scatter's axis and ticks in matplotlib.pyplot - python

I'm currently doing my numerical analysis homework. I use python to analyze the influence of different parameter's values (which is w in the code) on the backward error in an algorithm. I want to use matplotlib.pyplot to plot a scatter to show the result. But, it seems that the scatter doesn't look like what I want.
As you can see from the figure, the values on y-axis is not ascending from bottom to top, they distribute randomly, and all the points seems like they are at the same line. I've tried a lot of methods to fix it but failed.
Here's the wrong piece of code and data file "SOR2".
import matplotlib.pyplot as plt
import numpy as np
# read SOR2
SOR2 = open("SOR2", 'r')
w = []
e = []
for line in SOR2:
data = line.strip().split()
w.append(data[0])
e.append(data[1])
SOR2.close()
# plot scatter
plt.xlabel("w")
plt.ylabel("backward error")
plt.scatter(w, e)
plt.show()
The data in file "SOR2", the left column is w, and the right column is backward error:
0.50 1.05549
0.51 1.01085
0.52 0.96795
0.53 0.92669
0.54 0.88701
0.55 0.84883
0.56 0.81210
0.57 0.77676
0.58 0.74274
0.59 0.70999
0.60 0.67847
0.61 0.64811
0.62 0.61889
0.63 0.59075
0.64 0.56366
0.65 0.53758
0.66 0.51247
0.67 0.48829
0.68 0.46502
0.69 0.44263
0.70 0.42107
0.71 0.40034
0.72 0.38039
0.73 0.36120
0.74 0.34276
0.75 0.32503
0.76 0.30799
0.77 0.29163
0.78 0.27592
0.79 0.26084
0.80 0.24638
0.81 0.23251
0.82 0.21921
0.83 0.20648
0.84 0.19429
0.85 0.18263
0.86 0.17148
0.87 0.16083
0.88 0.15067
0.89 0.14097
0.90 0.13173
0.91 0.12293
0.92 0.11457
0.93 0.10662
0.94 0.09908
0.95 0.09193
0.96 0.08516
0.97 0.07876
0.98 0.07272
0.99 0.06702
1.00 0.06166
1.01 0.05663
1.02 0.05190
1.03 0.04748
1.04 0.04335
1.05 0.03950
1.06 0.03599
1.07 0.03276
1.08 0.02977
1.09 0.02699
1.10 0.02442
1.11 0.02208
1.12 0.01993
1.13 0.01794
1.14 0.01609
1.15 0.01438
1.16 0.01280
1.17 0.01139
1.18 0.01009
1.19 0.00890
1.20 0.00791
1.21 0.00706
1.22 0.00630
1.23 0.00560
1.24 0.00498
1.25 0.00441
1.26 0.00402
1.27 0.00384
1.28 0.00434
1.29 0.00514
1.30 0.00610
1.31 0.00723
1.32 0.00856
1.33 0.01013
1.34 0.01196
1.35 0.01408
1.36 0.01655
1.37 0.01940
1.38 0.02268
1.39 0.02645
1.40 0.03077
1.41 0.03571
1.42 0.04133
1.43 0.04773
1.44 0.05498
1.45 0.06319
1.46 0.07246
1.47 0.08291
1.48 0.09466
1.49 0.10786
And the result looks like this:

As #krm commented, data needs to be converted to float:
w.append(float(data[0]))
e.append(float(data[1]))
Alternatively you can use pandas to simplify all the parsing and plotting down to 2 lines with pandas.read_fwf() and DataFrame.plot.scatter():
import pandas as pd
df = pd.read_fwf('SOR2', header=None, names=['w', 'e'])
df.plot.scatter(x='w', y='e', ylabel='backward error')

