L1-python/source/Plotting II .ipynb

Reading files

import pandas as pd
# pandas is a Python package providing fast, flexible, and expressive data 
# structures designed to make working with “relational” or “labeled” data 
# both easy and intuitive. 

CSV files

!cat data.csv

x;y;er

1;1;0.2

2;4;0.2

3;8;0.3

4;17;0.4

5;24;0.2

# read file
d = pd.read_csv("data.csv",delimiter=";")
print(d)

   x   y   er
0  1   1  0.2
1  2   4  0.2
2  3   8  0.3
3  4  17  0.4
4  5  24  0.2

# taking values from headers
x=d["x"]
y=d["y"]
e=d["er"]
print(x,y)

0    1
1    2
2    3
3    4
4    5
Name: x, dtype: int64 0     1
1     4
2     8
3    17
4    24
Name: y, dtype: int64

# taking values from colon  .iloc[row,col] or .loc[row,col]
x=d.iloc[:,0]
y=d.iloc[:,1]
e=d.iloc[:,2]
print(x,y)

0    1
1    2
2    3
3    4
4    5
Name: x, dtype: int64 0     1
1     4
2     8
3    17
4    24
Name: y, dtype: int64

Raw files with headers

# text files
!cat data.txt

x y er

1 1 0.2

2 4 0.2

3 8 0.3

# read file
dtext = pd.read_csv("data.txt")
print(dtext)

    x y er
0  1 1 0.2
1  2 4 0.2
2  3 8 0.3

Raw files without headers

# raw values
!cat data.raw

1 1 0.2

2 4 0.2

3 8 0.3

# read raw file
draw = pd.read_csv("data.raw",delimiter=" ", header = None)
print(draw)

   0  1    2
0  1  1  0.2
1  2  4  0.2
2  3  8  0.3

draw.iloc[:,2]

0    0.2
1    0.2
2    0.3
Name: 2, dtype: float64

Formatted file

# read raw file
fwidths = [11,10]
dexpe = pd.read_fwf("expe.dat",delimiter=" ",header = None, widths = fwidths)
print(dexpe)

         0         1
0      0.0  0.894520
1    100.0  0.849616
2    200.0  0.806313
3    300.0  0.763861
4    400.0  0.722299
5    500.0  0.681680
6    600.0  0.642211
7    700.0  0.603411
8    800.0  0.565199
9    900.0  0.528873
10  1000.0  0.492817

More formats

see references

# import image module
from IPython.display import Image
  
# get the image
Image(url="02_io_readwrite.svg", width=500, height=500)

Plotting data

import matplotlib.pyplot as plt

# read raw file
fwidths = [11,10]
dexpe = pd.read_fwf("expe.dat",delimiter=" ",header = None, widths = fwidths)
print(dexpe)

         0         1
0      0.0  0.894520
1    100.0  0.849616
2    200.0  0.806313
3    300.0  0.763861
4    400.0  0.722299
5    500.0  0.681680
6    600.0  0.642211
7    700.0  0.603411
8    800.0  0.565199
9    900.0  0.528873
10  1000.0  0.492817

# get x and y
x=dexpe.iloc[:,0]
y=dexpe.iloc[:,1]

# plot
fig = plt.figure(figsize=(6,6)) # s
ax = plt.subplot() 
ax.plot(x,y,"o",label="Experimental data")
ax.set_ylim([0,2])
ax.set_xlabel("t[s]")
ax.set_ylabel("V[t]")
ax.set_title("Temporal Evolution of Volume")
ax.legend()

<matplotlib.legend.Legend at 0x7fbffabe9df0>

Fitting : linear

from scipy.optimize import curve_fit
import numpy as np

def func(x, a, b):
    return a * x + b

popt= curve_fit(func, x, y)
print(popt)

(array([-4.01437909e-04,  8.87155318e-01]), array([[ 1.96723961e-11, -9.83619802e-09],
       [-9.83619802e-09,  6.88533863e-06]]))

[a,b]=popt[0]

fig = plt.figure(figsize=(6,6)) # 
ax = plt.subplot() 
ax.plot(x,y,"o",label="Experimental data")
ax.plot(x,func(x,*popt[0]),label="Linear model")
ax.set_ylim([0,2])
ax.set_xlabel("t[s]")
ax.set_ylabel("V[t]")
ax.set_title("Temporal Evolution of Volume")
ax.legend()

<matplotlib.legend.Legend at 0x7fbffb10bf40>

Fitting : non linear

# read raw file
fwidths = [6,6,6]
dexpe = pd.read_fwf("power.dat",delimiter=" ",header = None, widths = fwidths)
print(dexpe)

