I am having problems interpolating some data points using Scipy. I guess that it might depend on the fact that the function I'm trying to interpolate is discontinuous at x roughly 4.
Here is the code I'm using to interpolate:
from scipy import *
y_interpolated = interp1d(x,y,buonds_error=False,fill_value=0.,kind='cubic')
new_x_array = arange(min(x),max(x),0.05)
plot(new_x_array,x_interpolated(new_x_array),'r-')
The error I get is
File "<stdin>", line 2, in <module>
File "/Library/Frameworks/EPD64.framework/Versions/7.1/lib/python2.7/site-packages/scipy/interpolate/interpolate.py", line 357, in __call__
out_of_bounds = self._check_bounds(x_new)
File "/Library/Frameworks/EPD64.framework/Versions/7.1/lib/python2.7/site-packages/scipy/interpolate/interpolate.py", line 415, in _check_bounds
raise ValueError("A value in x_new is above the interpolation "
ValueError: A value in x_new is above the interpolation range.
These are my data points:
1.56916432074 -27.9998263169
1.76773750527 -27.6198430485
1.98360238449 -27.2397962268
2.25133982943 -26.8596491107
2.49319293195 -26.5518194791
2.77823462692 -26.1896935372
3.07201297519 -25.9540514619
3.46090507092 -25.7362456112
3.65968688527 -25.6453922172
3.84116464506 -25.53652509
3.97070419447 -25.3374215879
4.03087127145 -24.8493356465
4.08217147954 -24.0540196233
4.12470899596 -23.0960856364
4.17612639206 -22.4634289328
4.19318305992 -22.1380894034
4.2708234589 -21.902951035
4.3745696768 -21.9027079759
4.52158254627 -21.9565591238
4.65985875536 -21.8839570732
4.80666329863 -21.6486676004
4.91026629192 -21.4496126386
5.05709528961 -21.2685401725
5.29054655428 -21.2860476871
5.54129211534 -21.3215908912
5.73174988353 -21.6645019816
6.06035782465 -21.772138994
6.30243916407 -21.7715483093
6.59656410998 -22.0238656166
6.86481948673 -22.3665921479
7.01182409559 -22.4385289076
7.17609125906 -22.4200564296
7.37494987052 -22.4376476472
7.60844044988 -22.5093814451
7.79869207061 -22.5812017094
8.00616642549 -22.5445612485
8.17903446593 -22.4899243886
8.29141325457 -22.4715846981
What version of scipy are you using?
The script you posted has some syntax errors (I assume due to wrong copy and paste).
This script works, with scipy.__version__ == 0.9.0. .
import sys
from scipy import *
from scipy.interpolate import *
from pylab import plot
x = []
y = []
for line in sys.stdin:
a, b = line.split()
x.append(float(a))
y.append(float(b))
y_interpolated = interp1d(x,y,bounds_error=False,fill_value=0.,kind='cubic')
new_x_array = arange(min(x),max(x),0.05)
plot(new_x_array,y_interpolated(new_x_array),'r-')
Related
I'm trying to do some plots of some symbolic data. I have some expression from a regression in the form:
expr = '(((((((((1.0)*(2.0)))-(ER)))-(-0.37419122066665467))*0.006633039574629684)*(0.006633039574629684*((((T)-(((1.0)+(P)))))-(P))))+0.1451920626347467)'
Where expr here is some prediction: f = f(T, P, ER). I know this particular example is a crazy expression but it's not really super important. Basically, supposing I have some dataframe, plotdata, I am trying to produce plots with:
import pandas
import sympy
import numexpr
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
expr = '(((((((((1.0)*(2.0)))-(ER)))-(-0.37419122066665467))*0.006633039574629684)*(0.006633039574629684*((((T)-(((1.0)+(P)))))-(P))))+0.1451920626347467)'
#Extract some data for surface plot but fixing one variable
plotdata = plotdata.loc[(plotdata.P == 1)]
#Extract data as lists for plotting
x = list(plotdata['T'])
y = list(plotdata['ER'])
f_real = list(plotdata['f'])
T_sympy = sympy.Symbol('T')
P_sympy = sympy.Symbol('P')
ER_sympy = sympy.Symbol('ER')
f_pred = numexpr.evaluate(expr)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(x,y,f_real, alpha = 0.3)
ax.plot_surface(x,y,f_pred)
However, I am getting an error with f_pred.
