def drawingStayAreaCenter(data,
fname,
dir='imgresults',
title='Result for Finding StayArea'):
'''
画用户停留区域结果图，只画出停留区域中心点-v1.1
:param data: 结果数据集 list[type, lat,longti,time]类型
:param fname: 保存图片文件名
:param dir: 文件保存目录名
:param title: 图的标题
:return:
'''
loop = 1
for line in data:
print('Drawing center point [%d], left [%d]' %
(loop, len(data) - loop))
content = line.rstrip().split(',')
if content[0] == 'Center':
color_r = random.uniform(0, 1)
color_b = random.uniform(0, 1)
color_g = random.uniform(0, 1)
plt.plot(float(content[1]),
float(content[2]),
'.',
color=(color_r, color_g, color_b),
linewidth=0.5)
loop += 1
plt.xlabel('Latitude')
plt.ylabel('Lontitude')
plt.title(title)
plt.grid(True)
fname = combileFileName(dirs=dir, filename=fname)
plt.savefig(fname)
plt.clf()
print('Drawing end')
def seperateUserTrajectory(user_trajectory,
dir=r'classifytxtresults',
filename=r'10&8&40user.txt'):
'''
将用户轨迹分离，分为被检测用户的轨迹以及其他用户的轨迹
:param user_trajectory: 一组相似用户的所有轨迹类型为：dict{'user', list[Trajectory]}
:param dir: 原始相似用户保存的文件目录名
:param filename: 原始相似用户保存的文件名，文件的第一条用户即为待检测的用户
:return: 被检测用户的轨迹列表类型为：list[Trajectory]和其他用户的轨迹列表类型为：list[Trajectory]
'''
filename = combileFileName(dirs=dir, filename=filename)
input_file = open(filename)
detected_user = input_file.readline().rstrip()
input_file.close()
detected_trajectorys = []
others_trajectorys = []
for key in user_trajectory.keys():
if key == detected_user:
trajectorys = user_trajectory.get(key).copy()
for trajectory in trajectorys:
detected_trajectorys.append(trajectory)
else:
trajectorys = user_trajectory.get(key).copy()
for trajectory in trajectorys:
others_trajectorys.append(trajectory)
return detected_trajectorys, others_trajectorys
def drawingStayAreaDetail(data,
fname,
dir='imgresults',
minlat=31,
maxlat=32,
minlon=121,
maxlon=122,
title='StayArea Result for Finding Staypoint'):
'''
画用户停留区域结果图，画出某范围内的停留区域中心点以及其邻居节点的详细画图-v1.1
:param data: 结果数据集 list[type, lat,longti,time]类型
:param fname: 保存图片文件名
:param dir: 文件保存目录名
:param minlat: 最小纬度
:param maxlat: 最大纬度
:param minlon: 最小经度
:param maxlon: 最大经度
:param title: 图的标题
:return:
'''
loop = 1
color_index = 0
ret_color = []
skip = 0
for line in data:
print('Drawing detail point [%d], left [%d], minlat [%d]' %
(loop, len(data) - loop, minlat))
content = line.rstrip().split(',')
lat = float(content[1])
lon = float(content[2])
if (maxlat >= lat >= minlat) and (maxlon >= lon >= minlon):
if content[0] == 'Center':
color_r = random.uniform(0, 1)
color_b = random.uniform(0, 1)
color_g = random.uniform(0, 1)
ret_color.append([color_r, color_g, color_b])
color_index += 1
plt.plot(lat, lon, '.', color=(color_r, color_g, color_b))
plt.text(lat, lon, 'C' + str(color_index))
else:
if skip % 2 == 0:
plt.plot(lat,
lon,
'.',
color=(ret_color[color_index - 1][0],
ret_color[color_index - 1][1],
ret_color[color_index - 1][2]))
loop += 1
skip += 1
plt.xlabel('Latitude/°')
plt.ylabel('Lontitude/°')
plt.title(title)
plt.grid(True)
fname = combileFileName(dirs=dir, filename=fname)
plt.savefig(fname)
plt.clf()
print('Drawing end')
def loadTxtSimilarityUser(dir=r'classifytxtresults',
filename=r'10&8&40user.txt'):
'''
加载用户分类结果，用轨迹挖掘提供用户信息
:param dir: 目录名
:param filename: 文件名
:return: 将相似用户按组划分，类型为list[user]
'''
filename = combileFileName(dirs=dir, filename=filename)
