Ingredients

Libraries

In [22]:
import numpy as np, scipy, matplotlib.pyplot as plt, pandas as pd, seaborn as sns
sns.set(style='whitegrid')
plt.rcParams['figure.figsize'] = (20.0, 10.0)
import IPython.display as ipd
%run detect_peaks.py
plt.rcParams['figure.figsize'] = [10, 5]
%matplotlib inline

Data

In [36]:
data = pd.DataFrame.from_csv('./HackLab_Vol1_Excerpts/HackLab_Vol1_Excerpts/Vol1_P2_1_Exrpt.csv')
data = data.drop('Sample',axis=1)
In [85]:
samples = pd.DataFrame.from_csv('./HackLab_Vol1_Excerpts/HackLab_Vol1_Excerpts/Vol1_P2_1_Exrpt.csv')
samples = samples['Sample']
In [37]:
data.plot(figsize = (16,10))
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f3fece690>

Preparations

In [26]:
from scipy import signal
In [40]:
sig = data['P1']
In [41]:
len(sig)
Out[41]:
901
In [42]:
f,ax = plt.subplots(5,2,figsize = (32,20), sharex=True)
ax[0,0].plot(data['P1'],color = 'red')
ax[1,0].plot(data['P2'],color = 'orange')
ax[2,0].plot(data['P3'],color = 'yellow')
ax[3,0].plot(data['P4'],color = 'green')
ax[4,0].plot(data['P5'],color = 'brown')
ax[0,1].plot(data['P6'],color = 'blue')
ax[1,1].plot(data['P7'],color = 'grey')
ax[2,1].plot(data['P8'],color = 'purple')
ax[3,1].plot(data['P9'],color = 'black')
Out[42]:
[<matplotlib.lines.Line2D at 0x7f8f3ff89990>]
In [46]:
data.hist(figsize=(16,10))
Out[46]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3f003c50>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3eff73d0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3efbc110>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3ef4fc50>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3eec9650>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3ee85610>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3ee500d0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3ee1f110>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3eddfb90>]],
      dtype=object)
In [63]:
from pandas.plotting import scatter_matrix
scatter_matrix(data, alpha=0.2, figsize=(16, 16), diagonal='kde')
Out[63]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3ca17f10>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3ceeb790>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c9a9ad0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c80ae10>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c87d750>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c971690>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c93b150>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c885190>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c7c7c10>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c8e3c10>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3d5109d0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3da5fc50>,
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        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c61b590>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c697dd0>,
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       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f3c34fa10>,
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       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8f379bd510>,
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        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8f374e5ad0>]],
      dtype=object)
In [66]:
emgdesc1
Out[66]:
P1 P2 P3 P4 P5 P6 P7 P8 P9
count 901.000000 901.000000 901.000000 901.000000 901.000000 901.000000 901.000000 901.000000 901.000000
mean 19.605993 3.872919 6.068313 5.182364 54.426526 0.782675 2.198613 16.472829 10.629956
std 6.151543 0.139780 3.986588 2.339354 0.191358 0.663637 3.443335 25.871947 0.328572
min 14.000000 3.490000 2.450000 3.770000 53.800000 0.485000 1.080000 0.404000 9.990000
25% 17.700000 3.790000 3.570000 4.200000 54.300000 0.558000 1.390000 1.130000 10.600000
50% 19.200000 3.860000 4.220000 4.340000 54.400000 0.583000 1.520000 2.860000 10.600000
75% 20.800000 3.930000 7.310000 4.670000 54.500000 0.707000 1.690000 17.700000 10.600000
max 141.000000 5.280000 32.100000 24.300000 55.100000 8.250000 45.800000 96.200000 17.100000
In [74]:
data.describe()
desc=data.describe()
desc = desc.drop('count')
In [75]:
desc.plot(figsize=(16,10))
Out[75]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f35ee1810>
In [77]:
import seaborn as sns
sns.set_style("whitegrid")             


paper_rc = {'lines.linewidth': 1, 'lines.markersize': 10}     
fig,ax =plt.subplots(figsize=(16, 10))
ax = sns.violinplot(x=desc,
                     split = True,inner="stick",scale="count",bw=.3,palette="RdBu", color_codes=True)
plt.xlabel('Participant',fontsize='32')
plt.ylabel('Profile',fontsize = '28')
# plt.setp(ax.get_legend().get_texts(), fontsize='28') # for legend text
# plt.setp(ax.get_legend().get_title(), fontsize='2') # for legend title
Out[77]:
Text(0,0.5,u'Profile')
In [93]:
sns.jointplot(data['P1'], data['P2'], color="g")
Out[93]:
<seaborn.axisgrid.JointGrid at 0x7f8f3135f110>
In [95]:
data['P1']
Out[95]:
[<matplotlib.lines.Line2D at 0x7f8f32111650>]
In [102]:
%run smooth.py

<matplotlib.figure.Figure at 0x7f8f32262b10>