Related

How to solve NaN values error using Lmfit with Python

I'm trying to fit a set of data taken by an external simulation, and stored in a vector, with the Lmfit library.
Below there's my code:
import numpy as np
import matplotlib.pyplot as plt
from lmfit import Model
from lmfit import Parameters
def DGauss3Par(x,I1,sigma1,sigma2):
I2 = 2.63 - I1
return (I1/np.sqrt(2*np.pi*sigma1))*np.exp(-(x*x)/(2*sigma1*sigma1)) + (I2/np.sqrt(2*np.pi*sigma2))*np.exp(-(x*x)/(2*sigma2*sigma2))
#TAKE DATA
xFull = []
yFull = []
fileTypex = np.dtype([('xFull', np.float)])
fileTypey = np.dtype([('yFull', np.float)])
fDatax = "xValue.dat"
fDatay = "yValue.dat"
xFull = np.loadtxt(fDatax, dtype=fileTypex)
yFull = np.loadtxt(fDatay, dtype=fileTypey)
xGauss = xFull[:]["xFull"]
yGauss = yFull[:]["yFull"]
#MODEL'S DEFINITION
gmodel = Model(DGauss3Par)
params = Parameters()
params.add('I1', value=1.66)
params.add('sigma1', value=1.04)
params.add('sigma2', value=1.2)
result3 = gmodel.fit(yGauss, x=xGauss, params=params)
#PLOTS
plt.plot(xGauss, result3.best_fit, 'y-')
plt.show()
When I run it, I get this error:
File "Overlap.py", line 133, in <module>
result3 = gmodel.fit(yGauss, x=xGauss, params=params)
ValueError: The input contains nan values
These are the values of the data contained in the vector xGauss (related to the x axis):
[-3.88 -3.28 -3.13 -3.08 -3.03 -2.98 -2.93 -2.88 -2.83 -2.78 -2.73 -2.68
-2.63 -2.58 -2.53 -2.48 -2.43 -2.38 -2.33 -2.28 -2.23 -2.18 -2.13 -2.08
-2.03 -1.98 -1.93 -1.88 -1.83 -1.78 -1.73 -1.68 -1.63 -1.58 -1.53 -1.48
-1.43 -1.38 -1.33 -1.28 -1.23 -1.18 -1.13 -1.08 -1.03 -0.98 -0.93 -0.88
-0.83 -0.78 -0.73 -0.68 -0.63 -0.58 -0.53 -0.48 -0.43 -0.38 -0.33 -0.28
-0.23 -0.18 -0.13 -0.08 -0.03 0.03 0.08 0.13 0.18 0.23 0.28 0.33
0.38 0.43 0.48 0.53 0.58 0.63 0.68 0.73 0.78 0.83 0.88 0.93
0.98 1.03 1.08 1.13 1.18 1.23 1.28 1.33 1.38 1.43 1.48 1.53
1.58 1.63 1.68 1.73 1.78 1.83 1.88 1.93 1.98 2.03 2.08 2.13
2.18 2.23 2.28 2.33 2.38 2.43 2.48 2.53 2.58 2.63 2.68 2.73
2.78 2.83 2.88 2.93 2.98 3.03 3.08 3.13 3.28 3.88]
And these ones the ones in the vector yGauss (related to y axis):
[0.00173977 0.00986279 0.01529543 0.0242624 0.0287456 0.03238484
0.03285927 0.03945234 0.04615091 0.05701618 0.0637672 0.07194268
0.07763934 0.08565687 0.09615262 0.1043281 0.11350606 0.1199406
0.1260062 0.14093328 0.15079665 0.16651464 0.18065023 0.1938894
0.2047541 0.21794024 0.22806706 0.23793043 0.25164404 0.2635118
0.28075974 0.29568682 0.30871501 0.3311846 0.34648062 0.36984661
0.38540666 0.40618835 0.4283945 0.45002014 0.48303911 0.50746062
0.53167057 0.5548792 0.57835128 0.60256181 0.62566436 0.65704847
0.68289386 0.71332794 0.73258027 0.769608 0.78769989 0.81407275
0.83358852 0.85210239 0.87109068 0.89456217 0.91618782 0.93760247
0.95680234 0.96919757 0.9783219 0.98486193 0.9931429 0.9931429
0.98486193 0.9783219 0.96919757 0.95680234 0.93760247 0.91618782
0.89456217 0.87109068 0.85210239 0.83358852 0.81407275 0.78769989
0.769608 0.73258027 0.71332794 0.68289386 0.65704847 0.62566436
0.60256181 0.57835128 0.5548792 0.53167057 0.50746062 0.48303911