similarity_user = []
for line in open(filename):
if line != '':
similarity_user.append(line.rstrip())
return similarity_user
def outputClassifyPklResults(data,
dir=r'classifypklresults',
filename=r'classifyresult.pkl'):
'''
保存用户分类结果数据信息，以二进制形式保存，将结果序列化保存-v1.0
:param data: 用户分类结果数据信息 list[UserClassify]
:param dir: 目录名
:param filename: 保存数据的文件名
:return:
'''
print('Output beginlocation')
filename = str(len(data)) + '&' + filename
filename = combileFileName(dir, filename)
out_file = open(filename, 'wb')
pickle.dump(data, out_file)
out_file.close()
print('Output endlocation')
def outputPklResults(data,
user='******',
dir=r'saveresults',
filename=r'dbscanresult.pkl'):
'''
保存停留区域或者停留位置挖掘结果，以二进制形式保存，将结果序列化保存-v1.1
:param data: 需要保存的停留区域以及停留位置数据 list[StayArea]或list[StayPoint]
:param dir: 目录名
:param filename: 保存数据的文件名
:return:
'''
print('Output beginlocation')
filename = user + '&' + str(len(data)) + '&' + filename
filename = combileFileName(dir, filename)
out_file = open(filename, 'wb')
pickle.dump(data, out_file)
out_file.close()
print('Output endlocation')
def drawingStayAreaCenter(data,
fname,
dir='imgresults',
title='MSUserA Rawdata Distribute',
minlat_2=39,
maxlat_2=41,
minlon_2=116,
maxlon_2=117):
'''
minlat_2=6.6, maxlat_2=7, minlon_2=46, maxlon_2=48
minlat_2=39, maxlat_2=41, minlon_2=116, maxlon_2=117
画用户停留区域结果图，只画出停留区域中心点-v1.1
:param data: 结果数据集 list[type, lat,longti,time]类型
:param fname: 保存图片文件名
:param dir: 文件保存目录名
:param title: 图的标题
:return:
'''
loop = 1
for line in data:
print('Drawing center point [%d], left [%d]' %
(loop, len(data) - loop))
content = line.rstrip().split(',')
lat = float(content[0])
lon = float(content[1])
if (maxlat_2 >= lat >= minlat_2) and (maxlon_2 >= lon >= minlon_2):
color_r = random.uniform(0, 1)
color_b = random.uniform(0, 1)
color_g = random.uniform(0, 1)
plt.plot(lat,
lon,
'.',
color=(color_r, color_g, color_b),
linewidth=0.5)
loop += 1
plt.xlabel('Latitude/°')
plt.ylabel('Lontitude/°')
plt.title(title)
plt.grid(True)
fname = combileFileName(dirs=dir, filename=fname)
plt.savefig(fname)
plt.clf()
print('Drawing end')
def outputClassifyTxtResults(data, dir=r'classifytxtresults'):
'''
保存用户分类结果数据信息，以文本文件形式保存-v1.0
:param data: 用户分类结果数据信息 list[UserClassify]
:param dir: 目录名
:return:
'''
index = 1
for item in data:
print('Output begin user similarity %d group' % index)
filename = str(index) + '&' + str(len(
item.similarityuser)) + '&' + item.user + '.txt'
filename = combileFileName(dir, filename)
outfile = open(filename, 'w')
outfile.write(item.user)
outfile.write('\n')
for key in item.similarityuser.keys():
outfile.write(key)
outfile.write('\n')
outfile.close()
index += 1
def loadUserData(similarity_user, dirs=r'rawsdata'):
'''
加载一组相似用户的地理位置数据
:param similarity_user: 一组相似用户类型为：list[user]
:param dirs: 原始文件目录名
:return: 加载完一组相似用户地理位置数据类型为：dict{'user':list[TLocation]}
'''
user_location = {}
for item in similarity_user:
filename = item + '.txt'
print('begins %s' % filename)
filename = combileFileName(dirs, filename)
locations = []
for line in open(filename):
content = line.rstrip().split(',')
tlocation = TLocation()
tlocation.lat = float(content[0])
tlocation.longti = float(content[1])
tlocation.timer = datetime.strptime(content[2], str_formate)