numexpr.evaluate(expr)
Traceback (most recent call last):
File "/anaconda3/lib/python3.7/site-packages/numexpr/necompiler.py", line 744, in getArguments
a = local_dict[name]
KeyError: 'ER'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<ipython-input-100-c765b0f1e5ce>", line 1, in <module>
numexpr.evaluate(expr)
File "/anaconda3/lib/python3.7/site-packages/numexpr/necompiler.py", line 818, in evaluate
arguments = getArguments(names, local_dict, global_dict)
File "/anaconda3/lib/python3.7/site-packages/numexpr/necompiler.py", line 746, in getArguments
a = global_dict[name]
KeyError: 'ER'
I am not super familiar with the numexpr package. However, I have been building this up from a 1D regression to now a 3D regression. ER was my 1D variable and was working fine. I have obviously slightly altered my code since the 1D case but I am still slightly at a loss as to why this error is popping up.
Any pointers would be greatly appreciated.
I've figured it out. Pretty silly error in the end. I needed to change:
#Extract data as lists for plotting
x = list(plotdata['T'])
y = list(plotdata['ER'])
to:
T = list(plotdata['T'])
ER = list(plotdata['ER'])
P = list(plotdata['P'])
i.e. numexpr.evaluate was looking for the input data, not the symbol!
This is my code to plot a Power spectral density signal. I need some help as im having error coding in python.
import math
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, ifft
from scipy import signal
t=np.linspace(0,1,106)
Fs=1000
y=np.sin(2*math.pi*15*t) + np.sin(2*math.pi*30*t)
Y=fft(y,512)
f = np.arange(0,len(Y)-1)*(Fs-1)/len(Y)
P = abs(Y)*2/len(Y)
plt.plot(f,P)
plt.show()
This is the error i have when i try to run it.
Traceback (most recent call last):
File "C:\Users\keehu\Desktop\trial.py", line 13, in <module>
plt.plot(f,P)
File "C:\Users\keehu\AppData\Local\Programs\Python\Python38-32\lib\site-packages\matplotlib\pyplot.py", line 2761, in plot
return gca().plot(
File "C:\Users\keehu\AppData\Local\Programs\Python\Python38-32\lib\site-packages\matplotlib\axes\_axes.py", line 1646, in plot
lines = [*self._get_lines(*args, data=data, **kwargs)]
File "C:\Users\keehu\AppData\Local\Programs\Python\Python38-32\lib\site-packages\matplotlib\axes\_base.py", line 216, in __call__
yield from self._plot_args(this, kwargs)
File "C:\Users\keehu\AppData\Local\Programs\Python\Python38-32\lib\site-packages\matplotlib\axes\_base.py", line 342, in _plot_args
raise ValueError(f"x and y must have same first dimension, but "
ValueError: x and y must have same first dimension, but have shapes (511,) and (512,)
>>>
Your f is of size len(Y)-1 as you requested, but P has size len(Y).
The size of f is 511 and Y is 512. I have changed the code to solve that.
import math
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, ifft
from scipy import signal
t=np.linspace(0,1,106)
Fs=1000
y=np.sin(2*math.pi*15*t) + np.sin(2*math.pi*30*t)
Y=fft(y,512)
f = np.arange(0,len(Y))*(Fs-1)/len(Y) #changed here
P = abs(Y)*2/len(Y)
plt.plot(f,P)
plt.show()
"ValueError: x and y must have same first dimension, but have shapes (511,) and (512,)" means that your arrays, f and P don't have the same size. f is one element to small. Use:
f = np.arange(0,len(Y))*(Fs-1)/len(Y)
I have been using lmfit for about a day now and needless to say I know very little about the library. I have been using several built-in models for curve fitting and all of them work flawlessly with the data except the Lognormal Model.
