetime(2013, 2, 8, 8, 51, 46, 261000), datetime.datetime(2013, 2, 12, 5, 56, 37, 233000), datetime.datetime(2013, 2, 12, 16, 6, 25, 126000), datetime.datetime(2013, 2, 13, 7, 45, 33, 448000), datetime.datetime(2013, 2, 13, 10, 43, 15, 749000), datetime.datetime(2013, 2, 14, 6, 10, 27, 562000), datetime.datetime(2013, 2, 14, 16, 44, 45, 469000), datetime.datetime(2013, 2, 15, 6, 3, 12, 787000), datetime.datetime(2013, 2, 15, 14, 8, 40, 281000), datetime.datetime(2013, 2, 17, 11, 46, 41, 983000), datetime.datetime(2013, 2, 20, 15, 32, 52, 455000), datetime.datetime(2013, 2, 21, 16, 0, 40, 165000), datetime.datetime(2013, 2, 22, 9, 12, 55, 32000), datetime.datetime(2013, 2, 22, 15, 11, 45, 979000), datetime.datetime(2013, 2, 25, 6, 52, 49, 991000), datetime.datetime(2013, 2, 25, 8, 52, 8, 947000), datetime.datetime(2013, 2, 25, 9, 27, 7, 716000), datetime.datetime(2013, 2, 25, 9, 33, 21, 121000), datetime.datetime(2013, 2, 26, 7, 15, 0, 135000), datetime.datetime(2013, 2, 26, 16, 15, 39, 693000), datetime.datetime(2013, 2, 27, 6, 33, 23, 745000), datetime.datetime(2013, 2, 27, 17, 28, 47, 793000), datetime.datetime(2013, 2, 28, 5, 43, 32, 479000), datetime.datetime(2013, 2, 28, 17, 22, 15, 510000), datetime.datetime(2013, 3, 1, 6, 54, 21, 676000), datetime.datetime(2013, 3, 1, 15, 47, 19, 912000), datetime.datetime(2013, 3, 4, 17, 39, 55, 809000), datetime.datetime(2013, 3, 5, 6, 40, 35, 101000), datetime.datetime(2013, 3, 5, 17, 5, 4, 324000), datetime.datetime(2013, 3, 6, 6, 39, 42, 235000), datetime.datetime(2013, 3, 6, 16, 6, 29, 410000), datetime.datetime(2013, 3, 7, 6, 32, 56, 197000), datetime.datetime(2013, 3, 7, 17, 31, 39, 249000), datetime.datetime(2013, 3, 8, 6, 56, 44, 369000), datetime.datetime(2013, 3, 11, 7, 17, 20, 748000), datetime.datetime(2013, 3, 11, 17, 27, 43, 102000), datetime.datetime(2013, 3, 12, 7, 10, 24, 751000), datetime.datetime(2013, 3, 12, 10, 23, 44, 759000), datetime.datetime(2013, 3, 12, 15, 42, 20, 461000), datetime.datetime(2013, 3, 13, 7, 12, 40, 494000), datetime.datetime(2013, 3, 13, 12, 7, 24, 986000), datetime.datetime(2013, 3, 14, 6, 52, 10, 779000), datetime.datetime(2013, 3, 14, 16, 39, 12, 776000), datetime.datetime(2013, 3, 15, 7, 4, 26, 454000), datetime.datetime(2013, 3, 15, 16, 40, 37, 98000), datetime.datetime(2013, 3, 18, 6, 53, 56, 937000), datetime.datetime(2013, 3, 18, 16, 53, 26, 914000), datetime.datetime(2013, 3, 19, 6, 34, 41, 813000), datetime.datetime(2013, 3, 19, 17, 19, 59, 721000), datetime.datetime(2013, 3, 20, 6, 57, 37, 141000), datetime.datetime(2013, 3, 20, 15, 15, 43, 458000), datetime.datetime(2013, 3, 21, 15, 36, 12, 949000), datetime.datetime(2013, 3, 22, 6, 57, 21, 973000), datetime.datetime(2013, 3, 22, 15, 36, 14, 388000), datetime.datetime(2013, 3, 25, 7, 0, 43, 602000), datetime.datetime(2013, 3, 25, 18, 27, 0, 693000), datetime.datetime(2013, 3, 26, 17, 20, 48, 194000), datetime.datetime(2013, 3, 27, 7, 11, 17, 665000), datetime.datetime(2013, 3, 27, 18, 27, 41, 894000), datetime.datetime(2013, 3, 28, 7, 2, 8, 624000), datetime.datetime(2013, 3, 28, 11, 12, 22, 53000), datetime.datetime(2013, 4, 3, 5, 45, 23, 995000), datetime.datetime(2013, 4, 4, 6, 5, 39, 243000), datetime.datetime(2013, 4, 8, 6, 4, 34, 667000), datetime.datetime(2013, 4, 8, 17, 6, 8, 718000), datetime.datetime(2013, 4, 9, 6, 2, 32, 813000), datetime.datetime(2013, 4, 9, 15, 16, 46, 622000), datetime.datetime(2013, 4, 10, 5, 26, 16, 694000), datetime.datetime(2013, 4, 10, 18, 50, 54, 809000), datetime.datetime(2013, 4, 11, 15, 12, 29, 376000), datetime.datetime(2013, 4, 12, 6, 9, 38, 925000), datetime.datetime(2013, 4, 12, 14, 42, 32, 607000), datetime.datetime(2013, 4, 15, 10, 0, 59, 995000), datetime.datetime(2013, 4, 15, 10, 11, 42, 16000), datetime.datetime(2013, 4, 16, 6, 8, 3, 838000), datetime.datetime(2013, 4, 16, 15, 27, 35, 147000), datetime.datetime(2013, 4, 17, 6, 4, 44, 272000), datetime.datetime(2013, 4, 17, 15, 23, 0, 924000), datetime.datetime(2013, 4, 18, 6, 9, 55, 