body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
d72e52ed388d13398a86567769cc6bf559d0cf927c6d954ca6613f189ceb38f6 | def while_E():
" Upper case Alphabet letter 'E' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 4):
if ((col == 0) or ((row % 3) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'E' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_E | SaikumarS2611/ANSPatterns | 0 | python | def while_E():
" "
row = 0
while (row < 7):
col = 0
while (col < 4):
if ((col == 0) or ((row % 3) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_E():
" "
row = 0
while (row < 7):
col = 0
while (col < 4):
if ((col == 0) or ((row % 3) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'E' pattern using Python while loop<|endoftext|> |
f74f41c2d96fed30274a45eddcb7c59bba387da2de0592b6741e97d9384f232d | def for_F():
" Upper case Alphabet letter 'F' pattern using Python for loop"
for row in range(7):
for col in range(4):
if ((col == 0) or (((row % 3) == 0) and (row != 6))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'F' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_F | SaikumarS2611/ANSPatterns | 0 | python | def for_F():
" "
for row in range(7):
for col in range(4):
if ((col == 0) or (((row % 3) == 0) and (row != 6))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_F():
" "
for row in range(7):
for col in range(4):
if ((col == 0) or (((row % 3) == 0) and (row != 6))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'F' pattern using Python for loop<|endoftext|> |
f1b49a2267db9870dce977bdcf8d3a6838c9f61beb4705450264a83b781b473c | def while_F():
" Upper case Alphabet letter 'F' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 4):
if ((col == 0) or (((row % 3) == 0) and (row != 6))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'F' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_F | SaikumarS2611/ANSPatterns | 0 | python | def while_F():
" "
row = 0
while (row < 7):
col = 0
while (col < 4):
if ((col == 0) or (((row % 3) == 0) and (row != 6))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_F():
" "
row = 0
while (row < 7):
col = 0
while (col < 4):
if ((col == 0) or (((row % 3) == 0) and (row != 6))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'F' pattern using Python while loop<|endoftext|> |
e66e9767c61fd85aab481c51c49ce249b77745dbea890faefaffadbad4765bd0 | def for_G():
" Upper case Alphabet letter 'G' pattern using Python for loop"
for row in range(7):
for col in range(5):
if (((col == 0) and (row > 0) and (row < 6)) or (((row % 3) == 0) and (col > 0) and (col < 4) and (row != 3)) or ((row == 3) and (col > 1)) or ((col == 4) and (((row > 2) and (row < 6)) or (row == 1)))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'G' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_G | SaikumarS2611/ANSPatterns | 0 | python | def for_G():
" "
for row in range(7):
for col in range(5):
if (((col == 0) and (row > 0) and (row < 6)) or (((row % 3) == 0) and (col > 0) and (col < 4) and (row != 3)) or ((row == 3) and (col > 1)) or ((col == 4) and (((row > 2) and (row < 6)) or (row == 1)))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_G():
" "
for row in range(7):
for col in range(5):
if (((col == 0) and (row > 0) and (row < 6)) or (((row % 3) == 0) and (col > 0) and (col < 4) and (row != 3)) or ((row == 3) and (col > 1)) or ((col == 4) and (((row > 2) and (row < 6)) or (row == 1)))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'G' pattern using Python for loop<|endoftext|> |
b40cd10e7d20a9a52aad20664afdf535fba4f726d287cb3f318ae644df5f7568 | def while_G():
" Upper case Alphabet letter 'G' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 5):
if (((col == 0) and (row > 0) and (row < 6)) or (((row % 3) == 0) and (col > 0) and (col < 4) and (row != 3)) or ((row == 3) and (col > 1)) or ((col == 4) and (((row > 2) and (row < 6)) or (row == 1)))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'G' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_G | SaikumarS2611/ANSPatterns | 0 | python | def while_G():
" "
row = 0
while (row < 7):
col = 0
while (col < 5):
if (((col == 0) and (row > 0) and (row < 6)) or (((row % 3) == 0) and (col > 0) and (col < 4) and (row != 3)) or ((row == 3) and (col > 1)) or ((col == 4) and (((row > 2) and (row < 6)) or (row == 1)))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_G():
" "
row = 0
while (row < 7):
col = 0
while (col < 5):
if (((col == 0) and (row > 0) and (row < 6)) or (((row % 3) == 0) and (col > 0) and (col < 4) and (row != 3)) or ((row == 3) and (col > 1)) or ((col == 4) and (((row > 2) and (row < 6)) or (row == 1)))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'G' pattern using Python while loop<|endoftext|> |
8f73b0962a7f71967de46eb94e85e20dfedf64ef12da020e11e554f0df71cf8e | def for_H():
" Upper case Alphabet letter 'H' pattern using Python for loop"
for row in range(7):
for col in range(5):
if (((col % 4) == 0) or (row == 3)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'H' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_H | SaikumarS2611/ANSPatterns | 0 | python | def for_H():
" "
for row in range(7):
for col in range(5):
if (((col % 4) == 0) or (row == 3)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_H():
" "
for row in range(7):
for col in range(5):
if (((col % 4) == 0) or (row == 3)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'H' pattern using Python for loop<|endoftext|> |
e17e7ec0a4eaad6d979ba97525977ec5efd3102150508b0af77c8215c0a3c2d1 | def while_H():
" Upper case Alphabet letter 'H' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 5):
if (((col % 4) == 0) or (row == 3)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'H' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_H | SaikumarS2611/ANSPatterns | 0 | python | def while_H():
" "
row = 0
while (row < 7):
col = 0
while (col < 5):
if (((col % 4) == 0) or (row == 3)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_H():
" "
row = 0
while (row < 7):
col = 0
while (col < 5):
if (((col % 4) == 0) or (row == 3)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'H' pattern using Python while loop<|endoftext|> |
31c7530be66e08026724df46dac36967b3cc871fd253da170b154fd0981b9ff4 | def for_I():
" Upper case Alphabet letter 'I' pattern using Python for loop"
for row in range(6):
for col in range(5):
if ((row in (0, 5)) or (col == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'I' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_I | SaikumarS2611/ANSPatterns | 0 | python | def for_I():
" "
for row in range(6):
for col in range(5):
if ((row in (0, 5)) or (col == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_I():
" "
for row in range(6):
for col in range(5):
if ((row in (0, 5)) or (col == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'I' pattern using Python for loop<|endoftext|> |
f43bacbe0e5a4369fcf42d0b34129e911eca59592c2e9d24c71a190a2744d532 | def while_I():
" Upper case Alphabet letter 'I' pattern using Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((row in (0, 5)) or (col == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'I' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_I | SaikumarS2611/ANSPatterns | 0 | python | def while_I():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((row in (0, 5)) or (col == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_I():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((row in (0, 5)) or (col == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'I' pattern using Python while loop<|endoftext|> |
97fd6b9e6a59d85d748048a369bb2a9b7bc501d779100c785e79913575b0343b | def for_J():
" Upper case Alphabet letter 'J' pattern using Python Python for loop"
for row in range(6):
for col in range(5):
if ((row == 0) or ((col == 2) and (row < 5)) or (((row + col) == 6) and (col < 3)) or ((col == 0) and (row == 4))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'J' pattern using Python Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_J | SaikumarS2611/ANSPatterns | 0 | python | def for_J():
" "
for row in range(6):
for col in range(5):
if ((row == 0) or ((col == 2) and (row < 5)) or (((row + col) == 6) and (col < 3)) or ((col == 0) and (row == 4))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_J():
" "
for row in range(6):
for col in range(5):
if ((row == 0) or ((col == 2) and (row < 5)) or (((row + col) == 6) and (col < 3)) or ((col == 0) and (row == 4))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'J' pattern using Python Python for loop<|endoftext|> |
e97ec7a6a8b9626f97422e042506720e860080822d52657b6042abf2fe95c067 | def while_J():
" Upper case Alphabet letter 'J' pattern using Python Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((row == 0) or ((col == 2) and (row < 5)) or (((row + col) == 6) and (col < 3)) or ((col == 0) and (row == 4))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'J' pattern using Python Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_J | SaikumarS2611/ANSPatterns | 0 | python | def while_J():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((row == 0) or ((col == 2) and (row < 5)) or (((row + col) == 6) and (col < 3)) or ((col == 0) and (row == 4))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_J():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((row == 0) or ((col == 2) and (row < 5)) or (((row + col) == 6) and (col < 3)) or ((col == 0) and (row == 4))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'J' pattern using Python Python while loop<|endoftext|> |
5ef910a54222f9a8ca7035a7175ee2b136cbf5daedd66c081030f7b7586de623 | def for_K():
" Upper case Alphabet letter 'K' pattern using Python Python for loop"
for row in range(6):
for col in range(4):
if ((col == 0) or ((row + col) == 3) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'K' pattern using Python Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_K | SaikumarS2611/ANSPatterns | 0 | python | def for_K():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or ((row + col) == 3) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_K():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or ((row + col) == 3) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'K' pattern using Python Python for loop<|endoftext|> |
59bef891bab8f031ae36fd078d73db4ef40b7bcef163a4cd3326e99f4827b1ff | def while_K():
" Upper case Alphabet letter 'K' pattern using Python Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 4):
if ((col == 0) or ((row + col) == 3) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'K' pattern using Python Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_K | SaikumarS2611/ANSPatterns | 0 | python | def while_K():
" "
row = 0
while (row < 6):
col = 0
while (col < 4):
if ((col == 0) or ((row + col) == 3) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_K():
" "
row = 0
while (row < 6):
col = 0
while (col < 4):
if ((col == 0) or ((row + col) == 3) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'K' pattern using Python Python while loop<|endoftext|> |
3a1d026b1f9dd56c4eded03cd1b0b9e4a0a9f58321121ec8aea98be9da688704 | def for_L():
" Upper case Alphabet letter 'L' pattern using Python Python for loop"
for row in range(6):
for col in range(4):
if ((col == 0) or (row == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'L' pattern using Python Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_L | SaikumarS2611/ANSPatterns | 0 | python | def for_L():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or (row == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_L():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or (row == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'L' pattern using Python Python for loop<|endoftext|> |
f26766bad9fd8170994e0c63b1f73cda4fa28c461fa45ef43ddf96ca6d15f157 | def while_L():
" Upper case Alphabet letter 'L' pattern using Python Python while loop"
for row in range(6):
for col in range(4):
if ((col == 0) or (row == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'L' pattern using Python Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_L | SaikumarS2611/ANSPatterns | 0 | python | def while_L():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or (row == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def while_L():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or (row == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'L' pattern using Python Python while loop<|endoftext|> |
7a1ef6f2ae5b915718243f8fab915035c2fb4334ad92e3113e047fb8f586c69d | def for_M():
" Upper case Alphabet letter 'M' pattern using Python Python for loop"
for row in range(6):
for col in range(5):
if ((col in (0, 4)) or ((((row - col) == 0) or ((row + col) == 4)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'M' pattern using Python Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_M | SaikumarS2611/ANSPatterns | 0 | python | def for_M():
" "
for row in range(6):
for col in range(5):
if ((col in (0, 4)) or ((((row - col) == 0) or ((row + col) == 4)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_M():
" "
for row in range(6):
for col in range(5):
if ((col in (0, 4)) or ((((row - col) == 0) or ((row + col) == 4)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'M' pattern using Python Python for loop<|endoftext|> |
1127b51ac869901e6ed4cfc2fc60a4512cedaef3e1717f5014535c9e18c76235 | def while_M():
" Upper case Alphabet letter 'M' pattern using Python Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((col in (0, 4)) or ((((row - col) == 0) or ((row + col) == 4)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'M' pattern using Python Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_M | SaikumarS2611/ANSPatterns | 0 | python | def while_M():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((col in (0, 4)) or ((((row - col) == 0) or ((row + col) == 4)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_M():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((col in (0, 4)) or ((((row - col) == 0) or ((row + col) == 4)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'M' pattern using Python Python while loop<|endoftext|> |
2d12608a844e2d4c52176b90d38391f19527ce78760dc55f8e5804fc29d6c5bf | def for_N():
" Upper case Alphabet letter 'N' pattern using Python Python for loop"
for row in range(5):
for col in range(5):
if ((col in (0, 4)) or ((row - col) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'N' pattern using Python Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_N | SaikumarS2611/ANSPatterns | 0 | python | def for_N():
" "
for row in range(5):
for col in range(5):
if ((col in (0, 4)) or ((row - col) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_N():
" "
for row in range(5):
for col in range(5):
if ((col in (0, 4)) or ((row - col) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'N' pattern using Python Python for loop<|endoftext|> |
efaa4043ac40bb767cd1a03a1bfdbf755384218cd4e4bbecce8f384a858d11cc | def while_N():
" Upper case Alphabet letter 'N' pattern using Python Python while loop"
row = 0
while (row < 5):
col = 0
while (col < 5):
if ((col in (0, 4)) or ((row - col) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'N' pattern using Python Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_N | SaikumarS2611/ANSPatterns | 0 | python | def while_N():
" "
row = 0
while (row < 5):
col = 0
while (col < 5):
if ((col in (0, 4)) or ((row - col) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_N():
" "
row = 0
while (row < 5):
col = 0
while (col < 5):
if ((col in (0, 4)) or ((row - col) == 0)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'N' pattern using Python Python while loop<|endoftext|> |
24714367f5ab7e20e7afbe2a09f6fbdf927fd3d8ec84bed74520498672c65e8f | def for_O():
" Upper case Alphabet letter 'O' pattern using Python for loop"
for row in range(6):
for col in range(5):
if (((row in (0, 5)) and (col > 0) and (col < 4)) or ((col in (0, 4)) and (row > 0) and (row < 5))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'O' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_O | SaikumarS2611/ANSPatterns | 0 | python | def for_O():
" "
for row in range(6):
for col in range(5):
if (((row in (0, 5)) and (col > 0) and (col < 4)) or ((col in (0, 4)) and (row > 0) and (row < 5))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_O():
" "
for row in range(6):
for col in range(5):
if (((row in (0, 5)) and (col > 0) and (col < 4)) or ((col in (0, 4)) and (row > 0) and (row < 5))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'O' pattern using Python for loop<|endoftext|> |
259fcbef6e97cbed7a86b775452de439d323cc8cf4d6d9175f563be7b5b6f789 | def while_O():
" Upper case Alphabet letter 'O' pattern using Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 5):
if (((row in (0, 5)) and (col > 0) and (col < 4)) or ((col in (0, 4)) and (row > 0) and (row < 5))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'O' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_O | SaikumarS2611/ANSPatterns | 0 | python | def while_O():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if (((row in (0, 5)) and (col > 0) and (col < 4)) or ((col in (0, 4)) and (row > 0) and (row < 5))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_O():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if (((row in (0, 5)) and (col > 0) and (col < 4)) or ((col in (0, 4)) and (row > 0) and (row < 5))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'O' pattern using Python while loop<|endoftext|> |
6bd4255483656b37b08da7509fc1f70eb4b159ed06d020379e647580e5adeff4 | def for_P():
" Upper case Alphabet letter 'P' pattern using Python for loop"
for row in range(6):
for col in range(4):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'P' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_P | SaikumarS2611/ANSPatterns | 0 | python | def for_P():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_P():
" "
for row in range(6):
for col in range(4):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'P' pattern using Python for loop<|endoftext|> |
45efbec993d4bc7e976cbc443820727f5873e8574dbfbe5d0ad9235423015990 | def while_P():
" Upper case Alphabet letter 'P' pattern using Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 4):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'P' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_P | SaikumarS2611/ANSPatterns | 0 | python | def while_P():
" "
row = 0
while (row < 6):
col = 0
while (col < 4):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_P():
" "
row = 0
while (row < 6):
col = 0
while (col < 4):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'P' pattern using Python while loop<|endoftext|> |
abe031ea51ea0997468b208a7bd10f4eddc87409ce47a8d4ab3cbbe7ff6e8b19 | def for_Q():
" Upper case Alphabet letter 'Q' pattern using Python for loop"
for row in range(5):
for col in range(5):
if (((col in (0, 4)) and (row > 0) and (row < 4)) or ((row in (0, 4)) and (col > 0) and (col < 4)) or (((col - row) == 0) and (row > 2))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'Q' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_Q | SaikumarS2611/ANSPatterns | 0 | python | def for_Q():
" "
for row in range(5):
for col in range(5):
if (((col in (0, 4)) and (row > 0) and (row < 4)) or ((row in (0, 4)) and (col > 0) and (col < 4)) or (((col - row) == 0) and (row > 2))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_Q():
" "
for row in range(5):
for col in range(5):
if (((col in (0, 4)) and (row > 0) and (row < 4)) or ((row in (0, 4)) and (col > 0) and (col < 4)) or (((col - row) == 0) and (row > 2))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'Q' pattern using Python for loop<|endoftext|> |
2d423f96ed63158d15395131617b717d2581ecf73603387e9cf49f2902970098 | def while_Q():
" Upper case Alphabet letter 'Q' pattern using Python while loop"
row = 0
while (row < 5):
col = 0
while (col < 5):
if (((col in (0, 4)) and (row > 0) and (row < 4)) or ((row in (0, 4)) and (col > 0) and (col < 4)) or (((col - row) == 0) and (row > 2))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'Q' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_Q | SaikumarS2611/ANSPatterns | 0 | python | def while_Q():
" "
row = 0
while (row < 5):
col = 0
while (col < 5):
if (((col in (0, 4)) and (row > 0) and (row < 4)) or ((row in (0, 4)) and (col > 0) and (col < 4)) or (((col - row) == 0) and (row > 2))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_Q():
" "
row = 0
while (row < 5):
col = 0
while (col < 5):
if (((col in (0, 4)) and (row > 0) and (row < 4)) or ((row in (0, 4)) and (col > 0) and (col < 4)) or (((col - row) == 0) and (row > 2))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'Q' pattern using Python while loop<|endoftext|> |
27ba3a7c55e512b81efd140e6acbb4fd4db877656f61cc9d67e9339a3d2e7b76 | def for_R():
" Upper case Alphabet letter 'R' pattern using Python for loop"
for row in range(6):
for col in range(5):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3)) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'R' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_R | SaikumarS2611/ANSPatterns | 0 | python | def for_R():
" "
for row in range(6):
for col in range(5):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3)) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_R():
" "
for row in range(6):
for col in range(5):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3)) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'R' pattern using Python for loop<|endoftext|> |
fb92d17cd3b46e0c8ca5de687fbdcdc8c38cfd2b2f68146e48ad3e84f8d89c61 | def while_R():
