def __init__(self, curva_id, **kwargs):
#variáveis básicas
self.curva_id = curva_id
self.titulo = ""
self.descricao = ""
self.title = ""
self.description = ""
self.ndim = 0
self.tags = []
# self.x = np.array()
# TODO: abstrair melhor essa merda
self.ue = [] # vetor de ue de acordo com os tags
ue = {'original': '', 'used': ''}
self.val = None
# self.tags_ = tag
self.metadata = []
metadata = {
'x_tag': None,
'y_tag': None,
'z_tag': None,
'x_options': {},
'y_options': {},
'z_options': {}
}
self.x_values = np.Array()
self.y_values = np.Array()
self.z_values = np.Array()
if (kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def sparse_pearsonr(a, b):
length = a.shape[1]
mean_a = a.mean()
mean_b = b.mean()
mean_product_ab = mean_a * mean_b
mean_product_aa = mean_a ** 2
mean_product_bb = mean_b ** 2
am = a - np.Array([a.mean()] * length)
bm = b - np.Array([b.mean()] * length)
return cosine_similarity(am, bm)
def _get_data(self):
try:
ret = self.section.data.data_points
#self.pointsx = ret[:, 0]
#self.pointsy = ret[:, 1]
self.plotdata.set_data('x', ret[:, 0])
self.plotdata.set_data('y', ret[:, 1])
return ret
except AttributeError:
return numpy.Array([[], []])
def __init__(self, *coeffs):
"""Creates a Polynomial with the coefficients, starting with the constant"""
self.coeffs = np.Array(coeffs)
self.order = len(self.coeffs) - 1
def main():
arr, mode1 = load_img_arr('work/form1.1.npy')
image_data_size = arr.size
thread_x, thread_y, thread_z = GPU_ATTRIBUTES['MAX_THREADS_PER_BLOCK']/GPU_ATTRIBUTES['MULTIPROCESSOR_COUNT'], 1, 1
block_size = (thread_x, thread_y, thread_z)
# arr <- all rows
# arr[0] <- pixels in first row
# arr[0][0] <- first pixel in first row (RGB[A])
#TODO:
# make a strip of images up to max images, max grid dim, or max image count
# if there are images left over, start making another strip (same conditions as above)
# if we finished a whole strip, add the strip to the existing strips
# repeat until all images are used up or we have more strips than that grid dim
pairs_processed = 0
total_image_pairs = 50
while pairs_processed < total_image_pairs:
free_memory = cuda.mem_get_info()[0]
half_memory = free_memory / 2
max_group_count = (half_memory / image_data_size) # number of image pairs that can be loaded
y_strip_count = 0
group_count = 0
strip1 = numpy.Array()
strip2 = numpy.Array()
while group_count < max_group_count and y_strip_count < GPU_ATTRIBUTES['MAX_GRID_DIM_Y']:
i1 = load_img_arr('work/form1.%d.npy' % (pairs_processed+1))
i2 = load_img_arr('work/form2.%d.npy' % (pairs_processed+1))
group_count += 1
pairs_processed += 1
x_strip_count = 0
# make strip here
images_processed = 0
image_count = 50
block_y = 1
while images_processed < image_count:
free_memory = cuda.mem_get_info()[0]
half_memory = free_memory / 2
image_group_count = (half_memory / image_data_size) - 1 # number of image pairs that can be loaded
block1 = load_img_arr('work/form1.%d.npy'%(images_processed+1))[0]
block2 = load_img_arr('work/form2.%d.npy'%(images_processed+1))[0]
count = 1
for i in xrange(images_processed+1, images_processed+image_group_count):
if i > image_count:
break
i1 = load_img_arr('work/form1.%d.npy'%i)[0]
i2 = load_img_arr('work/form2.%d.npy'%i)[0]
h, w, d = len(block1), len(block1[0]), len(block1[0][0])
new_block_x = (block1.size + i1.size) / thread_x
if (block1.size + i1.size) % thread_x:
new_block_x += 1
new_block_y = block_y
if new_block_x > GPU_ATTRIBUTES['MAX_GRID_DIM_X']:
new_block_y += 1
if new_block_y > GPU_ATTRIBUTES['MAX_GRID_DIM_Y']:
raise Exception('Grid getting too big')
block1 = numpy.concatenate((block1,i1), axis=1)
block2 = numpy.concatenate((block2,i2), axis=1)
block_y = new_block_y
else:
block1 = numpy.concatenate((block1,i1), axis=0)
block2 = numpy.concatenate((block2,i2), axis=0)
block_x = new_block_x
count += 1
grid_size = (block_x, block_y)
print grid_size
save_image_arr(block1, mode1, 'grid_out.%d.png' % images_processed)
images_processed += image_group_count
#!/bin/python
import sys
import numpy
n = int(raw_input().strip())
unsorted = []
unsorted_i = 0
final = []
for unsorted_i in xrange(n):
unsorted_t = str(raw_input().strip())
unsorted.append(unsorted_t)
max = numpy.Array(unsorted)
print sorted(max)
# for i in range(n):
# for j in range(n-i-1):
# if long(unsorted[j])>long(unsorted[j+1]):
# t=unsorted[j]
# unsorted[j]=unsorted[j+1]
# unsorted[j+1]=t
#
# for i in unsorted:
# print i
56: >>> b[2] = AFC_east 57: >>> b ['spam', 2.0, ['New England Patriots', 'Buffalo Bills', 'New York Giants', 'New York Jets'], 10] 58: >>> len(b) 4 59: >>> a = [1, 2, 3] 60: >>> b = [4, 5, 6] 61: >>> c = a + b 62: >>> c [1, 2, 3, 4, 5, 6] 63: >>> import numpy as np 64: >>> a = np.Arry([1, 2, 3]) 65: >>> a = np.Array([1, 2, 3]) 66: >>> [0] * 4 [0, 0, 0, 0] 67: >>> ['Tom Brady', 'Bill Belichick']*3 ['Tom Brady', 'Bill Belichick', 'Tom Brady', 'Bill Belichick', 'Tom Brady', 'Bill Belichick'] 68: >>> t = ['a', 'b', 'c', 'd', 'e', 'f'] 69: >>> t[1:2] ['b'] 70: >>> t[1:3] ['b', 'c'] 71: >>> t[0:5]
d =[array([10, 1, 7, 3]),
array([ 0, 14, 12, 13]),
array([ 3, 10, 7, 8]),
array([7, 5]),
array([ 5, 12, 3]),
array([14, 8, 10])]
d =pd.Array([10, 1, 7, 3],
[ 0, 14, 12, 13],
[ 3, 10, 7, 8],
[7, 5],
[ 5, 12, 3],
[14, 8, 10])
import numpy as np
d =np.Array([10, 1, 7, 3],
[ 0, 14, 12, 13],
[ 3, 10, 7, 8],
[7, 5],
[ 5, 12, 3],
[14, 8, 10])
dir(np)
dir(np.array)
d =np.array([10, 1, 7, 3],
[ 0, 14, 12, 13],
[ 3, 10, 7, 8],
[7, 5],
[ 5, 12, 3],
[14, 8, 10])
d =np.array([[10, 1, 7, 3],
[ 0, 14, 12, 13],
[ 3, 10, 7, 8],
[7, 5],
[ 5, 12, 3],
import numpy as np mynumber = 65 myList = [67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113] myArray = np.Array(myList)