454000), datetime.datetime(2013, 4, 18, 15, 5, 43, 601000), datetime.datetime(2013, 4, 19, 6, 0, 38, 132000), datetime.datetime(2013, 4, 19, 16, 35, 26, 14000), datetime.datetime(2013, 4, 19, 17, 44, 17, 116000), datetime.datetime(2013, 4, 19, 17, 51, 48, 43000), datetime.datetime(2013, 4, 19, 17, 54, 30, 44000), datetime.datetime(2013, 4, 21, 14, 58, 56, 363000), datetime.datetime(2013, 4, 21, 15, 8, 11, 276000), datetime.datetime(2013, 4, 23, 6, 24, 57, 124000), datetime.datetime(2013, 4, 23, 15, 44, 30, 503000), datetime.datetime(2013, 4, 25, 6, 13, 9, 269000), datetime.datetime(2013, 4, 25, 15, 41, 11, 370000), datetime.datetime(2013, 4, 26, 6, 2, 17, 877000), datetime.datetime(2013, 4, 27, 16, 17, 34, 97000), datetime.datetime(2013, 4, 27, 18, 20, 57, 975000), datetime.datetime(2013, 4, 29, 10, 17, 41, 746000), datetime.datetime(2013, 4, 29, 16, 45, 18, 65000), datetime.datetime(2013, 4, 30, 6, 13, 2, 333000), datetime.datetime(2013, 4, 30, 15, 3, 22, 343000), datetime.datetime(2013, 5, 1, 7, 22, 40, 401000), datetime.datetime(2013, 5, 1, 11, 16, 38, 525000), datetime.datetime(2013, 5, 2, 6, 7, 7, 749000), datetime.datetime(2013, 5, 3, 12, 48, 22, 617000), datetime.datetime(2013, 5, 6, 6, 1, 1, 168000), datetime.datetime(2013, 5, 6, 14, 56, 48, 236000), datetime.datetime(2013, 5, 7, 16, 47, 4, 597000), datetime.datetime(2013, 5, 8, 15, 26, 52, 105000), datetime.datetime(2013, 5, 10, 6, 10, 39, 379000), datetime.datetime(2013, 5, 13, 6, 9, 57, 990000), datetime.datetime(2013, 5, 13, 19, 56, 15, 354000), datetime.datetime(2013, 5, 15, 16, 39, 9, 127000), datetime.datetime(2013, 5, 16, 5, 59, 27, 609000), datetime.datetime(2013, 5, 16, 14, 18, 33, 253000), datetime.datetime(2013, 5, 17, 6, 20, 11, 853000), datetime.datetime(2013, 5, 21, 15, 38, 10, 53000), datetime.datetime(2013, 5, 22, 5, 59, 8, 126000), datetime.datetime(2013, 5, 22, 15, 48, 55, 877000), datetime.datetime(2013, 5, 23, 5, 47, 4, 779000), datetime.datetime(2013, 5, 23, 16, 59, 16, 948000), datetime.datetime(2013, 5, 24, 10, 57, 34, 831000), datetime.datetime(2013, 5, 24, 12, 29, 17, 332000), datetime.datetime(2013, 5, 27, 17, 0, 14, 513000), datetime.datetime(2013, 6, 20, 7, 28, 45, 975000), datetime.datetime(2013, 6, 20, 13, 31, 13, 228000), datetime.datetime(2013, 6, 21, 6, 18, 47, 789000), datetime.datetime(2013, 7, 1, 6, 12, 3, 640000), datetime.datetime(2013, 7, 1, 14, 33, 9, 251000), datetime.datetime(2013, 7, 2, 14, 59, 0, 421000), datetime.datetime(2013, 7, 3, 6, 12, 58, 282000), datetime.datetime(2013, 7, 3, 17, 23, 38, 745000), datetime.datetime(2013, 7, 5, 13, 40, 44, 719000), datetime.datetime(2013, 7, 9, 14, 51, 27, 348000), datetime.datetime(2013, 7, 10, 5, 12, 3, 104000)]
It should be ordered by date. What I need to know is how many days apear on this array. If there are many dates of the same day, I'll count only 1.
I could do it "by hand", iterating over each point and checking to a temp variable and count the days, but isn't there a faster, proper way to "unique" by days?
thanks
You can use the datetime.date() method:
s = {d.date() for d in dates}
print len(s)
Since dates are hashable, you can put them in a set just fine...
Note that you could also get a count of how many times each date appeared:
import collections
print collections.Counter(d.date() for d in dates)
Or, even do a list of datetime instances keyed by date:
import collections
d = collections.defaultdict(list)
for dt in dates:
d[dt.date()].append(dt)
Although, I suppose that since the input is sorted, you could do the same thing more or less with itertools.groupby:
for date, dt_group in itertools.groupby(dates, key=lambda dt: dt.date()):
print date, list(dt_group)
If you simply want to count how many unique days there are, the following works:
print len({(i.day,i.month,i.year) for i in dates})
This is to ensure that it is infact the same date and not just the same day number, since 1st of November has the say .day as the 1st of december but they are obviously not the same day.