locations.append(tlocation)
user_location[item] = locations.copy()
return user_location
def outputTxtResults(data,
user='******',
dir=r'showresults',
filename=r'dbscanresult.txt'):
'''
将挖掘停留区域结果数据导出为文本文件，给画图程序提供源数据-v1.1
:param data: 挖掘停留区域或停留位置的结果集，类型为list[StayArea]或list[StayPoint]
:param dir: 目录名
:param filename: 输出数据文件名
:return:
'''
filename = user + '&' + str(len(data)) + '&' + filename
filename = combileFileName(dir, filename)
print('Output beginlocation')
outfile = open(filename, 'w')
print('Total StayArea|StayPoint center: %d ' % (len(data)))
for item in data:
print('StayArea|StayPoint [%d] has [%d] nerborhoods' %
(data.index(item), len(item.nerborhoods)))
outfile.write('Center')
outfile.write(',')
outfile.write(str(item.centerlat))
outfile.write(',')
outfile.write(str(item.centerlon))
outfile.write('\n')
for nerbor in item.nerborhoods:
outfile.write('Nerborhood')
outfile.write(',')
outfile.write(str(nerbor.lat))
outfile.write(',')
outfile.write(str(nerbor.longti))
outfile.write(',')
outfile.write(str(nerbor.timer))
outfile.write('\n')
outfile.close()
print('Output endlocation')
def drawingStayArea(data,
fname,
dir='imgresults',
title_1='Result for All StayArea Center(a)',
title_2='Leftup Part(b)',
title_3='Rightdown Part(c_1)',
title_4='Rightdown Part(c_2)',
minlat_1=31,
maxlat_1=32,
minlon_1=121,
maxlon_1=122,
minlat_2=39,
maxlat_2=41,
minlon_2=116,
maxlon_2=117,
minlat_3=39.8,
maxlat_3=40.4,
minlon_3=116.25,
maxlon_3=116.45):
'''
画用户停留区域结果图，将中心点，详细点全部画于一张图上-v1.1
:param data: list[type,lat,longti,time]类型
:param fname: 保存结果文件名
:param dir: 保存结果目录名
:param title_1: 标题1
:param title_2: 标题2
:param title_3: 标题3
:param title_4: 标题4
:param minlat_1: 最小纬度1
:param maxlat_1: 最大纬度1
:param minlon_1: 最小经度1
:param maxlon_1: 最大经度1
:param minlat_2: 最小纬度2
:param maxlat_2: 最大纬度2
:param minlon_2: 最小经度2
:param maxlon_2: 最大经度2
:param minlat_3: 最小纬度3
:param maxlat_3: 最大纬度3
:param minlon_3: 最小经度3
:param maxlon_3: 最大经度3
:return:
'''
gc.collect()
fig = plt.figure()
xlable = 'Latitude'
ylable = 'Lontitude'
skip_3 = 0
skip_4 = 0
ax_1 = fig.add_subplot(221,
title=title_1,
xlabel=xlable,
ylabel=ylable,
autoscale_on=True)
ax_2 = fig.add_subplot(222,
title=title_2,
xlabel=xlable,
ylabel=ylable,
autoscale_on=True)
ax_3 = fig.add_subplot(223,
title=title_3,
xlabel=xlable,
ylabel=ylable,
autoscale_on=True)
ax_4 = fig.add_subplot(224,
title=title_4,
xlabel=xlable,
ylabel=ylable,
autoscale_on=True)
loop = 1
color_index = 0
ret_color = []
all_num = len(data)
if all_num >= 300000:
skip_num = 6
elif 300000 > all_num >= 200000:
skip_num = 5
elif 200000 > all_num >= 100000:
skip_num = 4
elif 100000 > all_num >= 70000:
skip_num = 2
else:
skip_num = 1
for line in data:
print('Drawing detail point [%d], left [%d]' %
(loop, len(data) - loop))
content = line.rstrip().split(',')
lat = float(content[1])
lon = float(content[2])
if content[0] == 'Center':
color_r = random.uniform(0, 1)
color_b = random.uniform(0, 1)
color_g = random.uniform(0, 1)
ret_color.append([color_r, color_g, color_b])
color_index += 1
ax_1.plot(lat, lon, 'o', color=(color_r, color_g, color_b))
if (maxlat_1 >= lat >= minlat_1) and (maxlon_1 >= lon >= minlon_1):
ax_2.plot(lat, lon, '.', color=(color_r, color_g, color_b))
ax_2.text(lat, lon, 'C' + str(color_index))
