我在使用Python 2.7.6时遇到了一些麻烦，从字典中获取信息并使用它做一些有用的事情。我已经在下面附上了我的全部代码，因为我不确定具体是什么错误，这可能不是我期待的。
我正在尝试生成一些测试数据;一堆随机分布的图像源（1＆＃39; s），它们从正确的位置移动一小部分。我使用字典单独跟踪每个源，并在字典中为包含移位源的每个图像使用字典。
我的问题是当我想拍摄图像中的源的平均运动时。我已经找到了这个地方我相信问题是明确的（大约一半）。我已经尝试了几种不同的技术，它们被注释掉了。目前我只使用3张图片，但我打算大幅增加这个数字。如果我只坚持只有3个，我就会采用不同的方法，并且在很长的路上写下了很多。
我已经考虑过这样的其他问题，但没有找到任何与我的问题有关的内容，这可能是因为我不知道我正在尝试做什么的行话。如果以前曾经问过这个并且已经解决了，那就道歉了。
# Source position-offset tracker
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import copy
import random
from pylab import boxplot
#FUNCTIONS
def random_movement(source_positions):
source_positions_changed={}
for n in range(len(source_positions)): # n = [0,1]
key = source_positions.keys()[n]
del_x = source_positions[key][0]+random.randint(0,1)
del_y = source_positions[key][1]+random.randint(0,1)
source_positions_changed[key] = (del_x,del_y)
return source_positions_changed
#OTHER CODE
# put in original positions
# -> randomly distributed
# -> of values 0 or 1 only
original_positions = np.random.randint(2,size=(10,10))
# Tag each source within the image to keep track of them
source_positions = {}
source_count=0
for x in range(len(original_positions)):
for y in range(len(original_positions[0])):
if original_positions[x,y] == 1: # finding all sources
source_count += 1
index = 'S'+str(source_count)
source_positions[index] = (x,y)
# attach a source name to its position
source_numbers = len(source_positions)
number_timesteps = 2 # how many images were taken NOT including the original
# create a dictionary for the timesteps of shifted sources
# timesteps are the images where the sources have moves from the correct position
dictionary = {}
for x in range(1,number_timesteps+1):
#exec('dictionary%s = copy.copy(random_movement(source_positions))'%x)
dictionary['position_changed{0}'.format(x)] = copy.copy(random_movement(source_positions))
# finding the distances from the sources original positions
#source_distance_sum = {}
#################################################
### THIS IS WHERE I THINK I'M HAVING PROBLEMS ###
#################################################
# this should take make the motion of any sources that appear outside the range of the image -1
# and for sources that remain in range should find the motion from the correct position
# using equation: a^2 = b^2 + c^2
# should end up with source_distance_sum1 and source_distance_sum2 that have the motions from the correct positions of each source for the images, whose positional information was stored in dictionary['position_changed1'] and dictionary['position_changed2'] respectively
#source_distance_sum=[]
#distance_moved=[]
for source in range(1,source_numbers+1):
#source_distance_sum['S{0}'.format(source)]=0
for tstep in range(1,number_timesteps+1):
exec('source_distance_sum%s=[]'%tstep)
if dictionary['position_changed{0}'.format(tstep)]['S{0}'.format(source)][0]>=len(original_positions) or dictionary['position_changed{0}'.format(tstep)]['S{0}'.format(source)][1]>=len(original_positions[0]):
#if 'dictionary%s[S%s][0]>=len(original_positions) or dictionary%s[S%s][1]>=len(original_positions[0])'%(tstep,source,tstep,source)
#source_distance_sum['S{0}'.format(source)]=-1
exec('source_distance_sum%s.append(-1)'%tstep)
#print 'if 1: '+str(source_distance_sum1)
#print 'if 2: '+str(source_distance_sum2)
# dealing with sources moved out of range
else:
distance_moved=np.sqrt((source_positions['S{0}'.format(source)][0]-dictionary['position_changed{0}'.format(tstep)]['S{0}'.format(source)][0])**2+(source_positions['S{0}'.format(source)][1]-dictionary['position_changed{0}'.format(tstep)]['S{0}'.format(source)][1])**2)
# I have tried changing distance_moved as well, in similar ways to source_distance_sum, but I have as yet had no luck.
#source_distance_sum['S{0}'.format(source)]=distance_moved
exec('source_distance_sum%s.append(distance_moved)'%tstep)
# why does this not work!!!!????? I really feel like it should...
# for movement that stays in range
#print 'else 1: '+str(source_distance_sum1)
#print 'else 2: '+str(source_distance_sum2)
# then I want to use the information from the source_distance_sum1 & 2 and find the averages. I realise the following code will not work, but I cannot get the previous paragraph to work, so have not moved on to fixing the following.
