I want to plot the solution of my differential equation but I got this:
'ValueError: x and y must have same first dimension, but have shapes
(360,) and (1,)'
When I write _plt.plot(t,final[:1])_ I got
'Equality object is not subscriptable'
statement.
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
from sympy.abc import *
import sympy as sy
L= float(input('L:'))
R= float(input('R:'))
v=220*sy.sqrt(2)
i=sy.Function('i')
q=sy.dsolve(sy.Eq(sy.Derivative(i(t)*L,t)+i(t)*R,v*sy.sin(t)),i(t)).evalf()
constant=sy.solve(q.subs(i(t),0),dict=True)
t=np.linspace(0,360,360)
final=q.subs(constant[0]).evalf()
plt.plot(t,final)
plt.show()
What should I do?
It's obvious from the code that t has 360 elements
t=np.linspace(0,360,360)
The error complains that final has an initial dimension of 1, where as it should be 360 like t. While it is possible the final has (1,) shape (1 element, containing another array or list), more likely it is (1, n).
When you get shape errors, you need to look at the shape of relevant arrays,
print(final.shape, final.dtype)
and decide from that the correct way of adjusting the shapes.
plot can handle a second argument that is (360,m), where m is the number of lines that it should plot.
Related
I have a 2D array of shape 5 and 10. So 5 different arrays with 10 values. I am hoping to get a histogram and see which array is on the lower end versus higher end of a histogram. Hope that makes sense. I am attaching an image of an example of what I mean (labeled example).
Looking for one histogram but the histogram is organized by the distribution of the highest and lowest of each array.
I'm having trouble doing this with Python. I tried a few ways of doing this:
# setting up 2d array
import numpy as np
from scipy import signal
np.random.seed(1234)
array_2d = np.random.random((5,20))
I thought you could maybe just plot all the histograms of each array (5 of them) like this:
for i in range(5):
plt.hist(signal.detrend(array_2d[i,:],type='constant'),bins=20)
plt.show()
And then looking to see which array's histogram is furthest to the right or left, but not sure if that makes too much sense...
Then also considered using .ravel to make the 2D array into a 1D array which makes a nice histogram. But all the values within each array are being shifted around so it's difficult to tell which array is on the lower or higher end of the histogram:
plt.hist(signal.detrend(array_2d.ravel(),type='constant'),bins=20)
plt.xticks(np.linspace(-1,1,10));
How might I get a histogram of the 5 arrays (shape 5, 10) and get the range of the arrays with the lowest values versus array with highest values?
Also please let me know if this is unclear or not possible at all too haha. Thanks!
Maybe you could use a kdeplot? This would replace each input value with a small Gaussian curve and sum them.
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
np.random.seed(1234)
array_2d = np.random.random((5, 20))
sns.kdeplot(data=pd.DataFrame(array_2d.T, columns=range(1, 6)), palette='Set1', multiple='layer')
My code generates values and corresponding standard deviations in sets of 3, i.e. 3x1 arrays. I want to plot them all together as a categorical errorbar plot. For specifying the yerr, since it only accepts scalar or (N,) or N x 2, I used np.ravel to convert all the 3x1 arrays to one single N x 1 array. But I still get the error ValueError: err must be [ scalar | N, Nx1 or 2xN array-like ]
Here is the code:
import numpy as np
import matplotlib.pyplot as plt
names_p=['p1','p1','p1','p2','p2','p2','p3','p3','p3','p4','p4','p4','p5','p5','p5','p6','p6','p6'] #### The names are repeated three times because for each variable I have three values
y=(p1sdm2N_ratem,p2sdm2N_ratem,p3sdm2N_ratem,p4sdm2N_ratem,p5sdm2N_ratem,p6sdm2N_ratem) #### each of these 6 elements is 3 x 1 E.g. p1sdm2N_ratem=(0.04,0.02,0.03)
c=np.ravel((p1sdm2N_ratestd,p2sdm2N_ratestd,p3sdm2N_ratestd,p4sdm2N_ratestd,p5sdm2N_ratestd,p6sdm2N_ratestd)) ### each of these 6 elements is 3x1 e.g. p1sdm2N_ratestd=(0.001,0.003,0.001)
plt. errorbar(names_p,y,yerr=c)
This gives the error I mentioned before, even though c is an 18x1 array. (It's not an array of an array, I checked.)