Related
I have the code below, my problem is in the format "datetime.datetime", I can't fit it using a Gaussian function.
import numpy as np
import datetime as datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from scipy.optimize import curve_fit
#ydata
Temperature = ([1.05436258e+03, 1.09296819e+03, 1.05145602e+03, 1.05894926e+03,
1.08366115e+03, 1.08066721e+03, 1.04696163e+03, 1.01842441e+03,
1.04944307e+03, 1.06891551e+03, 1.01844764e+03, 1.03511906e+03,
1.02044599e+03, 9.93275818e+02, 1.03741013e+03, 1.00540470e+03,
1.02573646e+03, 9.75301913e+02, 1.00045743e+03, 1.03321562e+03,
1.03731104e+03, 9.68730834e+02, 9.07474634e+02, 9.30465587e+02,
9.98967526e+02, 9.11791887e+02, 9.15951873e+02, 8.29331306e+02,
9.31702088e+02, 8.90075633e+02, 8.30659093e+02, 8.78715978e+02,
8.66238768e+02, 8.97958014e+02, 6.77909787e+02, 2.23437657e-01,
9.40495055e+02, 8.41990924e+02, 8.75391469e+02, 8.98393043e+02,
9.25048353e+02, 9.31445104e+02, 9.04151363e+02, 2.28176362e-01,
9.65550728e+02, 9.16348809e+02, 9.36315168e+02, 9.00445995e+02,
8.87768320e+02, 8.75064126e+02, 8.81480871e+02, 8.78240278e+02,
8.62958271e+02, 8.93813659e+02, 8.83678318e+02, 9.23593998e+02,
9.15524580e+02, 8.77919073e+02, 8.91754242e+02, 9.19274917e+02,
8.62223914e+02, 8.81275387e+02, 8.62331470e+02, 8.69461632e+02,
8.90014577e+02, 9.02656117e+02, 8.74446393e+02, 8.76284046e+02,
8.66751916e+02, 8.54095049e+02, 8.44540741e+02, 8.70263794e+02,
8.66687327e+02, 8.18019291e+02, 8.21875267e+02, 8.13385138e+02,
8.43198211e+02, 8.70558259e+02, 7.94039978e+02, 8.13497634e+02,
8.12217789e+02, 8.01361143e+02, 8.00263045e+02, 7.47101493e+02,
7.35923635e+02, 7.32930255e+02, 7.75930026e+02, 7.83786631e+02,
7.75255742e+02, 7.74938671e+02, 7.04186773e+02, 7.47612911e+02,
7.29315237e+02, 6.94021293e+02, 7.42723487e+02, 7.09890191e+02,
7.60674339e+02, 7.51491228e+02, 7.23875166e+02, 7.41451471e+02,
7.49694410e+02, 7.43337883e+02, 7.00286359e+02, 7.20250078e+02,
7.32189596e+02, 6.93097572e+02, 7.82342462e+02, 7.11995854e+02,
6.84432159e+02, 7.61195087e+02, 7.46725427e+02, 7.44614939e+02,
6.48985204e+02, 6.76023106e+02, 6.89141056e+02, 6.27855922e+02,
7.07298358e+02, 6.52207871e+02, 6.52609278e+02, 6.80525240e+02,
6.89328581e+02, 6.78148423e+02, 7.28229663e+02, 6.91857497e+02,
7.43998987e+02, 6.96885527e+02, 7.33249599e+02, 7.22833678e+02,
7.34832942e+02, 7.19049095e+02, 7.03573908e+02, 7.11151460e+02,
6.89345427e+02, 6.14126253e+02, 4.60412424e+02])
#xdata
time=[datetime.datetime(2015, 11, 7, 18, 14, 24),
datetime.datetime(2015, 11, 7, 18, 19, 12),
datetime.datetime(2015, 11, 7, 18, 23, 9),
datetime.datetime(2015, 11, 7, 18, 26, 38),
datetime.datetime(2015, 11, 7, 18, 29, 55),
datetime.datetime(2015, 11, 7, 18, 32, 52),
datetime.datetime(2015, 11, 7, 18, 35, 36),
datetime.datetime(2015, 11, 7, 18, 38, 26),
datetime.datetime(2015, 11, 7, 18, 41, 13),
datetime.datetime(2015, 11, 7, 18, 44, 16),
datetime.datetime(2015, 11, 7, 18, 47, 12),
datetime.datetime(2015, 11, 7, 18, 50, 1),
datetime.datetime(2015, 11, 7, 18, 53, 2),
datetime.datetime(2015, 11, 7, 18, 56, 17),
datetime.datetime(2015, 11, 7, 18, 59, 45),
datetime.datetime(2015, 11, 7, 19, 3, 14),
datetime.datetime(2015, 11, 7, 19, 6, 28),
datetime.datetime(2015, 11, 7, 19, 10, 4),
datetime.datetime(2015, 11, 7, 19, 13, 46),
datetime.datetime(2015, 11, 7, 19, 17, 47),
datetime.datetime(2015, 11, 7, 19, 21, 35),
datetime.datetime(2015, 11, 7, 19, 25, 15),
datetime.datetime(2015, 11, 7, 19, 29, 22),
datetime.datetime(2015, 11, 7, 19, 33, 41),
datetime.datetime(2015, 11, 7, 19, 38, 38),
datetime.datetime(2015, 11, 7, 19, 43, 16),
datetime.datetime(2015, 11, 7, 19, 47, 53),
datetime.datetime(2015, 11, 7, 19, 53, 21),
datetime.datetime(2015, 11, 7, 19, 59, 4),
datetime.datetime(2015, 11, 7, 20, 5, 14),
datetime.datetime(2015, 11, 7, 20, 11, 6),
datetime.datetime(2015, 11, 7, 20, 17, 7),
datetime.datetime(2015, 11, 7, 20, 24, 11),
datetime.datetime(2015, 11, 7, 20, 31, 5),
datetime.datetime(2015, 11, 7, 20, 38, 1),
datetime.datetime(2015, 11, 7, 20, 44, 39),
datetime.datetime(2015, 11, 7, 20, 50, 8),
datetime.datetime(2015, 11, 7, 20, 54, 31),
datetime.datetime(2015, 11, 7, 20, 59, 28),
datetime.datetime(2015, 11, 7, 21, 4, 54),
datetime.datetime(2015, 11, 7, 21, 10, 24),
datetime.datetime(2015, 11, 7, 21, 15, 56),
datetime.datetime(2015, 11, 7, 21, 21, 50),
datetime.datetime(2015, 11, 7, 21, 27, 38),
datetime.datetime(2015, 11, 7, 21, 33, 24),
datetime.datetime(2015, 11, 7, 21, 37, 54),
datetime.datetime(2015, 11, 7, 21, 42, 24),
datetime.datetime(2015, 11, 7, 21, 47, 20),
datetime.datetime(2015, 11, 7, 21, 52, 12),
datetime.datetime(2015, 11, 7, 21, 57, 3),