# average distance:
source_distance = []
for source in range(1,len(source_distance_sum)+1):
if source_distance_sum['S{0}'.format(source)] > -1:
source_distance.append(source_distance_sum['S{0}'.format(source)])
average = sum(source_distance)/float(len(source_distance))
# set range of graph
#axx_max = np.ceil(max(distance_travelled))
#axy_max = np.ceil(max(number_of_sources))
# plot graph
fig = plt.figure()
#plt.axis([-1,axx_max+1,-1,axy_max+1])
plt.xlabel('Data set')
plt.ylabel('Average distance travelled')
plt.title('There are %s source(s) with %s valid' % (source_count,len(source_distance)))
ax1 = fig.add_subplot(111)
ax1.scatter(1, average, s=10, c='b', marker="+", label='First timestep')
#ax1.scatter(x[40:],y[40:], s=10, c='r', marker="o", label='second')
plt.legend(loc='upper left');
plt.show()
# NOTES AND REMOVED CODE
# Move sources around over time
# -> keep within a fixed range of motion
# -> randomly generate motion
# Calculate motion of sources from images
# -> ignore direction
# -> all that move by same magnitude get stored together
# -> Number of sources against magnitude of motion
# Make dictionary of number of sources that have moved a certain amount.
#source_motion_count = {} # make length of sources, values all 0
#for elem in range(len(source_distance)):
# if type(source_distance[elem])!=str and source_distance[elem]>-1:
# source_motion_count[source_distance[elem]] = 0
#for elem in range(len(source_distance)):
# if type(source_distance[elem])!=str and source_distance[elem]>-1:
# source_motion_count[source_distance[elem]] += 1
# Compile count of sources based on movement into graph
#number_of_sources = []
#distance_travelled = []
#for n in range(len(source_motion_count)):
# key=source_motion_count.keys()[n]
# number_of_sources.append(source_motion_count[key])
# distance_travelled.append(key)
答案 0 :(得分：0)
我通过将该部分转换为自己的功能来修复它。我也改变了一些我想要它做的事情，所以从最初的想法到下面的内容有一些变化。
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import copy
import random
from pylab import boxplot
#------------------------------------------------------------------------#
#--------------------------FUNCTIONS-------------------------------------#
#------------------------------------------------------------------------#
def original_image():
# create image
original_positions = np.random.randint(2,size=(11,11))
# make sure image has uneven lengths - will be useful later
y = original_positions.shape[0]
x = original_positions.shape[1]
if y%2 == 0:
y-=1
if x%2 == 0:
x-=1
original_positions = original_positions[0:y,0:x]
return original_positions
def random_movement(source_positions):
source_positions_changed={}
# create some random movement in x and y axis, within a certain range
for n in range(len(source_positions)):
key = source_positions.keys()[n] # original source positions
del_x = source_positions[key][0]+random.randint(-1,1)
del_y = source_positions[key][1]+random.randint(-1,1)
source_positions_changed[key] = (del_x,del_y)
return source_positions_changed
def tag_sources(original_positions):
source_positions = {}
source_count=0
# keeping track of all the sources (1's) from original image
for x in range(len(original_positions)):
for y in range(len(original_positions[0])):
if original_positions[x,y] == 1: # finding all sources
source_count += 1
index = 'S'+str(source_count)
source_positions[index] = (x,y)
return source_positions
def calc_motion(position_dict_changed, position_dict_original,xaxis_len,yaxis_len):
position_dict_motion = {}
for source_num in range(1,len(position_dict_original)+1):
# make sources that go outside the image range -1
if position_dict_changed['S{0}'.format(source_num)][1]>=yaxis_len or position_dict_changed['S{0}'.format(source_num)][0]>=xaxis_len:
position_dict_motion['S{0}'.format(source_num)] = -1
else:
# determine x and y motion from original position
# this is the main difference from the original idea as do not want to average the motion
x_motion = position_dict_original['S{0}'.format(source_num)][1] - position_dict_changed['S{0}'.format(source_num)][1]
y_motion = position_dict_original['S{0}'.format(source_num)][0] - position_dict_changed['S{0}'.format(source_num)][0]
position_dict_motion['S{0}'.format(source_num)] = (y_motion,x_motion)
return position_dict_motion
#------------------------------------------------------------------------#
#--------------------------OTHER CODE------------------------------------#
#------------------------------------------------------------------------#
# creating random distribution of sources
original_positions = original_image()
orig_xaxis_len = len(original_positions[0])
orig_yaxis_len = len(original_positions)
# tag sources in original_positions
source_positions = tag_sources(original_positions)
source_numbers = len(source_positions)
# how many images were taken NOT including the original
number_timesteps = 2
# create a dictionary for the timesteps of shifted sources
positions_dict = {}
for x in range(1,number_timesteps+1):
positions_dict['position_changed{0}'.format(x)] = copy.copy(random_movement(source_positions))
# create a dictionary of the motion from the original position for each image
for x in range(1,number_timesteps+1):
motion_dict['position_changed{0}'.format(x)] = copy.copy(calc_motion(positions_dict['position_changed{0}'.format(x)],source_positions,orig_xaxis_len,orig_yaxis_len))
print motion_dict