Trying to do some plotting in SymPy -
As per this video I have written :
from sympy.plotting import plot, plot_parametric
e = sin(2*sin(x**3))
plot(e, (x, 0, 5));
But after evaling that cell I don't get any output? There isn't an error or anything, it just doesn't display anything.
Another test :
from sympy import *
from sympy.plotting import plot, plot_parametric
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
expr = x**2 + sqrt(3)*x - Rational(1, 3)
lf = lambdify(x, expr)
fig = plt.figure()
axes = fig.add_subplot(111)
x_vals = np.linspace(-5., 5.)
y_vals = lf(x_vals)
axes.grid()
axes.plot(x_vals, y_vals)
plt.show();
So Im not sure what I'm doing wrong here, I'm not getting any errors though?
If the virtual environment content is of any interest here's a tree of that : venv
I'm running this on Linux Ubuntu. The virtual environment that it's running in can be seen in the above paste link
Jupyter Notebook - Big Data Visualization Tool IPython kernel of Jupyter notebook is able to display plots of code in input cells. It works seamlessly with matplotlib library. The inline option with the %matplotlib magic function renders the plot out cell even if show() function of plot object is not called.
To display the image, the Ipython. display() method necessitates the use of a function. In the notebook, you can also specify the width and height of the image.
The %matplotlib magic command sets up your Jupyter Notebook for displaying plots with Matplotlib. The standard Matplotlib graphics backend is used by default, and your plots will be displayed in a separate window.
You need to use the magic functions, more specifically the ones for matplotlib:
%matplotlib qt # displays a pop-up of the plot
%matplotlib inline # keeps it within the notebook
Runnable example using Python 3.4 Nov '15:
from sympy import *
from sympy.plotting import plot, plot_parametric
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
expr = x**2 + sqrt(3)*x - Rational(1, 3)
lf = lambdify(x, expr)
fig = plt.figure()
axes = fig.add_subplot(111)
x_vals = np.linspace(-5., 5.)
y_vals = lf(x_vals)
axes.grid()
axes.plot(x_vals, y_vals)
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