def main():
"""Make a jazz noise here"""
args = get_args()
min_len = args.min_len
files = args.FILE
hamm = args.hamming_distance
logfile = args.logfile
table = args.table
logging.basicConfig(
filename=logfile,
filemode='w',
level=logging.DEBUG if args.debug else logging.CRITICAL)
file1 = files[0]
file2 = files[1]
logging.debug('file1 = {}, file2 = {}'.format(file1, file2))
if hamm < 0:
die('--distance "{}" must be > 0'.format(hamm))
words1 = sorted(uniq_words(file1, min_len))
words2 = sorted(uniq_words(file2, min_len))
matches = {}
for str1 in words1:
for str2 in words2:
d = dist(str1, str2)
if d <= hamm:
matches[(str1, str2)] = d
if len(matches) > 0:
t = []
for pairs, count in matches.items():
col1 = pairs[0]
col2 = pairs[1]
col3 = count
column = col1, col2, col3
t.append(column)
if table:
print(
tbl(t, headers=['word1', 'word2', 'distance'],
tablefmt='psql'))
else:
print('{}\t{}\t{}'.format('word1', 'word2', 'distance'))
for row in t:
print('{}\t{}\t{}'.format(row[0], row[1], row[2]))
else:
print('No words in common.')
# https://github.com/dhava-stmkg/interpolation
import numpy as np # Import modul numpy sebagai np
import sympy as sy # Import modul sympy sebagai sy
from tabulate import tabulate as tbl # Import sub-modul tabulate dari modul tabulate sebagai tbl
# Define t as symbol using sympy lib
t = sy.Symbol('t')
# Data
x = np.array([0, 10, 15, 20, 22.5, 30])
y = np.array([0, 227.04, 362.78, 517.35, 602.97, 901.67])
tabel = []
for i in range(len(x)):
tabel.append([x[i], y[i]])
table = tbl(tabel, headers=['t(s)', 'v(t) (mm/s)'], tablefmt='orgtbl')
print(table)
# Function
def direct_method(x, y):
vander_matrix = np.vander(
x, increasing=False
) # Membuat matrix Vandermonde dengan menggunakan modul numpy
coef = np.linalg.solve(vander_matrix,
y) # Menyelesaikan persamaan dengan modul numpy
return coef # Mengembalikan nilai koefisien
def newt_method(x, y):
m = len(x) # Mengambil jumlah data
c.get_rate(base_cur, MXN, date_obj),
c.get_rate(base_cur, BGN, date_obj),
c.get_rate(base_cur, HUF, date_obj),
c.get_rate(base_cur, MYR, date_obj),
c.get_rate(base_cur, HRK, date_obj),
c.get_rate(base_cur, THB, date_obj),
c.get_rate(base_cur, NOK, date_obj),
c.get_rate(base_cur, PLN, date_obj),
c.get_rate(base_cur, CZK, date_obj)
]]
tbl_header = (USD, GBP, ZAR, EUR, AUD, SGD, CAD, CNY, JPY, NZD, INR, CHF, MXN,
BGN, HUF, MYR, HRK, THB, NOK, PLN, CZK)
print(">>Table Results:")
print('>>Base Currency = ', base_cur)
print(tbl(tbl_data, tbl_header, tablefmt="fancy_grid"))
print(">>DOne loading table...")
print("\n::Lets Check Bitcoin now...!")
print("Bitcoin price for => ", base_cur)
print("1 BITCOIN = %(base)s %(price).2F" % {
'base': base_cur,
'price': b.get_latest_price(base_cur)
})
print(">>Done checking bitcoin...")
print("Goodbye!")