" Upper case Alphabet letter 'R' pattern using Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3)) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'R' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_R | SaikumarS2611/ANSPatterns | 0 | python | def while_R():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3)) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_R():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((col == 0) or (((row % 3) == 0) and (col < 3)) or ((col == 3) and ((row % 3) != 0) and (row < 3)) or ((row - col) == 2)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'R' pattern using Python while loop<|endoftext|> |
b469c0d6c9cc052b3022ba2bca657fc8cf05ed5b650a49bc6b35612d346b5a34 | def for_S():
" Upper case Alphabet letter 'S' pattern using Python for loop"
for row in range(7):
for col in range(5):
if ((((row % 3) == 0) and (col > 0) and (col < 4)) or ((col == 0) and ((row % 3) != 0) and (row < 3)) or ((col == 4) and ((row % 3) != 0) and (row > 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'S' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_S | SaikumarS2611/ANSPatterns | 0 | python | def for_S():
" "
for row in range(7):
for col in range(5):
if ((((row % 3) == 0) and (col > 0) and (col < 4)) or ((col == 0) and ((row % 3) != 0) and (row < 3)) or ((col == 4) and ((row % 3) != 0) and (row > 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_S():
" "
for row in range(7):
for col in range(5):
if ((((row % 3) == 0) and (col > 0) and (col < 4)) or ((col == 0) and ((row % 3) != 0) and (row < 3)) or ((col == 4) and ((row % 3) != 0) and (row > 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'S' pattern using Python for loop<|endoftext|> |
ffac5489c31a2e897f30d9cdb4d168703dc2ac3f7fd8619faf366004d81944b9 | def while_S():
" Upper case Alphabet letter 'S' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 5):
if ((((row % 3) == 0) and (col > 0) and (col < 4)) or ((col == 0) and ((row % 3) != 0) and (row < 3)) or ((col == 4) and ((row % 3) != 0) and (row > 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'S' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_S | SaikumarS2611/ANSPatterns | 0 | python | def while_S():
" "
row = 0
while (row < 7):
col = 0
while (col < 5):
if ((((row % 3) == 0) and (col > 0) and (col < 4)) or ((col == 0) and ((row % 3) != 0) and (row < 3)) or ((col == 4) and ((row % 3) != 0) and (row > 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_S():
" "
row = 0
while (row < 7):
col = 0
while (col < 5):
if ((((row % 3) == 0) and (col > 0) and (col < 4)) or ((col == 0) and ((row % 3) != 0) and (row < 3)) or ((col == 4) and ((row % 3) != 0) and (row > 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'S' pattern using Python while loop<|endoftext|> |
61228693ea8d3fe7c9c94dc7718f93d3b9c7e0cb31bebb12c91072eb92e1b2b8 | def for_T():
" Upper case Alphabet letter 'T' pattern using Python for loop"
for row in range(5):
for col in range(3):
if ((row == 0) or (col == 1)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'T' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_T | SaikumarS2611/ANSPatterns | 0 | python | def for_T():
" "
for row in range(5):
for col in range(3):
if ((row == 0) or (col == 1)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_T():
" "
for row in range(5):
for col in range(3):
if ((row == 0) or (col == 1)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'T' pattern using Python for loop<|endoftext|> |
ac586c4dc3c7d55d152ba0e053f1c169dae2705e77a3b391ab17e39cb36cbc4a | def while_T():
" Upper case Alphabet letter 'T' pattern using Python while loop"
row = 0
while (row < 5):
col = 0
while (col < 3):
if ((row == 0) or (col == 1)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'T' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_T | SaikumarS2611/ANSPatterns | 0 | python | def while_T():
" "
row = 0
while (row < 5):
col = 0
while (col < 3):
if ((row == 0) or (col == 1)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_T():
" "
row = 0
while (row < 5):
col = 0
while (col < 3):
if ((row == 0) or (col == 1)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'T' pattern using Python while loop<|endoftext|> |
805c3302069c78982e962380881781ab7c1dd53223ba3957ab818d45f8f0363a | def for_U():
" Upper case Alphabet letter 'U' pattern using Python for loop"
for row in range(6):
for col in range(5):
if ((((col % 4) == 0) and (row < 5)) or ((row == 5) and (col > 0) and (col < 4))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'U' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_U | SaikumarS2611/ANSPatterns | 0 | python | def for_U():
" "
for row in range(6):
for col in range(5):
if ((((col % 4) == 0) and (row < 5)) or ((row == 5) and (col > 0) and (col < 4))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_U():
" "
for row in range(6):
for col in range(5):
if ((((col % 4) == 0) and (row < 5)) or ((row == 5) and (col > 0) and (col < 4))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'U' pattern using Python for loop<|endoftext|> |
4f886e789731025557cf8d827a8ce6314ecbf94ebf286a5a3b8883b909d9ef97 | def while_U():
" Upper case Alphabet letter 'U' pattern using Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((((col % 4) == 0) and (row < 5)) or ((row == 5) and (col > 0) and (col < 4))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'U' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_U | SaikumarS2611/ANSPatterns | 0 | python | def while_U():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((((col % 4) == 0) and (row < 5)) or ((row == 5) and (col > 0) and (col < 4))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_U():
" "
row = 0
while (row < 6):
col = 0
while (col < 5):
if ((((col % 4) == 0) and (row < 5)) or ((row == 5) and (col > 0) and (col < 4))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'U' pattern using Python while loop<|endoftext|> |
b47df7f78ef53ae5f46954319d49b75e0dda6d0c6893d6647bebb564dcf852eb | def for_V():
" Upper case Alphabet letter 'V' pattern using Python for loop"
for row in range(7):
for col in range(13):
if ((row == col) or ((row + col) == 12)):
print('*', end='')
else:
print(' ', end='')
print() | Upper case Alphabet letter 'V' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_V | SaikumarS2611/ANSPatterns | 0 | python | def for_V():
" "
for row in range(7):
for col in range(13):
if ((row == col) or ((row + col) == 12)):
print('*', end=)
else:
print(' ', end=)
print() | def for_V():
" "
for row in range(7):
for col in range(13):
if ((row == col) or ((row + col) == 12)):
print('*', end=)
else:
print(' ', end=)
print()<|docstring|>Upper case Alphabet letter 'V' pattern using Python for loop<|endoftext|> |
f774b4dccae19d8658bd24268e2e49c4a5dd8617524bbd16b59603947484e9b7 | def while_V():
" Upper case Alphabet letter 'V' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 13):
if ((row == col) or ((row + col) == 12)):
print('*', end=' ')
else:
print(' ', end='')
col += 1
print()
row += 1 | Upper case Alphabet letter 'V' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_V | SaikumarS2611/ANSPatterns | 0 | python | def while_V():
" "
row = 0
while (row < 7):
col = 0
while (col < 13):
if ((row == col) or ((row + col) == 12)):
print('*', end=' ')
else:
print(' ', end=)
col += 1
print()
row += 1 | def while_V():
" "
row = 0
while (row < 7):
col = 0
while (col < 13):
if ((row == col) or ((row + col) == 12)):
print('*', end=' ')
else:
print(' ', end=)
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'V' pattern using Python while loop<|endoftext|> |
dfb56d1a30cabaddce9097fecf5c216ba2862f15199ce0cce511dc0c1a840f5e | def for_W():
" Upper case Alphabet letter 'W' pattern using Python for loop"
for i in range(5):
for j in range(27):
if ((i == j) or (((i > 1) and ((i + j) == 8)) or ((i + j) == 13)) or ((i == 3) and ((i + j) == 11))):
print('*', end='')
else:
print(' ', end='')
print() | Upper case Alphabet letter 'W' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_W | SaikumarS2611/ANSPatterns | 0 | python | def for_W():
" "
for i in range(5):
for j in range(27):
if ((i == j) or (((i > 1) and ((i + j) == 8)) or ((i + j) == 13)) or ((i == 3) and ((i + j) == 11))):
print('*', end=)
else:
print(' ', end=)
print() | def for_W():
" "
for i in range(5):
for j in range(27):
if ((i == j) or (((i > 1) and ((i + j) == 8)) or ((i + j) == 13)) or ((i == 3) and ((i + j) == 11))):
print('*', end=)
else:
print(' ', end=)
print()<|docstring|>Upper case Alphabet letter 'W' pattern using Python for loop<|endoftext|> |
612d41b71805c082690abffd359109410a777ff603544e0764046675bb9f7a19 | def while_W():
" Upper case Alphabet letter 'W' pattern using Python while loop"
row = 0
while (row < 5):
col = 0
while (col < 27):
if ((row == col) or ((row > 1) and ((row + col) == 8)) or ((row + col) == 13) or ((row == 3) and ((row + col) == 11))):
print('*', end='')
else:
print(' ', end='')
col += 1
print()
row += 1 | Upper case Alphabet letter 'W' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_W | SaikumarS2611/ANSPatterns | 0 | python | def while_W():
" "
row = 0
while (row < 5):
col = 0
while (col < 27):
if ((row == col) or ((row > 1) and ((row + col) == 8)) or ((row + col) == 13) or ((row == 3) and ((row + col) == 11))):
print('*', end=)
else:
print(' ', end=)
col += 1
print()
row += 1 | def while_W():
" "
row = 0
while (row < 5):
col = 0
while (col < 27):
if ((row == col) or ((row > 1) and ((row + col) == 8)) or ((row + col) == 13) or ((row == 3) and ((row + col) == 11))):
print('*', end=)
else:
print(' ', end=)
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'W' pattern using Python while loop<|endoftext|> |
d9066864a118d81175f3ec80b88ad43906ae30bc5a36edd63d76bfaf9de5a70c | def for_X():
" Upper case Alphabet letter 'X' pattern using Python for loop"
for row in range(5):
for col in range(6):
if (((row - col) == 0) or ((row + col) == 4)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'X' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_X | SaikumarS2611/ANSPatterns | 0 | python | def for_X():
" "
for row in range(5):
for col in range(6):
if (((row - col) == 0) or ((row + col) == 4)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_X():
" "
for row in range(5):
for col in range(6):
if (((row - col) == 0) or ((row + col) == 4)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'X' pattern using Python for loop<|endoftext|> |
4f9201a11531d5066d8bb21a79ede139c829d8961844cc9fe40b2776f5355aed | def while_X():
" Upper case Alphabet letter 'X' pattern using Python while loop"
row = 0
while (row < 5):
col = 0
while (col < 6):
if (((row - col) == 0) or ((row + col) == 4)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'X' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_X | SaikumarS2611/ANSPatterns | 0 | python | def while_X():
" "
row = 0
while (row < 5):
col = 0
while (col < 6):
if (((row - col) == 0) or ((row + col) == 4)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_X():
" "
row = 0
while (row < 5):
col = 0
while (col < 6):
if (((row - col) == 0) or ((row + col) == 4)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'X' pattern using Python while loop<|endoftext|> |
ed0a347dbb2612d65fcf79e21d1f435fcc938899bd75f9bfc2d5e2e372519322 | def for_Y():
" Upper case Alphabet letter 'Y' pattern using Python for loop"
for row in range(7):
for col in range(7):
if (((col == 3) and (row > 2)) or ((((row - col) == 0) or ((row + col) == 6)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'Y' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_Y | SaikumarS2611/ANSPatterns | 0 | python | def for_Y():
" "
for row in range(7):
for col in range(7):
if (((col == 3) and (row > 2)) or ((((row - col) == 0) or ((row + col) == 6)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_Y():
" "
for row in range(7):
for col in range(7):
if (((col == 3) and (row > 2)) or ((((row - col) == 0) or ((row + col) == 6)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'Y' pattern using Python for loop<|endoftext|> |
319efd9dc32b56425f07e9f7aa10f92804a7bd7772cb3935b809f408a33c65e3 | def while_Y():
" Upper case Alphabet letter 'Y' pattern using Python while loop"
row = 0
while (row < 7):
col = 0
while (col < 7):
if (((col == 3) and (row > 2)) or ((((row - col) == 0) or ((row + col) == 6)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'Y' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_Y | SaikumarS2611/ANSPatterns | 0 | python | def while_Y():
" "
row = 0
while (row < 7):
col = 0
while (col < 7):
if (((col == 3) and (row > 2)) or ((((row - col) == 0) or ((row + col) == 6)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_Y():
" "
row = 0
while (row < 7):
col = 0
while (col < 7):
if (((col == 3) and (row > 2)) or ((((row - col) == 0) or ((row + col) == 6)) and (row < 3))):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'Y' pattern using Python while loop<|endoftext|> |
022690237bcee5f58a4818449f023d84b73dbad3397b1e357407841baf225192 | def for_Z():
" Upper case Alphabet letter 'Z' pattern using Python for loop"
for row in range(6):
for col in range(6):
if ((row in (0, 5)) or ((row + col) == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | Upper case Alphabet letter 'Z' pattern using Python for loop | ANSPatterns/uppercase_alphabets/ualp.py | for_Z | SaikumarS2611/ANSPatterns | 0 | python | def for_Z():
" "
for row in range(6):
for col in range(6):
if ((row in (0, 5)) or ((row + col) == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print() | def for_Z():
" "
for row in range(6):
for col in range(6):
if ((row in (0, 5)) or ((row + col) == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
print()<|docstring|>Upper case Alphabet letter 'Z' pattern using Python for loop<|endoftext|> |
18706947bc8fea85648396ecef7d19fe70cc2f8a0d835b4687cdd36448a00e82 | def while_Z():
" Upper case Alphabet letter 'Z' pattern using Python while loop"
row = 0
while (row < 6):
col = 0
while (col < 6):
if ((row in (0, 5)) or ((row + col) == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | Upper case Alphabet letter 'Z' pattern using Python while loop | ANSPatterns/uppercase_alphabets/ualp.py | while_Z | SaikumarS2611/ANSPatterns | 0 | python | def while_Z():
" "
row = 0
while (row < 6):
col = 0
while (col < 6):
if ((row in (0, 5)) or ((row + col) == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1 | def while_Z():
" "
row = 0
while (row < 6):
col = 0
while (col < 6):
if ((row in (0, 5)) or ((row + col) == 5)):
print('*', end=' ')
else:
print(' ', end=' ')
col += 1
print()
row += 1<|docstring|>Upper case Alphabet letter 'Z' pattern using Python while loop<|endoftext|> |
13fa701129d7f1a50b7dd075765c046bb323511075f07a03a9409bafb364f324 | def daemonize(self):
'Deamonize class. UNIX double fork mechanism.'
try:
pid = os.fork()
if (pid > 0):
sys.exit(0)
except OSError as err:
sys.stderr.write('fork #1 failed: {0}\n'.format(err))
sys.exit(1)
os.chdir('/')
os.setsid()
os.umask(0)
try:
pid = os.fork()
if (pid > 0):
sys.exit(0)
except OSError as err:
sys.stderr.write('fork #2 failed: {0}\n'.format(err))
sys.exit(1)
sys.stdout.flush()
sys.stderr.flush()
si = open(os.devnull, 'r')
so = open(os.devnull, 'a+')
se = open(os.devnull, 'a+')
os.dup2(si.fileno(), sys.stdin.fileno())
os.dup2(so.fileno(), sys.stdout.fileno())
os.dup2(se.fileno(), sys.stderr.fileno())
atexit.register(self.delpid)
pid = str(os.getpid())
with open(self.pidfile, 'w+') as f:
f.write((pid + '\n')) | Deamonize class. UNIX double fork mechanism. | modules/kif/files/kif.py | daemonize | jennyb2911/infrastructure-puppet | 1 | python | def daemonize(self):
try:
pid = os.fork()
if (pid > 0):
sys.exit(0)
except OSError as err:
sys.stderr.write('fork #1 failed: {0}\n'.format(err))
sys.exit(1)
os.chdir('/')
os.setsid()
os.umask(0)
try:
pid = os.fork()
if (pid > 0):
sys.exit(0)
except OSError as err:
sys.stderr.write('fork #2 failed: {0}\n'.format(err))
sys.exit(1)
sys.stdout.flush()
sys.stderr.flush()
si = open(os.devnull, 'r')
so = open(os.devnull, 'a+')
se = open(os.devnull, 'a+')
os.dup2(si.fileno(), sys.stdin.fileno())
os.dup2(so.fileno(), sys.stdout.fileno())
os.dup2(se.fileno(), sys.stderr.fileno())
atexit.register(self.delpid)
pid = str(os.getpid())
with open(self.pidfile, 'w+') as f:
f.write((pid + '\n')) | def daemonize(self):
try:
pid = os.fork()
if (pid > 0):
sys.exit(0)
except OSError as err:
sys.stderr.write('fork #1 failed: {0}\n'.format(err))
sys.exit(1)
os.chdir('/')
os.setsid()
os.umask(0)
try:
pid = os.fork()
if (pid > 0):
sys.exit(0)
except OSError as err:
sys.stderr.write('fork #2 failed: {0}\n'.format(err))
sys.exit(1)
sys.stdout.flush()
sys.stderr.flush()
si = open(os.devnull, 'r')
so = open(os.devnull, 'a+')
se = open(os.devnull, 'a+')
os.dup2(si.fileno(), sys.stdin.fileno())
os.dup2(so.fileno(), sys.stdout.fileno())
os.dup2(se.fileno(), sys.stderr.fileno())
atexit.register(self.delpid)
pid = str(os.getpid())
with open(self.pidfile, 'w+') as f:
f.write((pid + '\n'))<|docstring|>Deamonize class. UNIX double fork mechanism.<|endoftext|> |
08fd4ca027b4fbb087e168ad5445af106093c02c013f70c9922e12c1acbe5f0a | def start(self, args):
'Start the daemon.'
try:
with open(self.pidfile, 'r') as pf:
pid = int(pf.read().strip())
except IOError:
pid = None
if pid:
message = ('pidfile {0} already exist. ' + 'Daemon already running?\n')
sys.stderr.write(message.format(self.pidfile))
sys.exit(1)
self.daemonize()
self.run(args) | Start the daemon. | modules/kif/files/kif.py | start | jennyb2911/infrastructure-puppet | 1 | python | def start(self, args):
try:
with open(self.pidfile, 'r') as pf:
pid = int(pf.read().strip())
except IOError:
pid = None
if pid:
message = ('pidfile {0} already exist. ' + 'Daemon already running?\n')
sys.stderr.write(message.format(self.pidfile))
sys.exit(1)
self.daemonize()
self.run(args) | def start(self, args):
try:
with open(self.pidfile, 'r') as pf:
pid = int(pf.read().strip())
except IOError:
pid = None
if pid:
message = ('pidfile {0} already exist. ' + 'Daemon already running?\n')
sys.stderr.write(message.format(self.pidfile))
sys.exit(1)
self.daemonize()
self.run(args)<|docstring|>Start the daemon.<|endoftext|> |
50d41e4383e0141fd6d74b79db8b444d30368614e7783a711fe2d063bcc60c84 | def stop(self):
'Stop the daemon.'
try:
with open(self.pidfile, 'r') as pf:
pid = int(pf.read().strip())
except IOError:
pid = None
if (not pid):
message = ('pidfile {0} does not exist. ' + 'Daemon not running?\n')
sys.stderr.write(message.format(self.pidfile))
return
try:
while 1:
os.kill(pid, signal.SIGTERM)
time.sleep(0.1)
except OSError as err:
e = str(err.args)
if (e.find('No such process') > 0):
if os.path.exists(self.pidfile):
os.remove(self.pidfile)
else:
print(str(err.args))
sys.exit(1) | Stop the daemon. | modules/kif/files/kif.py | stop | jennyb2911/infrastructure-puppet | 1 | python | def stop(self):
try:
with open(self.pidfile, 'r') as pf:
pid = int(pf.read().strip())
except IOError:
pid = None
if (not pid):
message = ('pidfile {0} does not exist. ' + 'Daemon not running?\n')
sys.stderr.write(message.format(self.pidfile))
return
try:
while 1:
os.kill(pid, signal.SIGTERM)
time.sleep(0.1)
except OSError as err:
e = str(err.args)
if (e.find('No such process') > 0):
if os.path.exists(self.pidfile):
os.remove(self.pidfile)
else:
print(str(err.args))
sys.exit(1) | def stop(self):
try:
with open(self.pidfile, 'r') as pf:
pid = int(pf.read().strip())
except IOError:
pid = None
if (not pid):
message = ('pidfile {0} does not exist. ' + 'Daemon not running?\n')
sys.stderr.write(message.format(self.pidfile))
return
try:
while 1:
os.kill(pid, signal.SIGTERM)
time.sleep(0.1)
except OSError as err:
e = str(err.args)
if (e.find('No such process') > 0):
if os.path.exists(self.pidfile):
os.remove(self.pidfile)
else:
print(str(err.args))
sys.exit(1)<|docstring|>Stop the daemon.<|endoftext|> |
fa02a2f0e5a5d60699c1b9a991b00aef9570dbbaf012ba5e983d63775b7663e3 | def restart(self):
'Restart the daemon.'
self.stop()
self.start() | Restart the daemon. | modules/kif/files/kif.py | restart | jennyb2911/infrastructure-puppet | 1 | python | def restart(self):
self.stop()
self.start() | def restart(self):
self.stop()
self.start()<|docstring|>Restart the daemon.<|endoftext|> |
11cecd60d91e6412d44e2f7fbb4390041c38383ad5f0c23cab023e26ac2d49b2 | def run(self):
'You should override this method when you subclass Daemon.\n\n It will be called after the process has been daemonized by\n start() or restart().' | You should override this method when you subclass Daemon.
It will be called after the process has been daemonized by
start() or restart(). | modules/kif/files/kif.py | run | jennyb2911/infrastructure-puppet | 1 | python | def run(self):
'You should override this method when you subclass Daemon.\n\n It will be called after the process has been daemonized by\n start() or restart().' | def run(self):
'You should override this method when you subclass Daemon.\n\n It will be called after the process has been daemonized by\n start() or restart().'<|docstring|>You should override this method when you subclass Daemon.
It will be called after the process has been daemonized by
start() or restart().<|endoftext|> |
ef51dea034adb4b64976c2f8c77106ad63782a762b58905f2441236f14665b83 | def log_mean_exp(mtx):
'\n Возвращает логарифм среднего по каждому столбцу от экспоненты данной матрицы.\n Вход: Tensor - матрица размера n x k.\n Выход: Tensor, вектор длины n.\n '
(m, _) = torch.max(mtx, dim=1, keepdim=True)
outputs = (m + (mtx - m).exp().mean(dim=1, keepdim=True).log())
outputs = outputs.squeeze(1)
return outputs | Возвращает логарифм среднего по каждому столбцу от экспоненты данной матрицы.