Here is my code:
from numpy import *
from lmfit.models import LognormalModel
import pandas as pd
import scipy.integrate as integrate
import matplotlib.pyplot as plt
data = pd.read_csv('./data.csv', delimiter = ",")
x = data.ix[:, 0]
y = data.ix[:, 1]
print (x)
print (y)
mod = LognormalModel()
pars = mod.guess(y, x=x)
out = mod.fit(y, pars , x=x)
print(out.best_values)
print(out.fit_report(min_correl=0.25))
out.plot()
plt.plot(x, y, 'bo')
plt.plot(x, out.init_fit, 'k--')
plt.plot(x, out.best_fit, 'r-')
plt.show()
and the error output is:
Traceback (most recent call last):
File "Cs_curve_fit.py", line 17, in <module>
pvout = pvmod.fit(y, amplitude= 1, center = 1, sigma =1 , x=x)
File "C:\Users\NAME\Anaconda3\lib\site-packages\lmfit\model.py", line 731, in fit
output.fit(data=data, weights=weights)
File "C:\Users\NAME\Anaconda3\lib\site-packages\lmfit\model.py", line 944, in fit
self.init_fit = self.model.eval(params=self.params, **self.userkws)
File "C:\Users\NAME\Anaconda3\lib\site-packages\lmfit\model.py", line 569, in eval
return self.func(**self.make_funcargs(params, kwargs))
File "C:\Users\NAME\Anaconda3\lib\site-packages\lmfit\lineshapes.py", line 162, in lognormal
x[where(x <= 1.e-19)] = 1.e-19
File "C:\Users\NAME\Anaconda3\lib\site-packages\pandas\core\series.py", line 773, in __setitem__
setitem(key, value)
File "C:\Users\NAME\Anaconda3\lib\site-packages\pandas\core\series.py", line 755, in setitem
raise ValueError("Can only tuple-index with a MultiIndex")
ValueError: Can only tuple-index with a MultiIndex
First, the error message you show cannot have come from the code you post. The error message says that line 17 of the file "Cs_curve_fit.py" reads
pvout = pvmod.fit(y, amplitude= 1, center = 1, sigma =1 , x=x)
but that is not anywhere in your code. Please post the actual code and the actual output.
Second, the problem appears to happening because the data for x is cannot be turned into a 1D numpy array. Not being able to trust your code or output, I would just suggest converting the data to 1D numpy arrays yourself as a first test. Lmfit should be able to handle Pandas series, but it just does a simple coercion to 1D numpy arrays.
from __future__ import division
import functools
import warnings
import numpy as np
import scipy as sp
from scipy import integrate
from numpy import exp, pi
import matplotlib.pyplot as plt
warnings.simplefilter("ignore", np.ComplexWarning)
def legendrePoly(a,n):
def integrand(t):
return ( ((a + exp(2j*pi*t))**2 - 1)/(2*exp(2j*pi*t)) )**n
return sp.integrate.quad(integrand,0,1)[0]
basisDim = 6
legendreBasis = [functools.partial(legendrePoly, n=i) for i in range(basisDim)]
integrand = [lambda x,i=i: exp(x) * legendreBasis[i](x) for i in range(basisDim)]
normalizingConst = [lambda x,i=i: legendreBasis[i](x)**2 for i in range(basisDim)]
basisCoeff = [sp.integrate.quad(integrand[i],-1,1)[0]
/sp.integrate.quad(normalizingConst[i],-1,1)[0] for i in range(basisDim)]
approxPoly = lambda x: sum(basisCoeff[i]*legendreBasis[i](x) for i in range(basisDim))
t = np.arange(-1, 1, 1e-3)
plt.plot(t,exp(t),'b')
plt.plot(t,approxPoly(t),'r')
plt.show()
I'm using Legendre polynomials as a basis for a polynomial approximation to the exponential function. I'm also using Cauchy's Integral Formula to evaluate them, rather than importing them directly from numpy.
Everything runs fine up to and including defining approxPoly, and approxPoly returns the expected value for any input I enter. But for some reason when I try to plot approxPoly(t), it returns the error: Supplied function does not return a valid float.
