prompt large_stringlengths 72 9.34k | completion large_stringlengths 0 7.61k |
|---|---|
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | for j in range(i + 1, i + neighborhood + 1):
if input[j] >= input[i]: |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
<|fim_middle... | i = cuda.grid(1)
r = 0
if i < input.shape[0] - 2 * neighborhood:
i += neighborhood
for j in range(i - neighborhood, i):
if input[j] >= input[i]:
r += powers[j - i + neighborhood]
for j in range(i + 1, i + neighborhood + 1):
if input[j] >= ... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | maxThread = 512
blockDim = maxThread
d_input = cuda.to_device(input)
hist = np.zeros(2 ** (2 * neighborhood), dtype='int32')
gridDim = (len(input) - 2 * neighborhood + blockDim) / blockDim
d_hist = cuda.to_device(hist)
lbp_kernel[gridDim, blockDim](d_input, neighborhood, d_powers, d_hist... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | maxThread = 512
blockDim = maxThread
gridDim = (len(input) - 2 * neighborhood + blockDim) / blockDim
for block in range(0, gridDim):
for thread in range(0, blockDim):
r = 0
i = blockDim * block + thread
if i < input.shape[0] - 2 * neighborhood:
... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | maxThread = 512
blockDim = maxThread
gridDim = (len(input) - 2 * neighborhood + blockDim) / blockDim
for block in range(0, gridDim):
for thread in range(0, blockDim):
r = 0
i = blockDim * block + thread
if i < input.shape[0] - 2 * neighborhood:
... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | """
Extract the 1d lbp pattern on CPU
"""
res = np.zeros(1 << (2 * neighborhood))
for i in range(neighborhood, len(input) - neighborhood):
left = input[i - neighborhood : i]
right = input[i + 1 : i + neighborhood + 1]
both = np.r_[left, right]
res[np.sum(p [both >= in... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | i += neighborhood
for j in range(i - neighborhood, i):
if input[j] >= input[i]:
r += powers[j - i + neighborhood]
for j in range(i + 1, i + neighborhood + 1):
if input[j] >= input[i]:
r += powers[j - i + neighborhood - 1]
cuda... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | r += powers[j - i + neighborhood] |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | r += powers[j - i + neighborhood - 1] |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | i += neighborhood
for j in range(i - neighborhood, i):
if input[j] >= input[i]:
r += powers[j - i + neighborhood]
for j in range(i + 1, i + neighborhood + 1):
if input[j] >= input[i]:
... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | r += powers[j - i + neighborhood] |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | r += powers[j - i + neighborhood - 1] |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | i += neighborhood
for j in range(i - neighborhood, i):
if input[j] >= input[i]:
r += powers[j - i + neighborhood]
for j in range(i + 1, i + neighborhood + 1):
if input[j] >= input[i]:
... |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | r += powers[j - i + neighborhood] |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | r += powers[j - i + neighborhood - 1] |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def <|fim_middle|>(input, neighborhood, powers, h):
i = cuda... | lbp_kernel |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | extract_1dlbp_gpu |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | extract_1dlbp_gpu_debug |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | extract_1dlbp_cpu_jit |
<|file_name|>1dlbp_tests.py<|end_file_name|><｜fim▁begin｜>import numpy as np
import matplotlib.pylab as plt
from numba import cuda, uint8, int32, uint32, jit
from timeit import default_timer as timer
@cuda.jit('void(uint8[:], int32, int32[:], int32[:])')
def lbp_kernel(input, neighborhood, powers, h):
i = cuda.gri... | extract_1dlbp_cpu |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | # Ironic will register each baremetal node as a 'hypervisor',
# so the hypervisor list can contain many hypervisors of type |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | """Tests Hypervisors API that require admin privileges"""
@classmethod
def setup_clients(cls):
super(HypervisorAdminTestJSON, cls).setup_clients()
cls.client = cls.os_adm.hypervisor_client
def _list_hypervisors(self):
# List of hypervisors
hypers = self.client.list_hype... |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | super(HypervisorAdminTestJSON, cls).setup_clients()
cls.client = cls.os_adm.hypervisor_client |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self.client.list_hypervisors()['hypervisors']
return hypers |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | self.assertTrue(len(hypers) > 0, "No hypervisors found: %s" % hypers) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self._list_hypervisors()
self.assertHypervisors(hypers) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self.client.list_hypervisors(detail=True)['hypervisors']
self.assertHypervisors(hypers) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self._list_hypervisors()
self.assertHypervisors(hypers)
details = self.client.show_hypervisor(hypers[0]['id'])['hypervisor']
self.assertTrue(len(details) > 0)
self.assertEqual(details['hypervisor_hostname'],
hypers[0]['hypervisor_hostname']) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self._list_hypervisors()
self.assertHypervisors(hypers)
hostname = hypers[0]['hypervisor_hostname']
hypervisors = (self.client.list_servers_on_hypervisor(hostname)
['hypervisors'])
self.assertTrue(len(hypervisors) > 0) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | stats = (self.client.show_hypervisor_statistics()
['hypervisor_statistics'])
self.assertTrue(len(stats) > 0) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self._list_hypervisors()