Вход: Tensor - матрица размера n x k.
Выход: Tensor, вектор длины n. | zo/log_likelihood.py | log_mean_exp | severilov/zo | 1 | python | def log_mean_exp(mtx):
'\n Возвращает логарифм среднего по каждому столбцу от экспоненты данной матрицы.\n Вход: Tensor - матрица размера n x k.\n Выход: Tensor, вектор длины n.\n '
(m, _) = torch.max(mtx, dim=1, keepdim=True)
outputs = (m + (mtx - m).exp().mean(dim=1, keepdim=True).log())
outputs = outputs.squeeze(1)
return outputs | def log_mean_exp(mtx):
'\n Возвращает логарифм среднего по каждому столбцу от экспоненты данной матрицы.\n Вход: Tensor - матрица размера n x k.\n Выход: Tensor, вектор длины n.\n '
(m, _) = torch.max(mtx, dim=1, keepdim=True)
outputs = (m + (mtx - m).exp().mean(dim=1, keepdim=True).log())
outputs = outputs.squeeze(1)
return outputs<|docstring|>Возвращает логарифм среднего по каждому столбцу от экспоненты данной матрицы.
Вход: Tensor - матрица размера n x k.
Выход: Tensor, вектор длины n.<|endoftext|> |
6a7a2ea54322a152e6f0bcc0d28ab15da9b9c5ab0ae44b6856cc833ea3b64e73 | def log_likelihood(generated_set, validation_set, test_set):
'\n Возвращает оценку логарифма правдоподобия модели GAN методом\n Парзеновского окна со стандартным нормальным ядром.\n Вход: generated_set - сэмплы из генеративной модели.\n validation_set - валидационная выборка.\n test_set - тестовая выборка.\n Выход: float - оценка логарифма правдоподобия.\n '
M = generated_set.shape[0]
N = validation_set.shape[0]
D = generated_set.shape[1]
validation_tensor = validation_set.unsqueeze(1).repeat(1, M, 1)
test_tensor = test_set.unsqueeze(1).repeat(1, M, 1)
generated_tensor = generated_set.unsqueeze(0).repeat(N, 1, 1)
sigma_space = np.logspace((- 4), 4, 100)
grid_ll = np.zeros_like(sigma_space)
for (i, sigma) in enumerate(sigma_space):
mtx = ((- ((validation_tensor - generated_tensor) ** 2)) / (2 * (sigma ** 2)))
mtx = mtx.sum(dim=2)
ll_vector = (log_mean_exp(mtx) - (D * np.log((((2 * np.pi) ** 0.5) * sigma))))
ll = ll_vector.mean().item()
grid_ll[i] = ll
best_sigma = sigma_space[np.argmax(grid_ll)]
mtx = ((- ((test_tensor - generated_tensor) ** 2)) / (2 * (best_sigma ** 2)))
mtx = mtx.sum(dim=2)
ll_vector = (log_mean_exp(mtx) - (D * np.log((((2 * np.pi) ** 0.5) * best_sigma))))
ll = ll_vector.mean().item()
return ll | Возвращает оценку логарифма правдоподобия модели GAN методом
Парзеновского окна со стандартным нормальным ядром.
Вход: generated_set - сэмплы из генеративной модели.
validation_set - валидационная выборка.
test_set - тестовая выборка.
Выход: float - оценка логарифма правдоподобия. | zo/log_likelihood.py | log_likelihood | severilov/zo | 1 | python | def log_likelihood(generated_set, validation_set, test_set):
'\n Возвращает оценку логарифма правдоподобия модели GAN методом\n Парзеновского окна со стандартным нормальным ядром.\n Вход: generated_set - сэмплы из генеративной модели.\n validation_set - валидационная выборка.\n test_set - тестовая выборка.\n Выход: float - оценка логарифма правдоподобия.\n '
M = generated_set.shape[0]
N = validation_set.shape[0]
D = generated_set.shape[1]
validation_tensor = validation_set.unsqueeze(1).repeat(1, M, 1)
test_tensor = test_set.unsqueeze(1).repeat(1, M, 1)
generated_tensor = generated_set.unsqueeze(0).repeat(N, 1, 1)
sigma_space = np.logspace((- 4), 4, 100)
grid_ll = np.zeros_like(sigma_space)
for (i, sigma) in enumerate(sigma_space):
mtx = ((- ((validation_tensor - generated_tensor) ** 2)) / (2 * (sigma ** 2)))
mtx = mtx.sum(dim=2)
ll_vector = (log_mean_exp(mtx) - (D * np.log((((2 * np.pi) ** 0.5) * sigma))))
ll = ll_vector.mean().item()
grid_ll[i] = ll
best_sigma = sigma_space[np.argmax(grid_ll)]
mtx = ((- ((test_tensor - generated_tensor) ** 2)) / (2 * (best_sigma ** 2)))
mtx = mtx.sum(dim=2)
ll_vector = (log_mean_exp(mtx) - (D * np.log((((2 * np.pi) ** 0.5) * best_sigma))))
ll = ll_vector.mean().item()
return ll | def log_likelihood(generated_set, validation_set, test_set):
'\n Возвращает оценку логарифма правдоподобия модели GAN методом\n Парзеновского окна со стандартным нормальным ядром.\n Вход: generated_set - сэмплы из генеративной модели.\n validation_set - валидационная выборка.\n test_set - тестовая выборка.\n Выход: float - оценка логарифма правдоподобия.\n '
M = generated_set.shape[0]
N = validation_set.shape[0]
D = generated_set.shape[1]
validation_tensor = validation_set.unsqueeze(1).repeat(1, M, 1)
test_tensor = test_set.unsqueeze(1).repeat(1, M, 1)
generated_tensor = generated_set.unsqueeze(0).repeat(N, 1, 1)
sigma_space = np.logspace((- 4), 4, 100)
grid_ll = np.zeros_like(sigma_space)
for (i, sigma) in enumerate(sigma_space):
mtx = ((- ((validation_tensor - generated_tensor) ** 2)) / (2 * (sigma ** 2)))
mtx = mtx.sum(dim=2)
ll_vector = (log_mean_exp(mtx) - (D * np.log((((2 * np.pi) ** 0.5) * sigma))))
ll = ll_vector.mean().item()
grid_ll[i] = ll
best_sigma = sigma_space[np.argmax(grid_ll)]
mtx = ((- ((test_tensor - generated_tensor) ** 2)) / (2 * (best_sigma ** 2)))
mtx = mtx.sum(dim=2)
ll_vector = (log_mean_exp(mtx) - (D * np.log((((2 * np.pi) ** 0.5) * best_sigma))))
ll = ll_vector.mean().item()
return ll<|docstring|>Возвращает оценку логарифма правдоподобия модели GAN методом
Парзеновского окна со стандартным нормальным ядром.
Вход: generated_set - сэмплы из генеративной модели.
validation_set - валидационная выборка.
test_set - тестовая выборка.
Выход: float - оценка логарифма правдоподобия.<|endoftext|> |
5eea38de7ed8b777023fd4e94c94181a21dda9e2ebd89da1e618bb056b044850 | def config_cam(azimuth, elevation, dist, tilt=0):
'\n Author: Amit Raj\n Utility function to generate camera location and rotation Vectors \n Parameters:\n azimuth: float\n Azimuth angle in radians\n elevation: float\n Elevation angle in radians\n dist : float\n Distance of viewing sphere\n tilt : float\n In Plane rotation in radians \n Returns:\n location : mathutils.Vector\n Camera location setting\n rotation : mathutils.Vector\n Camera rotation setting\n '
z = (dist * np.sin(elevation))
y = (((- dist) * np.cos(azimuth)) * np.cos(elevation))
x = ((dist * np.sin(azimuth)) * np.cos(elevation))
location = Vector((x, y, z))
xr = ((np.pi / 2) - elevation)
yr = tilt
zr = azimuth
rotation = Vector((xr, yr, zr))
return (location, rotation) | Author: Amit Raj
Utility function to generate camera location and rotation Vectors
Parameters:
azimuth: float
Azimuth angle in radians
elevation: float
Elevation angle in radians
dist : float
Distance of viewing sphere
tilt : float
In Plane rotation in radians
Returns:
location : mathutils.Vector
Camera location setting
rotation : mathutils.Vector
Camera rotation setting | image_generation/render_images_custom.py | config_cam | swarrier246/clevr-dataset-gen | 0 | python | def config_cam(azimuth, elevation, dist, tilt=0):
'\n Author: Amit Raj\n Utility function to generate camera location and rotation Vectors \n Parameters:\n azimuth: float\n Azimuth angle in radians\n elevation: float\n Elevation angle in radians\n dist : float\n Distance of viewing sphere\n tilt : float\n In Plane rotation in radians \n Returns:\n location : mathutils.Vector\n Camera location setting\n rotation : mathutils.Vector\n Camera rotation setting\n '
z = (dist * np.sin(elevation))
y = (((- dist) * np.cos(azimuth)) * np.cos(elevation))
x = ((dist * np.sin(azimuth)) * np.cos(elevation))
location = Vector((x, y, z))
xr = ((np.pi / 2) - elevation)
yr = tilt
zr = azimuth
rotation = Vector((xr, yr, zr))
return (location, rotation) | def config_cam(azimuth, elevation, dist, tilt=0):
'\n Author: Amit Raj\n Utility function to generate camera location and rotation Vectors \n Parameters:\n azimuth: float\n Azimuth angle in radians\n elevation: float\n Elevation angle in radians\n dist : float\n Distance of viewing sphere\n tilt : float\n In Plane rotation in radians \n Returns:\n location : mathutils.Vector\n Camera location setting\n rotation : mathutils.Vector\n Camera rotation setting\n '
z = (dist * np.sin(elevation))
y = (((- dist) * np.cos(azimuth)) * np.cos(elevation))
x = ((dist * np.sin(azimuth)) * np.cos(elevation))
location = Vector((x, y, z))
xr = ((np.pi / 2) - elevation)
yr = tilt
zr = azimuth
rotation = Vector((xr, yr, zr))
return (location, rotation)<|docstring|>Author: Amit Raj
Utility function to generate camera location and rotation Vectors
Parameters:
azimuth: float
Azimuth angle in radians
elevation: float
Elevation angle in radians
dist : float
Distance of viewing sphere
tilt : float
In Plane rotation in radians
Returns:
location : mathutils.Vector
Camera location setting
rotation : mathutils.Vector
Camera rotation setting<|endoftext|> |
56927aa5ae98c711e11d32427b354548c19547ebca8d070db1975aaafe55d00f | def convert_to_array(matrix_obj):
' Input: 3x3 matrix from mathutils\n Output: Array type, for bpy rotate function\n '
array = np.array(np.vstack([matrix_obj[i] for i in range(len(matrix_obj))]))
print(array)
return array | Input: 3x3 matrix from mathutils
Output: Array type, for bpy rotate function | image_generation/render_images_custom.py | convert_to_array | swarrier246/clevr-dataset-gen | 0 | python | def convert_to_array(matrix_obj):
' Input: 3x3 matrix from mathutils\n Output: Array type, for bpy rotate function\n '
array = np.array(np.vstack([matrix_obj[i] for i in range(len(matrix_obj))]))
print(array)
return array | def convert_to_array(matrix_obj):
' Input: 3x3 matrix from mathutils\n Output: Array type, for bpy rotate function\n '
array = np.array(np.vstack([matrix_obj[i] for i in range(len(matrix_obj))]))
print(array)
return array<|docstring|>Input: 3x3 matrix from mathutils
Output: Array type, for bpy rotate function<|endoftext|> |
40e5efddb769e345cdaa65b5aeb7019bc7191f9baf8df9628a8fd3805cb3b232 | def add_random_objects(scene_struct, num_objects, args, camera):
'\n Add random objects to the current blender scene\n '
with open(args.properties_json, 'r') as f:
properties = json.load(f)
color_name_to_rgba = {}
for (name, rgb) in properties['colors'].items():
rgba = ([(float(c) / 255.0) for c in rgb] + [1.0])
color_name_to_rgba[name] = rgba
material_mapping = [(v, k) for (k, v) in properties['materials'].items()]
object_mapping = [(v, k) for (k, v) in properties['shapes'].items()]
leaf_disk = False
leaf_properties = properties['shapes'].copy()
leaf_properties.pop('sphere')
leaf_obj_mapping = [(v, k) for (k, v) in leaf_properties.items()]
size_mapping = list(properties['sizes'].items())
print('size mapping: ', size_mapping)
shape_color_combos = None
if (args.shape_color_combos_json is not None):
with open(args.shape_color_combos_json, 'r') as f:
shape_color_combos = list(json.load(f).items())
positions = []
objects = []
blender_objects = []
init_rot = 0
(size_name, r) = random.choice(size_mapping)
(color_name, rgba) = random.choice(list(color_name_to_rgba.items()))
"\n WORKFLOW: First select nb of rows - 1 being bottom-most\n For each row, select nb of leaves - each row has a tilt angle that's a function of the row\n For each leaf, select angular position of leaf around z-axis. ensure leaves don't fully overlap\n "
nb_rows = random.randint(1, 3)
size_map_sorted = sorted(size_mapping, key=(lambda x: x[1]))
size_map_sorted.reverse()
x = 0.0
y = 0.0
for row in range(nb_rows):
nb_leaves_per_row = random.randint(3, 7)
for i in range(nb_leaves_per_row):
theta = ((math.pi * random.choice(list(np.arange((row * 20), ((row + 1) * 20), 0.5)))) / 180)
(size_name, r) = random.choice(size_mapping)
(color_name, rgba) = random.choice(list(color_name_to_rgba.items()))
if (not positions):
z = r
obj_name_out = str('sphere')
obj_name = properties['shapes']['sphere']
else:
z = 0.0
if (leaf_disk == True):
(obj_name, obj_name_out) = random.choice(leaf_obj_mapping)
else:
obj_name_out = str('leaf')
obj_name = str('leaf')
utils.add_object(args.shape_dir, obj_name, r, (x, y, z), theta=theta)
obj = bpy.context.object
blender_objects.append(obj)
positions.append((x, y, r))
print('theta: ', theta, ' for obj: ', obj)
(mat_name, mat_name_out) = random.choice(material_mapping)
utils.add_material(mat_name, Color=rgba)
(mat_name, mat_name_out) = random.choice(material_mapping)
utils.add_material(mat_name, Color=rgba)
pixel_coords = utils.get_camera_coords(camera, obj.location)
objects.append({'shape': obj_name_out, 'size': size_name, 'material': mat_name_out, '3d_coords': tuple(obj.location), 'rotation': theta, 'pixel_coords': pixel_coords, 'color': color_name})
parent_object = blender_objects[0]
bpy.context.object.rotation_mode = 'XYZ'
if ((len(positions) > 1) and (obj.type == 'MESH')):
obj.select = True
parent_object.select = True
obj.parent = parent_object
obj.matrix_parent_inverse = parent_object.matrix_world.inverted()
rot_angle_deg = (360.0 / float((nb_leaves_per_row - 1)))
rot_angle = ((3.14159 * rot_angle_deg) / 180)
init_rot = rot_angle
bpy.context.scene.objects.active = bpy.data.objects[parent_object.name]
bpy.context.object.rotation_euler[2] = (bpy.context.object.rotation_euler[2] + rot_angle)
parent_object.select = False
obj.select = False
bpy.ops.object.select_all(action='DESELECT')
all_visible = check_visibility(blender_objects, args.min_pixels_per_object)
return (objects, blender_objects) | Add random objects to the current blender scene | image_generation/render_images_custom.py | add_random_objects | swarrier246/clevr-dataset-gen | 0 | python | def add_random_objects(scene_struct, num_objects, args, camera):
'\n \n '
with open(args.properties_json, 'r') as f:
properties = json.load(f)
color_name_to_rgba = {}
for (name, rgb) in properties['colors'].items():
rgba = ([(float(c) / 255.0) for c in rgb] + [1.0])
color_name_to_rgba[name] = rgba
material_mapping = [(v, k) for (k, v) in properties['materials'].items()]
object_mapping = [(v, k) for (k, v) in properties['shapes'].items()]
leaf_disk = False
leaf_properties = properties['shapes'].copy()
leaf_properties.pop('sphere')
leaf_obj_mapping = [(v, k) for (k, v) in leaf_properties.items()]
size_mapping = list(properties['sizes'].items())
print('size mapping: ', size_mapping)
shape_color_combos = None
if (args.shape_color_combos_json is not None):
with open(args.shape_color_combos_json, 'r') as f:
shape_color_combos = list(json.load(f).items())
positions = []
objects = []
blender_objects = []
init_rot = 0
(size_name, r) = random.choice(size_mapping)
(color_name, rgba) = random.choice(list(color_name_to_rgba.items()))
"\n WORKFLOW: First select nb of rows - 1 being bottom-most\n For each row, select nb of leaves - each row has a tilt angle that's a function of the row\n For each leaf, select angular position of leaf around z-axis. ensure leaves don't fully overlap\n "
nb_rows = random.randint(1, 3)
size_map_sorted = sorted(size_mapping, key=(lambda x: x[1]))
size_map_sorted.reverse()
x = 0.0
y = 0.0
for row in range(nb_rows):
nb_leaves_per_row = random.randint(3, 7)
for i in range(nb_leaves_per_row):
theta = ((math.pi * random.choice(list(np.arange((row * 20), ((row + 1) * 20), 0.5)))) / 180)
(size_name, r) = random.choice(size_mapping)
(color_name, rgba) = random.choice(list(color_name_to_rgba.items()))
if (not positions):
z = r
obj_name_out = str('sphere')
obj_name = properties['shapes']['sphere']
else:
z = 0.0
if (leaf_disk == True):
(obj_name, obj_name_out) = random.choice(leaf_obj_mapping)
else:
obj_name_out = str('leaf')
obj_name = str('leaf')
utils.add_object(args.shape_dir, obj_name, r, (x, y, z), theta=theta)
obj = bpy.context.object
blender_objects.append(obj)
positions.append((x, y, r))
print('theta: ', theta, ' for obj: ', obj)
(mat_name, mat_name_out) = random.choice(material_mapping)
utils.add_material(mat_name, Color=rgba)
(mat_name, mat_name_out) = random.choice(material_mapping)
utils.add_material(mat_name, Color=rgba)
pixel_coords = utils.get_camera_coords(camera, obj.location)
objects.append({'shape': obj_name_out, 'size': size_name, 'material': mat_name_out, '3d_coords': tuple(obj.location), 'rotation': theta, 'pixel_coords': pixel_coords, 'color': color_name})
parent_object = blender_objects[0]
bpy.context.object.rotation_mode = 'XYZ'
if ((len(positions) > 1) and (obj.type == 'MESH')):
obj.select = True
parent_object.select = True
obj.parent = parent_object
obj.matrix_parent_inverse = parent_object.matrix_world.inverted()
rot_angle_deg = (360.0 / float((nb_leaves_per_row - 1)))
rot_angle = ((3.14159 * rot_angle_deg) / 180)
init_rot = rot_angle
bpy.context.scene.objects.active = bpy.data.objects[parent_object.name]
bpy.context.object.rotation_euler[2] = (bpy.context.object.rotation_euler[2] + rot_angle)
parent_object.select = False
obj.select = False
bpy.ops.object.select_all(action='DESELECT')
all_visible = check_visibility(blender_objects, args.min_pixels_per_object)
return (objects, blender_objects) | def add_random_objects(scene_struct, num_objects, args, camera):
'\n \n '
with open(args.properties_json, 'r') as f:
properties = json.load(f)
color_name_to_rgba = {}
for (name, rgb) in properties['colors'].items():
rgba = ([(float(c) / 255.0) for c in rgb] + [1.0])
color_name_to_rgba[name] = rgba
material_mapping = [(v, k) for (k, v) in properties['materials'].items()]
object_mapping = [(v, k) for (k, v) in properties['shapes'].items()]
leaf_disk = False
leaf_properties = properties['shapes'].copy()
leaf_properties.pop('sphere')
leaf_obj_mapping = [(v, k) for (k, v) in leaf_properties.items()]
size_mapping = list(properties['sizes'].items())
print('size mapping: ', size_mapping)
shape_color_combos = None
if (args.shape_color_combos_json is not None):
with open(args.shape_color_combos_json, 'r') as f:
shape_color_combos = list(json.load(f).items())
positions = []
objects = []
blender_objects = []
init_rot = 0
(size_name, r) = random.choice(size_mapping)
(color_name, rgba) = random.choice(list(color_name_to_rgba.items()))
"\n WORKFLOW: First select nb of rows - 1 being bottom-most\n For each row, select nb of leaves - each row has a tilt angle that's a function of the row\n For each leaf, select angular position of leaf around z-axis. ensure leaves don't fully overlap\n "
nb_rows = random.randint(1, 3)
size_map_sorted = sorted(size_mapping, key=(lambda x: x[1]))
size_map_sorted.reverse()
x = 0.0
y = 0.0
for row in range(nb_rows):
nb_leaves_per_row = random.randint(3, 7)
for i in range(nb_leaves_per_row):
theta = ((math.pi * random.choice(list(np.arange((row * 20), ((row + 1) * 20), 0.5)))) / 180)
(size_name, r) = random.choice(size_mapping)
(color_name, rgba) = random.choice(list(color_name_to_rgba.items()))
if (not positions):
z = r
obj_name_out = str('sphere')
obj_name = properties['shapes']['sphere']
else:
z = 0.0
if (leaf_disk == True):
(obj_name, obj_name_out) = random.choice(leaf_obj_mapping)
else:
obj_name_out = str('leaf')
obj_name = str('leaf')
utils.add_object(args.shape_dir, obj_name, r, (x, y, z), theta=theta)
obj = bpy.context.object
blender_objects.append(obj)
positions.append((x, y, r))
print('theta: ', theta, ' for obj: ', obj)
(mat_name, mat_name_out) = random.choice(material_mapping)
utils.add_material(mat_name, Color=rgba)
(mat_name, mat_name_out) = random.choice(material_mapping)
utils.add_material(mat_name, Color=rgba)
pixel_coords = utils.get_camera_coords(camera, obj.location)
objects.append({'shape': obj_name_out, 'size': size_name, 'material': mat_name_out, '3d_coords': tuple(obj.location), 'rotation': theta, 'pixel_coords': pixel_coords, 'color': color_name})
parent_object = blender_objects[0]
bpy.context.object.rotation_mode = 'XYZ'
if ((len(positions) > 1) and (obj.type == 'MESH')):
obj.select = True
parent_object.select = True
obj.parent = parent_object
obj.matrix_parent_inverse = parent_object.matrix_world.inverted()
rot_angle_deg = (360.0 / float((nb_leaves_per_row - 1)))
rot_angle = ((3.14159 * rot_angle_deg) / 180)
init_rot = rot_angle
bpy.context.scene.objects.active = bpy.data.objects[parent_object.name]
bpy.context.object.rotation_euler[2] = (bpy.context.object.rotation_euler[2] + rot_angle)
parent_object.select = False
obj.select = False
bpy.ops.object.select_all(action='DESELECT')
all_visible = check_visibility(blender_objects, args.min_pixels_per_object)
return (objects, blender_objects)<|docstring|>Add random objects to the current blender scene<|endoftext|> |
edd2f9e54e51ba45b197fd362c040615b67aac261e3ca2a25f99f8e275476fe6 | def compute_all_relationships(scene_struct, eps=0.2):
"\n Computes relationships between all pairs of objects in the scene.\n \n Returns a dictionary mapping string relationship names to lists of lists of\n integers, where output[rel][i] gives a list of object indices that have the\n relationship rel with object i. For example if j is in output['left'][i] then\n object j is left of object i.\n "
all_relationships = {}
for (name, direction_vec) in scene_struct['directions'].items():
if ((name == 'above') or (name == 'below')):
continue
all_relationships[name] = []
for (i, obj1) in enumerate(scene_struct['objects']):
coords1 = obj1['3d_coords']
related = set()
for (j, obj2) in enumerate(scene_struct['objects']):
if (obj1 == obj2):
continue
coords2 = obj2['3d_coords']
diff = [(coords2[k] - coords1[k]) for k in [0, 1, 2]]
dot = sum(((diff[k] * direction_vec[k]) for k in [0, 1, 2]))
if (dot > eps):
related.add(j)
all_relationships[name].append(sorted(list(related)))
return all_relationships | Computes relationships between all pairs of objects in the scene.