elif (maxlat_2 >= lat >= minlat_2) and (maxlon_2 >= lon >=
minlon_2):
skip_3 = 0
ax_3.plot(lat, lon, '.', color=(color_r, color_g, color_b))
ax_3.text(lat, lon, 'C' + str(color_index))
if (maxlat_3 >= lat >= minlat_3) and (maxlon_3 >= lon >= minlon_3):
skip_4 = 0
ax_4.plot(lat, lon, '.', color=(color_r, color_g, color_b))
ax_4.text(lat, lon, 'C' + str(color_index))
else:
if (maxlat_1 >= lat >= minlat_1) and (maxlon_1 >= lon >= minlon_1):
ax_2.plot(lat,
lon,
'.',
color=(ret_color[color_index - 1][0],
ret_color[color_index - 1][1],
ret_color[color_index - 1][2]))
elif (maxlat_2 >= lat >= minlat_2) and (
maxlon_2 >= lon >= minlon_2) and skip_3 % skip_num == 0:
#and skip_3 % 2 ==0
ax_3.plot(lat,
lon,
'.',
color=(ret_color[color_index - 1][0],
ret_color[color_index - 1][1],
ret_color[color_index - 1][2]))
if (maxlat_3 >= lat >= minlat_3) and (
maxlon_3 >= lon >= minlon_3) and skip_4 % skip_num == 0:
#and skip_4 % 2 ==0
ax_4.plot(lat,
lon,
'.',
color=(ret_color[color_index - 1][0],
ret_color[color_index - 1][1],
ret_color[color_index - 1][2]))
skip_3 += 1
skip_4 += 1
loop += 1
ax_1.grid(True)
ax_2.grid(True)
ax_3.grid(True)
ax_4.grid(True)
fname = combileFileName(dirs=dir, filename=fname)
fig.tight_layout(pad=1.1)
fig.set_figheight(6.5)
fig.set_figwidth(12)
fig.savefig(fname)
fig.clf()
print('Drawing end')
def drawingTrajectory(trajectorys,
fname,
type,
dir='imgresults',
title_1='All normal trajectory(a)',
title_2='Leftup Part(b)',
title_3='Rightdown Part(c_1)',
title_4='Rightdown Part(c_2)',
minlat_1=31,
maxlat_1=32,
minlon_1=121,
maxlon_1=122,
minlat_2=39,
maxlat_2=41,
minlon_2=116,
maxlon_2=117,
minlat_3=39.8,
maxlat_3=40.4,
minlon_3=116.25,
maxlon_3=116.45):
'''
画用户停留区域结果图，将中心点，详细点全部画于一张图上-v1.1
:param trajectorys: list[type,lat,longti,time]类型
:param fname: 保存结果文件名
:param type: 轨迹类型，正常和异常
:param dir: 保存结果目录名
:param title_1: 标题1
:param title_2: 标题2
:param title_3: 标题3
:param title_4: 标题4
:param minlat_1: 最小纬度1
:param maxlat_1: 最大纬度1
:param minlon_1: 最小经度1
:param maxlon_1: 最大经度1
:param minlat_2: 最小纬度2
:param maxlat_2: 最大纬度2
:param minlon_2: 最小经度2
:param maxlon_2: 最大经度2
:param minlat_3: 最小纬度3
:param maxlat_3: 最大纬度3
:param minlon_3: 最小经度3
:param maxlon_3: 最大经度3
:return:
'''
gc.collect()
fig = plt.figure()
xlable = 'Latitude'
ylable = 'Lontitude'
ax_1 = fig.add_subplot(111,
title=title_1,
xlabel=xlable,
ylabel=ylable,
autoscale_on=True)
loop = 1
for trajectory in trajectorys:
# if loop == 1:
minlat_1 = trajectory.beginlocation.lat
minlon_1 = trajectory.beginlocation.longti
maxlat_1 = trajectory.endlocation.lat
maxlon_1 = trajectory.endlocation.longti
print('Drawing %s trajectory [%d], left [%d]' %
(type, loop, len(trajectorys) - loop))
for point in trajectory.points:
ax_1.plot(point.lat, point.longti, 'r.', alpha=0.2)
# ax_1.text(point.lat, point.longti, str(trajectory.tid))
nerbors = trajectory.nerbors.copy()
for nerbors_trajectory in nerbors:
for point in nerbors_trajectory.points:
if (maxlat_1 >= point.lat >=
minlat_1) and (maxlon_1 >= point.longti >= minlon_1):
ax_1.plot(point.lat, point.longti, 'k.')
loop += 1
ax_1.grid(True)
fname = combileFileName(dirs=dir, filename=fname)
# fig.tight_layout(pad=1.1)
# fig.set_figheight(6.5)
# fig.set_figwidth(12)
fig.savefig(fname)
fig.clf()
print('Drawing end')