datetime.datetime(2015, 11, 7, 22, 1, 41),
datetime.datetime(2015, 11, 7, 22, 6, 21),
datetime.datetime(2015, 11, 7, 22, 11, 30),
datetime.datetime(2015, 11, 7, 22, 16, 44),
datetime.datetime(2015, 11, 7, 22, 21, 59),
datetime.datetime(2015, 11, 7, 22, 26, 56),
datetime.datetime(2015, 11, 7, 22, 32),
datetime.datetime(2015, 11, 7, 22, 37, 43),
datetime.datetime(2015, 11, 7, 22, 43, 21),
datetime.datetime(2015, 11, 7, 22, 48, 45),
datetime.datetime(2015, 11, 7, 22, 53, 49),
datetime.datetime(2015, 11, 7, 22, 58, 49),
datetime.datetime(2015, 11, 7, 23, 4, 4),
datetime.datetime(2015, 11, 7, 23, 9, 8),
datetime.datetime(2015, 11, 7, 23, 14, 3),
datetime.datetime(2015, 11, 7, 23, 18, 34),
datetime.datetime(2015, 11, 7, 23, 22, 58),
datetime.datetime(2015, 11, 7, 23, 27, 43),
datetime.datetime(2015, 11, 7, 23, 32, 22),
datetime.datetime(2015, 11, 7, 23, 36, 48),
datetime.datetime(2015, 11, 7, 23, 41, 9),
datetime.datetime(2015, 11, 7, 23, 45, 29),
datetime.datetime(2015, 11, 7, 23, 49, 59),
datetime.datetime(2015, 11, 7, 23, 54, 34),
datetime.datetime(2015, 11, 7, 23, 59, 6),
datetime.datetime(2015, 11, 8, 0, 3, 37),
datetime.datetime(2015, 11, 8, 0, 8, 17),
datetime.datetime(2015, 11, 8, 0, 13, 15),
datetime.datetime(2015, 11, 8, 0, 18, 22),
datetime.datetime(2015, 11, 8, 0, 23, 23),
datetime.datetime(2015, 11, 8, 0, 28, 28),
datetime.datetime(2015, 11, 8, 0, 33, 59),
datetime.datetime(2015, 11, 8, 0, 39, 51),
datetime.datetime(2015, 11, 8, 0, 45, 56),
datetime.datetime(2015, 11, 8, 0, 51, 57),
datetime.datetime(2015, 11, 8, 0, 57, 48),
datetime.datetime(2015, 11, 8, 1, 4, 2),
datetime.datetime(2015, 11, 8, 1, 10, 47),
datetime.datetime(2015, 11, 8, 1, 17, 43),
datetime.datetime(2015, 11, 8, 1, 24, 22),
datetime.datetime(2015, 11, 8, 1, 30, 39),
datetime.datetime(2015, 11, 8, 1, 37, 2),
datetime.datetime(2015, 11, 8, 1, 43, 51),
datetime.datetime(2015, 11, 8, 1, 50, 38),
datetime.datetime(2015, 11, 8, 1, 57, 23),
datetime.datetime(2015, 11, 8, 2, 4, 3),
datetime.datetime(2015, 11, 8, 2, 10, 46),
datetime.datetime(2015, 11, 8, 2, 18, 6),
datetime.datetime(2015, 11, 8, 2, 25, 14),
datetime.datetime(2015, 11, 8, 2, 32, 30),
datetime.datetime(2015, 11, 8, 2, 39, 35),
datetime.datetime(2015, 11, 8, 2, 46, 49),
datetime.datetime(2015, 11, 8, 2, 54, 43),
datetime.datetime(2015, 11, 8, 3, 2, 33),
datetime.datetime(2015, 11, 8, 3, 10, 15),
datetime.datetime(2015, 11, 8, 3, 17, 28),
datetime.datetime(2015, 11, 8, 3, 24, 30),
datetime.datetime(2015, 11, 8, 3, 32, 8),
datetime.datetime(2015, 11, 8, 3, 39, 13),
datetime.datetime(2015, 11, 8, 3, 46, 10),
datetime.datetime(2015, 11, 8, 3, 52, 48),
datetime.datetime(2015, 11, 8, 3, 59, 1),
datetime.datetime(2015, 11, 8, 4, 5, 39),
datetime.datetime(2015, 11, 8, 4, 11, 59),
datetime.datetime(2015, 11, 8, 4, 18, 27),
datetime.datetime(2015, 11, 8, 4, 24, 49),
datetime.datetime(2015, 11, 8, 4, 31, 7),
datetime.datetime(2015, 11, 8, 4, 39, 18),
datetime.datetime(2015, 11, 8, 4, 46, 26),
datetime.datetime(2015, 11, 8, 4, 53, 13),
datetime.datetime(2015, 11, 8, 5, 0, 11),
datetime.datetime(2015, 11, 8, 5, 8, 57),
datetime.datetime(2015, 11, 8, 5, 15, 45),
datetime.datetime(2015, 11, 8, 5, 22, 6),
datetime.datetime(2015, 11, 8, 5, 28, 5),
datetime.datetime(2015, 11, 8, 5, 34, 57),
datetime.datetime(2015, 11, 8, 5, 40, 4),
datetime.datetime(2015, 11, 8, 5, 44, 45),
datetime.datetime(2015, 11, 8, 5, 49, 8),
datetime.datetime(2015, 11, 8, 5, 54, 41),
datetime.datetime(2015, 11, 8, 5, 58, 46),
datetime.datetime(2015, 11, 8, 6, 2, 35),
datetime.datetime(2015, 11, 8, 6, 6, 18),
datetime.datetime(2015, 11, 8, 6, 11, 11),
datetime.datetime(2015, 11, 8, 6, 14, 45)]
#ploting data
#plt.plot(time, Temperature)
#plt.show()
#curve_fit
def fun(x, A, B) :
return A*np.exp(-1*B*x**2)
parameters, covariance = curve_fit(fun, time, Temperature)
plt.plot(time, fun(time, *parameters))
plt.show()
this is the result of execution it :
Traceback (most recent call last): File "datetime_fit.py", line 187,
in
parameters, covariance = curve_fit(fun, time, Temperature) File "/home/lhoussine/anaconda3/lib/python3.8/site-packages/scipy/optimize/minpack.py",
line 742, in curve_fit
xdata = np.asarray_chkfinite(xdata, float) File "/home/lhoussine/anaconda3/lib/python3.8/site-packages/numpy/lib/function_base.py",
line 486, in asarray_chkfinite
a = asarray(a, dtype=dtype, order=order) TypeError: float() argument must be a string or a number, not 'datetime.datetime'
You'll need to use a numeric representation of date and time, e.g. Unix time which you can obtain from the datetime object's timestamp method. Ex:
def fun(x, A, B) :
return A*x + B # a simple linear fit for illustration