        0      1      2
0   0.102  0.319  0.565
1   0.204  0.452  0.672
2   0.306  0.553  0.744
3   0.408  0.639  0.799
4   0.510  0.714  0.845
5   0.612  0.782  0.885
6   0.714  0.845  0.919
7   0.816  0.904  0.951
8   0.918  0.958  0.979
9   1.020  1.010  1.005
10  1.122  1.059  1.029
11  1.224  1.107  1.052
12  1.327  1.152  1.073
13  1.429  1.195  1.093
14  1.531  1.237  1.112
15  1.633  1.278  1.130
16  1.735  1.317  1.148
17  1.837  1.355  1.164
18  1.939  1.392  1.180
19  2.041  1.429  1.195
20  2.143  1.464  1.210
21  2.245  1.498  1.224
22  2.347  1.532  1.238
23  2.449  1.565  1.251
24  2.551  1.597  1.264
25  2.653  1.629  1.276
26  2.755  1.660  1.288
27  2.857  1.690  1.300
28  2.959  1.720  1.312
29  3.061  1.750  1.323
30  3.163  1.779  1.334
31  3.265  1.807  1.344
32  3.367  1.835  1.355
33  3.469  1.863  1.365
34  3.571  1.890  1.375
35  3.673  1.917  1.384
36  3.776  1.943  1.394
37  3.878  1.969  1.403
38  3.980  1.995  1.412
39  4.082  2.020  1.421
40  4.184  2.045  1.430
41  4.286  2.070  1.439
42  4.388  2.095  1.447
43  4.490  2.119  1.456
44  4.592  2.143  1.464
45  4.694  2.167  1.472
46  4.796  2.190  1.480
47  4.898  2.213  1.488
48  5.000  2.236  1.495

# get x and y
x =dexpe.iloc[:,0]
y1=dexpe.iloc[:,1]
y2=dexpe.iloc[:,2]

# plot
fig = plt.figure(figsize=(6,6)) # s
ax = plt.subplot() 
ax.plot(x,y1,"o",label="Experimental data 1")
ax.plot(x,y2,"o",label="Experimental data 2")
ax.set_ylim([0,2])
ax.set_xlabel("t[s]")
ax.set_ylabel("V[t]")
ax.set_title("Temporal Evolution of Volume")
ax.legend()

<matplotlib.legend.Legend at 0x7fbfe90ebfd0>

log-log

# plot
fig = plt.figure(figsize=(6,6)) # s
ax = plt.subplot() 
ax.plot(x,y1,"o",label="Experimental data 1")
ax.plot(x,y2,"o",label="Experimental data 2")
ax.set_xlabel("t[s]")
ax.set_ylabel("V[t]")
ax.set_title("Temporal Evolution of Volume")
ax.legend()
ax.loglog()

[]

def func(x, a, b):
    return a * x + b

popt= curve_fit(func, np.log(x), np.log(y1))

popt[0]
a=popt[0][0]
b=popt[0][1]
print("%.3f" % a)
print("%.3f" % b)

0.500
-0.000

# plot
fig = plt.figure(figsize=(6,6)) # s
ax = plt.subplot() 
ax.plot(x,y1,"o",label="Experimental data 1")
ax.plot(x,x**a+b,"-",label="Model 1")
ax.set_xlabel("t[s]")
ax.set_ylabel("V[t]")
ax.set_title("Temporal Evolution of Volume")
ax.legend()
ax.loglog()

[]

Errors bar

# read file
d = pd.read_csv("data.csv",delimiter=";")
# taking values from headers
x=d["x"]
y=d["y"]
e=d["er"]
print(x,y,e)

0    1
1    2
2    3
3    4
4    5
Name: x, dtype: int64 0     1
1     4
2     8
3    17
4    24
Name: y, dtype: int64 0    0.2
1    0.2
2    0.3
3    0.4
4    0.2
Name: er, dtype: float64

fig = plt.figure(figsize=(6,6)) # s
ax = plt.subplot() 
ax.errorbar(x,0.1*y,e*2, marker='.', linestyle="none", label="data")
ax.legend()

