主要问题:
相同类型和相同大小的 numpy 数组不会使用 np.hstack 列堆叠在一起。 , np.column_stack ，或np.concatenate(axis=1) .
说明:
我不明白 numpy 数组的哪些属性可以改变 numpy.hstack , numpy.column_stack和numpy.concatenate(axis=1)不能正常工作。我在让我的真实程序按列堆叠时遇到问题 - 它只附加到行。 numpy 数组是否有某些属性会导致这种情况成立？它不会抛出错误，它只是不执行“正确”或“正常”的行为。
我尝试了一个简单的案例，它的工作原理符合我的预期:
input:
a = np.array([['1', '2'], ['3', '4']], dtype=object)
b = np.array([['5', '6'], ['7', '8']], dtype=object)
np.hstack(a, b)
output:
np.array([['1', '2', '5', '6'], ['3', '4', '7', '8']], dtype=object)
这对我来说完全没问题，也是我想要的。
但是，我从程序中得到的是:
First array:
[['29.8989', '0'] ['29.8659', '-8.54805e-005'] ['29.902', '-0.00015875']
..., ['908.791', '-0.015765'] ['908.073', '-0.0154842'] []]
Second array (to be added on in columns):
[['29.8989', '26.8556'] ['29.8659', '26.7969'] ['29.902', '29.0183'] ...,
['908.791', '943.621'] ['908.073', '940.529'] []]
What should be the two arrays side by side or in columns:
[['29.8989', '0'] ['29.8659', '-8.54805e-005'] ['29.902', '-0.00015875']
..., ['908.791', '943.621'] ['908.073', '940.529'] []]
显然，这不是正确的答案。
产生这个问题的模块相当长(我将在底部给出它)，但这里是它的简化，它仍然可以像第一个示例一样工作(执行正确的列堆叠):
import numpy as np
def contiguous_regions(condition):
d = np.diff(condition)
idx, = d.nonzero()
idx += 1
if condition[0]:
idx = np.r_[0, idx]
if condition[-1]:
idx = np.r_[idx, condition.size]
idx.shape = (-1,2)
return idx
def is_number(s):
try:
np.float64(s)
return True
except ValueError:
return False
total_array = np.array([['1', '2'], ['3', '4'], ['strings','here'], ['5', '6'], ['7', '8']], dtype=object)
where_number = np.array(map(is_number, total_array))
contig_ixs = contiguous_regions(where_number)
print contig_ixs
t = tuple(total_array[s[0]:s[1]] for s in contig_ixs)
print t
print np.hstack(t)
它基本上查看列表数组并找到最长的连续数字集。如果这些数据集的长度相同，我想对它们进行列堆叠。
这是提供问题的真实模块:
import numpy as np
def retrieve_XY(file_path):
# XY data is read in from a file in text format
file_data = open(file_path).readlines()
# The list of strings (lines in the file) is made into a list of lists while splitting by whitespace and removing commas
file_data = np.array(map(lambda line: line.rstrip('\n').replace(',',' ').split(), file_data))
# Remove empty lists, make into numpy array
xy_array = np.array(filter(None, column_stacked_data_chain))
# Each line is searched to make sure that all items in the line are a number
where_num = np.array(map(is_number, file_data))
# The data is searched for the longest contiguous chain of numbers
contig = contiguous_regions(where_num)
try:
# Data lengths (number of rows) for each set of data in the file
data_lengths = contig[:,1] - contig[:,0]
# Get the maximum length of data (max number of contiguous rows) in the file
maxs = np.amax(data_lengths)
# Find the indices for where this long list of data is (index within the indices array of the file)
# If there are two equally long lists of data, get both indices
longest_contig_idx = np.where(data_lengths == maxs)
except ValueError:
print 'Problem finding contiguous data'
return np.array([])
###############################################################################################
###############################################################################################
# PROBLEM ORIGINATES HERE
# Starting and stopping indices of the contiguous data are stored
ss = contig[longest_contig_idx]
# The file data with this longest contiguous chain of numbers
# If there are multiple sets of data of the same length, they are added in columns
longest_data_chains = tuple([file_data[i[0]:i[1]] for i in ss])
print "First array:"
print longest_data_chains[0]
print
print "Second array (to be added on in columns):"
print longest_data_chains[1]
column_stacked_data_chain = np.concatenate(longest_data_chains, axis=1)
print
print "What should be the two arrays side by side or in columns:"
print column_stacked_data_chain
###############################################################################################
###############################################################################################
xy = np.array(zip(*xy_array), dtype=float)
return xy
#http://stackoverflow.com/questions/4494404/find-large-number-of-consecutive-values-fulfilling-condition-in-a-numpy-array
def contiguous_regions(condition):
"""Finds contiguous True regions of the boolean array "condition". Returns
a 2D array where the first column is the start index of the region and the
second column is the end index."""