ts = np.array([t.timestamp() for t in time])
parameters, covariance = curve_fit(fun, ts, Temperature)
plt.plot(time, Temperature)
plt.plot(time, fun(ts, *parameters))
plt.show()
I'm interested in reordering the bits within a number, and since I want to do it several trillion times, I want to do it fast.
Here are the details: given a number num and an order matrix order.
order contains up to ~6000 lines of permutations of the numbers 0..31.
These are the positions to which the bits change.
Simplified example: binary(num) = 1001, order[1]=[0,1,3,2], reordered number for order[1] would be 1010 (binary).
Now I want to know, if my input number num is the smallest of these (~6000) reordered numbers. I'm searching for all 32-Bit numbers which fullfill this criterion.
My current approach is to slow, so I'm looking for a speedup.
minimal-reproducible-example:
num = 1753251840
order = [[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[ 3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8, 15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20, 27, 26, 25, 24, 31, 30, 29, 28],
[15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16],
[31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
[ 0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23, 8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31],
[21, 20, 23, 22, 29, 28, 31, 30, 17, 16, 19, 18, 25, 24, 27, 26, 5, 4, 7, 6, 13, 12, 15, 14, 1, 0, 3, 2, 9, 8, 11, 10]]
patterns=set()
bits = format(num, '032b')
for perm in order:
bitsn = [bits[perm[i]] for i in range(32)]
patterns.add(int(''.join(bitsn),2))
print( min(patterns)==num)
Where can I start to improve this?
Extracting bits using string is generally very inefficient (whatever the language). The same thing also apply for parsing. Moreover, for such a fast low-level operation, you need to use a JIT or a compiled language as comments already pointed out.
Here is a prototype using the Numba's JIT (assume all numbers are unsigned):
npOrder = np.array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[ 3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8, 15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20, 27, 26, 25, 24, 31, 30, 29, 28],
[15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16],
[31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
[ 0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23, 8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31],
[21, 20, 23, 22, 29, 28, 31, 30, 17, 16, 19, 18, 25, 24, 27, 26, 5, 4, 7, 6, 13, 12, 15, 14, 1, 0, 3, 2, 9, 8, 11, 10]], dtype=np.uint32)
#njit
def extractBits(num):
bits = np.empty(32, dtype=np.int32)
for i in range(32):
bits[i] = (num >> i) & 0x01
return bits
#njit
def permuteAndMerge(bits, perm):
bitsnFinal = 0
for i in range(32):
bitsnFinal |= bits[31-perm[i]] << i
return bitsnFinal
#njit
def computeOptimized(num):
bits = extractBits(num)
permCount = npOrder.shape[0]
patterns = np.empty(permCount, dtype=np.uint32)
for i in range(permCount):
patterns[i] = permuteAndMerge(bits, npOrder[i])
# The array can be converted to a set if needed here with: set(patterns)
return min(patterns) == num
This code is about 25 time faster than the original one on my machine (ran 5 000 000 times).
You can also use Numba to accelerate and parallelize the loop that run the function computeOptimized resulting in a significant additional speed-up.
Note that this code can be again much faster in C or C++ using low-level processor instructions (available for example on many x86_64 processors). With that and parallelism, the order of magnitude of the execution speed should be close to a billion of permutation per second.
Couple of possible speed-ups, staying with Python and the current algorithm:
Bail out as soon as you find a pattern less than num; once one like that is found, the condition cannot possibly be true. (You also don't need to store patterns; at most a flag whether an equal one was found, if that's not guaranteed by the problem.)
bitsn could be a generator expression, and doesn't need to be in a variable; you'll have to measure whether that's faster.
More fundamental improvements:
If you want to find all the numbers (rather than just test a particular one), it feels like there ought to be a faster algorithm by considering what the bits mean. A couple of hours thinking could potentially let you process just the 6000 lists, rather than all 2³² integers.
As others have written, if you're after pure speed, python is not the ideal language. That depends on the balance of how much time you want to spend on programming vs on running the program.
Side note:
Are the 32-bit integers signed or unsigned?