# Ironic will register each baremetal node as a 'hypervisor',
# so the hypervisor list can contain many hypervisors of type
# 'ironic'. If they are ALL ironic, skip this test since ironic
# doesn't support hypervisor uptime. Otherwise, remove th... |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers = self._list_hypervisors()
self.assertHypervisors(hypers)
hypers = self.client.search_hypervisor(
hypers[0]['hypervisor_hostname'])['hypervisors']
self.assertHypervisors(hypers) |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | hypers_without_ironic.append(hyper)
ironic_only = False |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | raise self.skipException(
"Ironic does not support hypervisor uptime") |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | has_valid_uptime = True
break |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | setup_clients |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | _list_hypervisors |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | assertHypervisors |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_get_hypervisor_list |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_get_hypervisor_list_details |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_get_hypervisor_show_details |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_get_hypervisor_show_servers |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_get_hypervisor_stats |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_get_hypervisor_uptime |
<|file_name|>test_hypervisor.py<|end_file_name|><｜fim▁begin｜># Copyright 2013 IBM Corporation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | test_search_hypervisor |
<|file_name|>11.py<|end_file_name|><｜fim▁begin｜># Created by PyCharm Community Edition
# User: Kaushik Talukdar
# Date: 22-03-2017
# Time: 03:52 PM
#python doesn't allow you to mix strings and numbers directly. numbers must be converted to strings
<｜fim▁hole｜>
print... |
age = 28
|
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | print s.subcribe() |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | s.login(username, password)
print "logging in..." |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | print s.createRegistrationToken()
print s.subcribe()
print "creating endpoint and registrationToken..." |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | print s.createRegistrationToken()
print s.subcribe()
data = s.pull() |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | continue |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | continue |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | print "%s to %s" % (sender, receiver) |
<|file_name|>test.py<|end_file_name|><｜fim▁begin｜>from messenger import Skype
import keyring
import utils
token = keyring.get_password('messagesReceiver', 'skypeToken')
registrationToken = keyring.get_password('messagesReceiver', 'skypeRegistrationToken')
username = keyring.get_password('messagesReceiver', 'skypeUsern... | print "From %s" % sender |
<|file_name|>burrows_wheeler.py<|end_file_name|><｜fim▁begin｜>def burrows_wheeler(text):
"""Calculates the burrows wheeler transform of <text>.
<｜fim▁hole｜> text += "$"
all_permutations = []
for i in range(len(text)):
all_permutations.append((text[i:] + text[:i],i))
all_permutations.sort()
... | returns the burrows wheeler string and the suffix array indices
The text is assumed to not contain the character $"""
|
<|file_name|>burrows_wheeler.py<|end_file_name|><｜fim▁begin｜>
def burrows_wheeler(text):
<|fim_middle|>
<｜fim▁end｜> | """Calculates the burrows wheeler transform of <text>.
returns the burrows wheeler string and the suffix array indices
The text is assumed to not contain the character $"""
text += "$"
all_permutations = []
for i in range(len(text)):
all_permutations.append((text[i:] + text[:i],i))
... |
<|file_name|>burrows_wheeler.py<|end_file_name|><｜fim▁begin｜>
def <|fim_middle|>(text):
"""Calculates the burrows wheeler transform of <text>.
returns the burrows wheeler string and the suffix array indices
The text is assumed to not contain the character $"""
text += "$"
all_permutations = []
... | burrows_wheeler |
<|file_name|>utils.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# Some utils
import hashlib
import uuid
def get_hash(data):<｜fim▁hole｜> """Returns hashed string"""
return hashlib.sha256(data).hexdigest()
def get_token():
return str(uuid.uuid4())<｜fim▁end｜> | |
<|file_name|>utils.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# Some utils
import hashlib
import uuid
def get_hash(data):
<|fim_middle|>
def get_token():
return str(uuid.uuid4())
<｜fim▁end｜> | """Returns hashed string"""
return hashlib.sha256(data).hexdigest() |
<|file_name|>utils.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# Some utils
import hashlib
import uuid
def get_hash(data):
"""Returns hashed string"""
return hashlib.sha256(data).hexdigest()
def get_token():
<|fim_middle|>
<｜fim▁end｜> | return str(uuid.uuid4()) |
<|file_name|>utils.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# Some utils
import hashlib
import uuid
def <|fim_middle|>(data):
"""Returns hashed string"""
return hashlib.sha256(data).hexdigest()
def get_token():
return str(uuid.uuid4())
<｜fim▁end｜> | get_hash |
<|file_name|>utils.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# Some utils
import hashlib
import uuid
def get_hash(data):
"""Returns hashed string"""
return hashlib.sha256(data).hexdigest()
def <|fim_middle|>():
return str(uuid.uuid4())
<｜fim▁end｜> | get_token |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>''' The `Filter` hierarchy contains Transformer classes that take a `Stim`
of one type as input and return a `Stim` of the same type as output (but with
some changes to its data).
