Operator Activity Analysis via Spatial Hand Position Distributions

Sebastian Hell                       contact@sebastianhell.com

Table of Contents

1  Loading Full Experimental Data
2  Ploting
2.1  Import Skeleton Tracking Data
2.2  Labels
2.3  Creating a Dataframe
2.4  Ploting
2.5  Clear Tracking errors
2.6  Only Labeld Data
2.6.1  Comparing Labeled Data
2.6.1.1  Rest
2.6.1.2  Work
2.6.1.3  Fetch
2.6.1.4  Tab
2.6.1.5  Combined Plot
2.6.1.6  Handmade KDE plot with adjustable bandwith
2.6.2  Heatmap Plots
2.6.3  KDE Explanation
2.6.4  Old and New Data Combined
2.6.5  New Data
2.6.6  Old Data
2.6.7  Old vs Comdbined

Loading Full Experimental Data

In [1]:
import numpy as np
import pandas as pd
import os
import pickle
from scipy import stats
# setup paths
path = ' /dat/'
(_, dirnames, filenames) = os.walk(path).next()
filelabel = ['Label27.txt', 'Label28.txt',
             'Label32.txt', 'Label37.txt', 'Label40.txt']
pathL = '/Label/'
# inti Dataframe
Dataset = pd.DataFrame()


# creating column names
joints = 'SpineBase SpineMid Neck Head ShoulderLeft ElbowLeft WristLeft HandLeft ShoulderRight ElbowRight WristRight HandRight HipLeft KneeLeft AnkleLeft FootLeft HipRight KneeRight AnkleRight FootRight SpineShoulder HandTipLeft ThumbLeft HandTipRight ThumbRight'.split(' ')
columnS = ['MaxP', 'BodyNum', 'Time']
for item in joints:
    list.append(columnS, item + '_x')
    list.append(columnS, item + '_y')
    list.append(columnS, item + '_z')
    list.append(columnS, item + '_state')
list.append(columnS, 'Testperson')
list.append(columnS, 'Label')

# loop over skeleton files
for fi in filenames:
    print(fi)
    data = np.loadtxt(path + fi, delimiter=',').T
    pers = np.ones([1, data.shape[1]]) * int(fi[-6:-4])
    LabDum = np.ones([1, data.shape[1]]) * np.NaN
    data = np.append(data, pers, axis=0)
    data = np.append(data, LabDum, axis=0)
    # compare label files with skelton if there are lables available
    for items in filelabel:
        if fi[-6:-4] == items[-6:-4]:
            Lab = np.array(np.loadtxt(pathL + items, delimiter=','))
            data[-1, :] = Lab
        else:
            pass
    dfTemp = pd.DataFrame(data.T, columns=columnS)
    Dataset = Dataset.append(dfTemp, ignore_index=True)
#"write"
with pd.HDFStore('/FullDataset.h5',  mode='w') as store:
    store.append('RRRU', Dataset, data_columns=Dataset.columns, format='table')

skeleton_23.txt
skeleton_27.txt
skeleton_28.txt
skeleton_29.txt
skeleton_30.txt
skeleton_32.txt
skeleton_33.txt
skeleton_34.txt
skeleton_35.txt
skeleton_36.txt
skeleton_37.txt
skeleton_39.txt
skeleton_40.txt

Ploting

In [1]:
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

Import Skeleton Tracking Data

In [72]:
with pd.HDFStore('/FullDataset.h5',  mode='r') as newstore:
    Dataset = newstore.select('RRRU')
Dataset[0:3]
Out[72]:
MaxP BodyNum Time SpineBase_x SpineBase_y SpineBase_z SpineBase_state SpineMid_x SpineMid_y SpineMid_z ... HandTipRight_x HandTipRight_y HandTipRight_z HandTipRight_state ThumbRight_x ThumbRight_y ThumbRight_z ThumbRight_state Testperson Label
0 6.0 0.0 14670808.0 0.314792 -0.050282 1.56754 2.0 0.323520 0.183996 1.52702 ... 0.524438 -0.052018 1.59915 2.0 0.485295 -0.045905 1.52667 2.0 23.0 NaN
1 6.0 0.0 15670791.0 0.309842 -0.048114 1.56087 2.0 0.319482 0.185231 1.52420 ... 0.512685 -0.051019 1.58592 2.0 0.504713 0.009075 1.55690 2.0 23.0 NaN
2 6.0 0.0 16330042.0 0.295937 -0.050413 1.54759 2.0 0.301963 0.184655 1.51578 ... 0.496801 -0.048134 1.57029 2.0 0.483440 -0.014053 1.56123 2.0 23.0 NaN

3 rows × 105 columns

Labels

Label Action
1 wait
2 work
3 fetch
4 Tab
5 undefined
9 untracked

Creating a Dataframe

  • Selecting the Labels and Joinst for Plotting
  • Determine size of smaller Dataset
  • Pick Random sample of the Datasets with the size of the smaller one
In [18]:
dfa = Dataset.query('Label == 1')[
    ['HandRight_x', 'HandRight_y', 'HandRight_z']]
dfb = Dataset.query(' Label == 2')[
    ['HandRight_x', 'HandRight_y', 'HandRight_z']]
idmin = np.min([dfb.shape[0], dfa.shape[0]])
df = pd.concat([dfa.sample(n=idmin, random_state=1),
                dfb.sample(n=idmin, random_state=1)])