I have the following code, which on first glance should produce 10 Jobs with 3 Tasks each.
class Job:
id = None
tasks = {}
class Task:
id = None
cnt = 0
jobs = []
for i in range(0, 10):
job = Job()
job.id = i
for ii in range(0, 3):
task = Task()
task.id = cnt
job.tasks[task.id] = task
cnt += 1
jobs.append(job)
for job in jobs:
print("job {}, tasks: {}".format(job.id, job.tasks.keys()))
The result is somehow surprising - we have 30 Tasks shared by each Job:
job 0, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 1, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 2, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 3, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 4, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 5, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 6, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 7, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 8, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
job 9, tasks: dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
Can someone explain what is going on in here?
UPDATE
tasks is a class variable shared by all the instances.
In your Job class you need to do this
class Job:
id = None
def __init__(self):
self.tasks = {}
tasks is in your class and each time you are appending to the class tasks which is shared by all the instances.
I'm trying to save some data using pandas as csv and then I'm gonna load and use those data in somewhere else. This is my code for saving data
shuffledInteractionsFirstSeq = [[11, 15, 19, 9, 6, 8, 15, 9, 12, 21, 14, 7, 10, 20, 15, 6, 6, 17, 10, 10, 10, 6, 11, 10, 11, 8, 2, 16, 1, 19, 4, 9, 10, 20, 19, 17, 19, 21, 21, 6, 19, 13, 19, 20, 9, 4, 1, 17, 17, 17, 10, 5, 2, 1, 16, 3, 1, 9, 1, 21, 3, 17, 4, 19, 7, 12, 19, 20, 1, 17, 7, 1, 2, 19, 13, 17, 3, 13, 12, 13, 14, 4, 8, 19, 10, 4, 12, 19, 17, 4, 12, 5, 12, 11, 20, 9, 12, 12, 11, 19, 4, 14, 11, 7, 4, 3, 8, 8, 16, 10, 20, 3, 14, 16, 10, 9, 13, 2, 19, 9, 10, 17, 13, 10, 2, 19, 17, 10, 7, 2, 17, 12, 10, 9, 12, 1, 17, 12, 17, 9, 16, 16, 12, 20, 9, 4, 11, 3, 15, 6, 4, 8, 9, 12, 2, 16, 5, 9, 19, 17, 17, 16, 8, 15, 12, 9, 11, 14, 9, 4, 21, 1, 10, 5, 21, 9, 10, 3, 19, 19, 13, 8, 3, 12, 3, 12, 17, 16, 21, 9, 10, 8, 12, 2, 12, 17, 16, 19, 8, 17, 14, 1, 2, 13, 9, 19, 16, 5, 4, 13, 8, 13, 8, 7, 21, 2, 1, 13, 1, 6, 5, 1, 8, 10, 9, 2, 12, 3, 9, 9, 5, 12, 6, 16, 6, 13, 2, 17, 12, 19, 16, 17, 19, 14, 2, 17, 7, 6, 8, 15, 13, 19, 19, 16, 17, 14, 10, 10, 10, 12, 6, 16, 10, 1, 4, 4, 6, 19, 19, 8, 15, 16, 4, 12, 5, 17, 3, 12, 1, 9, 17, 8, 8, 19, 14, 10, 9, 4, 16, 19, 4, 8, 12, 2, 17, 15, 13, 12, 12, 12, 17, 15, 9, 16, 8, 17, 8, 6, 13, 6, 15, 1, 5, 21, 1, 17, 6, 3, 8, 8, 6, 3, 8, 15, 14, 1, 7, 2, 12, 8, 16, 6, 4, 9, 20, 12, 12, 17, 10, 9, 14, 8, 19, 17, 9, 10, 14, 1, 14, 5, 6, 12, 9, 17, 8, 19, 5, 9, 14, 16, 16, 6, 6, 3, 13, 4, 8, 19, 11, 7, 16, 5, 12, 2, 6, 6, 4, 5, 5, 21, 2, 12, 16, 17, 14, 10, 5, 12, 16, 17, 20, 12, 12, 17, 8, 6, 13, 12, 12, 17, 12, 6, 17, 8, 17, 10, 13, 2, 15, 8, 9, 14, 8, 8, 12, 15, 20, 14, 4, 19, 6, 9, 1, 11, 21, 1, 13, 13, 8, 15, 6, 14, 8, 15, 2, 16, 16, 12, 8, 17, 6, 10, 10, 10, 17, 15, 3, 6, 6, 9, 4, 8, 16, 12, 17, 17, 4, 8, 5, 15, 13, 6, 6, 6, 3, 11, 15, 3, 12, 20, 15, 16, 4, 10, 21, 9, 21, 9, 19, 19, 9, 8, 4, 13, 10, 6, 19, 1, 13, 17, 9, 1, 9, 15, 15, 19, 19, 14, 15, 4, 9, 15, 1, 19, 17, 10, 6, 1, 11, 5, 10, 6, 5, 10, 6, 1, 1, 6, 16, 17, 11, 6, 1, 15, 16, 10, 17, 10, 17, 19, 14, 1, 15, 14, 10, 10, 16, 6, 8, 19, 14, 14, 14, 12, 12, 10, 10, 15, 1, 8, 