0.45002014 0.4283945 0.40618835 0.38540666 0.36984661 0.34648062
0.3311846 0.30871501 0.29568682 0.28075974 0.2635118 0.25164404
0.23793043 0.22806706 0.21794024 0.2047541 0.1938894 0.18065023
0.16651464 0.15079665 0.14093328 0.1260062 0.1199406 0.11350606
0.1043281 0.09615262 0.08565687 0.07763934 0.07194268 0.0637672
0.05701618 0.04615091 0.03945234 0.03285927 0.03238484 0.0287456
0.0242624 0.01529543 0.00986279 0.00173977]
I've also tried to print the values returned by my function, to see if there really were some NaN values:
params = Parameters()
params.add('I1', value=1.66)
params.add('sigma1', value=1.04)
params.add('sigma2', value=1.2)
func = DGauss3Par(xGauss,I1,sigma1,sigma2)
print func
but what I obtained is:
[0.04835225 0.06938855 0.07735839 0.08040181 0.08366964 0.08718237
0.09096169 0.09503048 0.0994128 0.10413374 0.10921938 0.11469669
0.12059333 0.12693754 0.13375795 0.14108333 0.14894236 0.15736337
0.16637406 0.17600115 0.18627003 0.19720444 0.20882607 0.22115413
0.23420498 0.24799173 0.26252377 0.27780639 0.29384037 0.3106216
0.32814069 0.34638266 0.3653266 0.38494543 0.40520569 0.42606735
0.44748374 0.46940149 0.49176057 0.51449442 0.5375301 0.56078857
0.58418507 0.60762948 0.63102687 0.65427809 0.6772804 0.69992818
0.72211377 0.74372824 0.76466232 0.78480729 0.80405595 0.82230355
0.83944875 0.85539458 0.87004937 0.88332762 0.89515085 0.90544838
0.91415806 0.92122688 0.92661155 0.93027889 0.93220625 0.93220625
0.93027889 0.92661155 0.92122688 0.91415806 0.90544838 0.89515085
0.88332762 0.87004937 0.85539458 0.83944875 0.82230355 0.80405595
0.78480729 0.76466232 0.74372824 0.72211377 0.69992818 0.6772804
0.65427809 0.63102687 0.60762948 0.58418507 0.56078857 0.5375301
0.51449442 0.49176057 0.46940149 0.44748374 0.42606735 0.40520569
0.38494543 0.3653266 0.34638266 0.32814069 0.3106216 0.29384037
0.27780639 0.26252377 0.24799173 0.23420498 0.22115413 0.20882607
0.19720444 0.18627003 0.17600115 0.16637406 0.15736337 0.14894236
0.14108333 0.13375795 0.12693754 0.12059333 0.11469669 0.10921938
0.10413374 0.0994128 0.09503048 0.09096169 0.08718237 0.08366964
0.08040181 0.07735839 0.06938855 0.04835225]
So it doesn't seems that there are NaN values, I'm not understanding for which reason it returns me that error.
Could anyone help me, please? Thanks!
If you add a print function to your fit function, printing out sigma1 and sigma2, you'll find that
DGauss3Par is evaluated already a few times before the error occurs.
Both sigma variables have a negative value at the time the error occurs.
Taking the square root of a negative value causes, of course, a NaN.
You should add a min bound or similar to your sigma1 and sigma2 parameters to prevent this. Using min=0.0 as an additional argument to params.add(...) will result in a good fit.
Be aware that for some analyses, setting explicit bounds to your fitting parameters may make these analyses invalid. For most cases, you'll be fine, but for some cases, you'll need to check whether the fitting parameters should be allowed to vary from negative infinity to positive infinity, or are allowed to be bounded.