Returns a dictionary mapping string relationship names to lists of lists of
integers, where output[rel][i] gives a list of object indices that have the
relationship rel with object i. For example if j is in output['left'][i] then
object j is left of object i. | image_generation/render_images_custom.py | compute_all_relationships | swarrier246/clevr-dataset-gen | 0 | python | def compute_all_relationships(scene_struct, eps=0.2):
"\n Computes relationships between all pairs of objects in the scene.\n \n Returns a dictionary mapping string relationship names to lists of lists of\n integers, where output[rel][i] gives a list of object indices that have the\n relationship rel with object i. For example if j is in output['left'][i] then\n object j is left of object i.\n "
all_relationships = {}
for (name, direction_vec) in scene_struct['directions'].items():
if ((name == 'above') or (name == 'below')):
continue
all_relationships[name] = []
for (i, obj1) in enumerate(scene_struct['objects']):
coords1 = obj1['3d_coords']
related = set()
for (j, obj2) in enumerate(scene_struct['objects']):
if (obj1 == obj2):
continue
coords2 = obj2['3d_coords']
diff = [(coords2[k] - coords1[k]) for k in [0, 1, 2]]
dot = sum(((diff[k] * direction_vec[k]) for k in [0, 1, 2]))
if (dot > eps):
related.add(j)
all_relationships[name].append(sorted(list(related)))
return all_relationships | def compute_all_relationships(scene_struct, eps=0.2):
"\n Computes relationships between all pairs of objects in the scene.\n \n Returns a dictionary mapping string relationship names to lists of lists of\n integers, where output[rel][i] gives a list of object indices that have the\n relationship rel with object i. For example if j is in output['left'][i] then\n object j is left of object i.\n "
all_relationships = {}
for (name, direction_vec) in scene_struct['directions'].items():
if ((name == 'above') or (name == 'below')):
continue
all_relationships[name] = []
for (i, obj1) in enumerate(scene_struct['objects']):
coords1 = obj1['3d_coords']
related = set()
for (j, obj2) in enumerate(scene_struct['objects']):
if (obj1 == obj2):
continue
coords2 = obj2['3d_coords']
diff = [(coords2[k] - coords1[k]) for k in [0, 1, 2]]
dot = sum(((diff[k] * direction_vec[k]) for k in [0, 1, 2]))
if (dot > eps):
related.add(j)
all_relationships[name].append(sorted(list(related)))
return all_relationships<|docstring|>Computes relationships between all pairs of objects in the scene.
Returns a dictionary mapping string relationship names to lists of lists of
integers, where output[rel][i] gives a list of object indices that have the
relationship rel with object i. For example if j is in output['left'][i] then
object j is left of object i.<|endoftext|> |
e6beb8d362a000944aa5c0ed440e6988088ad4555a000783d6b332fa8b95a9df | def check_visibility(blender_objects, min_pixels_per_object):
'\n Check whether all objects in the scene have some minimum number of visible\n pixels; to accomplish this we assign random (but distinct) colors to all\n objects, and render using no lighting or shading or antialiasing; this\n ensures that each object is just a solid uniform color. We can then count\n the number of pixels of each color in the output image to check the visibility\n of each object.\n\n Returns True if all objects are visible and False otherwise.\n '
(f, path) = tempfile.mkstemp(suffix='.png')
object_colors = render_shadeless(blender_objects, path=path)
img = bpy.data.images.load(path)
p = list(img.pixels)
color_count = Counter(((p[i], p[(i + 1)], p[(i + 2)], p[(i + 3)]) for i in range(0, len(p), 4)))
os.remove(path)
if (len(color_count) != (len(blender_objects) + 1)):
return False
for (_, count) in color_count.most_common():
if (count < min_pixels_per_object):
return False
return True | Check whether all objects in the scene have some minimum number of visible
pixels; to accomplish this we assign random (but distinct) colors to all
objects, and render using no lighting or shading or antialiasing; this
ensures that each object is just a solid uniform color. We can then count
the number of pixels of each color in the output image to check the visibility
of each object.
Returns True if all objects are visible and False otherwise. | image_generation/render_images_custom.py | check_visibility | swarrier246/clevr-dataset-gen | 0 | python | def check_visibility(blender_objects, min_pixels_per_object):
'\n Check whether all objects in the scene have some minimum number of visible\n pixels; to accomplish this we assign random (but distinct) colors to all\n objects, and render using no lighting or shading or antialiasing; this\n ensures that each object is just a solid uniform color. We can then count\n the number of pixels of each color in the output image to check the visibility\n of each object.\n\n Returns True if all objects are visible and False otherwise.\n '
(f, path) = tempfile.mkstemp(suffix='.png')
object_colors = render_shadeless(blender_objects, path=path)
img = bpy.data.images.load(path)
p = list(img.pixels)
color_count = Counter(((p[i], p[(i + 1)], p[(i + 2)], p[(i + 3)]) for i in range(0, len(p), 4)))
os.remove(path)
if (len(color_count) != (len(blender_objects) + 1)):
return False
for (_, count) in color_count.most_common():
if (count < min_pixels_per_object):
return False
return True | def check_visibility(blender_objects, min_pixels_per_object):
'\n Check whether all objects in the scene have some minimum number of visible\n pixels; to accomplish this we assign random (but distinct) colors to all\n objects, and render using no lighting or shading or antialiasing; this\n ensures that each object is just a solid uniform color. We can then count\n the number of pixels of each color in the output image to check the visibility\n of each object.\n\n Returns True if all objects are visible and False otherwise.\n '
(f, path) = tempfile.mkstemp(suffix='.png')
object_colors = render_shadeless(blender_objects, path=path)
img = bpy.data.images.load(path)
p = list(img.pixels)
color_count = Counter(((p[i], p[(i + 1)], p[(i + 2)], p[(i + 3)]) for i in range(0, len(p), 4)))
os.remove(path)
if (len(color_count) != (len(blender_objects) + 1)):
return False
for (_, count) in color_count.most_common():
if (count < min_pixels_per_object):
return False
return True<|docstring|>Check whether all objects in the scene have some minimum number of visible
pixels; to accomplish this we assign random (but distinct) colors to all
objects, and render using no lighting or shading or antialiasing; this
ensures that each object is just a solid uniform color. We can then count
the number of pixels of each color in the output image to check the visibility
of each object.
Returns True if all objects are visible and False otherwise.<|endoftext|> |
3d520c2ede4b11bf5e3df43a6522910df686127b897cbd1251b85052ffb24479 | def render_shadeless(blender_objects, path='flat.png'):
'\n Render a version of the scene with shading disabled and unique materials\n assigned to all objects, and return a set of all colors that should be in the\n rendered image. The image itself is written to path. This is used to ensure\n that all objects will be visible in the final rendered scene.\n '
render_args = bpy.context.scene.render
old_filepath = render_args.filepath
old_engine = render_args.engine
old_use_antialiasing = render_args.use_antialiasing
render_args.filepath = path
render_args.engine = 'BLENDER_RENDER'
render_args.use_antialiasing = False
utils.set_layer(bpy.data.objects['Lamp_Key'], 2)
utils.set_layer(bpy.data.objects['Lamp_Fill'], 2)
utils.set_layer(bpy.data.objects['Lamp_Back'], 2)
utils.set_layer(bpy.data.objects['Ground'], 2)
object_colors = set()
old_materials = []
for (i, obj) in enumerate(blender_objects):
old_materials.append(obj.data.materials[0])
bpy.ops.material.new()
mat = bpy.data.materials['Material']
mat.name = ('Material_%d' % i)
while True:
(r, g, b) = [random.random() for _ in range(3)]
if ((r, g, b) not in object_colors):
break
object_colors.add((r, g, b))
mat.diffuse_color = [r, g, b]
mat.use_shadeless = True
obj.data.materials[0] = mat
bpy.ops.render.render(write_still=True)
for (mat, obj) in zip(old_materials, blender_objects):
obj.data.materials[0] = mat
utils.set_layer(bpy.data.objects['Lamp_Key'], 0)
utils.set_layer(bpy.data.objects['Lamp_Fill'], 0)
utils.set_layer(bpy.data.objects['Lamp_Back'], 0)
utils.set_layer(bpy.data.objects['Ground'], 0)
render_args.filepath = old_filepath
render_args.engine = old_engine
render_args.use_antialiasing = old_use_antialiasing
return object_colors | Render a version of the scene with shading disabled and unique materials
assigned to all objects, and return a set of all colors that should be in the
rendered image. The image itself is written to path. This is used to ensure
that all objects will be visible in the final rendered scene. | image_generation/render_images_custom.py | render_shadeless | swarrier246/clevr-dataset-gen | 0 | python | def render_shadeless(blender_objects, path='flat.png'):
'\n Render a version of the scene with shading disabled and unique materials\n assigned to all objects, and return a set of all colors that should be in the\n rendered image. The image itself is written to path. This is used to ensure\n that all objects will be visible in the final rendered scene.\n '
render_args = bpy.context.scene.render
old_filepath = render_args.filepath
old_engine = render_args.engine
old_use_antialiasing = render_args.use_antialiasing
render_args.filepath = path
render_args.engine = 'BLENDER_RENDER'
render_args.use_antialiasing = False
utils.set_layer(bpy.data.objects['Lamp_Key'], 2)
utils.set_layer(bpy.data.objects['Lamp_Fill'], 2)
utils.set_layer(bpy.data.objects['Lamp_Back'], 2)
utils.set_layer(bpy.data.objects['Ground'], 2)
object_colors = set()
old_materials = []
for (i, obj) in enumerate(blender_objects):
old_materials.append(obj.data.materials[0])
bpy.ops.material.new()
mat = bpy.data.materials['Material']
mat.name = ('Material_%d' % i)
while True:
(r, g, b) = [random.random() for _ in range(3)]
if ((r, g, b) not in object_colors):
break
object_colors.add((r, g, b))
mat.diffuse_color = [r, g, b]
mat.use_shadeless = True
obj.data.materials[0] = mat
bpy.ops.render.render(write_still=True)
for (mat, obj) in zip(old_materials, blender_objects):
obj.data.materials[0] = mat
utils.set_layer(bpy.data.objects['Lamp_Key'], 0)
utils.set_layer(bpy.data.objects['Lamp_Fill'], 0)
utils.set_layer(bpy.data.objects['Lamp_Back'], 0)
utils.set_layer(bpy.data.objects['Ground'], 0)
render_args.filepath = old_filepath
render_args.engine = old_engine
render_args.use_antialiasing = old_use_antialiasing
return object_colors | def render_shadeless(blender_objects, path='flat.png'):
'\n Render a version of the scene with shading disabled and unique materials\n assigned to all objects, and return a set of all colors that should be in the\n rendered image. The image itself is written to path. This is used to ensure\n that all objects will be visible in the final rendered scene.\n '
render_args = bpy.context.scene.render
old_filepath = render_args.filepath
old_engine = render_args.engine
old_use_antialiasing = render_args.use_antialiasing
render_args.filepath = path
render_args.engine = 'BLENDER_RENDER'
render_args.use_antialiasing = False
utils.set_layer(bpy.data.objects['Lamp_Key'], 2)
utils.set_layer(bpy.data.objects['Lamp_Fill'], 2)
utils.set_layer(bpy.data.objects['Lamp_Back'], 2)
utils.set_layer(bpy.data.objects['Ground'], 2)
object_colors = set()
old_materials = []
for (i, obj) in enumerate(blender_objects):
old_materials.append(obj.data.materials[0])
bpy.ops.material.new()
mat = bpy.data.materials['Material']
mat.name = ('Material_%d' % i)
while True:
(r, g, b) = [random.random() for _ in range(3)]
if ((r, g, b) not in object_colors):
break
object_colors.add((r, g, b))
mat.diffuse_color = [r, g, b]
mat.use_shadeless = True
obj.data.materials[0] = mat
bpy.ops.render.render(write_still=True)
for (mat, obj) in zip(old_materials, blender_objects):
obj.data.materials[0] = mat
utils.set_layer(bpy.data.objects['Lamp_Key'], 0)
utils.set_layer(bpy.data.objects['Lamp_Fill'], 0)
utils.set_layer(bpy.data.objects['Lamp_Back'], 0)
utils.set_layer(bpy.data.objects['Ground'], 0)
render_args.filepath = old_filepath
render_args.engine = old_engine
render_args.use_antialiasing = old_use_antialiasing
return object_colors<|docstring|>Render a version of the scene with shading disabled and unique materials
assigned to all objects, and return a set of all colors that should be in the
rendered image. The image itself is written to path. This is used to ensure
that all objects will be visible in the final rendered scene.<|endoftext|> |
3236ba4085ae0b06e4ff0fb885a29282ed47a7ae85ba8fbe17eab8795a6e8353 | def minimumTotal(self, triangle):
'\n https://shenjie1993.gitbooks.io/leetcode-python/120%20Triangle.html\n\n 典型的动态规划问题，先将问题转化一下，把每一行的数列都左对齐，如下：\n [\n [2],\n [3,4],\n [6,5,7],\n [4,1,8,3]\n ]\n 可以看出来，其实上一行到下一行就两个选择，横坐标不变或加一。\n dp[i]表示从底层到这一层的第i个元素所有路径中最小的和。\n 递推关系就是 dp[j] = triangle[i][j] + min(dp[j], dp[j + 1])，\n 即下一行与它相邻的两个节点中和比较小的再加上它自己的值。\n\n :type triangle: List[List[int]]\n :rtype: int\n '
n = len(triangle)
dp = triangle[(- 1)]
for i in range((n - 2), (- 1), (- 1)):
for j in range((i + 1)):
dp[j] = (triangle[i][j] + min(dp[j], dp[(j + 1)]))
return dp[0] | https://shenjie1993.gitbooks.io/leetcode-python/120%20Triangle.html
典型的动态规划问题，先将问题转化一下，把每一行的数列都左对齐，如下：
[
[2],
[3,4],
[6,5,7],
[4,1,8,3]
]
可以看出来，其实上一行到下一行就两个选择，横坐标不变或加一。
dp[i]表示从底层到这一层的第i个元素所有路径中最小的和。
递推关系就是 dp[j] = triangle[i][j] + min(dp[j], dp[j + 1])，
即下一行与它相邻的两个节点中和比较小的再加上它自己的值。
:type triangle: List[List[int]]
:rtype: int | 120_triangle.py | minimumTotal | gengwg/leetcode | 2 | python | def minimumTotal(self, triangle):
'\n https://shenjie1993.gitbooks.io/leetcode-python/120%20Triangle.html\n\n 典型的动态规划问题，先将问题转化一下，把每一行的数列都左对齐，如下：\n [\n [2],\n [3,4],\n [6,5,7],\n [4,1,8,3]\n ]\n 可以看出来，其实上一行到下一行就两个选择，横坐标不变或加一。\n dp[i]表示从底层到这一层的第i个元素所有路径中最小的和。\n 递推关系就是 dp[j] = triangle[i][j] + min(dp[j], dp[j + 1])，\n 即下一行与它相邻的两个节点中和比较小的再加上它自己的值。\n\n :type triangle: List[List[int]]\n :rtype: int\n '
n = len(triangle)
dp = triangle[(- 1)]
for i in range((n - 2), (- 1), (- 1)):
for j in range((i + 1)):
dp[j] = (triangle[i][j] + min(dp[j], dp[(j + 1)]))
return dp[0] | def minimumTotal(self, triangle):
'\n https://shenjie1993.gitbooks.io/leetcode-python/120%20Triangle.html\n\n 典型的动态规划问题，先将问题转化一下，把每一行的数列都左对齐，如下：\n [\n [2],\n [3,4],\n [6,5,7],\n [4,1,8,3]\n ]\n 可以看出来，其实上一行到下一行就两个选择，横坐标不变或加一。\n dp[i]表示从底层到这一层的第i个元素所有路径中最小的和。\n 递推关系就是 dp[j] = triangle[i][j] + min(dp[j], dp[j + 1])，\n 即下一行与它相邻的两个节点中和比较小的再加上它自己的值。\n\n :type triangle: List[List[int]]\n :rtype: int\n '
n = len(triangle)
dp = triangle[(- 1)]
for i in range((n - 2), (- 1), (- 1)):
for j in range((i + 1)):
dp[j] = (triangle[i][j] + min(dp[j], dp[(j + 1)]))
return dp[0]<|docstring|>https://shenjie1993.gitbooks.io/leetcode-python/120%20Triangle.html
典型的动态规划问题，先将问题转化一下，把每一行的数列都左对齐，如下：
[
[2],
[3,4],
[6,5,7],
[4,1,8,3]
]
可以看出来，其实上一行到下一行就两个选择，横坐标不变或加一。
dp[i]表示从底层到这一层的第i个元素所有路径中最小的和。
递推关系就是 dp[j] = triangle[i][j] + min(dp[j], dp[j + 1])，
即下一行与它相邻的两个节点中和比较小的再加上它自己的值。
:type triangle: List[List[int]]
:rtype: int<|endoftext|> |
dee3bee4467402447b3cc6c3b7a418435f390c639dcd17e96f36bedcde7fe333 | def forward(self, sk_feat, im_feat, bs, bi):
'\n :param sk_feat: features of sketches. bs * m.\n :param im_feat: features of images. bs * m.\n :param bs: hash codes of sketches. bs * m.\n :param bi: hash codes of images. bs * m.\n :return: loss\n '
return (torch.mean(((self.d(sk_feat, bs) ** 2) + (self.d(im_feat, bi) ** 2))) * self.gamma) | :param sk_feat: features of sketches. bs * m.