4, 1, 14, 14, 7, 10, 10, 14, 10, 17, 19, 20, 6, 8, 9, 14, 10, 14, 1, 15, 19, 10, 1, 19, 4, 15, 21, 10, 9, 3, 14, 14, 10, 10, 6, 8, 20, 6, 2, 16, 6, 9, 10, 8, 2, 17, 17, 1, 19, 13, 20, 12, 1, 16, 20, 16, 12, 9, 16, 10, 3, 14, 8, 20, 12, 12, 11, 17, 20, 11, 4, 20, 4, 15, 4, 8, 3, 12, 21, 17, 12, 10, 8, 21, 17, 10, 8, 4, 4, 16, 14, 12, 14, 14, 4, 9, 12, 4, 14, 4, 10, 10, 4, 10, 3, 9, 20, 1, 16, 10, 20, 12, 20, 5, 3, 8, 16, 9, 20, 10, 20, 21, 8, 9, 8, 5, 8, 11, 8, 19, 6, 6, 10, 19, 6, 10, 15, 8, 19, 5, 17, 19, 10, 16, 8, 19, 12, 15, 19, 15, 14, 6, 21, 16, 13, 10, 16, 5, 14, 17, 15, 5, 13, 1, 13, 15, 6, 13, 3, 15, 13, 4, 6, 8, 4, 4, 4, 6, 6, 4, 15, 3, 15, 3, 15, 16, 16, 13, 10, 19, 7, 6, 10, 10, 1, 10, 8, 20, 3, 3, 10, 15, 16, 10, 2, 10, 5, 16, 21, 7, 15, 10, 15, 3, 10, 8, 10, 8, 1, 1, 15, 8, 19, 4, 10, 10, 6, 15, 15, 6, 20, 4, 1, 10, 9, 21, 20, 6, 12, 10, 10, 14, 21, 20, 8, 14, 4, 10, 9, 12, 16, 1, 19, 16, 10, 5, 3, 1, 8, 1, 8, 1, 19, 1, 4, 6, 17, 3, 15, 8, 8, 4, 19, 1, 14, 15, 8, 6, 15, 1, 5, 10, 7, 8, 13, 15, 15, 8, 15, 14, 6, 5, 4, 15, 1, 10, 10]]
shuffledInteractionsSecondSeq = [[11, 15, 17, 10, 1, 8, 10, 1, 1, 8, 10, 10, 19, 1, 10, 14, 1, 14, 1, 4, 13, 10, 14, 1, 15, 1, 3, 4, 19, 1, 1, 1, 13, 4, 14, 8, 1, 1, 3, 8, 13, 4, 19, 19, 19, 16, 10, 1, 20, 3, 4, 16, 10, 1, 13, 9, 7, 13, 6, 16, 15, 9, 12, 11, 1, 2, 21, 2, 15, 8, 13, 1, 2, 8, 1, 6, 4, 15, 15, 21, 6, 17, 2, 8, 21, 14, 6, 15, 10, 20, 1, 5, 2, 2]]
print(max([len(seq) for seq in shuffledInteractionsFirstSeq]))
# 847
print(max([len(seq) for seq in shuffledInteractionsSecondSeq]))
# 94
DataFrame(data={'Sequence1' : shuffledInteractionsFirstSeq, 'Sequence2' : shuffledInteractionsSecondSeq}).to_csv('interactionDataSet.csv', index = False)
interactionsCSV = read_csv('interactionDataSet.csv')
interactionsSequence1 = list(interactionsCSV.get('Sequence1'))
interactionsSequence2 = list(interactionsCSV.get('Sequence2'))
print(max([len(seq) for seq in interactionsSequence1]))
# 3026
print(max([len(seq) for seq in interactionsSequence2]))
# 327
as you can see the data is changing after saving. I know maybe I'm doing something wrong but I couldn't figure it out
This occures because read_csv doesn't recognize complex types like list and reads them as strings:
type(interactionsCSV.at[0, 'Sequence1'])
# <class 'str'>
One possible work around is to use pandas.eval function:
interactionsCSV['Sequence1'] = pd.eval(interactionsCSV['Sequence1'])
type(interactionsCSV.at[0, 'Sequence1'])
# <class 'list'>
max([len(s) for s in interactionsCSV.get('Sequence1')])
# 847
I am really sorry for the long program I am complaining about it here, I am just trying to make up my own DES encryption code using python with the little knowledge I have. So I have written the following code: It returned an error saying :" m = (B[j][0] << 1) + B[j][5]
IndexError: bitarray index out of range". How can I solve that?
from bitarray import bitarray
iptable=[57, 49, 41, 33, 25, 17, 9, 1,
59, 51, 43, 35, 27, 19, 11, 3,
61, 53, 45, 37, 29, 21, 13, 5,
63, 55, 47, 39, 31, 23, 15, 7,
56, 48, 40, 32, 24, 16, 8, 0,
58, 50, 42, 34, 26, 18, 10, 2,
60, 52, 44, 36, 28, 20, 12, 4,
62, 54, 46, 38, 30, 22, 14, 6
]
pc1=[56, 48, 40, 32, 24, 16, 8,
0, 57, 49, 41, 33, 25, 17,
9, 1, 58, 50, 42, 34, 26,
18, 10, 2, 59, 51, 43, 35,
62, 54, 46, 38, 30, 22, 14,
6, 61, 53, 45, 37, 29, 21,
13, 5, 60, 52, 44, 36, 28,
20, 12, 4, 27, 19, 11, 3
]
expTable=[31, 0, 1, 2, 3, 4,
3, 4, 5, 6, 7, 8,
7, 8, 9, 10, 11, 12,
11, 12, 13, 14, 15, 16,
15, 16, 17, 18, 19, 20,