Ploting

In [23]:
 sns.set(style="white")
 g = sns.PairGrid(Dataset[['HandRight_x', 'HandRight_y', 'HandRight_z']].sample(n=50000, random_state=1), diag_sharey=False)
 g.map_lower(sns.kdeplot,rasterized=True, cmap="Blues_d")
 g.map_upper(plt.scatter,s = 10,alpha =0.3)
 g.map_diag(sns.kdeplot, lw=2,)
 g.axes[0,0].yaxis.set_label_text('x (m)')
 g.axes[1,0].yaxis.set_label_text('y (m)')
 g.axes[2,0].yaxis.set_label_text('z (m)')
 g.axes[2,0].xaxis.set_label_text('x (m)')
 g.axes[2,1].xaxis.set_label_text('y (m)')
 g.axes[2,2].xaxis.set_label_text('z (m)')
 #plt.savefig('full.pdf')
In [20]:
sns.set(style="white")
g = sns.PairGrid(df, diag_sharey=False)
g.map_lower(sns.kdeplot,rasterized=True, cmap="Blues_d")
g.map_upper(plt.scatter,s = 10,alpha =0.3)
g.map_diag(sns.kdeplot, lw=2,)
g.axes[0,0].yaxis.set_label_text('x (m)')
g.axes[1,0].yaxis.set_label_text('y (m)')
g.axes[2,0].yaxis.set_label_text('z (m)')
g.axes[2,0].xaxis.set_label_text('x (m)')
g.axes[2,1].xaxis.set_label_text('y (m)')
g.axes[2,2].xaxis.set_label_text('z (m)')
Out[20]:
<matplotlib.text.Text at 0x1db6c5c0>

Clear Tracking errors

In [27]:
df = Dataset.query('(((HandRight_x < 0.24)|(HandRight_x > 0.282)) |((HandRight_z < 0.58)|(HandRight_z > 0.62))|((HandRight_y < -0.061)|(HandRight_y > -0.039)))&(Label != 9) ' )
dfclear = df
df = df[['HandRight_x','HandRight_y','HandRight_z']].sample(n= 50000,random_state = 1)
dfclear.shape
Out[27]:
(198436, 105)
  • Plot of 50000 randome samples
  • Right Hand
  • cleaned Errors
In [28]:
sns.set(style="white")
g = sns.PairGrid(df, diag_sharey=False)
g.map_lower(sns.kdeplot,rasterized=True, cmap="Blues_d")
g.map_upper(plt.scatter,s = 10,alpha =0.3)
g.map_diag(sns.kdeplot, lw=2,)
g.axes[0,0].yaxis.set_label_text('x (m)')
g.axes[1,0].yaxis.set_label_text('y (m)')
g.axes[2,0].yaxis.set_label_text('z (m)')
g.axes[2,0].xaxis.set_label_text('x (m)')
g.axes[2,1].xaxis.set_label_text('y (m)')
g.axes[2,2].xaxis.set_label_text('z (m)')
plt.savefig('redu.pdf')
In [48]:
sns.set(style="white")

dfbla = Dataset.query('Label == 3')[
    ['HandRight_x', 'HandRight_y', 'HandRight_z']]
dfblb = Dataset.query(' Label == 4')[
    ['HandRight_x', 'HandRight_y', 'HandRight_z']]
idmin = np.min([dfbla.shape[0], dfblb.shape[0]])
dfH = pd.concat([dfbla.sample(n=idmin, random_state=1),
                dfblb.sample(n=idmin, random_state=1)])
g = sns.PairGrid(dfH, diag_sharey=False)
g.map_lower(sns.kdeplot,rasterized=True, cmap="Blues_d")
g.map_upper(plt.scatter,s = 10,alpha =0.3)
g.map_diag(sns.kdeplot, lw=2,)
g.axes[0,0].yaxis.set_label_text('x (m)')
g.axes[1,0].yaxis.set_label_text('y (m)')
g.axes[2,0].yaxis.set_label_text('z (m)')
g.axes[2,0].xaxis.set_label_text('x (m)')
g.axes[2,1].xaxis.set_label_text('y (m)')
g.axes[2,2].xaxis.set_label_text('z (m)')
plt.savefig('Tippen_Holen.pdf')
dfH.shape
Out[48]:
(4864, 3)

Compare if Datsetsize Changes KDE alot

  • Plot of 100 randome samples
  • Right Hand X-Data
  • cleaned Errors
In [66]:
df = dfclear[['HandRight_x','HandRight_y','HandRight_z']]
sns.distplot(df.sample(n = 100,random_state= 1).HandRight_x, hist=False, rug=True, color="r")
Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x26f0492e8>
  • Plot of 1000 randome samples
  • Right Hand Z-Data
  • cleaned Errors
In [68]:
sns.distplot(df.sample(n = 1000,random_state= 1).HandRight_z, hist=False, rug=True, color="r")
Out[68]:
<matplotlib.axes._subplots.AxesSubplot at 0x2718d6390>
  • Plot of 1000 randome samples
  • Right Hand Y-Data
  • cleaned Errors
In [46]:
sns.distplot(df.sample(n = 1000,random_state= 1).HandRight_y, hist=False, rug=True, color="r")
Out[46]:
<matplotlib.axes._subplots.AxesSubplot at 0x267439dd8>

Looking at Lefthand Data

  • Plot of 1000 randome samples
  • LeftHand y-Data
  • cleaned Errors
In [72]:
sns.distplot(dfclear.sample(n = 1000,random_state= 1).HandLeft_y, hist=False, rug=True, color="r")
Out[72]:
<matplotlib.axes._subplots.AxesSubplot at 0x273af0eb8>