This error seems to suggest that when my functions in legendreBasis call to scipy.integrate.quad, that something goes wrong there, but if that was the case then approxPoly wouldn't work, yet if you evaluate it at 2000 points between -1 and 1 manually, and plot these points, everything works fine, but is that not exactly what plt.plot doing when it tries to graph my function?
Traceback:
Traceback (most recent call last):
File "/private/var/folders/mb/yyp8v3_95l538z3g7jsttq540000gn/T/Cleanup At Startup/Exercise-413072597.643.py", line 34, in <module>
plt.plot(t,approxPoly(t),'r')
File "/private/var/folders/mb/yyp8v3_95l538z3g7jsttq540000gn/T/Cleanup At Startup/Exercise-413072597.643.py", line 29, in <lambda>
approxPoly = lambda x: sum(basisCoeff[i]*legendreBasis[i](x) for i in range(basisDim))
File "/private/var/folders/mb/yyp8v3_95l538z3g7jsttq540000gn/T/Cleanup At Startup/Exercise-413072597.643.py", line 29, in <genexpr>
approxPoly = lambda x: sum(basisCoeff[i]*legendreBasis[i](x) for i in range(basisDim))
File "/private/var/folders/mb/yyp8v3_95l538z3g7jsttq540000gn/T/Cleanup At Startup/Exercise-413072597.643.py", line 18, in legendrePoly
return sp.integrate.quad(integrand,0,1)[0]
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/integrate/quadpack.py", line 247, in quad
retval = _quad(func,a,b,args,full_output,epsabs,epsrel,limit,points)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/integrate/quadpack.py", line 312, in _quad
return _quadpack._qagse(func,a,b,args,full_output,epsabs,epsrel,limit)
quadpack.error: Supplied function does not return a valid float.
logout
scipy.integrate.quad doesn't integrate functions that return arrays. That means when you try to call approxPoly(t), t gets passed around until it ends up in this function:
def legendrePoly(a,n):
def integrand(t):
return ( ((a + exp(2j*pi*t))**2 - 1)/(2*exp(2j*pi*t)) )**n
return sp.integrate.quad(integrand,0,1)[0]
integrand returns an array, and sp.integrate.quad chokes. The same problem occurs a number of times in your code. Everything seems to be written assuming scalar arguments.
You can probably fix this by calling vectorize on approxPoly:
plt.plot(t,np.vectorize(approxPoly)(t),'r')
NumPy will then call approxPoly on each element of t separately.
I seem to be getting an error when I use the root-finder in scipy. I was wondering if anyone could point out what I'm doing wrong.
The function I'm finding the root of is just an easy example, and not particularly important.
If I run this code with scipy 0.9.0:
import numpy as np
from scipy.optimize import fsolve
tmpFunc = lambda xIn: (xIn[0]-4)**2 + (xIn[1]-5)**2 + (xIn[2]-7)**3
x0 = [3,4,5]
xFinal = fsolve(tmpFunc, x0 )
print xFinal
I get the following error message:
Traceback (most recent call last):
File "tmpStack.py", line 7, in <module>
xFinal = fsolve(tmpFunc, x0 )
File "/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py", line 115, in fsolve
_check_func('fsolve', 'func', func, x0, args, n, (n,))
File "/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py", line 26, in _check_func
raise TypeError(msg)
TypeError: fsolve: there is a mismatch between the input and output shape of the 'func' argument '<lambda>'.
Well it looks like I was trying to use this routine incorrectly. This routine requires the same number of equations and variables vs. the one equation with three variables I gave it. So if the input to the function to be minimized is a 3-D array the output should be a 3-D array. This code works:
import numpy as np
from scipy.optimize import fsolve
tmpFunc = lambda xIn: np.array( [(xIn[0]-4)**2 + xIn[1], (xIn[1]-5)**2 - xIn[2]) \
, (xIn[2]-7)**3 + xIn[0] ] )
x0 = [3,4,5]
xFinal = fsolve(tmpFunc, x0 )
print xFinal
Which represents solving three equations simultaneously.