Note, with the way I've set up my variables,
plt.scatter(names_p,y)
and
plt. errorbar(names_p,y,yerr=None)
work, but without the errorbars, of course.
I'd appreciate any help!
I have a 3D numpy array that is a stack of 2D (m,n) images at certain timestamps, t. So my array is of shape (t, m, n). I want to plot the value of one of the pixels as a function of time.
e.g.:
import numpy as np
import matplotlib.pyplot as plt
data_cube = []
for i in xrange(10):
a = np.random(100,100)
data_cube.append(a)
So my (t, m, n) now has shape (10,100,100). Say I wanted a 1D plot the value of index [12][12] at each of the 10 steps I would do:
plt.plot(data_cube[:][12][12])
plt.show()
But I'm getting index out of range errors. I thought I might have my indices mixed up, but every plot I generate seems to be in the 'wrong' axis, i.e. across one of the 2D arrays, but instead I want it 'through' the vertical stack. Thanks in advance!
Here is the solution: Since you are already using numpy, convert you final list to an array and just use slicing. The problem in your case was two-fold:
First: Your final data_cube was not an array. For a list, you will have to iterate over the values
Second: Slicing was incorrect.
import numpy as np
import matplotlib.pyplot as plt
data_cube = []
for i in range(10):
a = np.random.rand(100,100)
data_cube.append(a)
data_cube = np.array(data_cube) # Added this step
plt.plot(data_cube[:,12,12]) # Modified the slicing
Output
A less verbose version that avoids iteration:
data_cube = np.random.rand(10, 100,100)
plt.plot(data_cube[:,12,12])
I am new to machine learning. I was teaching myself data visualization with MATPLOTLIB. my code is pretty simple.
It takes a numpy array (x = np.random.rand(1,100)) of shape=(1, 100)).
It converts numpy array x into y(y = np.sin(x)).
Final task is to visualise this in a BAR(plt.bar(x, y, label="BAR", color='r'))
But it is throwing VALUE ERROR.Even though there are already answers to this question, but none seems to work so far for me.
In one answer for this question By unutbu
he explains that this error is raised "whenever one tries to evaluate an array in boolean context".
I am unable to understand how I am using these arrays as boolean?
MY CODE:
import matplotlib.pyplot as plt
import numpy as np
#arguments are shape: 1=row; 100=columns
x = np.random.rand(1, 100)
y = np.cos(x)
#bars
plt.bar(x, y, label='Bars1', color='pink')
#legends
plt.legend()
#show the figure
plt.show()
You need to replace
x = np.random.rand(1, 100)
with
x = np.random.rand(100)
The reason is that the former gives you an array of arrays (with one array inside, but it is still a 2D array overall with dimensions 1-by-100), while the latter gives you a 1D array (of length 100). In order to visualize it with plt, you need the latter.
How I can change that code for running? It is necessary to run graphic that produces a function w(omega) = 1/(1 + 1j*omega) on the real and imaginary axis.
import matplotlib.pyplot as plt
import numpy as np
def func(a):
for x in range(len(a)):
plt.plot([0, a[x].real], [0, a[x].imag], 'ro-', label='python')
limit=np.max(np.ceil(np.absolute(a))) # set limits for axis
plt.xlim((-limit,limit))
plt.ylim((-limit,limit))
plt.ylabel('Imaginary')
plt.xlabel('Real')
plt.show()
omega = np.linspace(-4, 4, 251)
a = np.arange(1) + 1j*np.arange(omega, 1)
func(a)
To answer your specific question: the error arises from the fact that omega is an array, but arange expects scalar arguments: the from-to-step values. Since it gets a length-1 array (omega), it can't determine which value of omega it should choose as the starting point of the range. Hence the error: omega cannot be converted to a scalar (which would make arange work).
But it's still unclear why you're exactly plotting this way, and exactly what you want to put in the array a.