Looking at Lefthand Data

  • Plot of 1000 randome samples
  • LeftHand y-Data
  • cleaned Errors
  • y >0
In [62]:
df = Dataset.query('((HandLeft_y > 0)) ' )
sns.distplot(df.sample(n = 1000,random_state= 1).HandLeft_y, hist=False, rug=True, color="r")
Out[62]:
<matplotlib.axes._subplots.AxesSubplot at 0x26b8c1e48>

Only Labeld Data

In [29]:
dfLabel= Dataset[pd.notnull(Dataset['Label'])]
In [70]:
df = dfLabel.query('((Label != 9)&(Label != 5))')
dfa = Dataset.query('Label == 1' )
dfb = Dataset.query(' Label == 2' )
dfc = Dataset.query(' Label == 3' )
dfd = Dataset.query(' Label == 4' )
idmin = np.min([dfb.shape[0],dfa.shape[0],dfc.shape[0],dfd.shape[0]])
df= pd.concat([dfa.sample(n= idmin,random_state = 1),dfb.sample(n=idmin,random_state = 1),dfc.sample(n=idmin,random_state = 1),dfd.sample(n=idmin,random_state = 1)])
print(idmin)
print([dfa.shape[0],dfb.shape[0],dfc.shape[0],dfd.shape[0]])

2432
[12575, 24077, 2432, 2704]
Variabl Label
dfa wait
dfb work
dfc fetch
dfd Tab
In [33]:
sns.set(style="white")
g = sns.PairGrid(df[['HandRight_x','HandRight_y','HandRight_z']], diag_sharey=False)
g.map_lower(sns.kdeplot,rasterized=True, cmap="Blues_d")
g.map_upper(plt.scatter,s = 10,alpha =0.3)
g.map_diag(sns.kdeplot, lw=2,)
g.axes[0,0].yaxis.set_label_text('x (m)')
g.axes[1,0].yaxis.set_label_text('y (m)')
g.axes[2,0].yaxis.set_label_text('z (m)')
g.axes[2,0].xaxis.set_label_text('x (m)')
g.axes[2,1].xaxis.set_label_text('y (m)')
g.axes[2,2].xaxis.set_label_text('z (m)')
plt.savefig('evenSamples.pdf')

Comparing Labeled Data

Ploting x-, y-, and z-RightHand Kernel-Density

Rest

In [32]:
f, axes = plt.subplots(1, 3, figsize=(21, 6))

sns.distplot(dfa.sample(n = idmin ,random_state= 1).HandRight_x, hist=False, rug=True, color="r", ax = axes[0])
sns.distplot(dfa.sample(n = idmin ,random_state= 1).HandRight_y, hist=False, rug=True, color="r", ax = axes[1])
sns.distplot(dfa.sample(n = idmin ,random_state= 1).HandRight_z, hist=False, rug=True, color="r", ax = axes[2])
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0xff6716d8>

Work

In [28]:
f, axes = plt.subplots(1, 3, figsize=(21, 6))
sns.distplot(dfb.sample(n = idmin ,random_state= 1).HandRight_x, hist=False, rug=True, color="r", ax = axes[0])
sns.distplot(dfb.sample(n = idmin ,random_state= 1).HandRight_y, hist=False, rug=True, color="r", ax = axes[1])
sns.distplot(dfb.sample(n = idmin ,random_state= 1).HandRight_z, hist=False, rug=True, color="r", ax = axes[2])
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0xb86dbbe0>

Fetch

In [29]:
f, axes = plt.subplots(1, 3, figsize=(21, 6))
sns.distplot(dfc.sample(n = idmin ,random_state= 1).HandRight_x, hist=False, rug=True, color="r", ax = axes[0])
sns.distplot(dfc.sample(n = idmin ,random_state= 1).HandRight_y, hist=False, rug=True, color="r", ax = axes[1])
sns.distplot(dfc.sample(n = idmin ,random_state= 1).HandRight_z, hist=False, rug=True, color="r", ax = axes[2])
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0xc9269630>

Tab

In [30]:
f, axes = plt.subplots(1, 3, figsize=(21, 6))
sns.distplot(dfd.sample(n = idmin ,random_state= 1).HandRight_x, hist=False, rug=True, color="r", ax = axes[0])
sns.distplot(dfd.sample(n = idmin ,random_state= 1).HandRight_y, hist=False, rug=True, color="r", ax = axes[1])
sns.distplot(dfd.sample(n = idmin ,random_state= 1).HandRight_z, hist=False, rug=True, color="r", ax = axes[2])
Out[30]:
<matplotlib.axes._subplots.AxesSubplot at 0xd95aa128>