Convert elements in masked astropy Table to np.nan

Consider the simple process of reading a data file with some non-valid entries. This is my test.dat file:
16 1035.22 1041.09 24.54 0.30 1.39 0.30 1.80 0.30 2.26 0.30 1.14 0.30 0.28 0.30 0.2884
127 824.57 1105.52 25.02 0.29 0.87 0.29 1.30 0.29 2.12 0.29 0.66 0.29 0.10 0.29 0.2986
182 1015.83 904.93 INDEF 0.28 1.80 0.28 1.64 0.28 2.38 0.28 1.04 0.28 0.06 0.28 0.3271
185 1019.15 1155.09 24.31 0.28 1.40 0.28 1.78 0.28 2.10 0.28 0.87 0.28 0.35 0.28 0.3290
192 1024.80 1045.57 24.27 0.27 1.24 0.27 2.01 0.27 2.40 0.27 0.90 0.27 0.09 0.27 0.3328
197 1035.99 876.04 24.10 0.27 1.23 0.27 1.52 0.27 2.59 0.27 0.45 0.27 0.25 0.27 0.3357
198 1110.80 1087.97 24.53 0.27 1.49 0.27 1.71 0.27 2.33 0.27 0.22 0.27 0.00 0.27 0.3362
1103 1168.39 1065.97 24.35 0.27 1.28 0.27 1.29 0.27 2.68 0.27 0.43 0.27 0.26 0.27 0.3388
And this is the code to read it, and replace the "bad" values (INDEF) with a float (99.999)
import numpy as np
from astropy.io import ascii
data = ascii.read("test.dat", fill_values=[('INDEF', '0')])
data = data.filled(99.999)
This works just fine, but if I instead try to replace the bad values with a np.nan (i.e., I use the line data = data.filled(np.nan)) I get:
ValueError: cannot convert float NaN to integer
why is this and how can I get around it?
As noted the issue is that the numpy MaskedArray.filled() method seems to try converting the fill value to the appropriate type before checking if there is actually anything to fill. Since the table in the example has an int column, this fails within numpy (and astropy.Table is just calling the filled() method on each column).
This should work:
In [44]: def fill_cols(tbl, fill=np.nan, kind='f'):
...: """
...: In-place fill of ``tbl`` columns which have dtype ``kind``
...: with ``fill`` value.
...: """
...: for col in tbl.itercols():
...: if col.dtype.kind == kind:
...: col[...] = col.filled(fill)
...:
In [45]: t = simple_table(masked=True)
In [46]: t
Out[46]:
<Table masked=True length=3>
a b c
int64 float64 str1
----- ------- ----
-- 1.0 c
2 2.0 --
3 -- e
In [47]: fill_cols(t)
In [48]: t
Out[48]:
<Table masked=True length=3>
a b c
int64 float64 str1
----- ------- ----
-- 1.0 c
2 2.0 --
3 nan e
I don't think it's primarily a numpy problem, as it works with individual columns:
>>> data['col4'].filled(np.nan)
<Column name='col4' dtype='float64' length=8>
24.54
25.02
nan
24.31
24.27
24.1
24.53
24.35
but you still can't construct a Table from this -
Table([data[n].filled(np.nan) for n in data.colnames])
raises the same error in np.ma.core.
You can explicitly set
data['col4'] = data['col4'].filled(np.nan)
but this apparently lets the table lose its .filled() method...
I am not that familiar with masked arrays and tables, but as you've already filed a related issue on Github, you might want to add this problem.
This is happening fairly deep in numpy, in numpy.ma.filled. fill values have to be scalars, basically.
A messy solution that fills with nan's and still returns a table could look like:
import numpy as np
from astropy.io import ascii
from astropy.table import Table
def fill_with_nan(t):
arr = t.as_array()
arr_list = arr.tolist()
arr = np.array(arr_list)
arr[np.equal(arr, None)] = np.nan
arr = np.array(arr.tolist())
return Table(arr)
data = ascii.read("test.dat", fill_values=[('INDEF', '0')])
data = fill_with_nan(data)
Cut out the middleman? fill_values=[('INDEF', np.nan)]) seems to work.