# Find the indicies of changes in "condition"
d = np.diff(condition)
idx, = d.nonzero()
# We need to start things after the change in "condition". Therefore,
# we'll shift the index by 1 to the right.
idx += 1
if condition[0]:
# If the start of condition is True prepend a 0
idx = np.r_[0, idx]
if condition[-1]:
# If the end of condition is True, append the length of the array
idx = np.r_[idx, condition.size] # Edit
# Reshape the result into two columns
idx.shape = (-1,2)
return idx
def is_number(s):
try:
np.float64(s)
return True
except ValueError:
return False
更新:
我在 @hpaulj 的帮助下让它工作。显然，数据的结构类似于 np.array([['1','2'],['3','4']])在这两种情况下都不够，因为我使用的真实案例有 dtype=object列表中有一些字符串。因此，numpy 看到的是一维数组而不是二维数组，这是必需的。
解决此问题的解决方案是调用 map(float, data)到 readlines 给出的每个列表功能。
这是我最终得到的结果:
import numpy as np
def retrieve_XY(file_path):
# XY data is read in from a file in text format
file_data = open(file_path).readlines()
# The list of strings (lines in the file) is made into a list of lists while splitting by whitespace and removing commas
file_data = map(lambda line: line.rstrip('\n').replace(',',' ').split(), file_data)
# Remove empty lists, make into numpy array
xy_array = np.array(filter(None, file_data))
# Each line is searched to make sure that all items in the line are a number
where_num = np.array(map(is_number, xy_array))
# The data is searched for the longest contiguous chain of numbers
contig = contiguous_regions(where_num)
try:
# Data lengths
data_lengths = contig[:,1] - contig[:,0]
# All maximums in contiguous data
maxs = np.amax(data_lengths)
longest_contig_idx = np.where(data_lengths == maxs)
except ValueError:
print 'Problem finding contiguous data'
return np.array([])
# Starting and stopping indices of the contiguous data are stored
ss = contig[longest_contig_idx]
print ss
# The file data with this longest contiguous chain of numbers
# Float must be cast to each value in the lists of the contiguous data and cast to a numpy array
longest_data_chains = np.array([[map(float, n) for n in xy_array[i[0]:i[1]]] for i in ss])
# If there are multiple sets of data of the same length, they are added in columns
column_stacked_data_chain = np.hstack(longest_data_chains)
xy = np.array(zip(*column_stacked_data_chain), dtype=float)
return xy
#http://stackoverflow.com/questions/4494404/find-large-number-of-consecutive-values-fulfilling-condition-in-a-numpy-array
def contiguous_regions(condition):
"""Finds contiguous True regions of the boolean array "condition". Returns
a 2D array where the first column is the start index of the region and the
second column is the end index."""
# Find the indicies of changes in "condition"
d = np.diff(condition)
idx, = d.nonzero()
# We need to start things after the change in "condition". Therefore,
# we'll shift the index by 1 to the right.
idx += 1
if condition[0]:
# If the start of condition is True prepend a 0
idx = np.r_[0, idx]
if condition[-1]:
# If the end of condition is True, append the length of the array
idx = np.r_[idx, condition.size] # Edit
# Reshape the result into two columns
idx.shape = (-1,2)
return idx
def is_number(s):
try:
np.float64(s)
return True
except ValueError:
return False
此函数现在将接收一个文件并输出在其中找到的最长连续数字类型数据。如果找到多个具有相同长度的数据集，则会将它们按列堆叠。
最佳答案
数组末尾的空列表导致了您的问题:
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[1, 2], [3, 4], []])
>>> a.shape
(2L, 2L)
>>> a.dtype
dtype('int32')
>>> b.shape
(3L,)
>>> b.dtype
dtype('O')
由于末尾的空列表，它不是创建一个 2D 数组，而是创建一个 1D 数组，其中每个项目都包含一个包含两个项目的长列表对象。
关于python - numpy的columnstack/hstack功能不一致，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/21925577/