Combined Plot

  • Labled Data
  • Smallest size of Labeld Dataset {{idmin}} 2432
  • To equal the Dataset sizes Pick randome Samples from larger datasets with the size of smallest
In [68]:
f, axes = plt.subplots(1, 3, figsize=(27, 6))
sns.set(style="darkgrid", color_codes=True)
sns.distplot(dfa.sample(n = idmin ,random_state= 1).HandRight_x, hist=False,color="r", ax = axes[0],label = 'Rest')
sns.distplot(dfb.sample(n = idmin ,random_state= 1).HandRight_x, hist=False, color="g", ax = axes[0],label = 'Work')
sns.distplot(dfc.sample(n = idmin ,random_state= 1).HandRight_x, hist=False,color="b", ax = axes[0],label = 'Fetch')
sns.distplot(dfd.sample(n = idmin ,random_state= 1).HandRight_x, hist=False, color="k", ax = axes[0],label = 'Tab')
axes[0].legend(frameon=True,fontsize=14)
axes[0].xaxis.set_label_text('x (m)',fontsize=15)
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
sns.distplot(dfa.sample(n = idmin ,random_state= 1).HandRight_y, hist=False, color="r", ax = axes[1],label = 'Rest')
sns.distplot(dfb.sample(n = idmin ,random_state= 1).HandRight_y, hist=False, color="g", ax = axes[1],label = 'Work')
sns.distplot(dfc.sample(n = idmin ,random_state= 1).HandRight_y, hist=False,color="b", ax = axes[1],label = 'Fetch')
sns.distplot(dfd.sample(n = idmin ,random_state= 1).HandRight_y, hist=False,color="k", ax = axes[1],label = 'Tab')
axes[1].legend(frameon=True,fontsize=14)
axes[1].xaxis.set_label_text('y (m)',fontsize=15)
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
sns.distplot(dfa.sample(n = idmin ,random_state= 1).HandRight_z, hist=False, color="r", ax = axes[2],label = 'Rest')
sns.distplot(dfb.sample(n = idmin ,random_state= 1).HandRight_z, hist=False, color="g", ax = axes[2],label = 'Work')
sns.distplot(dfc.sample(n = idmin ,random_state= 1).HandRight_z, hist=False,  color="b", ax = axes[2],label = 'Fetch')
sns.distplot(dfd.sample(n = idmin ,random_state= 1).HandRight_z, hist=False,  color="k", ax = axes[2],label = 'Tab')
axes[2].legend(frameon=True,fontsize=14)
axes[2].xaxis.set_label_text('z (m)',fontsize=15)
axes[2].tick_params(axis='x',labelsize=12)
axes[2].tick_params(axis='y',labelsize=12)

plt.savefig('actionDensityxyz.pdf')

Evaluation of the Plots
Matrix shows along which axes to actions are good to seperate. The tilde ~ stands for semi-good

In [30]:
from matplotlib.colors import LinearSegmentedColormap
fig, ax = plt.subplots()

min_val, max_val, diff = 0., 4., 1.
bababa = np.array([[' ','~x\n~y\n~z','y\nz','x'],['~x\n~y\n~z',' ',' z\n~x\n~y',' x\n~z'],['y\nz',' z\n~x\n~y',' ','x\ny\nz'],['x',' x\n~z','x\ny\nz','']])

#imshow portion
N_points = 4
#imshow_data = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]])
imshow_data = np.array([[[0,0.4,0],[1,1,0.6],[0.6,1,0.6],[0.9,1,0.6]],[[1,1,0.6],[0,0.4,0],[0.6,1,0.6],[0.8,1,0.6]],[[0.6,1,0.6],[0.6,1,0.6],[0,0.4,0],[0.6,1,0.6]],[[0.9,1,0.6],[0.8,1,0.6],[0.6,1,0.6],[0,0.4,0]]])
colors = [ (1,1,0.6),(0.6,1,0.6) ]
n_bin = 100
cmap_name = 'my_list'
cm = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bin)

cax = ax.imshow(imshow_data, interpolation='nearest',cmap=cm)
#ax.pcolor(imshow_data, cmap=plt.cm.Blues)
#text portion
ind_array = np.arange(min_val, max_val, diff)
x, y = np.meshgrid(ind_array, ind_array)
i = 0
b = bababa.flatten()
for  x_val, y_val in zip(x.flatten(), y.flatten()):
    c= b[i]
    ax.text(x_val, y_val, c, va='center', ha='center')
    i+= 1
# #set tick marks for grid
# ax.set_xticks(np.arange(min_val-diff/2, max_val-diff/2))
# ax.set_yticks(np.arange(min_val-diff/2, max_val-diff/2))
# ax.set_xticklabels(['Rest','Work','Fetch','Tab'])
# ax.set_yticklabels(['Rest','Work','Fetch','Tab'])
# ax.set_xlim(min_val-diff/2, max_val-diff/2)
# ax.set_ylim(min_val-diff/2, max_val-diff/2)
labels =['Rest','Work','Fetch','Tab']
for axis in [ax.xaxis, ax.yaxis]:
    axis.set(ticks=np.arange(0, len(labels)), ticklabels=labels)
#ax.invert_yaxis()

ax.xaxis.tick_top()
ax.grid(which='major')
ax.set_xticks(np.arange(-.5, 4, 1), minor=True);
ax.set_yticks(np.arange(-.5, 4, 1), minor=True);
ax.grid(which='minor', color='k', linestyle='-', linewidth=1)

cbar = fig.colorbar(cax, ticks=[-1, 0, 1])
cbar.ax.set_yticklabels([ 'medium \nseparable', 'well \nseparable'])  # vertically oriented colorbar
plt.savefig('matrix.pdf')

plt.show()