:param im_feat: features of images. bs * m.
:param bs: hash codes of sketches. bs * m.
:param bi: hash codes of images. bs * m.
:return: loss | src/package/loss/dsh_loss.py | forward | Jiangtong-Li/ZHSIR | 8 | python | def forward(self, sk_feat, im_feat, bs, bi):
'\n :param sk_feat: features of sketches. bs * m.\n :param im_feat: features of images. bs * m.\n :param bs: hash codes of sketches. bs * m.\n :param bi: hash codes of images. bs * m.\n :return: loss\n '
return (torch.mean(((self.d(sk_feat, bs) ** 2) + (self.d(im_feat, bi) ** 2))) * self.gamma) | def forward(self, sk_feat, im_feat, bs, bi):
'\n :param sk_feat: features of sketches. bs * m.\n :param im_feat: features of images. bs * m.\n :param bs: hash codes of sketches. bs * m.\n :param bi: hash codes of images. bs * m.\n :return: loss\n '
return (torch.mean(((self.d(sk_feat, bs) ** 2) + (self.d(im_feat, bi) ** 2))) * self.gamma)<|docstring|>:param sk_feat: features of sketches. bs * m.
:param im_feat: features of images. bs * m.
:param bs: hash codes of sketches. bs * m.
:param bi: hash codes of images. bs * m.
:return: loss<|endoftext|> |
2642e37e0731c0921e6c0f300f1bf1eee5a2fcdf1c5666aaf5cef9b7b96e027f | @nb.vectorize('float64(float64, float64, float64)')
def norm_pdf(x, mu, sigma):
'\n Return probability density of normal distribution.\n '
z = ((x - mu) / sigma)
c = (1.0 / np.sqrt((2 * np.pi)))
return ((np.exp(((- 0.5) * (z ** 2))) * c) / sigma) | Return probability density of normal distribution. | numba_stats/stats.py | norm_pdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64)')
def norm_pdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
c = (1.0 / np.sqrt((2 * np.pi)))
return ((np.exp(((- 0.5) * (z ** 2))) * c) / sigma) | @nb.vectorize('float64(float64, float64, float64)')
def norm_pdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
c = (1.0 / np.sqrt((2 * np.pi)))
return ((np.exp(((- 0.5) * (z ** 2))) * c) / sigma)<|docstring|>Return probability density of normal distribution.<|endoftext|> |
96d57aecada5803e4b120e9a2a522a1d6f9e35a0f2390d397956e882861fa747 | @nb.vectorize('float64(float64, float64, float64)')
def norm_cdf(x, mu, sigma):
'\n Evaluate cumulative distribution function of normal distribution.\n '
z = ((x - mu) / sigma)
z *= (1.0 / np.sqrt(2))
return (0.5 * (1.0 + erf(z))) | Evaluate cumulative distribution function of normal distribution. | numba_stats/stats.py | norm_cdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64)')
def norm_cdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
z *= (1.0 / np.sqrt(2))
return (0.5 * (1.0 + erf(z))) | @nb.vectorize('float64(float64, float64, float64)')
def norm_cdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
z *= (1.0 / np.sqrt(2))
return (0.5 * (1.0 + erf(z)))<|docstring|>Evaluate cumulative distribution function of normal distribution.<|endoftext|> |
cc2dd1e2ef46b2b22adffd8f40e048b7f367baad1724eed82904057f99ce048e | @nb.vectorize('float64(float64, float64, float64)')
def norm_ppf(p, mu, sigma):
'\n Return quantile of normal distribution for given probability.\n '
z = (np.sqrt(2) * erfinv(((2 * p) - 1)))
return ((sigma * z) + mu) | Return quantile of normal distribution for given probability. | numba_stats/stats.py | norm_ppf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64)')
def norm_ppf(p, mu, sigma):
'\n \n '
z = (np.sqrt(2) * erfinv(((2 * p) - 1)))
return ((sigma * z) + mu) | @nb.vectorize('float64(float64, float64, float64)')
def norm_ppf(p, mu, sigma):
'\n \n '
z = (np.sqrt(2) * erfinv(((2 * p) - 1)))
return ((sigma * z) + mu)<|docstring|>Return quantile of normal distribution for given probability.<|endoftext|> |
5aba5ce6efb5e99ab7441bf13942f10a84926ac58ac4be412f1b6a6b43ecc0f6 | @nb.vectorize('float64(intp, float64)')
def poisson_pmf(k, mu):
'\n Return probability mass for Poisson distribution.\n '
logp = ((xlogy(k, mu) - gammaln((k + 1.0))) - mu)
return np.exp(logp) | Return probability mass for Poisson distribution. | numba_stats/stats.py | poisson_pmf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(intp, float64)')
def poisson_pmf(k, mu):
'\n \n '
logp = ((xlogy(k, mu) - gammaln((k + 1.0))) - mu)
return np.exp(logp) | @nb.vectorize('float64(intp, float64)')
def poisson_pmf(k, mu):
'\n \n '
logp = ((xlogy(k, mu) - gammaln((k + 1.0))) - mu)
return np.exp(logp)<|docstring|>Return probability mass for Poisson distribution.<|endoftext|> |
e437ca191bc622b37562bda9c6c5846c6a80e5adda6cf6a16c6d7f5d8c85e5ad | @nb.vectorize('float64(intp, float64)')
def poisson_cdf(x, mu):
'\n Evaluate cumulative distribution function of Poisson distribution.\n '
k = np.floor(x)
return pdtr(k, mu) | Evaluate cumulative distribution function of Poisson distribution. | numba_stats/stats.py | poisson_cdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(intp, float64)')
def poisson_cdf(x, mu):
'\n \n '
k = np.floor(x)
return pdtr(k, mu) | @nb.vectorize('float64(intp, float64)')
def poisson_cdf(x, mu):
'\n \n '
k = np.floor(x)
return pdtr(k, mu)<|docstring|>Evaluate cumulative distribution function of Poisson distribution.<|endoftext|> |
5731ab5869d85ed93318f848d3b12a821bd23c6a935b91cd37d0ebbd7f8d5ee5 | @nb.vectorize('float64(float64, float64, float64)')
def expon_pdf(x, mu, sigma):
'\n Return probability density of exponential distribution.\n '
z = ((x - mu) / sigma)
return (np.exp((- z)) / sigma) | Return probability density of exponential distribution. | numba_stats/stats.py | expon_pdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64)')
def expon_pdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
return (np.exp((- z)) / sigma) | @nb.vectorize('float64(float64, float64, float64)')
def expon_pdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
return (np.exp((- z)) / sigma)<|docstring|>Return probability density of exponential distribution.<|endoftext|> |
21de248762798f310a220372bba66cb43fbac5a118bedc8fe934eda0807ec895 | @nb.vectorize('float64(float64, float64, float64)')
def expon_cdf(x, mu, sigma):
'\n Evaluate cumulative distribution function of exponential distribution.\n '
z = ((x - mu) / sigma)
return (- expm1((- z))) | Evaluate cumulative distribution function of exponential distribution. | numba_stats/stats.py | expon_cdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64)')
def expon_cdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
return (- expm1((- z))) | @nb.vectorize('float64(float64, float64, float64)')
def expon_cdf(x, mu, sigma):
'\n \n '
z = ((x - mu) / sigma)
return (- expm1((- z)))<|docstring|>Evaluate cumulative distribution function of exponential distribution.<|endoftext|> |
f5f8d4c408a6474b038b9a91563d41b4563a5cc2faa184c967938575d6af278c | @nb.vectorize('float64(float64, float64, float64)')
def expon_ppf(p, mu, sigma):
'\n Return quantile of exponential distribution for given probability.\n '
z = (- log1p((- p)))
x = ((z * sigma) + mu)
return x | Return quantile of exponential distribution for given probability. | numba_stats/stats.py | expon_ppf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64)')
def expon_ppf(p, mu, sigma):
'\n \n '
z = (- log1p((- p)))
x = ((z * sigma) + mu)
return x | @nb.vectorize('float64(float64, float64, float64)')
def expon_ppf(p, mu, sigma):
'\n \n '
z = (- log1p((- p)))
x = ((z * sigma) + mu)
return x<|docstring|>Return quantile of exponential distribution for given probability.<|endoftext|> |
48322bdd46b1c3838ef714e11118a3d1ed21953a2f3a96b8356611dc8595012b | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_pdf(x, df, mu, sigma):
"\n Return probability density of student's distribution.\n "
z = ((x - mu) / sigma)
k = (0.5 * (df + 1))
p = np.exp((gammaln(k) - gammaln((0.5 * df))))
p /= (np.sqrt((df * np.pi)) * ((1 + ((z ** 2) / df)) ** k))
return (p / sigma) | Return probability density of student's distribution. | numba_stats/stats.py | t_pdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_pdf(x, df, mu, sigma):
"\n \n "
z = ((x - mu) / sigma)
k = (0.5 * (df + 1))
p = np.exp((gammaln(k) - gammaln((0.5 * df))))
p /= (np.sqrt((df * np.pi)) * ((1 + ((z ** 2) / df)) ** k))
return (p / sigma) | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_pdf(x, df, mu, sigma):
"\n \n "
z = ((x - mu) / sigma)
k = (0.5 * (df + 1))
p = np.exp((gammaln(k) - gammaln((0.5 * df))))
p /= (np.sqrt((df * np.pi)) * ((1 + ((z ** 2) / df)) ** k))
return (p / sigma)<|docstring|>Return probability density of student's distribution.<|endoftext|> |
4cdf7082e5d73424f2da0140b6616f0d4a3055910c28d7f576f5c000e768ea00 | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_cdf(x, df, mu, sigma):
"\n Return probability density of student's distribution.\n "
z = ((x - mu) / sigma)
return stdtr(df, z) | Return probability density of student's distribution. | numba_stats/stats.py | t_cdf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_cdf(x, df, mu, sigma):
"\n \n "
z = ((x - mu) / sigma)
return stdtr(df, z) | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_cdf(x, df, mu, sigma):
"\n \n "
z = ((x - mu) / sigma)
return stdtr(df, z)<|docstring|>Return probability density of student's distribution.<|endoftext|> |
74c7d252fba03647c1d98a49803b4009dca8ca875705ffc99da88e97886f2f3c | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_ppf(p, df, mu, sigma):
"\n Return probability density of student's distribution.\n "
if (p == 0):
return (- np.inf)
elif (p == 1):
return np.inf
z = stdtrit(df, p)
return ((sigma * z) + mu) | Return probability density of student's distribution. | numba_stats/stats.py | t_ppf | chrisburr/numba-stats | 0 | python | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_ppf(p, df, mu, sigma):
"\n \n "
if (p == 0):
return (- np.inf)
elif (p == 1):
return np.inf
z = stdtrit(df, p)
return ((sigma * z) + mu) | @nb.vectorize('float64(float64, float64, float64, float64)')
def t_ppf(p, df, mu, sigma):
"\n \n "
if (p == 0):
return (- np.inf)
elif (p == 1):
return np.inf
z = stdtrit(df, p)
return ((sigma * z) + mu)<|docstring|>Return probability density of student's distribution.<|endoftext|> |
da678a55fd2a6051c9cb8f9322639177a4dec3552119f461020841aac3982d39 | def main():
'Go Main Go'
nt = network.Table('AWOS')
qdict = loadqc()
pgconn = get_dbconn('iem', user='nobody')
icursor = pgconn.cursor()
now12z = datetime.datetime.utcnow()
now12z = now12z.replace(hour=12, minute=0, second=0, microsecond=0, tzinfo=pytz.utc)
today6z = now12z.replace(hour=6)
today0z = now12z.replace(hour=0)
yesterday6z = (today6z - datetime.timedelta(days=1))
yesterday12z = (now12z - datetime.timedelta(days=1))
fmt = '%-6s:%-19s: %3s / %3s / %5s / %4s / %2s\n'
shef_fn = '/tmp/awos_rtp.shef'
out = open(shef_fn, 'w')
out.write(('\n\n\n.BR DMX %s Z DH06/TAIRVX/DH12/TAIRVP/PPDRVZ/SFDRVZ/SDIRVZ\n: IOWA AWOS RTP FIRST GUESS PROCESSED BY THE IEM\n: 06Z to 06Z HIGH TEMPERATURE FOR %s\n: 00Z TO 12Z TODAY LOW TEMPERATURE\n: 12Z YESTERDAY TO 12Z TODAY RAINFALL\n: ...BASED ON REPORTED OBS...\n' % (now12z.strftime('%m%d'), yesterday6z.strftime('%d %b %Y').upper())))
highs = {}
sql = "SELECT id,\n round(max(tmpf)::numeric,0) as max_tmpf,\n count(tmpf) as obs FROM current_log c, stations t\n WHERE t.iemid = c.iemid and t.network = 'AWOS' and valid >= %s\n and valid < %s\n and tmpf > -99 GROUP by id "
args = (yesterday6z, today6z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('tmpf'):
continue
highs[row[0]] = row[1]
pcpn = {}
sql = "\n select id, sum(precip) from\n (select id, extract(hour from valid) as hour,\n max(phour) as precip from current_log c, stations t\n WHERE t.network = 'AWOS' and t.iemid = c.iemid\n and valid >= %s and valid < %s\n GROUP by id, hour) as foo\n GROUP by id\n "
args = (yesterday12z, now12z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('precip'):
continue
pcpn[row[0]] = ('%5.2f' % (row[1],))
lows = {}
sql = "\n SELECT id, round(min(tmpf)::numeric,0) as min_tmpf,\n count(tmpf) as obs FROM\n current_log c JOIN stations t on (t.iemid = c.iemid)\n WHERE t.network = 'AWOS' and valid >= %s\n and valid < %s and tmpf > -99 GROUP by id\n "
args = (today0z, now12z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('tmpf'):
continue
lows[row[0]] = row[1]
ids = list(nt.sts.keys())
ids.sort()
for myid in ids:
out.write((fmt % (myid, nt.sts[myid]['name'], highs.get(myid, 'M'), lows.get(myid, 'M'), pcpn.get(myid, 'M'), 'M', 'M')))
out.write('.END\n')
out.close()
cmd = ("/home/ldm/bin/pqinsert -p 'plot ac %s0000 awos_rtp.shef awos_rtp.shef shef' %s" % (now12z.strftime('%Y%m%d'), shef_fn))
subprocess.call(cmd, shell=True)
os.unlink(shef_fn) | Go Main Go | scripts/12z/awos_rtp.py | main | trentford/iem | 1 | python | def main():
nt = network.Table('AWOS')
qdict = loadqc()
pgconn = get_dbconn('iem', user='nobody')
icursor = pgconn.cursor()
now12z = datetime.datetime.utcnow()
now12z = now12z.replace(hour=12, minute=0, second=0, microsecond=0, tzinfo=pytz.utc)
today6z = now12z.replace(hour=6)
today0z = now12z.replace(hour=0)
yesterday6z = (today6z - datetime.timedelta(days=1))
yesterday12z = (now12z - datetime.timedelta(days=1))
fmt = '%-6s:%-19s: %3s / %3s / %5s / %4s / %2s\n'
shef_fn = '/tmp/awos_rtp.shef'
out = open(shef_fn, 'w')
out.write(('\n\n\n.BR DMX %s Z DH06/TAIRVX/DH12/TAIRVP/PPDRVZ/SFDRVZ/SDIRVZ\n: IOWA AWOS RTP FIRST GUESS PROCESSED BY THE IEM\n: 06Z to 06Z HIGH TEMPERATURE FOR %s\n: 00Z TO 12Z TODAY LOW TEMPERATURE\n: 12Z YESTERDAY TO 12Z TODAY RAINFALL\n: ...BASED ON REPORTED OBS...\n' % (now12z.strftime('%m%d'), yesterday6z.strftime('%d %b %Y').upper())))
highs = {}
sql = "SELECT id,\n round(max(tmpf)::numeric,0) as max_tmpf,\n count(tmpf) as obs FROM current_log c, stations t\n WHERE t.iemid = c.iemid and t.network = 'AWOS' and valid >= %s\n and valid < %s\n and tmpf > -99 GROUP by id "
args = (yesterday6z, today6z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('tmpf'):
continue
highs[row[0]] = row[1]
pcpn = {}
sql = "\n select id, sum(precip) from\n (select id, extract(hour from valid) as hour,\n max(phour) as precip from current_log c, stations t\n WHERE t.network = 'AWOS' and t.iemid = c.iemid\n and valid >= %s and valid < %s\n GROUP by id, hour) as foo\n GROUP by id\n "
args = (yesterday12z, now12z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('precip'):
continue
pcpn[row[0]] = ('%5.2f' % (row[1],))
lows = {}
sql = "\n SELECT id, round(min(tmpf)::numeric,0) as min_tmpf,\n count(tmpf) as obs FROM\n current_log c JOIN stations t on (t.iemid = c.iemid)\n WHERE t.network = 'AWOS' and valid >= %s\n and valid < %s and tmpf > -99 GROUP by id\n "
args = (today0z, now12z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('tmpf'):
continue
lows[row[0]] = row[1]
ids = list(nt.sts.keys())
ids.sort()
for myid in ids:
out.write((fmt % (myid, nt.sts[myid]['name'], highs.get(myid, 'M'), lows.get(myid, 'M'), pcpn.get(myid, 'M'), 'M', 'M')))
out.write('.END\n')
out.close()
cmd = ("/home/ldm/bin/pqinsert -p 'plot ac %s0000 awos_rtp.shef awos_rtp.shef shef' %s" % (now12z.strftime('%Y%m%d'), shef_fn))
subprocess.call(cmd, shell=True)
os.unlink(shef_fn) | def main():
nt = network.Table('AWOS')
qdict = loadqc()
pgconn = get_dbconn('iem', user='nobody')
icursor = pgconn.cursor()
now12z = datetime.datetime.utcnow()
now12z = now12z.replace(hour=12, minute=0, second=0, microsecond=0, tzinfo=pytz.utc)
today6z = now12z.replace(hour=6)
today0z = now12z.replace(hour=0)
yesterday6z = (today6z - datetime.timedelta(days=1))
yesterday12z = (now12z - datetime.timedelta(days=1))
fmt = '%-6s:%-19s: %3s / %3s / %5s / %4s / %2s\n'
shef_fn = '/tmp/awos_rtp.shef'
out = open(shef_fn, 'w')
out.write(('\n\n\n.BR DMX %s Z DH06/TAIRVX/DH12/TAIRVP/PPDRVZ/SFDRVZ/SDIRVZ\n: IOWA AWOS RTP FIRST GUESS PROCESSED BY THE IEM\n: 06Z to 06Z HIGH TEMPERATURE FOR %s\n: 00Z TO 12Z TODAY LOW TEMPERATURE\n: 12Z YESTERDAY TO 12Z TODAY RAINFALL\n: ...BASED ON REPORTED OBS...\n' % (now12z.strftime('%m%d'), yesterday6z.strftime('%d %b %Y').upper())))
highs = {}
sql = "SELECT id,\n round(max(tmpf)::numeric,0) as max_tmpf,\n count(tmpf) as obs FROM current_log c, stations t\n WHERE t.iemid = c.iemid and t.network = 'AWOS' and valid >= %s\n and valid < %s\n and tmpf > -99 GROUP by id "
args = (yesterday6z, today6z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('tmpf'):
continue
highs[row[0]] = row[1]
pcpn = {}
sql = "\n select id, sum(precip) from\n (select id, extract(hour from valid) as hour,\n max(phour) as precip from current_log c, stations t\n WHERE t.network = 'AWOS' and t.iemid = c.iemid\n and valid >= %s and valid < %s\n GROUP by id, hour) as foo\n GROUP by id\n "
args = (yesterday12z, now12z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('precip'):
continue
pcpn[row[0]] = ('%5.2f' % (row[1],))
lows = {}
sql = "\n SELECT id, round(min(tmpf)::numeric,0) as min_tmpf,\n count(tmpf) as obs FROM\n current_log c JOIN stations t on (t.iemid = c.iemid)\n WHERE t.network = 'AWOS' and valid >= %s\n and valid < %s and tmpf > -99 GROUP by id\n "
args = (today0z, now12z)
icursor.execute(sql, args)
for row in icursor:
if qdict.get(row[0], {}).get('tmpf'):
continue
lows[row[0]] = row[1]
ids = list(nt.sts.keys())
ids.sort()
for myid in ids:
out.write((fmt % (myid, nt.sts[myid]['name'], highs.get(myid, 'M'), lows.get(myid, 'M'), pcpn.get(myid, 'M'), 'M', 'M')))
out.write('.END\n')
out.close()
cmd = ("/home/ldm/bin/pqinsert -p 'plot ac %s0000 awos_rtp.shef awos_rtp.shef shef' %s" % (now12z.strftime('%Y%m%d'), shef_fn))
subprocess.call(cmd, shell=True)
os.unlink(shef_fn)<|docstring|>Go Main Go<|endoftext|> |
6d65e209e9115ba9f407418946ae556769f722a826586d7e48375d9ef1094015 | def doc2opt(doc, user_input=True):
'\n doc : str, document to parse\n user_input : bool, optional.\n [default: True] for only options requiring user input\n '
RE = (RE_OPT_INPUT if user_input else RE_OPT)
return (('--' + i) for i in RE.findall(doc)) | doc : str, document to parse
user_input : bool, optional.