<matplotlib.legend.Legend at 0x7fbfc8070bb0>

# data generation
x=np.linspace(0,5)
y1 = np.power(x,0.5)
y2 = np.power(x,0.25)
#print(np.size(x))
#for i in range(50):
#    print("%.3f" % x[i],"%.3f" % np.power(x[i],0.5),"%.3f" % np.power(x[i],0.25))
#print(x,np.power(x,1./2))

sx = pd.Series(x)
sy1 = pd.Series(y1)
sy2 = pd.Series(y2)

nd = pd.concat([sx,sy1,sy2],axis=1)

nd.to_csv("test.csv")

d = pd.read_csv("test.csv",delimiter=",")

d

	Unnamed: 0	0	1	2
0	0	0.000000	0.000000	0.000000
1	1	0.102041	0.319438	0.565189
2	2	0.204082	0.451754	0.672126
3	3	0.306122	0.553283	0.743830
4	4	0.408163	0.638877	0.799298
5	5	0.510204	0.714286	0.845154
6	6	0.612245	0.782461	0.884568
7	7	0.714286	0.845154	0.919323
8	8	0.816327	0.903508	0.950530
9	9	0.918367	0.958315	0.978936
10	10	1.020408	1.010153	1.005063
11	11	1.122449	1.059457	1.029299
12	12	1.224490	1.106567	1.051935
13	13	1.326531	1.151751	1.073197
14	14	1.428571	1.195229	1.093265
15	15	1.530612	1.237179	1.112286
16	16	1.632653	1.277753	1.130377
17	17	1.734694	1.317078	1.147640
18	18	1.836735	1.355262	1.164157
19	19	1.938776	1.392399	1.180000
20	20	2.040816	1.428571	1.195229
21	21	2.142857	1.463850	1.209897
22	22	2.244898	1.498298	1.224050
23	23	2.346939	1.531972	1.237729
24	24	2.448980	1.564922	1.250968
25	25	2.551020	1.597191	1.263800
26	26	2.653061	1.628822	1.276253
27	27	2.755102	1.659850	1.288352
28	28	2.857143	1.690309	1.300119
29	29	2.959184	1.720228	1.311575
30	30	3.061224	1.749636	1.322738
31	31	3.163265	1.778557	1.333626
32	32	3.265306	1.807016	1.344253
33	33	3.367347	1.835033	1.354634
34	34	3.469388	1.862629	1.364782
35	35	3.571429	1.889822	1.374708
36	36	3.673469	1.916630	1.384424
37	37	3.775510	1.943067	1.393939
38	38	3.877551	1.969150	1.403264
39	39	3.979592	1.994891	1.412406
40	40	4.081633	2.020305	1.421374
41	41	4.183673	2.045403	1.430176
42	42	4.285714	2.070197	1.438818
43	43	4.387755	2.094697	1.447307
44	44	4.489796	2.118914	1.455649
45	45	4.591837	2.142857	1.463850
46	46	4.693878	2.166536	1.471916
47	47	4.795918	2.189959	1.479851
48	48	4.897959	2.213133	1.487660
49	49	5.000000	2.236068	1.495349

	Unnamed: 0	0	1	2
10	10	1.020408	1.010153	1.005063
11	11	1.122449	1.059457	1.029299
12	12	1.224490	1.106567	1.051935
13	13	1.326531	1.151751	1.073197
14	14	1.428571	1.195229	1.093265
15	15	1.530612	1.237179	1.112286
16	16	1.632653	1.277753	1.130377
17	17	1.734694	1.317078	1.147640
18	18	1.836735	1.355262	1.164157
19	19	1.938776	1.392399	1.180000
20	20	2.040816	1.428571	1.195229
21	21	2.142857	1.463850	1.209897
22	22	2.244898	1.498298	1.224050
23	23	2.346939	1.531972	1.237729
24	24	2.448980	1.564922	1.250968
25	25	2.551020	1.597191	1.263800
26	26	2.653061	1.628822	1.276253
27	27	2.755102	1.659850	1.288352
28	28	2.857143	1.690309	1.300119
29	29	2.959184	1.720228	1.311575
30	30	3.061224	1.749636	1.322738
31	31	3.163265	1.778557	1.333626
32	32	3.265306	1.807016	1.344253
33	33	3.367347	1.835033	1.354634
34	34	3.469388	1.862629	1.364782
35	35	3.571429	1.889822	1.374708
36	36	3.673469	1.916630	1.384424
37	37	3.775510	1.943067	1.393939
38	38	3.877551	1.969150	1.403264
39	39	3.979592	1.994891	1.412406
40	40	4.081633	2.020305	1.421374
41	41	4.183673	2.045403	1.430176
42	42	4.285714	2.070197	1.438818
43	43	4.387755	2.094697	1.447307
44	44	4.489796	2.118914	1.455649
45	45	4.591837	2.142857	1.463850
46	46	4.693878	2.166536	1.471916
47	47	4.795918	2.189959	1.479851
48	48	4.897959	2.213133	1.487660
49	49	5.000000	2.236068	1.495349