Best Tab and Fetch, worst Work and Rest

In [71]:
f, axes = plt.subplots(1, 3, figsize=(27, 6))
sns.set(style="darkgrid", color_codes=True)
sns.distplot(dfa.sample(n = 12575 ,random_state= 1).HandRight_x, hist=False,color="r", ax = axes[0],label = 'Rest')
sns.distplot(dfb.sample(n = 12575 ,random_state= 1).HandRight_x, hist=False, color="g", ax = axes[0],label = 'Work')

axes[0].legend(frameon=True,fontsize=14)
axes[0].xaxis.set_label_text('x (m)',fontsize=15)
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
sns.distplot(dfa.sample(n = 12575 ,random_state= 1).HandRight_y, hist=False, color="r", ax = axes[1],label = 'Rest')
sns.distplot(dfb.sample(n = 12575 ,random_state= 1).HandRight_y, hist=False, color="g", ax = axes[1],label = 'Work')

axes[1].legend(frameon=True,fontsize=14)
axes[1].xaxis.set_label_text('y (m)',fontsize=15)
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
sns.distplot(dfa.sample(n = 12575 ,random_state= 1).HandRight_z, hist=False, color="r", ax = axes[2],label = 'Rest')
sns.distplot(dfb.sample(n = 12575 ,random_state= 1).HandRight_z, hist=False, color="g", ax = axes[2],label = 'Work')

axes[2].legend(frameon=True,fontsize=14)
axes[2].xaxis.set_label_text('z (m)',fontsize=15)
axes[2].tick_params(axis='x',labelsize=12)
axes[2].tick_params(axis='y',labelsize=12)

Handmade KDE plot with adjustable bandwith

In [325]:
a = np.array([-200,-100,-100,-100,-100,100,100,100,100,100,100])
from scipy import stats
data = dfa.sample(n = 1000 ,random_state= 1).HandRight_x
b = stats.gaussian_kde(data,bw_method=0.1)
bla = np.array(data)
bla = np.sort(bla)

out = b(bla)
out = out / np.max(out)
plt.plot(bla,out)
plt.show()
sns.distplot(data)
print(b.integrate_box_1d(-0.1,0.5))
print(b(data.mean()))
print(np.max(out))

0.99992555643
[ 11.11401754]
1.0

Heatmap Plots

Heatmap wit same sample size in every class

In [372]:
import matplotlib.image as mpimg
img=mpimg.imread('FrontLabel4Color.png')
f, ax = plt.subplots(figsize=(8,8), ncols=2, nrows=2)

#ax.set_ylim(bottom=0.5, top=1.4)
#ax.set_xlim(left=0.5, right=1.4)

dfa = df.query('Label==1')
dfb = df.query('Label==2')
dfc = df.query('Label==3')
dfd = df.query('Label==4')

#xz
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_z,cmap="Reds", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_z,cmap="Blues", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfc.HandRight_x, dfc.HandRight_z,cmap="Greens", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_z,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[1][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[1][0].text(-0.06,0.8, "Work", size=16, color=blue)
ax[1][0].text(0.31, 1.18, "Rest", size=16, color=red)
ax[1][0].text(0.09, 0.6, "Fetch", size=16, color= green)
ax[1][0].text(0.5, 1, "Tab", size=16, color= pink)
ax[1][0].set(xlabel='x (m)', ylabel='z (m)')
ax[1][0].xaxis.label.set_size(15)
ax[1][0].yaxis.label.set_size(15)
ax[1][0].tick_params(axis='x',labelsize=12)
ax[1][0].tick_params(axis='y',labelsize=12)
ax[1][0].set_ylim(bottom=1.3, top=0.5)
ax[1][0].set_xlim(left=-0.2, right=0.6)

#f.savefig('KdeXZ.pdf')



#zy
sns.kdeplot(dfd.HandRight_z, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfa.HandRight_z, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfc.HandRight_z, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfb.HandRight_z, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][1])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][1].text(1.01,0.027, "Work", size=16, color=blue)
ax[0][1].text(1.16, -0.24, "Rest", size=16, color=red)
ax[0][1].text(0.67, 0.043, "Fetch", size=16, color= green)
#ax.text(0.52, -0.1, "Tab", size=16, color= pink)
ax[0][1].set(xlabel='z (m)', ylabel='')
ax[0][1].xaxis.label.set_size(15)
ax[0][1].tick_params(axis='x',labelsize=12)
ax[0][1].tick_params(axis='y',labelsize=12,label1On = False)
ax[0][1].set_xlim(left=0.4, right=1.4)
ax[0][1].set_ylim(bottom=-0.3, top=0.2)
#ax[0][1].yaxis.set_visible(False)

#f.savefig('KdeZY.pdf')

#xy

sns.kdeplot(dfc.HandRight_x, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][0].text(0.06,0.05, "Work", size=16, color=blue)
ax[0][0].text(0.2, -0.27, "Rest", size=16, color=red)
ax[0][0].text(0.36, 0.02, "Fetch", size=16, color= green)
ax[0][0].text(0.49, -0.1, "Tab", size=16, color= pink)
ax[0][0].set(xlabel='', ylabel='y (m)')
ax[0][0].set_xlim(left=-0.2, right=0.6)
ax[0][0].set_ylim(bottom=-0.3, top=0.2)
#ax[0][0].xaxis.set_visible(False)
ax[0][0].yaxis.label.set_size(15)
ax[0][0].tick_params(axis='x',labelsize=12,label1On = False)
ax[0][0].tick_params(axis='y',labelsize=12)


plt.imshow(img,axes=ax[1][1])
ax[1][1].xaxis.set_visible(False)
ax[1][1].yaxis.set_visible(False)

#ax[-1,-1].axis('off')
f.savefig('KdeSubplotsBlend.pdf')