[default: True] for only options requiring user input | .meta/mkcompletion.py | doc2opt | moble/tqdm | 22,617 | python | def doc2opt(doc, user_input=True):
'\n doc : str, document to parse\n user_input : bool, optional.\n [default: True] for only options requiring user input\n '
RE = (RE_OPT_INPUT if user_input else RE_OPT)
return (('--' + i) for i in RE.findall(doc)) | def doc2opt(doc, user_input=True):
'\n doc : str, document to parse\n user_input : bool, optional.\n [default: True] for only options requiring user input\n '
RE = (RE_OPT_INPUT if user_input else RE_OPT)
return (('--' + i) for i in RE.findall(doc))<|docstring|>doc : str, document to parse
user_input : bool, optional.
[default: True] for only options requiring user input<|endoftext|> |
647a9581cbbc7c5b6fa509b4f0162e1f5e020777f5a25bc04af9908f39382601 | def __init__(self, num_inputs, num_hidden, num_context, num_outputs):
'\n Initializes the network.\n\n Args:\n num_inputs (int): Number of neurons in the input layer.\n num_hidden (int): Number of neurons in the hidden layer.\n num_context (int): Number of context units.\n num_outputs (int): Number of output units.\n '
assert (num_hidden >= num_context), 'Context units exceed hidden'
self.num_inputs = num_inputs
self.num_hidden = num_hidden
self.num_context = num_context
self.num_outputs = num_outputs
self.weights_input = np.random.normal(size=(self.num_hidden, (self.num_inputs + self.num_context)))
self.bias_input = np.random.normal(size=(self.num_hidden, 1))
self.weights_hidden = np.random.normal(size=(self.num_outputs, self.num_hidden))
self.bias_hidden = np.random.normal(size=(self.num_outputs, 1)) | Initializes the network.
Args:
num_inputs (int): Number of neurons in the input layer.
num_hidden (int): Number of neurons in the hidden layer.
num_context (int): Number of context units.
num_outputs (int): Number of output units. | recurrent_neural_networks/elman_network.py | __init__ | sgalella/RecurrentNeuralNetworks | 0 | python | def __init__(self, num_inputs, num_hidden, num_context, num_outputs):
'\n Initializes the network.\n\n Args:\n num_inputs (int): Number of neurons in the input layer.\n num_hidden (int): Number of neurons in the hidden layer.\n num_context (int): Number of context units.\n num_outputs (int): Number of output units.\n '
assert (num_hidden >= num_context), 'Context units exceed hidden'
self.num_inputs = num_inputs
self.num_hidden = num_hidden
self.num_context = num_context
self.num_outputs = num_outputs
self.weights_input = np.random.normal(size=(self.num_hidden, (self.num_inputs + self.num_context)))
self.bias_input = np.random.normal(size=(self.num_hidden, 1))
self.weights_hidden = np.random.normal(size=(self.num_outputs, self.num_hidden))
self.bias_hidden = np.random.normal(size=(self.num_outputs, 1)) | def __init__(self, num_inputs, num_hidden, num_context, num_outputs):
'\n Initializes the network.\n\n Args:\n num_inputs (int): Number of neurons in the input layer.\n num_hidden (int): Number of neurons in the hidden layer.\n num_context (int): Number of context units.\n num_outputs (int): Number of output units.\n '
assert (num_hidden >= num_context), 'Context units exceed hidden'
self.num_inputs = num_inputs
self.num_hidden = num_hidden
self.num_context = num_context
self.num_outputs = num_outputs
self.weights_input = np.random.normal(size=(self.num_hidden, (self.num_inputs + self.num_context)))
self.bias_input = np.random.normal(size=(self.num_hidden, 1))
self.weights_hidden = np.random.normal(size=(self.num_outputs, self.num_hidden))
self.bias_hidden = np.random.normal(size=(self.num_outputs, 1))<|docstring|>Initializes the network.
Args:
num_inputs (int): Number of neurons in the input layer.
num_hidden (int): Number of neurons in the hidden layer.
num_context (int): Number of context units.
num_outputs (int): Number of output units.<|endoftext|> |
42bebc9747551d6c02f446f6bba786a56879497d6374a5d2c626095e7e1ac15b | def __repr__(self):
'Visualizes the network parameters when printing'
return f'ElmanNetwork(Inputs={self.num_inputs}, Hidden={self.num_hidden}, Contextual={self.num_context}, Outputs={self.num_outputs})' | Visualizes the network parameters when printing | recurrent_neural_networks/elman_network.py | __repr__ | sgalella/RecurrentNeuralNetworks | 0 | python | def __repr__(self):
return f'ElmanNetwork(Inputs={self.num_inputs}, Hidden={self.num_hidden}, Contextual={self.num_context}, Outputs={self.num_outputs})' | def __repr__(self):
return f'ElmanNetwork(Inputs={self.num_inputs}, Hidden={self.num_hidden}, Contextual={self.num_context}, Outputs={self.num_outputs})'<|docstring|>Visualizes the network parameters when printing<|endoftext|> |
e842978103f2b8cbd041b2c567fc99b1355cc4517e9def8be51a0702b2f740f2 | def sigmoid(self, a):
'Computes the sigmoid activation on a'
return (1 / (1 + np.exp((- a)))) | Computes the sigmoid activation on a | recurrent_neural_networks/elman_network.py | sigmoid | sgalella/RecurrentNeuralNetworks | 0 | python | def sigmoid(self, a):
return (1 / (1 + np.exp((- a)))) | def sigmoid(self, a):
return (1 / (1 + np.exp((- a))))<|docstring|>Computes the sigmoid activation on a<|endoftext|> |
b86ffb63c2c46b4d62aebc6a9b14d922c142d5908ed0ebd20bc8953db8084f51 | def forward_pass(self, X):
'\n Computes the forward pass on the network.\n\n Args:\n X (np.array): Vector containing the inputs and context units.\n '
self.H1 = self.sigmoid((np.dot(self.weights_input, X) + self.bias_input))
self.y_pred = self.sigmoid((np.dot(self.weights_hidden, self.H1) + self.bias_hidden))
if (self.y_pred.shape == (1, 1)):
self.y_pred = self.y_pred[0][0]
return self.y_pred | Computes the forward pass on the network.
Args:
X (np.array): Vector containing the inputs and context units. | recurrent_neural_networks/elman_network.py | forward_pass | sgalella/RecurrentNeuralNetworks | 0 | python | def forward_pass(self, X):
'\n Computes the forward pass on the network.\n\n Args:\n X (np.array): Vector containing the inputs and context units.\n '
self.H1 = self.sigmoid((np.dot(self.weights_input, X) + self.bias_input))
self.y_pred = self.sigmoid((np.dot(self.weights_hidden, self.H1) + self.bias_hidden))
if (self.y_pred.shape == (1, 1)):
self.y_pred = self.y_pred[0][0]
return self.y_pred | def forward_pass(self, X):
'\n Computes the forward pass on the network.\n\n Args:\n X (np.array): Vector containing the inputs and context units.\n '
self.H1 = self.sigmoid((np.dot(self.weights_input, X) + self.bias_input))
self.y_pred = self.sigmoid((np.dot(self.weights_hidden, self.H1) + self.bias_hidden))
if (self.y_pred.shape == (1, 1)):
self.y_pred = self.y_pred[0][0]
return self.y_pred<|docstring|>Computes the forward pass on the network.
Args:
X (np.array): Vector containing the inputs and context units.<|endoftext|> |
1c8801e40a1aef3009d3b92297fa366c814fce67f0e024efc0fce255228fe061 | def backpropagation(self, X, y, learning_rate):
'\n Computes the backpropagation algorithm in the network.\n\n Args:\n X (np.array): Vector containing the input and context units.\n y (np.array, float): Contains the output prediciton.\n learning_rate (float): Learning rate for upadting weights and biases.\n\n '
error = (self.y_pred - y)
delta_output = ((error * self.y_pred) * (1 - self.y_pred))
self.weights_hidden -= (learning_rate * np.dot(delta_output, self.H1.T))
self.bias_hidden -= (learning_rate * delta_output)
delta_input = ((np.dot(self.weights_hidden.T, delta_output) * self.H1) * (1 - self.H1))
self.weights_input -= (learning_rate * np.dot(delta_input, X.T))
self.bias_input -= (learning_rate * delta_input) | Computes the backpropagation algorithm in the network.
Args:
X (np.array): Vector containing the input and context units.
y (np.array, float): Contains the output prediciton.
learning_rate (float): Learning rate for upadting weights and biases. | recurrent_neural_networks/elman_network.py | backpropagation | sgalella/RecurrentNeuralNetworks | 0 | python | def backpropagation(self, X, y, learning_rate):
'\n Computes the backpropagation algorithm in the network.\n\n Args:\n X (np.array): Vector containing the input and context units.\n y (np.array, float): Contains the output prediciton.\n learning_rate (float): Learning rate for upadting weights and biases.\n\n '
error = (self.y_pred - y)
delta_output = ((error * self.y_pred) * (1 - self.y_pred))
self.weights_hidden -= (learning_rate * np.dot(delta_output, self.H1.T))
self.bias_hidden -= (learning_rate * delta_output)
delta_input = ((np.dot(self.weights_hidden.T, delta_output) * self.H1) * (1 - self.H1))
self.weights_input -= (learning_rate * np.dot(delta_input, X.T))
self.bias_input -= (learning_rate * delta_input) | def backpropagation(self, X, y, learning_rate):
'\n Computes the backpropagation algorithm in the network.\n\n Args:\n X (np.array): Vector containing the input and context units.\n y (np.array, float): Contains the output prediciton.\n learning_rate (float): Learning rate for upadting weights and biases.\n\n '
error = (self.y_pred - y)
delta_output = ((error * self.y_pred) * (1 - self.y_pred))
self.weights_hidden -= (learning_rate * np.dot(delta_output, self.H1.T))
self.bias_hidden -= (learning_rate * delta_output)
delta_input = ((np.dot(self.weights_hidden.T, delta_output) * self.H1) * (1 - self.H1))
self.weights_input -= (learning_rate * np.dot(delta_input, X.T))
self.bias_input -= (learning_rate * delta_input)<|docstring|>Computes the backpropagation algorithm in the network.
Args:
X (np.array): Vector containing the input and context units.
y (np.array, float): Contains the output prediciton.
learning_rate (float): Learning rate for upadting weights and biases.<|endoftext|> |
f7bdb97b2b12743198976455f6682664fdb0963263add123d99ed1d2d32e6bcf | def train(self, inputs, outputs, learning_rate, passes):
'\n Trains the network.\n\n Args;\n inputs (np.array): Vector containing the input units at each time step.\n outputs (np.arary): Vector containing the solutions for the inputs.\n learning_rate (float): Learning rate for upadting weights and biases.\n passes (int): Number of epochs.\n\n '
for _ in tqdm(range(passes)):
X = (0.5 * np.ones(((self.num_inputs + self.num_context), 1)))
for (x, y) in zip(inputs, outputs):
x = x.reshape(self.num_inputs, 1)
y = y.reshape(self.num_outputs, 1)
X[:self.num_inputs] = x
self.forward_pass(X)
self.backpropagation(X, y, learning_rate)
X[self.num_inputs:] = self.H1 | Trains the network.
Args;
inputs (np.array): Vector containing the input units at each time step.
outputs (np.arary): Vector containing the solutions for the inputs.
learning_rate (float): Learning rate for upadting weights and biases.
passes (int): Number of epochs. | recurrent_neural_networks/elman_network.py | train | sgalella/RecurrentNeuralNetworks | 0 | python | def train(self, inputs, outputs, learning_rate, passes):
'\n Trains the network.\n\n Args;\n inputs (np.array): Vector containing the input units at each time step.\n outputs (np.arary): Vector containing the solutions for the inputs.\n learning_rate (float): Learning rate for upadting weights and biases.\n passes (int): Number of epochs.\n\n '
for _ in tqdm(range(passes)):
X = (0.5 * np.ones(((self.num_inputs + self.num_context), 1)))
for (x, y) in zip(inputs, outputs):
x = x.reshape(self.num_inputs, 1)
y = y.reshape(self.num_outputs, 1)
X[:self.num_inputs] = x
self.forward_pass(X)
self.backpropagation(X, y, learning_rate)
X[self.num_inputs:] = self.H1 | def train(self, inputs, outputs, learning_rate, passes):
'\n Trains the network.\n\n Args;\n inputs (np.array): Vector containing the input units at each time step.\n outputs (np.arary): Vector containing the solutions for the inputs.\n learning_rate (float): Learning rate for upadting weights and biases.\n passes (int): Number of epochs.\n\n '
for _ in tqdm(range(passes)):
X = (0.5 * np.ones(((self.num_inputs + self.num_context), 1)))
for (x, y) in zip(inputs, outputs):
x = x.reshape(self.num_inputs, 1)
y = y.reshape(self.num_outputs, 1)
X[:self.num_inputs] = x
self.forward_pass(X)
self.backpropagation(X, y, learning_rate)
X[self.num_inputs:] = self.H1<|docstring|>Trains the network.
Args;
inputs (np.array): Vector containing the input units at each time step.
outputs (np.arary): Vector containing the solutions for the inputs.
learning_rate (float): Learning rate for upadting weights and biases.
passes (int): Number of epochs.<|endoftext|> |
56c4085bddad60ed41001089b9473d9cae707f4339569b678da32f84266621f1 | def predict(self, inputs, outputs):
'\n Tests the network.\n\n Args;\n inputs (np.array): Vector containing the input units at each time step.\n outputs (np.arary): Vector containing the solutions for the inputs.\n '
squared_error = np.zeros((1, len(outputs)))
X = (0.5 * np.ones(((self.num_inputs + self.num_context), 1)))
for i in range(len(outputs)):
x = inputs[i].reshape(self.num_inputs, 1)
y = outputs[i].reshape(self.num_outputs, 1)
X[:self.num_inputs] = x
self.forward_pass(X)
X[self.num_inputs:] = self.H1
squared_error[(0, i)] = ((self.y_pred - y) ** 2)
return squared_error | Tests the network.
Args;
inputs (np.array): Vector containing the input units at each time step.
outputs (np.arary): Vector containing the solutions for the inputs. | recurrent_neural_networks/elman_network.py | predict | sgalella/RecurrentNeuralNetworks | 0 | python | def predict(self, inputs, outputs):
'\n Tests the network.\n\n Args;\n inputs (np.array): Vector containing the input units at each time step.\n outputs (np.arary): Vector containing the solutions for the inputs.\n '
squared_error = np.zeros((1, len(outputs)))
X = (0.5 * np.ones(((self.num_inputs + self.num_context), 1)))
for i in range(len(outputs)):
x = inputs[i].reshape(self.num_inputs, 1)
y = outputs[i].reshape(self.num_outputs, 1)
X[:self.num_inputs] = x
self.forward_pass(X)
X[self.num_inputs:] = self.H1
squared_error[(0, i)] = ((self.y_pred - y) ** 2)
return squared_error | def predict(self, inputs, outputs):
'\n Tests the network.\n\n Args;\n inputs (np.array): Vector containing the input units at each time step.\n outputs (np.arary): Vector containing the solutions for the inputs.\n '
squared_error = np.zeros((1, len(outputs)))
X = (0.5 * np.ones(((self.num_inputs + self.num_context), 1)))
for i in range(len(outputs)):
x = inputs[i].reshape(self.num_inputs, 1)
y = outputs[i].reshape(self.num_outputs, 1)
X[:self.num_inputs] = x
self.forward_pass(X)
X[self.num_inputs:] = self.H1
squared_error[(0, i)] = ((self.y_pred - y) ** 2)
return squared_error<|docstring|>Tests the network.
Args;
inputs (np.array): Vector containing the input units at each time step.
outputs (np.arary): Vector containing the solutions for the inputs.<|endoftext|> |
cadb534bf149809497317f70eb5cc596a5d4f0b37894b5ca6e9f81a633b09815 | def __init__(self, description=None, domain_names=None, ip=None):
'Constructor for the HostEntry class'
self.description = description
self.domain_names = domain_names
self.ip = ip | Constructor for the HostEntry class | cohesity_management_sdk/models/host_entry.py | __init__ | nick6655/management-sdk-python | 18 | python | def __init__(self, description=None, domain_names=None, ip=None):
self.description = description
self.domain_names = domain_names
self.ip = ip | def __init__(self, description=None, domain_names=None, ip=None):
self.description = description
self.domain_names = domain_names
self.ip = ip<|docstring|>Constructor for the HostEntry class<|endoftext|> |
f73ac23a52dfc7ed25ef6f3581db1c06c5b71dc0a135969971362ec19ff7c38d | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match property names in the API description.\n\n Returns:\n object: An instance of this structure class.\n\n "
if (dictionary is None):
return None
description = dictionary.get('description')
domain_names = dictionary.get('domainNames')
ip = dictionary.get('ip')
return cls(description, domain_names, ip) | Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class. | cohesity_management_sdk/models/host_entry.py | from_dictionary | nick6655/management-sdk-python | 18 | python | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match property names in the API description.\n\n Returns:\n object: An instance of this structure class.\n\n "
if (dictionary is None):
return None
description = dictionary.get('description')
domain_names = dictionary.get('domainNames')
ip = dictionary.get('ip')
return cls(description, domain_names, ip) | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match property names in the API description.\n\n Returns:\n object: An instance of this structure class.\n\n "
if (dictionary is None):
return None
description = dictionary.get('description')
domain_names = dictionary.get('domainNames')
ip = dictionary.get('ip')
return cls(description, domain_names, ip)<|docstring|>Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class.<|endoftext|> |
11c1fd20e556c2fa9d3e1cf22830d5db785803e86bd72c529e33ffc16df220fc | def __init__(self, th_ratio: float, dur_multi: float, logger: Logger=NULL_LOGGER):
'Spectrogram based analysis method. This method looks at features of 2 adjacent timesteps and\n removes segments, for which the difference is small, thus reducing redundance in the signal.\n This method will remove silence and prolongations of sounds, syllables, words, or phrases.\n\n Args:\n th_ratio (float): Threshold ratio: greater value = more aggresive cuts\n dur_multi (float): Duration multiplier of segments selected for removal\n logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER.\n '
super().__init__('Spectrogram Analysis', [AnalysisDomain.AUDIO], logger)
self.th_ratio = th_ratio
self.dur_multi = dur_multi
self.logger = logger | Spectrogram based analysis method. This method looks at features of 2 adjacent timesteps and
removes segments, for which the difference is small, thus reducing redundance in the signal.