'''
from .audio import (AudioTrimmingFilter,
AudioResamplingFilt... | __all__ = [
'AudioTrimmingFilter', |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | def __init__(self, *args, **kwargs):
global scheduler
scheduler = "Current scheduler is PyCOMPSs"
self.task_instance = task(*args, **kwargs)
def __call__(self, f):
return self.task_instance.__call__(f) |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | global scheduler
scheduler = "Current scheduler is PyCOMPSs"
self.task_instance = task(*args, **kwargs) |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | return self.task_instance.__call__(f) |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | compss_barrier() |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | obj = compss_wait_on(obj)
return obj |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | compss_delete_object(obj) |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | compss_delete_file(file_path) |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | return obj |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | __init__ |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | __call__ |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | barrier |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | get_value_from_remote |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | delete_object |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | delete_file |
<|file_name|>ExaquteTaskPyCOMPSs.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/python
#
# Copyright 2002-2019 Barcelona Supercomputing Center (www.bsc.es)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | compute |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitude... |
def baseline_func_amp(self,z_data,f_data,lam,p,niter=10): |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
<|fim_middle|>
<｜fim▁end｜> | '''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitude(self,z_data,cal_ampdata):
return z_data/cal_ampdata
def normalize_phase(self,z_data,cal_phase):
return z_data*np.exp(-1j*cal_phase)
def normalize_by_func(s... |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
<|fim_middle|>
def normalize_amplitude(self,z_... | return z_data/cal_z_data |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | return z_data/cal_ampdata |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | return z_data*np.exp(-1j*cal_phase) |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | return z_data/func(f_data) |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | '''
see http://zanran_storage.s3.amazonaws.com/www.science.uva.nl/ContentPages/443199618.pdf
"Asymmetric Least Squares Smoothing" by P. Eilers and H. Boelens in 2005.
http://stackoverflow.com/questions/29156532/python-baseline-correction-library
"There are two parameters: p for asymmetry and lambda for smooth... |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | '''
for this to work, you need to analyze a large part of the baseline
tune lam and p until you get the desired result
'''
return self._baseline_als(np.absolute(z_data),lam,p,niter=niter) |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | '''
for this to work, you need to analyze a large part of the baseline
tune lam and p until you get the desired result
returns the baseline as a function
the points in between the datapoints are computed by cubic interpolation
'''
return interp1d(f_data, self._baseline_als(np.absolute(z_data),lam,p,niter=... |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | '''
for this to work, you need to analyze a large part of the baseline
tune lam and p until you get the desired result
returns the baseline as a function
the points in between the datapoints are computed by cubic interpolation
'''
return interp1d(f_data, self._baseline_als(np.angle(z_data),lam,p,niter=nit... |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | '''
for this to work, you need to analyze a large part of the baseline
tune lam and p until you get the desired result
'''
return self._baseline_als(np.angle(z_data),lam,p,niter=niter) |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | '''
A GUI to help you fit the baseline
'''
self.__lam = 1e6
self.__p = 0.9
niter = 10
self.__baseline = self._baseline_als(np.absolute(self.z_data_raw),self.__lam,self.__p,niter=niter)
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
fig, (ax0,ax1) = plt.subplots(nrows=2)
plt.... |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | self.__lam = 10**sSmooth.val
self.__p = sAsym.val
self.__baseline = sbcorr.val*self._baseline_als(np.absolute(self.z_data_raw),self.__lam,self.__p,niter=niter)
l0.set_ydata(np.absolute(self.z_data_raw))
l0b.set_ydata(np.absolute(self.__baseline))
l1.set_ydata(np.absolute(self.z_data_raw/self.__baselin... |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def <|fim_middle|>(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitude... | normalize_zdata |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def <|fim_middle|>(sel... | normalize_amplitude |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | normalize_phase |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | normalize_by_func |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | _baseline_als |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | fit_baseline_amp |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | baseline_func_amp |
<|file_name|>calibration.py<|end_file_name|><｜fim▁begin｜>
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitud... | baseline_func_phase |
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