KDE Explanation

In [428]:
np.random.seed(seed=4)
dataset = np.random.randn(4)
f, ax = plt.subplots(1, 1)
sns.rugplot(dataset,ax=ax)
plt.ylim(0,2)
sns.rugplot(dataset)
x_min = dataset.min() - 2
x_max = dataset.max() + 2
#100 equally spaced points from x_min to x_max
x_axis = np.linspace(x_min,x_max,100)
#bandwidth is a smooth factor
bandwidth = ((4*dataset.std()**5)/(3*len(dataset)))**.2
#create an empty kernel list
kernel_list = []
#plot each basis function
for data_point in dataset:
    #create a kernel for each point and append to list
    kernel = stats.norm(data_point,bandwidth).pdf(x_axis)
    kernel_list.append(kernel)
    #scale for plotting
    kernel = kernel/kernel.max()
    kernel = kernel*.4
    plt.plot(x_axis,kernel,color='black',alpha=1)
plt.ylim(0,2.1)
plt.plot(x_axis,kernel,color='black',alpha=1,label='Kernel')
sum_of_kde = np.sum(kernel_list,axis=0)
fig = plt.plot(x_axis,sum_of_kde,color='indianred',axes=ax,label='KDE')
sns.rugplot(dataset,c = 'indianred',ax=ax)
sns.rugplot(dataset[0:1],c = 'indianred',ax=ax,label='Datapoints')
plt.yticks()
ax.legend()
plt.savefig('KDEex.pdf')
In [130]:
with pd.HDFStore('/Hell.h5',  mode='r') as newstore:
    DataMod = newstore.select('RRRU')

DataMod.columns = ['HeadPos_x', 'HeadPos_y','HeadPos_z','HandLeft_x' ,'HandLeft_y', 'HandLeft_z' ,'HandRight_x' ,'HandRight_y' ,'HandRight_z' ,'Label' ,'Person' ,'serial']
mask = DataMod['Person']=='0'
DataMod.loc[mask,['HeadPos_x','HandLeft_x','HandRight_x' ]]= DataMod.loc[mask,['HeadPos_x','HandLeft_x','HandRight_x' ]].mul(-1)
DataHell = DataMod.query('Person=="Hell"')
DataMod0 = DataMod.query('Person=="0"')

Old and New Data Combined

In [129]:
f, ax = plt.subplots(figsize=(8,8), ncols=2, nrows=2)

#ax.set_ylim(bottom=0.5, top=1.4)
#ax.set_xlim(left=0.5, right=1.4)


dfa = DataMod.query('Label==1')
dfb = DataMod.query('Label==2')
dfc = DataMod.query('Label==3')
dfd = DataMod.query('Label==4')






#xz
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_z,cmap="Reds", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_z,cmap="Blues", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfc.HandRight_x, dfc.HandRight_z,cmap="Greens", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_z,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[1][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[1][0].text(-0.06,0.8, "Work", size=16, color=blue)
ax[1][0].text(0.31, 1.18, "Rest", size=16, color=red)
ax[1][0].text(0.09, 0.6, "Fetch", size=16, color= green)
ax[1][0].text(0.5, 1, "Tab", size=16, color= pink)
ax[1][0].set(xlabel='x (m)', ylabel='z (m)')
ax[1][0].xaxis.label.set_size(15)
ax[1][0].yaxis.label.set_size(15)
ax[1][0].tick_params(axis='x',labelsize=12)
ax[1][0].tick_params(axis='y',labelsize=12)
ax[1][0].set_ylim(bottom=1.3, top=0.5)
ax[1][0].set_xlim(left=-0.2, right=0.6)

#f.savefig('KdeXZ.pdf')



#zy
sns.kdeplot(dfd.HandRight_z, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfa.HandRight_z, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfc.HandRight_z, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfb.HandRight_z, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][1])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][1].text(1.01,0.027, "Work", size=16, color=blue)
ax[0][1].text(1.16, -0.24, "Rest", size=16, color=red)
ax[0][1].text(0.67, 0.043, "Fetch", size=16, color= green)
#ax.text(0.52, -0.1, "Tab", size=16, color= pink)
ax[0][1].set(xlabel='z (m)', ylabel='')
ax[0][1].xaxis.label.set_size(15)
ax[0][1].tick_params(axis='x',labelsize=12)
ax[0][1].tick_params(axis='y',labelsize=12,label1On = False)
ax[0][1].set_xlim(left=0.4, right=1.4)
ax[0][1].set_ylim(bottom=-0.3, top=0.2)
#ax[0][1].yaxis.set_visible(False)

#f.savefig('KdeZY.pdf')

#xy

sns.kdeplot(dfc.HandRight_x, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][0].text(0.06,0.05, "Work", size=16, color=blue)
ax[0][0].text(0.2, -0.27, "Rest", size=16, color=red)
ax[0][0].text(0.36, 0.02, "Fetch", size=16, color= green)
ax[0][0].text(0.49, -0.1, "Tab", size=16, color= pink)
ax[0][0].set(xlabel='', ylabel='y (m)')
ax[0][0].set_xlim(left=-0.2, right=0.6)
ax[0][0].set_ylim(bottom=-0.3, top=0.2)
#ax[0][0].xaxis.set_visible(False)
ax[0][0].yaxis.label.set_size(15)
ax[0][0].tick_params(axis='x',labelsize=12,label1On = False)
ax[0][0].tick_params(axis='y',labelsize=12)


ax[-1,-1].axis('off')
#f.savefig('KdeSubplotsBlend.pdf')
Out[129]:
(0.0, 1.0, 0.0, 1.0)