This method will remove silence and prolongations of sounds, syllables, words, or phrases.
Args:
th_ratio (float): Threshold ratio: greater value = more aggresive cuts
dur_multi (float): Duration multiplier of segments selected for removal
logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER. | src/speechless/processing/analysis/spectrogram.py | __init__ | Exepp/SpeechLess | 1 | python | def __init__(self, th_ratio: float, dur_multi: float, logger: Logger=NULL_LOGGER):
'Spectrogram based analysis method. This method looks at features of 2 adjacent timesteps and\n removes segments, for which the difference is small, thus reducing redundance in the signal.\n This method will remove silence and prolongations of sounds, syllables, words, or phrases.\n\n Args:\n th_ratio (float): Threshold ratio: greater value = more aggresive cuts\n dur_multi (float): Duration multiplier of segments selected for removal\n logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER.\n '
super().__init__('Spectrogram Analysis', [AnalysisDomain.AUDIO], logger)
self.th_ratio = th_ratio
self.dur_multi = dur_multi
self.logger = logger | def __init__(self, th_ratio: float, dur_multi: float, logger: Logger=NULL_LOGGER):
'Spectrogram based analysis method. This method looks at features of 2 adjacent timesteps and\n removes segments, for which the difference is small, thus reducing redundance in the signal.\n This method will remove silence and prolongations of sounds, syllables, words, or phrases.\n\n Args:\n th_ratio (float): Threshold ratio: greater value = more aggresive cuts\n dur_multi (float): Duration multiplier of segments selected for removal\n logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER.\n '
super().__init__('Spectrogram Analysis', [AnalysisDomain.AUDIO], logger)
self.th_ratio = th_ratio
self.dur_multi = dur_multi
self.logger = logger<|docstring|>Spectrogram based analysis method. This method looks at features of 2 adjacent timesteps and
removes segments, for which the difference is small, thus reducing redundance in the signal.
This method will remove silence and prolongations of sounds, syllables, words, or phrases.
Args:
th_ratio (float): Threshold ratio: greater value = more aggresive cuts
dur_multi (float): Duration multiplier of segments selected for removal
logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER.<|endoftext|> |
05d483014b1872aada5658b1a9317d9befb5a1146950fd4e357168919b49376f | @staticmethod
def setup_arg_parser(parser: ArgumentParser) -> ArgumentParser:
'Sets up a CLI argument parser for this submodule\n\n Returns:\n ArgumentParser: Configured parser\n '
parser.add_argument('-tr', f'--{CLI.ARG_TH_RATIO}', help='Threshold ratio: greater value = more aggresive cuts', type=float, action='store', default=CLI.DEFAULT_ARGS[CLI.ARG_TH_RATIO])
parser.add_argument('-m', f'--{CLI.ARG_DUR_MULTI}', help='Duration multiplier of segments selected for removal', type=float, action='store', default=CLI.DEFAULT_ARGS[CLI.ARG_DUR_MULTI])
parser.set_defaults(**{ARG_PREPARE_METHOD_FN: CLI.prepare_method}) | Sets up a CLI argument parser for this submodule
Returns:
ArgumentParser: Configured parser | src/speechless/processing/analysis/spectrogram.py | setup_arg_parser | Exepp/SpeechLess | 1 | python | @staticmethod
def setup_arg_parser(parser: ArgumentParser) -> ArgumentParser:
'Sets up a CLI argument parser for this submodule\n\n Returns:\n ArgumentParser: Configured parser\n '
parser.add_argument('-tr', f'--{CLI.ARG_TH_RATIO}', help='Threshold ratio: greater value = more aggresive cuts', type=float, action='store', default=CLI.DEFAULT_ARGS[CLI.ARG_TH_RATIO])
parser.add_argument('-m', f'--{CLI.ARG_DUR_MULTI}', help='Duration multiplier of segments selected for removal', type=float, action='store', default=CLI.DEFAULT_ARGS[CLI.ARG_DUR_MULTI])
parser.set_defaults(**{ARG_PREPARE_METHOD_FN: CLI.prepare_method}) | @staticmethod
def setup_arg_parser(parser: ArgumentParser) -> ArgumentParser:
'Sets up a CLI argument parser for this submodule\n\n Returns:\n ArgumentParser: Configured parser\n '
parser.add_argument('-tr', f'--{CLI.ARG_TH_RATIO}', help='Threshold ratio: greater value = more aggresive cuts', type=float, action='store', default=CLI.DEFAULT_ARGS[CLI.ARG_TH_RATIO])
parser.add_argument('-m', f'--{CLI.ARG_DUR_MULTI}', help='Duration multiplier of segments selected for removal', type=float, action='store', default=CLI.DEFAULT_ARGS[CLI.ARG_DUR_MULTI])
parser.set_defaults(**{ARG_PREPARE_METHOD_FN: CLI.prepare_method})<|docstring|>Sets up a CLI argument parser for this submodule
Returns:
ArgumentParser: Configured parser<|endoftext|> |
a21b09ac46902489a5691409a1d9fe5b30c57ab3622c9354e0e484592842c5df | def list(self, resource_group_name, account_name, **kwargs):
'List snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either SnapshotPoliciesList or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.netapp.models.SnapshotPoliciesList]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('SnapshotPoliciesList', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return (None, iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data) | List snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either SnapshotPoliciesList or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.netapp.models.SnapshotPoliciesList]
:raises: ~azure.core.exceptions.HttpResponseError | sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/operations/_snapshot_policies_operations.py | list | casperlehmann/azure-sdk-for-python | 1 | python | def list(self, resource_group_name, account_name, **kwargs):
'List snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either SnapshotPoliciesList or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.netapp.models.SnapshotPoliciesList]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('SnapshotPoliciesList', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return (None, iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data) | def list(self, resource_group_name, account_name, **kwargs):
'List snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either SnapshotPoliciesList or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.netapp.models.SnapshotPoliciesList]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('SnapshotPoliciesList', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return (None, iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)<|docstring|>List snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either SnapshotPoliciesList or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.netapp.models.SnapshotPoliciesList]
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|> |
6dad3b267b64d48c6b63a99325c8197af49f06ffdea9fd7d8899bd6d3054b840 | def get(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Get a snapshot Policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Get a snapshot Policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicy, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicy
:raises: ~azure.core.exceptions.HttpResponseError | sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/operations/_snapshot_policies_operations.py | get | casperlehmann/azure-sdk-for-python | 1 | python | def get(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Get a snapshot Policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | def get(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Get a snapshot Policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Get a snapshot Policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicy, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicy
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|> |
aa2135dc6f7aff20accf5040dbed1f5fac52313330393af58bf51fe28514cb6c | def create(self, resource_group_name, account_name, snapshot_policy_name, body, **kwargs):
'Create a snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :param body: Snapshot policy object supplied in the body of the operation.\n :type body: ~azure.mgmt.netapp.models.SnapshotPolicy\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
content_type = kwargs.pop('content_type', 'application/json')
accept = 'application/json'
url = self.create.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header('content_type', content_type, 'str')
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(body, 'SnapshotPolicy')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200, 201]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if (response.status_code == 200):
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if (response.status_code == 201):
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Create a snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:param body: Snapshot policy object supplied in the body of the operation.
:type body: ~azure.mgmt.netapp.models.SnapshotPolicy
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicy, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicy
:raises: ~azure.core.exceptions.HttpResponseError | sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/operations/_snapshot_policies_operations.py | create | casperlehmann/azure-sdk-for-python | 1 | python | def create(self, resource_group_name, account_name, snapshot_policy_name, body, **kwargs):
'Create a snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :param body: Snapshot policy object supplied in the body of the operation.\n :type body: ~azure.mgmt.netapp.models.SnapshotPolicy\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
content_type = kwargs.pop('content_type', 'application/json')
accept = 'application/json'
url = self.create.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header('content_type', content_type, 'str')
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(body, 'SnapshotPolicy')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200, 201]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if (response.status_code == 200):
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if (response.status_code == 201):
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | def create(self, resource_group_name, account_name, snapshot_policy_name, body, **kwargs):
'Create a snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :param body: Snapshot policy object supplied in the body of the operation.\n :type body: ~azure.mgmt.netapp.models.SnapshotPolicy\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
content_type = kwargs.pop('content_type', 'application/json')
accept = 'application/json'
url = self.create.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header('content_type', content_type, 'str')
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(body, 'SnapshotPolicy')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200, 201]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if (response.status_code == 200):
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if (response.status_code == 201):
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Create a snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:param body: Snapshot policy object supplied in the body of the operation.
:type body: ~azure.mgmt.netapp.models.SnapshotPolicy
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicy, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicy
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|> |
71b2a6c5799ddbfd324bd5118f366aa9c2060a58ba97296cc7e688314684904a | def update(self, resource_group_name, account_name, snapshot_policy_name, body, **kwargs):
'Patch a snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :param body: Snapshot policy object supplied in the body of the operation.\n :type body: ~azure.mgmt.netapp.models.SnapshotPolicyPatch\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
content_type = kwargs.pop('content_type', 'application/json')
accept = 'application/json'
url = self.update.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header('content_type', content_type, 'str')
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(body, 'SnapshotPolicyPatch')
body_content_kwargs['content'] = body_content
request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Patch a snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:param body: Snapshot policy object supplied in the body of the operation.
:type body: ~azure.mgmt.netapp.models.SnapshotPolicyPatch
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicy, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicy
:raises: ~azure.core.exceptions.HttpResponseError | sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/operations/_snapshot_policies_operations.py | update | casperlehmann/azure-sdk-for-python | 1 | python | def update(self, resource_group_name, account_name, snapshot_policy_name, body, **kwargs):
'Patch a snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :param body: Snapshot policy object supplied in the body of the operation.\n :type body: ~azure.mgmt.netapp.models.SnapshotPolicyPatch\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
content_type = kwargs.pop('content_type', 'application/json')
accept = 'application/json'
url = self.update.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header('content_type', content_type, 'str')
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(body, 'SnapshotPolicyPatch')
body_content_kwargs['content'] = body_content
request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | def update(self, resource_group_name, account_name, snapshot_policy_name, body, **kwargs):
'Patch a snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :param body: Snapshot policy object supplied in the body of the operation.\n :type body: ~azure.mgmt.netapp.models.SnapshotPolicyPatch\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicy, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicy\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
content_type = kwargs.pop('content_type', 'application/json')
accept = 'application/json'
url = self.update.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header('content_type', content_type, 'str')
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(body, 'SnapshotPolicyPatch')
body_content_kwargs['content'] = body_content
request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicy', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Patch a snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:param body: Snapshot policy object supplied in the body of the operation.
:type body: ~azure.mgmt.netapp.models.SnapshotPolicyPatch
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicy, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicy
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|> |
9e1e2c3f05586208237a2f62b690e3201916fd96c217aa4c1735fbd4da343082 | def begin_delete(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Delete snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of LROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._delete_initial(resource_group_name=resource_group_name, account_name=account_name, snapshot_policy_name=snapshot_policy_name, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
if (polling is True):
polling_method = ARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method) | Delete snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError: | sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/operations/_snapshot_policies_operations.py | begin_delete | casperlehmann/azure-sdk-for-python | 1 | python | def begin_delete(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Delete snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of LROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._delete_initial(resource_group_name=resource_group_name, account_name=account_name, snapshot_policy_name=snapshot_policy_name, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
if (polling is True):
polling_method = ARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method) | def begin_delete(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Delete snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of LROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._delete_initial(resource_group_name=resource_group_name, account_name=account_name, snapshot_policy_name=snapshot_policy_name, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
if (polling is True):
polling_method = ARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)<|docstring|>Delete snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:<|endoftext|> |
9e0db87b8dee5e80a4f311fc86870ecb9fc0b70da70a5345044acb9231f654af | def list_volumes(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Get volumes associated with snapshot policy.\n\n Get volumes associated with snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicyVolumeList, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicyVolumeList\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
url = self.list_volumes.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicyVolumeList', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | Get volumes associated with snapshot policy.
Get volumes associated with snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicyVolumeList, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicyVolumeList
:raises: ~azure.core.exceptions.HttpResponseError | sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/operations/_snapshot_policies_operations.py | list_volumes | casperlehmann/azure-sdk-for-python | 1 | python | def list_volumes(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Get volumes associated with snapshot policy.\n\n Get volumes associated with snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicyVolumeList, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicyVolumeList\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
url = self.list_volumes.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicyVolumeList', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | def list_volumes(self, resource_group_name, account_name, snapshot_policy_name, **kwargs):
'Get volumes associated with snapshot policy.\n\n Get volumes associated with snapshot policy.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param account_name: The name of the NetApp account.\n :type account_name: str\n :param snapshot_policy_name: The name of the snapshot policy target.\n :type snapshot_policy_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SnapshotPolicyVolumeList, or the result of cls(response)\n :rtype: ~azure.mgmt.netapp.models.SnapshotPolicyVolumeList\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2020-08-01'
accept = 'application/json'
url = self.list_volumes.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str', max_length=90, min_length=1, pattern='^[-\\w\\._\\(\\)]+$'), 'accountName': self._serialize.url('account_name', account_name, 'str'), 'snapshotPolicyName': self._serialize.url('snapshot_policy_name', snapshot_policy_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SnapshotPolicyVolumeList', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Get volumes associated with snapshot policy.
Get volumes associated with snapshot policy.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param account_name: The name of the NetApp account.
:type account_name: str
:param snapshot_policy_name: The name of the snapshot policy target.
:type snapshot_policy_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SnapshotPolicyVolumeList, or the result of cls(response)
:rtype: ~azure.mgmt.netapp.models.SnapshotPolicyVolumeList
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|> |
04a64dee480ba6ebb5aefa7dcf4927d07f0793aca63921d2837e8b7048ad5fc2 | @classmethod
def parse_json(cls, body):
'\n Get an instance from JSON string\n '
json_dict = json.loads(body)
if ('message' in json_dict):
text = json_dict['message']['result']['translatedText']
source = json_dict['message']['result']['srcLangType']
return Response(text=text, source=source)
else:
return Response(code=json_dict.get('errorCode'), message=json_dict.get('errorMessage')) | Get an instance from JSON string | papago/response.py | parse_json | anktdshmkh/papago_Example | 27 | python | @classmethod
def parse_json(cls, body):
'\n \n '
json_dict = json.loads(body)
if ('message' in json_dict):
text = json_dict['message']['result']['translatedText']
source = json_dict['message']['result']['srcLangType']
return Response(text=text, source=source)
else:
return Response(code=json_dict.get('errorCode'), message=json_dict.get('errorMessage')) | @classmethod
def parse_json(cls, body):
'\n \n '
json_dict = json.loads(body)
if ('message' in json_dict):
text = json_dict['message']['result']['translatedText']
source = json_dict['message']['result']['srcLangType']
return Response(text=text, source=source)
else:
return Response(code=json_dict.get('errorCode'), message=json_dict.get('errorMessage'))<|docstring|>Get an instance from JSON string<|endoftext|> |
a5a6dd87d9d8d34841305bef2cd623158fe0c4eb139a5dea7ab7608a6c542572 | def test_initial_state() -> None:
'It should set the initial state.'
subject = CommandStore()
assert (subject.state == CommandState(queue_status=QueueStatus.IMPLICITLY_ACTIVE, is_hardware_stopped=False, is_door_blocking=False, run_result=None, running_command_id=None, queued_command_ids=OrderedSet(), all_command_ids=[], commands_by_id=OrderedDict(), errors_by_id={})) | It should set the initial state. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_initial_state | Opentrons/labware | 2 | python | def test_initial_state() -> None:
subject = CommandStore()
assert (subject.state == CommandState(queue_status=QueueStatus.IMPLICITLY_ACTIVE, is_hardware_stopped=False, is_door_blocking=False, run_result=None, running_command_id=None, queued_command_ids=OrderedSet(), all_command_ids=[], commands_by_id=OrderedDict(), errors_by_id={})) | def test_initial_state() -> None:
subject = CommandStore()
assert (subject.state == CommandState(queue_status=QueueStatus.IMPLICITLY_ACTIVE, is_hardware_stopped=False, is_door_blocking=False, run_result=None, running_command_id=None, queued_command_ids=OrderedSet(), all_command_ids=[], commands_by_id=OrderedDict(), errors_by_id={}))<|docstring|>It should set the initial state.<|endoftext|> |
3ec85301c7a7a04c995e87c316b60ccc89a702274a6b5516587fc928c1e5bc80 | @pytest.mark.parametrize(QueueCommandSpec._fields, [QueueCommandSpec(command_request=commands.AspirateCreate(params=commands.AspirateParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name', volume=42, wellLocation=WellLocation())), expected_cls=commands.Aspirate), QueueCommandSpec(command_request=commands.DispenseCreate(params=commands.DispenseParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name', volume=42, wellLocation=WellLocation())), expected_cls=commands.Dispense), QueueCommandSpec(command_request=commands.DropTipCreate(params=commands.DropTipParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.DropTip), QueueCommandSpec(command_request=commands.LoadLabwareCreate(params=commands.LoadLabwareParams(location=DeckSlotLocation(slotName=DeckSlotName.SLOT_1), loadName='load-name', namespace='namespace', version=42)), expected_cls=commands.LoadLabware), QueueCommandSpec(command_request=commands.LoadPipetteCreate(params=commands.LoadPipetteParams(mount=MountType.LEFT, pipetteName=PipetteName.P300_SINGLE)), expected_cls=commands.LoadPipette), QueueCommandSpec(command_request=commands.PickUpTipCreate(params=commands.PickUpTipParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.PickUpTip), QueueCommandSpec(command_request=commands.MoveToWellCreate(params=commands.MoveToWellParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.MoveToWell), QueueCommandSpec(command_request=commands.PauseCreate(params=commands.PauseParams(message='hello world')), expected_cls=commands.Pause)])
def test_command_store_queues_commands(command_request: commands.CommandCreate, expected_cls: Type[commands.Command], created_at: datetime, command_id: str, command_key: str) -> None:
'It should add a command to the store.'