Scipy curve_fit bounds and conditions

I am trying to use curve_fit to fit some data. it is working great, I would just like to improve the fit with additional parameters to match assumptions (such as mechanical efficiency cannot be greater than 100% etc)
y_data = [0.90 0.90 0.90 0.90 0.90 0.90 0.90 1.30 1.30 1.30 1.30 1.20 1.65 1.65 1.65 1.65 1.65 1.65 1.80 1.80 1.80 1.80 1.80 1.80 1.80 1.80 1.80 3.50 6.60 6.60 6.70 6.70 6.70 6.70 6.70 8.50 12.70] # I am aware this does not have commas
x_data = [0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.46 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02] # ditto
def poly2(x, a, b, c): return a*x**2+ b*x+c
def poly3(x,a,b,c,d): return a*x**3+b*x**2+c*b*x+d
pars = fit(poly2, x_data, y_data, bounds=bounds)
But I would like to additionally specify bounds to relations between parameters eg.
B**2 -4*a*c > 0 #for poly2
b**2-3*a*c=0 #for poly3
To ensure that the fit has horizontal inflection.
Is there a way to achieve this?
Edit: I found this, it may help once I investigate:How do I put a constraint on SciPy curve fit?
How would this be done using lmfit as suggested?
So I believe I have solved this, based on #9dogs comment using lmfit.
relevant documentation here:
https://lmfit.github.io/lmfit-py/constraints.html
and a helpful tutorial here:
http://blog.danallan.com/projects/2013/model/
For my function poly3 this seams to work to enforce horizontal or positive inflection.
from lmfit import Parameters, Model
def poly3(x,a,b,c,d): return a*x**3+b*x**2+c*b*x+d
model = Model(poly3, independent_vars=['x'], )
params = Parameters()
apologies for teh terrible maths: the cubic dicriminant is given here as https://brilliant.org/wiki/cubic-discriminant/ b**2*c**2-4*a*c**3-4*b**3*d-27*a**2*d**2+18*a*b*c*d
params = Parameters()
params..add('a', value=1, min=0, vary=True)
params.add('b', value=1, vary=True)
params.add('c', value=1, vary=True)
params.add('d', value=1, vary=True)
params.add('discr', value = 0, vary= False, expr='(b**2*c**2-4*a*c**3-4*b**3*d-27*a**2*d**2+18*a*b*c*d)')
result = model.fit(y_data, x=x_data, params=params) # do the work
pars = [] # list that will contain the optimized parameters for analysis
# create a parameters list for use in the rest of code, this is a stopgap until I refactor the rest of my code
pars.append(result.values['a'])
pars.append(result.values['b'])
pars.append(result.values['c'])
pars.append(result.values['d'])
## rest of code such as plotting
If there are questions I will expand the example further.

Should stats.norm.pdf gives same result as stats.gaussian_kde in Python?

I was trying to estimate PDF of 1-D using gaussian_kde. However, when I plot pdf using stats.norm.pdf, it gives me different result. Please correct me if I am wrong, I think they should give quite similar result. Here's my code.
npeaks = 9
mean = np.array([0.2, 0.3, 0.38, 0.55, 0.65,0.7,0.75,0.8,0.82]) #peak locations
support = np.arange(0,1.01,0.01)
std = 0.03
pkfun = sum(stats.norm.pdf(support, loc=mean[i], scale=std) for i in range(0,npeaks))
df = pd.DataFrame(support)
X = df.iloc[:,0]
min_x, max_x = X.min(), X.max()
plt.figure(1)
plt.plot(support,pkfun)
kernel = stats.gaussian_kde(X)
grid = 100j
X= np.mgrid[min_x:max_x:grid]
Z = np.reshape(kernel(X), X.shape)
# plot KDE
plt.figure(2)
plt.plot(X, Z)
plt.show()
Also, when I get the first derivative of stats.gaussian_kde was far from the original signal. However, the result of first derivative of stats.norm.pdf does make sense. So, I am assuming I might have error in my code above.
Value of X= np.mgrid[min_x:max_x:grid]:
[
0. 0.01010101 0.02020202 0.03030303 0.04040404 0.05050505
0.06060606 0.07070707 0.08080808 0.09090909 0.1010101 0.11111111
0.12121212 0.13131313 0.14141414 0.15151515 0.16161616 0.17171717
0.18181818 0.19191919 0.2020202 0.21212121 0.22222222 0.23232323
0.24242424 0.25252525 0.26262626 0.27272727 0.28282828 0.29292929
0.3030303 0.31313131 0.32323232 0.33333333 0.34343434 0.35353535
0.36363636 0.37373737 0.38383838 0.39393939 0.4040404 0.41414141
0.42424242 0.43434343 0.44444444 0.45454545 0.46464646 0.47474747
0.48484848 0.49494949 0.50505051 0.51515152 0.52525253 0.53535354
0.54545455 0.55555556 0.56565657 0.57575758 0.58585859 0.5959596
0.60606061 0.61616162 0.62626263 0.63636364 0.64646465 0.65656566
0.66666667 0.67676768 0.68686869 0.6969697 0.70707071 0.71717172
0.72727273 0.73737374 0.74747475 0.75757576 0.76767677 0.77777778
0.78787879 0.7979798 0.80808081 0.81818182 0.82828283 0.83838384
0.84848485 0.85858586 0.86868687 0.87878788 0.88888889 0.8989899
0.90909091 0.91919192 0.92929293 0.93939394 0.94949495 0.95959596
0.96969697 0.97979798 0.98989899 1. ]
Value of X = df.iloc[:,0]:
[ 0. 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11
0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23
0.24 0.25 0.26 0.27 0.28 0.29 0.3 0.31 0.32 0.33 0.34 0.35
0.36 0.37 0.38 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47
0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59
0.6 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71
0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 0.81 0.82 0.83
0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 0.92 0.93 0.94 0.95
0.96 0.97 0.98 0.99 1. ]
In the row below you make pdf calculations in every peak-point along 100 datapoints with the std = 0,03. So you get a matrix with array with 100 elements per row then you summerize it elementwise, result:
Thus you get a graph with 9 narrow -because of std = 0,03- U-shape.
Are you sure, that this was your purpose with this row?
This will never get the similar graph as the kernel estimate base of the original data, result:
pkfun = sum(stats.norm.pdf(support, loc=mean[i], scale=std) for i in
range(0,npeaks))