New Data

In [132]:
f, ax = plt.subplots(figsize=(8,8), ncols=2, nrows=2)

#ax.set_ylim(bottom=0.5, top=1.4)
#ax.set_xlim(left=0.5, right=1.4)


dfa = DataHell.query('Label==1')
dfb = DataHell.query('Label==2')
dfc = DataHell.query('Label==3')
dfd = DataHell.query('Label==4')






#xz
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_z,cmap="Reds", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_z,cmap="Blues", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfc.HandRight_x, dfc.HandRight_z,cmap="Greens", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_z,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[1][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[1][0].text(-0.06,0.8, "Work", size=16, color=blue)
ax[1][0].text(0.31, 1.18, "Rest", size=16, color=red)
ax[1][0].text(0.09, 0.6, "Fetch", size=16, color= green)
ax[1][0].text(0.5, 1, "Tab", size=16, color= pink)
ax[1][0].set(xlabel='x (m)', ylabel='z (m)')
ax[1][0].xaxis.label.set_size(15)
ax[1][0].yaxis.label.set_size(15)
ax[1][0].tick_params(axis='x',labelsize=12)
ax[1][0].tick_params(axis='y',labelsize=12)
ax[1][0].set_ylim(bottom=1.3, top=0.5)
#ax[1][0].set_xlim(left=-0.2, right=0.6)

#f.savefig('KdeXZ.pdf')



#zy
sns.kdeplot(dfd.HandRight_z, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfa.HandRight_z, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfc.HandRight_z, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfb.HandRight_z, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][1])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][1].text(1.01,0.027, "Work", size=16, color=blue)
ax[0][1].text(1.16, -0.24, "Rest", size=16, color=red)
ax[0][1].text(0.67, 0.043, "Fetch", size=16, color= green)
#ax.text(0.52, -0.1, "Tab", size=16, color= pink)
ax[0][1].set(xlabel='z (m)', ylabel='')
ax[0][1].xaxis.label.set_size(15)
ax[0][1].tick_params(axis='x',labelsize=12)
ax[0][1].tick_params(axis='y',labelsize=12,label1On = False)
ax[0][1].set_xlim(left=0.4, right=1.4)
ax[0][1].set_ylim(bottom=-0.3, top=0.2)
#ax[0][1].yaxis.set_visible(False)

#f.savefig('KdeZY.pdf')

#xy

sns.kdeplot(dfc.HandRight_x, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][0].text(0.06,0.05, "Work", size=16, color=blue)
ax[0][0].text(0.2, -0.27, "Rest", size=16, color=red)
ax[0][0].text(0.36, 0.02, "Fetch", size=16, color= green)
ax[0][0].text(0.49, -0.1, "Tab", size=16, color= pink)
ax[0][0].set(xlabel='', ylabel='y (m)')
ax[0][0].set_xlim(left=-0.2, right=0.6)
ax[0][0].set_ylim(bottom=-0.3, top=0.2)
#ax[0][0].xaxis.set_visible(False)
ax[0][0].yaxis.label.set_size(15)
ax[0][0].tick_params(axis='x',labelsize=12,label1On = False)
ax[0][0].tick_params(axis='y',labelsize=12)


ax[-1,-1].axis('off')
#f.savefig('KdeSubplotsBlend.pdf')
Out[132]:
(0.0, 1.0, 0.0, 1.0)

Old Data

In [133]:
f, ax = plt.subplots(figsize=(8,8), ncols=2, nrows=2)

#ax.set_ylim(bottom=0.5, top=1.4)
#ax.set_xlim(left=0.5, right=1.4)

dfa = DataMod0.query('Label==1')
dfb = DataMod0.query('Label==2')
dfc = DataMod0.query('Label==3')
dfd = DataMod0.query('Label==4')






#xz
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_z,cmap="Reds", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_z,cmap="Blues", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfc.HandRight_x, dfc.HandRight_z,cmap="Greens", shade=True, shade_lowest=False,ax=ax[1][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_z,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[1][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[1][0].text(-0.06,0.8, "Work", size=16, color=blue)
ax[1][0].text(0.31, 1.18, "Rest", size=16, color=red)
ax[1][0].text(0.09, 0.6, "Fetch", size=16, color= green)
ax[1][0].text(0.5, 1, "Tab", size=16, color= pink)
ax[1][0].set(xlabel='x (m)', ylabel='z (m)')
ax[1][0].xaxis.label.set_size(15)
ax[1][0].yaxis.label.set_size(15)
ax[1][0].tick_params(axis='x',labelsize=12)
ax[1][0].tick_params(axis='y',labelsize=12)
ax[1][0].set_ylim(bottom=1.3, top=0.5)
#ax[1][0].set_xlim(left=-0.2, right=0.6)

#f.savefig('KdeXZ.pdf')



#zy
sns.kdeplot(dfd.HandRight_z, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfa.HandRight_z, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfc.HandRight_z, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][1])
sns.kdeplot(dfb.HandRight_z, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][1])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][1].text(1.01,0.027, "Work", size=16, color=blue)
ax[0][1].text(1.16, -0.24, "Rest", size=16, color=red)
ax[0][1].text(0.67, 0.043, "Fetch", size=16, color= green)
#ax.text(0.52, -0.1, "Tab", size=16, color= pink)
ax[0][1].set(xlabel='z (m)', ylabel='')
ax[0][1].xaxis.label.set_size(15)
ax[0][1].tick_params(axis='x',labelsize=12)
ax[0][1].tick_params(axis='y',labelsize=12,label1On = False)
#ax[0][1].set_xlim(left=0.4, right=1.4)
#ax[0][1].set_ylim(bottom=-0.3, top=0.2)
#ax[0][1].yaxis.set_visible(False)

