Avg

In [11]:

import pandas as pd
import os
import glob
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
sns.set_style("whitegrid")
sns.set_context("paper", font_scale=2)

import pandas as pd import os import glob import matplotlib.pyplot as plt import seaborn as sns sns.set() sns.set_style("whitegrid") sns.set_context("paper", font_scale=2)

In [16]:

df=pd.read_csv('L_corr.csv',delimiter=';',header=None)

df=pd.read_csv('L_corr.csv',delimiter=';',header=None)

In [18]:

plt.plot(df[0],df[1])

plt.plot(df[0],df[1])

Out[18]:

[<matplotlib.lines.Line2D at 0x19fe95c8dc0>]

In [30]:

main_path=os.getcwd()
pathR6= glob.glob(os.path.join(os.path.join(main_path,'Rib6'), '*.csv'))
pathR7= glob.glob(os.path.join(os.path.join(main_path,'Rib7'), '*.csv'))

main_path=os.getcwd() pathR6= glob.glob(os.path.join(os.path.join(main_path,'Rib6'), '*.csv')) pathR7= glob.glob(os.path.join(os.path.join(main_path,'Rib7'), '*.csv'))

In [31]:

plt.figure(figsize=(8,6))
for i in range(len(pathR6)):
    df=pd.read_csv(pathR6[i],header=None)
    plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR6[i]))[0]))

plt.legend()

plt.figure(figsize=(8,6)) for i in range(len(pathR6)): df=pd.read_csv(pathR6[i],header=None) plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR6[i]))[0])) plt.legend()

Out[31]:

<matplotlib.legend.Legend at 0x18996120340>

In [32]:

plt.figure(figsize=(8,6))
for i in range(len(pathR7)):
    df=pd.read_csv(pathR7[i],header=None)
    plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR7[i]))[0]))

plt.legend()

plt.figure(figsize=(8,6)) for i in range(len(pathR7)): df=pd.read_csv(pathR7[i],header=None) plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR7[i]))[0])) plt.legend()

Out[32]:

<matplotlib.legend.Legend at 0x189961ed430>

In [33]:

from scipy.interpolate import interp1d
import numpy as np

from scipy.interpolate import interp1d import numpy as np

In [89]:

# plt.figure(figsize=(8,6))
# for i in range(len(pathR6)):
#     df=pd.read_csv(pathR6[i],header=None)
#     plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR6[i]))[0]))

# # plt.legend()
# d = {}
# f={}
# xnew = np.linspace(0, 5, num=20, endpoint=True)
# zr=pd.DataFrame([[0] [0]],index =[0])
# for i in range(len(pathR6)):
#     d[i]=pd.read_csv(pathR6[i],header=None)
#     d[i] = pd.concat([zr, d[i]]).reset_index(drop = True)
#     f[i]=interp1d(d[i].iloc[:,0],d[i].iloc[:,1], kind='cubic')
#     # plt.plot(xnew, f[i](xnew),label='dem{}'.format(i+1),linewidth=1)
#     print(i)
# plt.plot(df.iloc[:,0],df.mean(axis=1),'k',linewidth=3,label='average')
# plt.legend()

d = {}
f={}
xnew = np.linspace(0, 5.6, num=100, endpoint=True)
df=pd.DataFrame(xnew)
zr=pd.DataFrame([[0] ,[0]]).T
for i in range(len(pathR6)):
    d[i]=pd.read_csv(pathR6[i],header=None)
    d[i] = pd.concat([zr, d[i]]).reset_index(drop = True)
    f[i]=interp1d(d[i].iloc[:,0],d[i].iloc[:,1],kind='linear')
    df['test{}'.format(i+1)]=f[i](xnew)
    # plt.plot(xnew, f[i](xnew),label='test{}'.format(i+1))
    # plt.plot(d[i].iloc[:,0],d[i].iloc[:,1])
# plt.plot(df.iloc[:,0],df.std(axis=1)/1000,'k',linewidth=3,label='average')
# plt.plot(df.iloc[:,0],df.std(axis=1)/1000,'k',linewidth=3,label='average')
plt.plot(df.iloc[:,0],df.mean(axis=1),'k',linewidth=3,label='average')
plt.fill_between(df.iloc[:,0],df.mean(axis=1)-df.std(axis=1), df.mean(axis=1)+df.std(axis=1), alpha=0.2)
# plt.plot(df.iloc[:,0],df.mean(axis=1)+df.std(axis=1),'k',linewidth=1)
# plt.plot(df.iloc[:,0],df.mean(axis=1)-df.std(axis=1),'k',linewidth=1)
# for i in range(len(pathR6)):
#     df=pd.read_csv(pathR6[i],header=None)
#     plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR6[i]))[0]))