19, 20, 21, 22, 23, 24,
23, 24, 25, 26, 27, 28,
27, 28, 29, 30, 31, 0]
pc2 = [13, 16, 10, 23, 0, 4,
2, 27, 14, 5, 20, 9,
22, 18, 11, 3, 25, 7,
15, 6, 26, 19, 12, 1,
40, 51, 30, 36, 46, 54,
29, 39, 50, 44, 32, 47,
43, 48, 38, 55, 33, 52,
45, 41, 49, 35, 28, 31]
# The (in)famous S-boxes
__sbox = [
# S1
[14, 4, 13, 1, 2, 15, 11, 8, 3, 10, 6, 12, 5, 9, 0, 7,
0, 15, 7, 4, 14, 2, 13, 1, 10, 6, 12, 11, 9, 5, 3, 8,
4, 1, 14, 8, 13, 6, 2, 11, 15, 12, 9, 7, 3, 10, 5, 0,
15, 12, 8, 2, 4, 9, 1, 7, 5, 11, 3, 14, 10, 0, 6, 13],
# S2
[15, 1, 8, 14, 6, 11, 3, 4, 9, 7, 2, 13, 12, 0, 5, 10,
3, 13, 4, 7, 15, 2, 8, 14, 12, 0, 1, 10, 6, 9, 11, 5,
0, 14, 7, 11, 10, 4, 13, 1, 5, 8, 12, 6, 9, 3, 2, 15,
13, 8, 10, 1, 3, 15, 4, 2, 11, 6, 7, 12, 0, 5, 14, 9],
# S3
[10, 0, 9, 14, 6, 3, 15, 5, 1, 13, 12, 7, 11, 4, 2, 8,
13, 7, 0, 9, 3, 4, 6, 10, 2, 8, 5, 14, 12, 11, 15, 1,
13, 6, 4, 9, 8, 15, 3, 0, 11, 1, 2, 12, 5, 10, 14, 7,
1, 10, 13, 0, 6, 9, 8, 7, 4, 15, 14, 3, 11, 5, 2, 12],
# S4
[7, 13, 14, 3, 0, 6, 9, 10, 1, 2, 8, 5, 11, 12, 4, 15,
13, 8, 11, 5, 6, 15, 0, 3, 4, 7, 2, 12, 1, 10, 14, 9,
10, 6, 9, 0, 12, 11, 7, 13, 15, 1, 3, 14, 5, 2, 8, 4,
3, 15, 0, 6, 10, 1, 13, 8, 9, 4, 5, 11, 12, 7, 2, 14],
# S5
[2, 12, 4, 1, 7, 10, 11, 6, 8, 5, 3, 15, 13, 0, 14, 9,
14, 11, 2, 12, 4, 7, 13, 1, 5, 0, 15, 10, 3, 9, 8, 6,
4, 2, 1, 11, 10, 13, 7, 8, 15, 9, 12, 5, 6, 3, 0, 14,
11, 8, 12, 7, 1, 14, 2, 13, 6, 15, 0, 9, 10, 4, 5, 3],
# S6
[12, 1, 10, 15, 9, 2, 6, 8, 0, 13, 3, 4, 14, 7, 5, 11,
10, 15, 4, 2, 7, 12, 9, 5, 6, 1, 13, 14, 0, 11, 3, 8,
9, 14, 15, 5, 2, 8, 12, 3, 7, 0, 4, 10, 1, 13, 11, 6,
4, 3, 2, 12, 9, 5, 15, 10, 11, 14, 1, 7, 6, 0, 8, 13],
# S7
[4, 11, 2, 14, 15, 0, 8, 13, 3, 12, 9, 7, 5, 10, 6, 1,
13, 0, 11, 7, 4, 9, 1, 10, 14, 3, 5, 12, 2, 15, 8, 6,
1, 4, 11, 13, 12, 3, 7, 14, 10, 15, 6, 8, 0, 5, 9, 2,
6, 11, 13, 8, 1, 4, 10, 7, 9, 5, 0, 15, 14, 2, 3, 12],
# S8
[13, 2, 8, 4, 6, 15, 11, 1, 10, 9, 3, 14, 5, 0, 12, 7,
1, 15, 13, 8, 10, 3, 7, 4, 12, 5, 6, 11, 0, 14, 9, 2,
7, 11, 4, 1, 9, 12, 14, 2, 0, 6, 10, 13, 15, 3, 5, 8,
2, 1, 14, 7, 4, 10, 8, 13, 15, 12, 9, 0, 3, 5, 6, 11],
]
msg= bitarray(endian='little')
msg.frombytes(b'ABCDEFGH')
perm = bitarray(endian='little')
key= bitarray(endian='little')
key.frombytes(b'FFQQSSMM')
keyPc1 = bitarray(endian='little')
keyPc2 = bitarray(endian='little')
exp = bitarray(endian='little')
for z in pc1:
keyPc1.append(key[z])
c0 = keyPc1[0:28]
d0 = keyPc1[28:]
key0 = c0 + d0
#permutation of key
for k in pc2:
keyPc2.append(key0[k])
#permutation of message
for x in iptable:
perm.append(msg[x])
l1 = perm[0:32]
r1 = perm[32:]
#Expansion of R
for y in expTable:
exp.append(r1[y])
#XORing R & key
xor_rk = keyPc2 ^ exp
#Working with S-boxes!
B = [xor_rk[0:6], xor_rk[6:14], xor_rk[14:20], xor_rk[20:26], xor_rk[26:32], xor_rk[32:38], xor_rk[38:42], xor_rk[42:47]]
j = 0
Bn = [0] * 32
pos = 0
while j < 8:
# Work out the offsets
m = (B[j][0] << 1) + B[j][5]
n = (B[j][1] << 3) + (B[j][2] << 2) + (B[j][3] << 1) + B[j][4]
# Find the permutation value
v = __sbox[j][(m << 4) + n]
# Turn value into bits, add it to result: Bn
Bn[pos] = (v & 8) >> 3
Bn[pos + 1] = (v & 4) >> 2
Bn[pos + 2] = (v & 2) >> 1
Bn[pos + 3] = v & 1
pos += 4
j += 1
f = Bn[0] + Bn[1] + Bn[2] + Bn[3] + Bn[4] +Bn[5] + Bn[6] +Bn[7]
xor_lf = l ^ f
Not all parts of your B list are the same length. For example, this part:
xor_rk[38:42]
has a length of 4, so you can't get the 5th element of that. Is it supposed to have a length of 4? Or did you mean to count by sixes and screw up?