#f.savefig('KdeZY.pdf')

#xy

sns.kdeplot(dfc.HandRight_x, dfc.HandRight_y,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_y,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfd.HandRight_x, dfd.HandRight_y,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0][0])
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_y,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0][0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0][0].text(0.06,0.05, "Work", size=16, color=blue)
ax[0][0].text(0.2, -0.27, "Rest", size=16, color=red)
ax[0][0].text(0.36, 0.02, "Fetch", size=16, color= green)
ax[0][0].text(0.49, -0.1, "Tab", size=16, color= pink)
ax[0][0].set(xlabel='', ylabel='y (m)')
#ax[0][0].set_xlim(left=-0.2, right=0.6)
#ax[0][0].set_ylim(bottom=-0.3, top=0.2)
#ax[0][0].xaxis.set_visible(False)
ax[0][0].yaxis.label.set_size(15)
ax[0][0].tick_params(axis='x',labelsize=12,label1On = False)
ax[0][0].tick_params(axis='y',labelsize=12)


ax[-1,-1].axis('off')
#f.savefig('KdeSubplotsBlend.pdf')
Out[133]:
(0.0, 1.0, 0.0, 1.0)

Old vs Comdbined

In [149]:
f, ax = plt.subplots(figsize=(8,4), ncols=2, nrows=1)

#ax.set_ylim(bottom=0.5, top=1.4)
#ax.set_xlim(left=0.5, right=1.4)

dfa0 = DataMod0.query('Label==1')
dfb0 = DataMod0.query('Label==2')
dfc0 = DataMod0.query('Label==3')
dfd0 = DataMod0.query('Label==4')



dfa = DataMod.query('Label==1')
dfb = DataMod.query('Label==2')
dfc = DataMod.query('Label==3')
dfd = DataMod.query('Label==4')


sns.kdeplot(dfa0.HandRight_x, dfa0.HandRight_z,cmap="Reds", shade=True, shade_lowest=False,ax=ax[0])
sns.kdeplot(dfb0.HandRight_x, dfb0.HandRight_z,cmap="Blues", shade=True, shade_lowest=False,ax=ax[0])
sns.kdeplot(dfc0.HandRight_x, dfc0.HandRight_z,cmap="Greens", shade=True, shade_lowest=False,ax=ax[0])
sns.kdeplot(dfd0.HandRight_x, dfd0.HandRight_z,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[0])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[0].text(-0.06,0.8, "Work", size=16, color=blue)
ax[0].text(0.31, 1.18, "Rest", size=16, color=red)
ax[0].text(0.09, 0.6, "Fetch", size=16, color= green)
ax[0].text(0.5, 1, "Tab", size=16, color= pink)
ax[0].set(xlabel='x (m)', ylabel='z (m)')
ax[0].xaxis.label.set_size(15)
ax[0].yaxis.label.set_size(15)
ax[0].tick_params(axis='x',labelsize=12)
ax[0].tick_params(axis='y',labelsize=12)
ax[0].set_ylim(bottom=1.3, top=0.5)
ax[0].set_xlim(left=-0.2, right=0.6)

sns.kdeplot(dfd.HandRight_x, dfd.HandRight_z,cmap="BuPu", shade=True, shade_lowest=False,ax=ax[1])
sns.kdeplot(dfa.HandRight_x, dfa.HandRight_z,cmap="Reds", shade=True, shade_lowest=False,ax=ax[1])
sns.kdeplot(dfc.HandRight_x, dfc.HandRight_z,cmap="Greens", shade=True, shade_lowest=False,ax=ax[1])
sns.kdeplot(dfb.HandRight_x, dfb.HandRight_z,cmap="Blues", shade=True, shade_lowest=False,ax=ax[1])
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
pink = sns.color_palette("BuPu")[-2]
ax[1].text(-0.06,0.8, "Work", size=16, color=blue)
ax[1].text(0.31, 1.18, "Rest", size=16, color=red)
ax[1].text(0.09, 0.6, "Fetch", size=16, color= green)
ax[1].text(0.5, 1, "Tab", size=16, color= pink)
ax[1].set(xlabel='x (m)', ylabel='')
ax[1].xaxis.label.set_size(15)
ax[1].tick_params(axis='x',labelsize=12)
ax[1].tick_params(axis='y',labelsize=12,label1On = False)
ax[1].set_xlim(left=-0.2, right=0.6)
ax[1].set_ylim(bottom=1.3, top=0.5)

#f.savefig('OldVsNew.pdf')
In [155]:
print(dfa.shape[0]-dfa0.shape[0])
print(dfb.shape[0]-dfb0.shape[0])
print(dfc.shape[0]-dfc0.shape[0])
print(dfd.shape[0]-dfd0.shape[0])
print(dfa0.shape)
print(dfb0.shape)
print(dfc0.shape)
print(dfd0.shape)
3088 + 7140 + 2225 +1193

3088
7140
2225
1193
(12575, 12)
(24077, 12)
(2432, 12)
(2704, 12)
Out[155]:
13646