Activity

`plot_activity`(data[, col, figsize, dpi, ...])	Plot the activity plot of individual
`entropy`(sequence)	Calculate entropy.
`entropy_rate`(sequence)	Calculate entropy rate.
`ellipse_params`(data[, col, confidence, epsg])	confidence ellipse parameter estimation for point data
`ellipse_plot`(ellip_params, ax, **kwargs)	Enter the parameters of the confidence ellipse and plot the confidence ellipse

Activity plot

transbigdata.plot_activity(data, col=['stime', 'etime', 'group'], figsize=(10, 5), dpi=250, shuffle=True, xticks_rotation=0, xticks_gap=1, yticks_gap=1, fontsize=12)

Plot the activity plot of individual

Parameters:

data (DataFrame) – activity information of one person
col (List) – The column name. [starttime,endtime,group] of activities, group control the color
figsize (List) – The figure size
dpi (Number) – The dpi of the figure
shuffle (bool) – Whether to shuffle the activity
xticks_rotation (Number) – rotation angle of xticks
xticks_gap (Number) – gap of xticks
yticks_gap (Number) – gap of yticks
fontsize (Number) – font size of xticks and yticks

Entropy

transbigdata.entropy(sequence)

Calculate entropy.

Parameters:: sequence (List,DataFrame,Series) – sequence data
Returns:: entropy
Return type:: Number

transbigdata.entropy_rate(sequence)

Calculate entropy rate. Reference: Goulet-Langlois, G., Koutsopoulos, H. N., Zhao, Z., & Zhao, J. (2017). Measuring regularity of individual travel patterns. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1583-1592.

Parameters:: sequence (List,DataFrame,Series) – sequence data
Returns:: entropy_rate
Return type:: Number

Confidence ellipse

transbigdata.ellipse_params(data, col=['lon', 'lat'], confidence=95, epsg=None)

confidence ellipse parameter estimation for point data

Parameters:

data (DataFrame) – point data
confidence (number) – confidence level: 99，95 or 90
epsg (number) – If given, the original coordinates are transformed from WGS84 to the given EPSG coordinate system for confidence ellipse parameter estimation
col (List) – Column names, [lon，lat]

Returns:

params – Centroid ellipse parameters[pos,width,height,theta,area,oblateness] Respectively [Center point coordinates, minor axis, major axis, angle, area, oblateness]

Return type:

List

transbigdata.ellipse_plot(ellip_params, ax, **kwargs)

Enter the parameters of the confidence ellipse and plot the confidence ellipse

Parameters:

ellip_params (List) – Centroid ellipse parameters[pos,width,height,theta,area,oblateness] Respectively[Center point coordinates, minor axis, major axis, angle , area, oblateness]
ax (matplotlib.axes._subplots.AxesSubplot) – Where to plot

用法

import pandas as pd
import transbigdata as tbd
import numpy as np
#生成测试用数据
data = np.random.uniform(1,10,(100,2))
data[:,1:] = 0.5*data[:,0:1]+np.random.uniform(-2,2,(100,1))
data = pd.DataFrame(data,columns = ['x','y'])

#绘制数据分布
import matplotlib.pyplot as plt
plt.figure(1,(5,5))
#绘制数据点
plt.scatter(data['x'],data['y'],s = 0.5)
#绘制坐标轴
plt.plot([-10,10],[0,0],c = 'k')
plt.plot([0,0],[-10,10],c = 'k')
plt.xlim(-15,15)
plt.ylim(-15,15)
plt.show()

输入数据与xy坐标所在列名，置信度，估计椭圆参数分别代表[中心点坐标，短轴，长轴，角度，面积，扁率

ellip_params = tbd.ellipse_params(data,confidence=95,col = ['x','y'])
ellip_params

[array([5.78928146, 2.88466235]),
 4.6981983145616875,
 14.04315715927693,
 -58.15524535916836,
 51.8186366184246,
 0.6654457212665993]

再用tbd.ellipse_plot绘制置信椭圆

#绘制数据分布
import matplotlib.pyplot as plt
plt.figure(1,(5,5))
ax = plt.subplot(111)
#绘制数据点
plt.scatter(data['x'],data['y'],s = 0.5)
#获取置信椭圆参数并绘制椭圆
#99%置信椭圆
ellip_params = tbd.ellipse_params(data,confidence=99,col = ['x','y'])
tbd.ellipse_plot(ellip_params,ax,fill = False,edgecolor = 'r',linewidth = 1)
#95%置信椭圆
ellip_params = tbd.ellipse_params(data,confidence=95,col = ['x','y'])
tbd.ellipse_plot(ellip_params,ax,fill = False,edgecolor = 'b',linewidth = 1)
#90%置信椭圆
ellip_params = tbd.ellipse_params(data,confidence=90,col = ['x','y'])
tbd.ellipse_plot(ellip_params,ax,fill = False,edgecolor = 'k',linewidth = 1)
#绘制坐标轴
plt.plot([-10,10],[0,0],c = 'k')
plt.plot([0,0],[-10,10],c = 'k')
plt.xlim(-15,15)
plt.ylim(-15,15)
plt.show()