print("PS, From [[[ElectronSz]]] :)")
dic3=lz4.frame.get_frame_info(compressed)
bs=dic3['block_size']
ty=nd.dtype.name
dic=[['LZ4 library version',lz4lver],
['LZ4 python module version',lz4ver],
['LZ4 block size',"{0}kB".format(int(np.round(bs/1024)))],
['LZ4 compression level',0],
['Zstd library version',zzs],
['Zstd Compression level',1],
['Zstd Compression Threads',4],
['Type of element',ty],
['Shape of data',sp],
['Original Size',"{0}kB".format(int(np.round(result3/1024)))],
['Compressed Size(LZ4)',"{0}kB".format(int(np.round(result2/1024)))],
['Compressed Size(Zstd)',"{0}kB".format(int(np.round(result4/1024)))],
['Compression Ratio(LZ4)',"{0:.6f}".format(result3/result2)],
['Compression Ratio(Zstd)',"{0:.6f}".format(result3/result4)],
['Compression Time(LZ4)',"{0:.6f}s".format(time)],
['Compression Time(Zstd)',"{0:.6f}s".format(time2)],
['Decompression Time(LZ4)',"{0:.6f}s".format(time4)],
['Decompression Time(Zstd)',"{0:.6f}s".format(time3)],
['Original == Compressed ?',result]]
with open('data.json', 'w') as fp:
json.dump(dic, fp)
with open('data.json', 'r') as fp:
dic2 = json.load(fp)
print(tbl(dic2,
tablefmt="fancy_grid",
headers=['Name','Value']))
def MoneyTable():
global all_users, money
out = tbl(list(zip(all_users, money)), tablefmt = 'psql', headers=('Players', 'Money'))
print('Current Money Distribution')
print(out)
winner = all_users[final_hands_original.index(final_hands[0])]
#Display a table with final hands of all players with winner marked separately
print(tbl
(list
(zip
(
[f'Winner >> [{i}]' if i is winner else i for i in all_users],
final_hands_original,
[_dispCards(*k) for k in \
[i.replace('10', 'T').split() for i in final_hands_original]
]
)
),
headers = (
'Player',
'Cards',
''
),
tablefmt='psql'
)
)
win_msg = f"player [{winner}] won with '{get_best_hand_of_user(winner)[1]}'\nCards are: {'['+', '.join(get_best_hand_of_user(winner)[0])+']'}"
Created on Tue Nov 19 12:07:06 2019
@author: agnib
"""
# Create an N x N magic square. N must be odd.
from tabulate import tabulate as tbl
import numpy as np
N = 9
magic_square = np.zeros((N, N), dtype=int)
i, j, n = 0, N // 2, 1
while n <= N**2:
magic_square[i, j] = n
n += 1
newi, newj = (i - 1) % N, (j + 1) % N
if magic_square[newi, newj]:
i += 1
else:
i, j = newi, newj
for i in magic_square:
for j in i:
print(j, end=" ")
print("\n")
print(tbl(magic_square, tablefmt="grid"))
headers += ['covariance']
headers += ['Linear mean [dBm]']
row_0 = [2]
row_1 = [5]
row_2 = [3.4]
for x, y, z in zip(qunatile_2m, qunatile_5m, qunatile_ant):
row_0.append(x)
row_1.append(y)
row_2.append(z)
row_0.append(cov_2m)
row_1.append(cov_5m)
row_0.append(linear_mean(rssi_2m))
row_1.append(linear_mean(rssi_5m))
row_2.append(cov_ant)
tble = [row_0, row_1]
table = tbl(tble, headers=headers, tablefmt='github')
print('*' * 150)
print(table)
print('*' * 150)
fig, ax = plt.subplots()
ax.plot(_2x, z2m, label='RSSI - 2m pdf')
ax.plot(_5x, z5m, label='RSSI - 5m pdf')
ax.set_title('PDF 2m, 5m')
ax.legend()
ax.grid()
plt.xlabel('power [dBm]')
fig.show()
fig, ax = plt.subplots()
ax.plot(_2x, cdf2m, label='RSSI - 2m CDF')
ax.plot(_5x, cdf5m, label='RSSI - 5m CDF')
ax.set_title('CDF 2m, 5m')