# plt.legend()
plt.legend()

# plt.figure(figsize=(8,6)) # for i in range(len(pathR6)): # df=pd.read_csv(pathR6[i],header=None) # plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR6[i]))[0])) # # plt.legend() # d = {} # f={} # xnew = np.linspace(0, 5, num=20, endpoint=True) # zr=pd.DataFrame([[0] [0]],index =[0]) # for i in range(len(pathR6)): # d[i]=pd.read_csv(pathR6[i],header=None) # d[i] = pd.concat([zr, d[i]]).reset_index(drop = True) # f[i]=interp1d(d[i].iloc[:,0],d[i].iloc[:,1], kind='cubic') # # plt.plot(xnew, f[i](xnew),label='dem{}'.format(i+1),linewidth=1) # print(i) # plt.plot(df.iloc[:,0],df.mean(axis=1),'k',linewidth=3,label='average') # plt.legend() d = {} f={} xnew = np.linspace(0, 5.6, num=100, endpoint=True) df=pd.DataFrame(xnew) zr=pd.DataFrame([[0] ,[0]]).T for i in range(len(pathR6)): d[i]=pd.read_csv(pathR6[i],header=None) d[i] = pd.concat([zr, d[i]]).reset_index(drop = True) f[i]=interp1d(d[i].iloc[:,0],d[i].iloc[:,1],kind='linear') df['test{}'.format(i+1)]=f[i](xnew) # plt.plot(xnew, f[i](xnew),label='test{}'.format(i+1)) # plt.plot(d[i].iloc[:,0],d[i].iloc[:,1]) # plt.plot(df.iloc[:,0],df.std(axis=1)/1000,'k',linewidth=3,label='average') # plt.plot(df.iloc[:,0],df.std(axis=1)/1000,'k',linewidth=3,label='average') plt.plot(df.iloc[:,0],df.mean(axis=1),'k',linewidth=3,label='average') plt.fill_between(df.iloc[:,0],df.mean(axis=1)-df.std(axis=1), df.mean(axis=1)+df.std(axis=1), alpha=0.2) # plt.plot(df.iloc[:,0],df.mean(axis=1)+df.std(axis=1),'k',linewidth=1) # plt.plot(df.iloc[:,0],df.mean(axis=1)-df.std(axis=1),'k',linewidth=1) # for i in range(len(pathR6)): # df=pd.read_csv(pathR6[i],header=None) # plt.plot(df[0],df[1],label=str(os.path.splitext(os.path.basename(pathR6[i]))[0])) # plt.legend() plt.legend()

Out[89]:

<matplotlib.legend.Legend at 0x1899a48e220>

In [90]:

df.to_csv('average_Rib6.csv')

df.to_csv('average_Rib6.csv')

In [64]:

df.std(axis=1)

df.std(axis=1)

Out[64]:

0      0.000000
1      1.027462
2      1.756313
3      2.529393
4      3.461033
        ...    
95    40.693390
96    40.422020
97    40.099291
98    39.786252
99    39.469463
Length: 100, dtype: float64

In [65]:

df.std(axis=1,ddof=0)

df.std(axis=1,ddof=0)

Out[65]:

0      0.000000
1      0.979647
2      1.674579
3      2.411682
4      3.299965
        ...    
95    38.799625
96    38.540884
97    38.233174
98    37.934703
99    37.632657
Length: 100, dtype: float64

In [ ]: