I am trying to join 2 randomly generated list into one but its adding them element wise. Suppose I generated 2 list each with 2 random numbers I want my output list to be of numbers. For example:
import numpy as np
x1 = np.random.uniform(0,0.1, 2)
x2 = np.random.uniform(0,0.1, 2)
x = x1 + x2
print(x1)
print(x2)
print(x)
The output is:
[0.06878713 0.03807816]
[0.01801809 0.06292975]
[0.08680523 0.10100791]
But I want my output as
[0.06878713 0.03807816]
[0.01801809 0.06292975]
[0.06878713 0.03807816 0.01801809 0.06292975]
If I use append() or extend() its giving me: AttributeError: 'numpy.ndarray' object has no attribute 'append'.
As listed here, you would do the following:
import numpy as np
x1 = np.random.uniform(0,0.1, 2)
x2 = np.random.uniform(0,0.1, 2)
x = np.concatenate((x1, x2))
print(x1)
print(x2)
print(x)
The output is:
[0.09031488 0.0600346]
[0.03298771 0.08265562]
[0.09031488 0.0600346 0.03298771 0.08265562]
Related
I am relatively new to coding in Python. I have mainly used MatLab in the past and am used to having vectors that can be referenced explicitly rather than appended lists. I have a script where I generate a list of x- and y- (z-, v-, etc) values. Later, I want to interpolate and then print a table of the values at specified points. Here is a MWE. The problem is at line 48:
yq = interp1d(x_list, y_list, xq(nn))#interp1(output1(:,1),output1(:,2),xq(nn))
I'm not sure I have the correct syntax for the last two lines either:
table[nn] = ('%.2f' %xq, '%.2f' %yq)
print(table)
Here is the full script for the MWE:
#This script was written to test how to interpolate after data was created in a loop and stored as a list. Can a list be accessed explicitly like a vector in matlab?
#
from scipy.interpolate import interp1d
from math import * #for ceil
from astropy.table import Table #for Table
import numpy as np
# define the initial conditions
x = 0 # initial x position
y = 0 # initial y position
Rmax = 10 # maxium range
""" initializing variables for plots"""
x_list = [x]
y_list = [y]
""" define functions"""
# not necessary for this MWE
"""create sample data for MWE"""
# x and y data are calculated using functions and appended to their respective lists
h = 1
t = 0
tf = 10
N=ceil(tf/h)
# Example of interpolation without a loop: https://docs.scipy.org/doc/scipy/tutorial/interpolate.html#d-interpolation-interp1d
#x = np.linspace(0, 10, num=11, endpoint=True)
#y = np.cos(-x**2/9.0)
#f = interp1d(x, y)
for i in range(N):
x = h*i
y = cos(-x**2/9.0)
""" appends selected data for ability to plot"""
x_list.append(x)
y_list.append(y)
## Interpolation after x- and y-lists are already created
intervals = 0.5
nfinal = ceil(Rmax/intervals)
NN = nfinal+1 # length of table
dtype = [('Range (units?)', 'f8'), ('Drop? (units)', 'f8')]
table = Table(data=np.zeros(N, dtype=dtype))
for nn in range(NN):#for nn = 1:NN
xq = 0.0 + (nn-1)*intervals #0.0 + (nn-1)*intervals
yq = interp1d(x_list, y_list, xq(nn))#interp1(output1(:,1),output1(:,2),xq(nn))
table[nn] = ('%.2f' %xq, '%.2f' %yq)
print(table)
Your help and patience will be greatly appreciated!
Best regards,
Alex
Your code has some glaring issues that made it really difficult to understand. Let's first take a look at some things I needed to fix:
for i in range(N):
x = h*1
y = cos(-x**2/9.0)
""" appends selected data for ability to plot"""
x_list.append(x)
y_list.append(y)
You are appending a single value without modifying it. What I presume you wanted is down below.
intervals = 0.5
nfinal = ceil(Rmax/intervals)
NN = nfinal+1 # length of table
dtype = [('Range (units?)', 'f8'), ('Drop? (units)', 'f8')]
table = Table(data=np.zeros(N, dtype=dtype))
for nn in range(NN):#for nn = 1:NN
xq = 0.0 + (nn-1)*intervals #0.0 + (nn-1)*intervals
yq = interp1d(x_list, y_list, xq(nn))#interp1(output1(:,1),output1(:,2),xq(nn))
table[nn] = ('%.2f' %xq, '%.2f' %yq)
This is where things get strange. First: use pandas tables, this is the more popular choice. Second: I have no idea what you are trying to loop over. What I presume you wanted was to vary the number of points for the interpolation, which I have done so below. Third: you are trying to interpolate a point, when you probably want to interpolate over a range of points (...interpolation). Lastly, you are using the interp1d function incorrectly. Please take a look at the code below or run it here; let me know what you exactly wanted (specifically: what should xq / xq(nn) be?), because the MRE you provided is quite confusing.
from scipy.interpolate import interp1d
from math import *
import numpy as np
Rmax = 10
h = 1
t = 0
tf = 10
N = ceil(tf/h)
x = np.arange(0,N+1)
y = np.cos(-x**2/9.0)
interval = 0.5
NN = ceil(Rmax/interval) + 1
ip_list = np.arange(1,interval*NN,interval)
xtable = []
ytable = []
for i,nn in enumerate(ip_list):
f = interp1d(x,y)
x_i = np.arange(0,nn+interval,interval)
xtable += [x_i]
ytable += [f(x_i)]
[print(i) for i in xtable]
[print(i) for i in ytable]
I am trying to copy a numpy array and change the value of the copied array.
When I create the x array using np.array, the addition doesn't work and it prints 2.00.
import numpy as np
import copy
x = np.array([2,3,4])
inc= np.array([0.2,0.3,0.4])
x_copy = copy.copy(x)
x_copy[0] = x_copy[0] + inc[0]
print("x_copy %.2f" % x_copy[0])
But when I create x without np.array, it works and it prints 2.20.
import numpy as np
import copy
x = [2,3,4]
inc= np.array([0.2,0.3,0.4])
x_copy = copy.copy(x)
x_copy[0] = x_copy[0] + inc[0]
print("x_copy %.2f" % x_copy[0])
I also tried to copy x using x.copy(), but it didn't make any difference.
You can do it in two ways:
either
x =np.array([2.0,3.0,4.0])
or
x = np.array([2,3,4])
x = x.astype(float)
i am trying to use the "subs" function for differential equation
but i get the error: "can't convert expression to float"
i tryed to check the type of the arrays, but they all float
import sympy as sym
from sympy.integrals import inverse_laplace_transform
from sympy.abc import s,t,y
import numpy as np
U = 1
G =(s+1)/(s*(s+2))
Y = G*U
y = inverse_laplace_transform(Y, s, t)
tm = np.linspace(0,2,3)
y_val = np.zeros(len(tm))
for i in range(len(tm)):
y_val[i] = y.subs(t, tm[i])
print(y)
print(y_val)
line 17
y_val[i] = y.subs(t, tm[i])
TypeError: can't convert expression to float
Ths issue here is that, because tm[0] == 0, the evaluated y in the first iteration of your loop is Heaviside(0), which has no defined real value by default (see https://docs.sympy.org/latest/modules/functions/special.html#heaviside). This is because you have
from sympy.functions import exp, Heaviside
assert y == Heaviside(t) / 2 + exp(-2 * t) * Heaviside(t) / 2
The simplest workaround here is defining a linear space excluding 0, for instance
epsilon = 1e-15
tm = np.linspace(epsilon, 2, 3)
Using y_val = np.zeros(len(tm)), the default datatype of array is float. After modifying the code, you find that one of y_val elements is an object, not float. You can use a list object as a placeholder or you can specify the datatype of numpy array as object:
import sympy as sym
from sympy.integrals import inverse_laplace_transform
from sympy.abc import s,t,y
import numpy as np
U = 1
G =(s+1)/(s*(s+2))
Y = G*U
y = inverse_laplace_transform(Y, s, t)
tm = np.linspace(0,2,3)
# y_val = [0 for _ in range(len(tm))]
y_val = np.zeros(len(tm), dtype=object)
for i in range(len(tm)):
y_val[i] = y.subs(t, tm[i])
print(y_val)
result: [Heaviside(0.0) 0.567667641618306 0.509157819444367]
I have similar problem and your answers work for me, but I still need to put the data into graph.. I modified my problem for this question:
import sympy as sym
from sympy.integrals import inverse_laplace_transform
from sympy.abc import s,t,y
import numpy as np
import matplotlib.pyplot as plt
Y = (5*(1 - 5*s))/(s*(4*(s**2) + s + 1))*(1/s)
y = inverse_laplace_transform(Y, s, t)
tm = np.linspace(1e-15, 20, 100)
y_val = np.zeros(len(tm), dtype=object)
for i in range(len(tm)):
y_val[i] = y.subs(t, tm[i])
plt.plot(y_val, tm)
plt.show()
Running this code I got same error:
TypeError: can't convert expression to float
I have a data as ndarray
a = [0,1,0,1,2,2]
b = ['x1','x2','x3','x4','x5','x6']
Then I tried:
univals = set(a)
serObj=pd.Series()
for ele in univals:
indexfound=np.where(a == ele)
Xpointsfromindex=np.take(b, indexfound)
serobj1=pd.Series(Xpointsfromindex[0],index=ele) ##error happening here
serObj.apend(serobj1)
print(serObj)
I expect output to be like
0 ['x1','x3']
1 ['x2','x4']
2 ['x5','x6']
But it is giving me an error like "TypeError: len() of unsized object"
Where am I doing wrong?
I believe here is possible create DataFrame if same length of lists and then create lists with groupby:
a = [0,1,0,1,2,2]
b = ['x1','x2','x3','x4','x5','x6']
df = pd.DataFrame({'a':a, 'b':b})
print(df)
a b
0 0 x1
1 1 x2
2 0 x3
3 1 x4
4 2 x5
5 2 x6
serObj = df.groupby('a')['b'].apply(list)
print (serObj)
a
0 [x1, x3]
1 [x2, x4]
2 [x5, x6]
Name: b, dtype: object
Just to stick to what OP was doing, here is the full code that works -
import pandas as pd
import numpy as np
a = [0,1,0,1,2,2]
b = ['x1','x2','x3','x4','x5','x6']
univals = set(a)
serObj=pd.Series()
for ele in univals:
indexfound=np.where([i==ele for i in a])
Xpointsfromindex=np.take(b, indexfound)
print(Xpointsfromindex)
serobj1=pd.Series(Xpointsfromindex[0],index=[ele for _ in range(np.shape(indexfound)[1])]) ##error happening here
serObj.append(serobj1)
print(serObj)
Output
[['x1' 'x3']]
[['x2' 'x4']]
[['x5' 'x6']]
Explanation
indexfound=np.where(a == ele) will always return False because you are trying to compare a list with a scalar. Changing it to list comprehension fetches the indices
The next change is using list comprehension at the index parameter of the pd.Series.
This will set you on your way to what you want to achieve
I have a folder with 38 files. The names are like this:
AWA_s1_features.mat, AWA_s2_features.mat......AWA_s38_features.mat
Each file is an array with 28 columns but with different # of rows. For example: AWA_s1_features.mat = (139,28), AWA_s2_features.mat = (199, 28) and so on.
As I am doing machine learning I need to join all these files in 1 huge array and label each row. So for the 139 rows of AWA_s1_features.mat there must be 139 1s; for AWA_s2_features.mat there must be 199 2s, and so on until AWA_s38_features.mat which must have a # of 38s.
This is what I mean:
I wrote some code. But I have found that the files are not called in order and therefore the labeling is wrong. For example, AWA_s1_features.mat is not the first file to be called and it has been labeled as 11. AWA_s2_features.mat has been labeled as 21.
So how can I improve my code so that it calls each file in the correct sequence?
Here is the code:
import numpy as np
import scipy.io as sio
import glob
read_files = glob.glob('I:/2D/Features 2D/AWA_s*.mat')
x = np.array([])
y = np.array([])
q = 1
for f in read_files:
l=sio.loadmat(f)['features']
x = np.concatenate((x, l), axis=0) if x.size else l
y_temp = q*np.ones((l.shape[0],1))
y = np.concatenate((y, y_temp), axis=0) if y.size else y_temp
q = q + 1
sio.savemat('AWA_FeaturesAll.mat', {'x':x, 'y':y})
The problem is that the default sorting is alphabetical, meaning that "11" comes before "2". You want numerical sorting and one way would be to use the sorted function with a key parameter, like so:
import numpy as np
import scipy.io as sio
import glob
read_files = glob.glob('I:/2D/Features 2D/AWA_s*.mat')
x = np.array([])
y = np.array([])
q = 1
for f in sorted(read_files, key=lambda f: int(f.split('_')[1][1:])):
l=sio.loadmat(f)['features']
x = np.concatenate((x, l), axis=0) if x.size else l
y_temp = q*np.ones((l.shape[0],1))
y = np.concatenate((y, y_temp), axis=0) if y.size else y_temp
q = q + 1
sio.savemat('AWA_FeaturesAll.mat', {'x':x, 'y':y})