python matplotlib plotfile explicitly use floating number

I have a simple data file to plot.
Here is the contents of a data file and I named it "ttry":
0.27 0
0.28 0
0.29 0
0.3 0
0.31 0
0.32 0
0.33 0
0.34 0
0.35 0
0.36 0
0.37 0
0.38 0.00728737997257
0.39 0.0600137174211
0.4 0.11488340192
0.41 0.157321673525
0.42 0.193158436214
0.43 0.233882030178
0.44 0.273319615912
0.45 0.311556927298
0.46 0.349879972565
0.47 0.387602880658
0.48 0.424211248285
0.49 0.460390946502
0.5 0.494855967078
0.51 0.529406721536
0.52 0.561814128944
0.53 0.594307270233
0.54 0.624228395062
0.55 0.654492455418
0.56 0.683984910837
0.57 0.711762688615
0.58 0.739368998628
0.59 0.765775034294
0.6 0.790895061728
0.61 0.815586419753
0.62 0.840192043896
0.63 0.863082990398
0.64 0.886231138546
0.65 0.906292866941
0.66 0.915809327846
0.67 0.911436899863
0.68 0.908179012346
0.69 0.904749657064
0.7 0.899519890261
0.71 0.895147462277
0.72 0.891632373114
0.73 0.888803155007
0.74 0.884687928669
0.75 0.879029492455
0.76 0.876114540466
0.77 0.872170781893
0.78 0.867541152263
0.79 0.86274005487
0.8 0.858367626886
0.81 0.854080932785
0.82 0.850994513032
0.83 0.997170781893
0.84 1.13477366255
0.85 1.24296982167
0.86 1.32690329218
0.87 1.40397805213
0.88 1.46836419753
0.89 1.52306241427
0.9 1.53232167353
0.91 1.52906378601
0.92 1.52211934156
0.93 1.516718107
0.94 1.51543209877
0.95 1.50660150892
0.96 1.50137174211
0.97 1.49408436214
0.98 1.48816872428
0.99 1.48088134431
1 1.4723079561
And then I use matplotlib.pyplot.plotfile to plot it. Here is my python script
from matplotlib import pyplot
pyplot.plotfile("ttry", cols=(0,1), delimiter=" ")
pyplot.show()
However the following error appears:
C:\WINDOWS\system32\cmd.exe /c ttry.py
Traceback (most recent call last):
File "E:\research\ttry.py", line 2, in <module>
pyplot.plotfile("ttry",col=(0,1),delimiter=" ")
File "C:\Python33\lib\site-packages\matplotlib\pyplot.py", line 2311, in plotfile
checkrows=checkrows, delimiter=delimiter, names=names)
File "C:\Python33\lib\site-packages\matplotlib\mlab.py", line 2163, in csv2rec
rows.append([func(name, val) for func, name, val in zip(converters, names, row)])
File "C:\Python33\lib\site-packages\matplotlib\mlab.py", line 2163, in <listcomp>
rows.append([func(name, val) for func, name, val in zip(converters, names, row)])
File "C:\Python33\lib\site-packages\matplotlib\mlab.py", line 2031, in newfunc
return func(val)
ValueError: invalid literal for int() with base 10: '0.00728737997257'
shell returned 1
Hit any key to close this window...
Obviously, python just considers yaxis data as int. So how to tell python I use float for yaxis data?
It implies int type of your second column based on first few values, which are all int's. To make it check all rows, add checkrows = 0 to arguments, that is:
pyplot.plotfile("ttry", cols=(0,1), delimiter=" ", checkrows = 0)
It's an argument coming from matplotlib.mlab.csv2rec, see more info here.

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