action = QueueCommandAction(request=command_request, created_at=created_at, command_id=command_id, command_key=command_key)
expected_command = expected_cls(id=command_id, key=command_key, createdAt=created_at, status=commands.CommandStatus.QUEUED, params=command_request.params)
subject = CommandStore()
subject.handle_action(action)
assert (subject.state.commands_by_id == {'command-id': CommandEntry(index=0, command=expected_command)})
assert (subject.state.all_command_ids == ['command-id'])
assert (subject.state.queued_command_ids == OrderedSet(['command-id'])) | It should add a command to the store. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_store_queues_commands | Opentrons/labware | 2 | python | @pytest.mark.parametrize(QueueCommandSpec._fields, [QueueCommandSpec(command_request=commands.AspirateCreate(params=commands.AspirateParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name', volume=42, wellLocation=WellLocation())), expected_cls=commands.Aspirate), QueueCommandSpec(command_request=commands.DispenseCreate(params=commands.DispenseParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name', volume=42, wellLocation=WellLocation())), expected_cls=commands.Dispense), QueueCommandSpec(command_request=commands.DropTipCreate(params=commands.DropTipParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.DropTip), QueueCommandSpec(command_request=commands.LoadLabwareCreate(params=commands.LoadLabwareParams(location=DeckSlotLocation(slotName=DeckSlotName.SLOT_1), loadName='load-name', namespace='namespace', version=42)), expected_cls=commands.LoadLabware), QueueCommandSpec(command_request=commands.LoadPipetteCreate(params=commands.LoadPipetteParams(mount=MountType.LEFT, pipetteName=PipetteName.P300_SINGLE)), expected_cls=commands.LoadPipette), QueueCommandSpec(command_request=commands.PickUpTipCreate(params=commands.PickUpTipParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.PickUpTip), QueueCommandSpec(command_request=commands.MoveToWellCreate(params=commands.MoveToWellParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.MoveToWell), QueueCommandSpec(command_request=commands.PauseCreate(params=commands.PauseParams(message='hello world')), expected_cls=commands.Pause)])
def test_command_store_queues_commands(command_request: commands.CommandCreate, expected_cls: Type[commands.Command], created_at: datetime, command_id: str, command_key: str) -> None:
action = QueueCommandAction(request=command_request, created_at=created_at, command_id=command_id, command_key=command_key)
expected_command = expected_cls(id=command_id, key=command_key, createdAt=created_at, status=commands.CommandStatus.QUEUED, params=command_request.params)
subject = CommandStore()
subject.handle_action(action)
assert (subject.state.commands_by_id == {'command-id': CommandEntry(index=0, command=expected_command)})
assert (subject.state.all_command_ids == ['command-id'])
assert (subject.state.queued_command_ids == OrderedSet(['command-id'])) | @pytest.mark.parametrize(QueueCommandSpec._fields, [QueueCommandSpec(command_request=commands.AspirateCreate(params=commands.AspirateParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name', volume=42, wellLocation=WellLocation())), expected_cls=commands.Aspirate), QueueCommandSpec(command_request=commands.DispenseCreate(params=commands.DispenseParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name', volume=42, wellLocation=WellLocation())), expected_cls=commands.Dispense), QueueCommandSpec(command_request=commands.DropTipCreate(params=commands.DropTipParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.DropTip), QueueCommandSpec(command_request=commands.LoadLabwareCreate(params=commands.LoadLabwareParams(location=DeckSlotLocation(slotName=DeckSlotName.SLOT_1), loadName='load-name', namespace='namespace', version=42)), expected_cls=commands.LoadLabware), QueueCommandSpec(command_request=commands.LoadPipetteCreate(params=commands.LoadPipetteParams(mount=MountType.LEFT, pipetteName=PipetteName.P300_SINGLE)), expected_cls=commands.LoadPipette), QueueCommandSpec(command_request=commands.PickUpTipCreate(params=commands.PickUpTipParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.PickUpTip), QueueCommandSpec(command_request=commands.MoveToWellCreate(params=commands.MoveToWellParams(pipetteId='pipette-id', labwareId='labware-id', wellName='well-name')), expected_cls=commands.MoveToWell), QueueCommandSpec(command_request=commands.PauseCreate(params=commands.PauseParams(message='hello world')), expected_cls=commands.Pause)])
def test_command_store_queues_commands(command_request: commands.CommandCreate, expected_cls: Type[commands.Command], created_at: datetime, command_id: str, command_key: str) -> None:
action = QueueCommandAction(request=command_request, created_at=created_at, command_id=command_id, command_key=command_key)
expected_command = expected_cls(id=command_id, key=command_key, createdAt=created_at, status=commands.CommandStatus.QUEUED, params=command_request.params)
subject = CommandStore()
subject.handle_action(action)
assert (subject.state.commands_by_id == {'command-id': CommandEntry(index=0, command=expected_command)})
assert (subject.state.all_command_ids == ['command-id'])
assert (subject.state.queued_command_ids == OrderedSet(['command-id']))<|docstring|>It should add a command to the store.<|endoftext|> |
d98e31f6f79d182126b4d8b0914be05e4041d15906346302181bd970fb577b27 | def test_command_queue_and_unqueue() -> None:
'It should queue on QueueCommandAction and dequeue on UpdateCommandAction.'
queue_1 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
queue_2 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2022, month=2, day=2), command_id='command-id-2', command_key='command-key-2')
update_1 = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
update_2 = UpdateCommandAction(command=create_running_command(command_id='command-id-2'))
subject = CommandStore()
subject.handle_action(queue_1)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1']))
subject.handle_action(queue_2)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1', 'command-id-2']))
subject.handle_action(update_2)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1']))
subject.handle_action(update_1)
assert (subject.state.queued_command_ids == OrderedSet()) | It should queue on QueueCommandAction and dequeue on UpdateCommandAction. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_queue_and_unqueue | Opentrons/labware | 2 | python | def test_command_queue_and_unqueue() -> None:
queue_1 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
queue_2 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2022, month=2, day=2), command_id='command-id-2', command_key='command-key-2')
update_1 = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
update_2 = UpdateCommandAction(command=create_running_command(command_id='command-id-2'))
subject = CommandStore()
subject.handle_action(queue_1)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1']))
subject.handle_action(queue_2)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1', 'command-id-2']))
subject.handle_action(update_2)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1']))
subject.handle_action(update_1)
assert (subject.state.queued_command_ids == OrderedSet()) | def test_command_queue_and_unqueue() -> None:
queue_1 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
queue_2 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2022, month=2, day=2), command_id='command-id-2', command_key='command-key-2')
update_1 = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
update_2 = UpdateCommandAction(command=create_running_command(command_id='command-id-2'))
subject = CommandStore()
subject.handle_action(queue_1)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1']))
subject.handle_action(queue_2)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1', 'command-id-2']))
subject.handle_action(update_2)
assert (subject.state.queued_command_ids == OrderedSet(['command-id-1']))
subject.handle_action(update_1)
assert (subject.state.queued_command_ids == OrderedSet())<|docstring|>It should queue on QueueCommandAction and dequeue on UpdateCommandAction.<|endoftext|> |
df625953201478cfd152c92824a0482cf29aedd5229830e1ba42710db19c9410 | def test_running_command_id() -> None:
"It should update the running command ID through a command's lifecycle."
queue = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
running_update = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
completed_update = UpdateCommandAction(command=create_succeeded_command(command_id='command-id-1'))
subject = CommandStore()
subject.handle_action(queue)
assert (subject.state.running_command_id is None)
subject.handle_action(running_update)
assert (subject.state.running_command_id == 'command-id-1')
subject.handle_action(completed_update)
assert (subject.state.running_command_id is None) | It should update the running command ID through a command's lifecycle. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_running_command_id | Opentrons/labware | 2 | python | def test_running_command_id() -> None:
queue = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
running_update = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
completed_update = UpdateCommandAction(command=create_succeeded_command(command_id='command-id-1'))
subject = CommandStore()
subject.handle_action(queue)
assert (subject.state.running_command_id is None)
subject.handle_action(running_update)
assert (subject.state.running_command_id == 'command-id-1')
subject.handle_action(completed_update)
assert (subject.state.running_command_id is None) | def test_running_command_id() -> None:
queue = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
running_update = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
completed_update = UpdateCommandAction(command=create_succeeded_command(command_id='command-id-1'))
subject = CommandStore()
subject.handle_action(queue)
assert (subject.state.running_command_id is None)
subject.handle_action(running_update)
assert (subject.state.running_command_id == 'command-id-1')
subject.handle_action(completed_update)
assert (subject.state.running_command_id is None)<|docstring|>It should update the running command ID through a command's lifecycle.<|endoftext|> |
91d3c78b358b940e4b92a8fa71e7b9c5fa89fd779a81b53d9344118f144b2cd8 | def test_running_command_no_queue() -> None:
'It should add a running command to state, even if there was no queue action.'
running_update = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
completed_update = UpdateCommandAction(command=create_succeeded_command(command_id='command-id-1'))
subject = CommandStore()
subject.handle_action(running_update)
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.running_command_id == 'command-id-1')
subject.handle_action(completed_update)
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.running_command_id is None) | It should add a running command to state, even if there was no queue action. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_running_command_no_queue | Opentrons/labware | 2 | python | def test_running_command_no_queue() -> None:
running_update = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
completed_update = UpdateCommandAction(command=create_succeeded_command(command_id='command-id-1'))
subject = CommandStore()
subject.handle_action(running_update)
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.running_command_id == 'command-id-1')
subject.handle_action(completed_update)
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.running_command_id is None) | def test_running_command_no_queue() -> None:
running_update = UpdateCommandAction(command=create_running_command(command_id='command-id-1'))
completed_update = UpdateCommandAction(command=create_succeeded_command(command_id='command-id-1'))
subject = CommandStore()
subject.handle_action(running_update)
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.running_command_id == 'command-id-1')
subject.handle_action(completed_update)
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.running_command_id is None)<|docstring|>It should add a running command to state, even if there was no queue action.<|endoftext|> |
c2324c01fb281279c567c69c96cc15d92009640c135d3942d85215e75915bb5c | def test_command_failure_clears_queue() -> None:
'It should clear the command queue on command failure.'
queue_1 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
queue_2 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-2', command_key='command-key-2')
running_1 = UpdateCommandAction(command=commands.Pause(id='command-id-1', key='command-key-1', createdAt=datetime(year=2021, month=1, day=1), startedAt=datetime(year=2022, month=2, day=2), params=commands.PauseParams(), status=commands.CommandStatus.RUNNING))
fail_1 = FailCommandAction(command_id='command-id-1', error_id='error-id', failed_at=datetime(year=2023, month=3, day=3), error=errors.ProtocolEngineError('oh no'))
expected_failed_1 = commands.Pause(id='command-id-1', key='command-key-1', error=errors.ErrorOccurrence(id='error-id', errorType='ProtocolEngineError', detail='oh no', createdAt=datetime(year=2023, month=3, day=3)), createdAt=datetime(year=2021, month=1, day=1), startedAt=datetime(year=2022, month=2, day=2), completedAt=datetime(year=2023, month=3, day=3), params=commands.PauseParams(), status=commands.CommandStatus.FAILED)
expected_failed_2 = commands.Pause(id='command-id-2', key='command-key-2', error=None, createdAt=datetime(year=2021, month=1, day=1), completedAt=datetime(year=2023, month=3, day=3), params=commands.PauseParams(), status=commands.CommandStatus.FAILED)
subject = CommandStore()
subject.handle_action(queue_1)
subject.handle_action(queue_2)
subject.handle_action(running_1)
subject.handle_action(fail_1)
assert (subject.state.running_command_id is None)
assert (subject.state.queued_command_ids == OrderedSet())
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=expected_failed_1), 'command-id-2': CommandEntry(index=1, command=expected_failed_2)}) | It should clear the command queue on command failure. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_failure_clears_queue | Opentrons/labware | 2 | python | def test_command_failure_clears_queue() -> None:
queue_1 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
queue_2 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-2', command_key='command-key-2')
running_1 = UpdateCommandAction(command=commands.Pause(id='command-id-1', key='command-key-1', createdAt=datetime(year=2021, month=1, day=1), startedAt=datetime(year=2022, month=2, day=2), params=commands.PauseParams(), status=commands.CommandStatus.RUNNING))
fail_1 = FailCommandAction(command_id='command-id-1', error_id='error-id', failed_at=datetime(year=2023, month=3, day=3), error=errors.ProtocolEngineError('oh no'))
expected_failed_1 = commands.Pause(id='command-id-1', key='command-key-1', error=errors.ErrorOccurrence(id='error-id', errorType='ProtocolEngineError', detail='oh no', createdAt=datetime(year=2023, month=3, day=3)), createdAt=datetime(year=2021, month=1, day=1), startedAt=datetime(year=2022, month=2, day=2), completedAt=datetime(year=2023, month=3, day=3), params=commands.PauseParams(), status=commands.CommandStatus.FAILED)
expected_failed_2 = commands.Pause(id='command-id-2', key='command-key-2', error=None, createdAt=datetime(year=2021, month=1, day=1), completedAt=datetime(year=2023, month=3, day=3), params=commands.PauseParams(), status=commands.CommandStatus.FAILED)
subject = CommandStore()
subject.handle_action(queue_1)
subject.handle_action(queue_2)
subject.handle_action(running_1)
subject.handle_action(fail_1)
assert (subject.state.running_command_id is None)
assert (subject.state.queued_command_ids == OrderedSet())
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=expected_failed_1), 'command-id-2': CommandEntry(index=1, command=expected_failed_2)}) | def test_command_failure_clears_queue() -> None:
queue_1 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-1', command_key='command-key-1')
queue_2 = QueueCommandAction(request=commands.PauseCreate(params=commands.PauseParams()), created_at=datetime(year=2021, month=1, day=1), command_id='command-id-2', command_key='command-key-2')
running_1 = UpdateCommandAction(command=commands.Pause(id='command-id-1', key='command-key-1', createdAt=datetime(year=2021, month=1, day=1), startedAt=datetime(year=2022, month=2, day=2), params=commands.PauseParams(), status=commands.CommandStatus.RUNNING))
fail_1 = FailCommandAction(command_id='command-id-1', error_id='error-id', failed_at=datetime(year=2023, month=3, day=3), error=errors.ProtocolEngineError('oh no'))
expected_failed_1 = commands.Pause(id='command-id-1', key='command-key-1', error=errors.ErrorOccurrence(id='error-id', errorType='ProtocolEngineError', detail='oh no', createdAt=datetime(year=2023, month=3, day=3)), createdAt=datetime(year=2021, month=1, day=1), startedAt=datetime(year=2022, month=2, day=2), completedAt=datetime(year=2023, month=3, day=3), params=commands.PauseParams(), status=commands.CommandStatus.FAILED)
expected_failed_2 = commands.Pause(id='command-id-2', key='command-key-2', error=None, createdAt=datetime(year=2021, month=1, day=1), completedAt=datetime(year=2023, month=3, day=3), params=commands.PauseParams(), status=commands.CommandStatus.FAILED)
subject = CommandStore()
subject.handle_action(queue_1)
subject.handle_action(queue_2)
subject.handle_action(running_1)
subject.handle_action(fail_1)
assert (subject.state.running_command_id is None)
assert (subject.state.queued_command_ids == OrderedSet())
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=expected_failed_1), 'command-id-2': CommandEntry(index=1, command=expected_failed_2)})<|docstring|>It should clear the command queue on command failure.<|endoftext|> |
9020979be205d366f659d4606fb1a88eee76b05274617eace7b0228f3e24b1d3 | def test_command_store_preserves_handle_order() -> None:
'It should store commands in the order they are handled.'
command_a = create_queued_command(command_id='command-id-1')
command_b = create_running_command(command_id='command-id-2')
command_c = create_succeeded_command(command_id='command-id-1')
subject = CommandStore()
subject.handle_action(UpdateCommandAction(command=command_a))
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_a)})
subject.handle_action(UpdateCommandAction(command=command_b))
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_a), 'command-id-2': CommandEntry(index=1, command=command_b)})
subject.handle_action(UpdateCommandAction(command=command_c))
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_c), 'command-id-2': CommandEntry(index=1, command=command_b)}) | It should store commands in the order they are handled. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_store_preserves_handle_order | Opentrons/labware | 2 | python | def test_command_store_preserves_handle_order() -> None:
command_a = create_queued_command(command_id='command-id-1')
command_b = create_running_command(command_id='command-id-2')
command_c = create_succeeded_command(command_id='command-id-1')
subject = CommandStore()
subject.handle_action(UpdateCommandAction(command=command_a))
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_a)})
subject.handle_action(UpdateCommandAction(command=command_b))
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_a), 'command-id-2': CommandEntry(index=1, command=command_b)})
subject.handle_action(UpdateCommandAction(command=command_c))
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_c), 'command-id-2': CommandEntry(index=1, command=command_b)}) | def test_command_store_preserves_handle_order() -> None:
command_a = create_queued_command(command_id='command-id-1')
command_b = create_running_command(command_id='command-id-2')
command_c = create_succeeded_command(command_id='command-id-1')
subject = CommandStore()
subject.handle_action(UpdateCommandAction(command=command_a))
assert (subject.state.all_command_ids == ['command-id-1'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_a)})
subject.handle_action(UpdateCommandAction(command=command_b))
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_a), 'command-id-2': CommandEntry(index=1, command=command_b)})
subject.handle_action(UpdateCommandAction(command=command_c))
assert (subject.state.all_command_ids == ['command-id-1', 'command-id-2'])
assert (subject.state.commands_by_id == {'command-id-1': CommandEntry(index=0, command=command_c), 'command-id-2': CommandEntry(index=1, command=command_b)})<|docstring|>It should store commands in the order they are handled.<|endoftext|> |
bb3b93daf59b779190a52b5f0b53056cbbe2a46c09941a5ec80108b7fc5568e1 | @pytest.mark.parametrize('pause_source', PauseSource)
def test_command_store_handles_pause_action(pause_source: PauseSource) -> None:
'It should clear the running flag on pause.'
subject = CommandStore()
subject.handle_action(PauseAction(source=pause_source))
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | It should clear the running flag on pause. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_store_handles_pause_action | Opentrons/labware | 2 | python | @pytest.mark.parametrize('pause_source', PauseSource)
def test_command_store_handles_pause_action(pause_source: PauseSource) -> None:
subject = CommandStore()
subject.handle_action(PauseAction(source=pause_source))
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | @pytest.mark.parametrize('pause_source', PauseSource)
def test_command_store_handles_pause_action(pause_source: PauseSource) -> None:
subject = CommandStore()
subject.handle_action(PauseAction(source=pause_source))
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))<|docstring|>It should clear the running flag on pause.<|endoftext|> |
db7c66a40686c4f1d8e62201ed7e70496dcf666162fc4dc44c449a87fb91d099 | @pytest.mark.parametrize('pause_source', PauseSource)
def test_command_store_handles_play_action(pause_source: PauseSource) -> None:
'It should set the running flag on play.'
subject = CommandStore()
subject.handle_action(PauseAction(source=pause_source))
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.ACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | It should set the running flag on play. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_store_handles_play_action | Opentrons/labware | 2 | python | @pytest.mark.parametrize('pause_source', PauseSource)
def test_command_store_handles_play_action(pause_source: PauseSource) -> None:
subject = CommandStore()
subject.handle_action(PauseAction(source=pause_source))
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.ACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | @pytest.mark.parametrize('pause_source', PauseSource)
def test_command_store_handles_play_action(pause_source: PauseSource) -> None:
subject = CommandStore()
subject.handle_action(PauseAction(source=pause_source))
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.ACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))<|docstring|>It should set the running flag on play.<|endoftext|> |
feac61b123e0d31074f108fdb7235bd82ac86745ab54f990911952f703d93370 | def test_command_store_handles_play_according_to_door_state() -> None:
'It should inactivate/activate command queue according to door state.'
subject = CommandStore(is_door_blocking=True)
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=True, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))
door_close_event = DoorStateNotification(new_state=DoorState.CLOSED, blocking=False)
subject.handle_action(HardwareEventAction(event=door_close_event))
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.ACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | It should inactivate/activate command queue according to door state. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_store_handles_play_according_to_door_state | Opentrons/labware | 2 | python | def test_command_store_handles_play_according_to_door_state() -> None:
subject = CommandStore(is_door_blocking=True)
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=True, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))
door_close_event = DoorStateNotification(new_state=DoorState.CLOSED, blocking=False)
subject.handle_action(HardwareEventAction(event=door_close_event))
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.ACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | def test_command_store_handles_play_according_to_door_state() -> None:
subject = CommandStore(is_door_blocking=True)
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=True, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))
door_close_event = DoorStateNotification(new_state=DoorState.CLOSED, blocking=False)
subject.handle_action(HardwareEventAction(event=door_close_event))
subject.handle_action(PlayAction())
assert (subject.state == CommandState(queue_status=QueueStatus.ACTIVE, run_result=None, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))<|docstring|>It should inactivate/activate command queue according to door state.<|endoftext|> |
1e2ec55f774132b63a505549ee95411e7f49a58d17e63260ef167cf3d7ef15ad | def test_command_store_handles_finish_action() -> None:
'It should change to a succeeded state with FinishAction.'
subject = CommandStore()
subject.handle_action(PlayAction())
subject.handle_action(FinishAction())
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=RunResult.SUCCEEDED, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | It should change to a succeeded state with FinishAction. | api/tests/opentrons/protocol_engine/state/test_command_store.py | test_command_store_handles_finish_action | Opentrons/labware | 2 | python | def test_command_store_handles_finish_action() -> None:
subject = CommandStore()
subject.handle_action(PlayAction())
subject.handle_action(FinishAction())
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=RunResult.SUCCEEDED, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={})) | def test_command_store_handles_finish_action() -> None:
subject = CommandStore()
subject.handle_action(PlayAction())
subject.handle_action(FinishAction())
assert (subject.state == CommandState(queue_status=QueueStatus.INACTIVE, run_result=RunResult.SUCCEEDED, is_hardware_stopped=False, is_door_blocking=False, running_command_id=None, all_command_ids=[], queued_command_ids=OrderedSet(), commands_by_id=OrderedDict(), errors_by_id={}))<|docstring|>It should change to a succeeded state with FinishAction.<|endoftext|> |
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