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Use environment variable to identify conda / mamba (#14515) Conda and mamba both set an environment variable which refers to the base environment's executable path, use that in preference to less reliable methods, but fall back on the other approaches if unable to locate the executable this way. Additionally, change the search to look for the bare command name rather than the command within the top level of the active environment, I'm dubious this approach works with any current conda / mamba version which usually place their executable links in a `condabin` directory or elsewhere not at the same level as the Python executable. I believe this will also address https://github.com/ipython/ipython/issues/14350, which I'm also seeing in a Windows context where the regex fails to parse and causes a traceback.

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Trapezoid Rule.ipynb
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Basic Numerical Integration: the Trapezoid RuleĀ¶

A simple illustration of the trapezoid rule for definite integration:

$$ \int_{a}^{b} f(x)\, dx \approx \frac{1}{2} \sum_{k=1}^{N} \left( x_{k} - x_{k-1} \right) \left( f(x_{k}) + f(x_{k-1}) \right). $$
First, we define a simple function and sample it between 0 and 10 at 200 points
InĀ [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
InĀ [2]:
def f(x):
    return (x-3)*(x-5)*(x-7)+85

x = np.linspace(0, 10, 200)
y = f(x)

Choose a region to integrate over and take only a few points in that region

InĀ [3]:
a, b = 1, 8 # the left and right boundaries
N = 5 # the number of points
xint = np.linspace(a, b, N)
yint = f(xint)

Plot both the function and the area below it in the trapezoid approximation

InĀ [4]:
plt.plot(x, y, lw=2)
plt.axis([0, 9, 0, 140])
plt.fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)
plt.text(0.5 * (a + b), 30,r"$\int_a^b f(x)dx$", horizontalalignment='center', fontsize=20);
No description has been provided for this image

Compute the integral both at high accuracy and with the trapezoid approximation

InĀ [5]:
from __future__ import print_function
from scipy.integrate import quad
integral, error = quad(f, a, b)
integral_trapezoid = sum( (xint[1:] - xint[:-1]) * (yint[1:] + yint[:-1]) ) / 2
print("The integral is:", integral, "+/-", error)
print("The trapezoid approximation with", len(xint), "points is:", integral_trapezoid)
The integral is: 565.2499999999999 +/- 6.275535646693696e-12
The trapezoid approximation with 5 points is: 559.890625