Commit 65d8c41c authored by Anton Y.'s avatar Anton Y.

task try 1

parent 85815026
#+TITLE: Your title #+TITLE: On the computation of pi
#+AUTHOR: Your name #+AUTHOR: Anton Y.
#+DATE: Today's date # #+DATE: 2025-07-07
#+LANGUAGE: en #+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export # #+PROPERTY: header-args :eval never-export
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* Some explanations * Asking the math library
This is an org-mode document with code examples in R. Once opened in My computer tells me that π is approximatively
Emacs, this document can easily be exported to HTML, PDF, and Office
formats. For more information on org-mode, see
https://orgmode.org/guide/.
When you type the shortcut =C-c C-e h o=, this document will be #+begin_src python :results output :session :exports both
exported as HTML. All the code in it will be re-executed, and the from math import *
results will be retrieved and included into the exported document. If pi
you do not want to re-execute all code each time, you can delete the #
and the space before ~#+PROPERTY:~ in the header of this document.
Like we showed in the video, Python code is included as follows (and
is exxecuted by typing ~C-c C-c~):
#+begin_src python :results output :exports both
print("Hello world!")
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: Hello world!
And now the same but in an Python session. With a session, Python's * * Buffon's needle
state, i.e. the values of all the variables, remains persistent from
one code block to the next. The code is still executed using ~C-c Applying the method of Buffon's needle, we get the approximation
C-c~.
#+begin_src python :results output :session :exports both #+begin_src python :results output :session :exports both
import numpy import numpy as np
x=numpy.linspace(-15,15) np.random.seed(seed=42)
print(x) N = 10000
x = np.random.uniform(size=N, low=0, high=1)
theta = np.random.uniform(size=N, low=0, high=pi/2)
2/(sum((x+np.sin(theta))>1)/N)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
#+begin_example
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041 * Using a surface fraction argument
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 A method that is easier to understand and does not make use of the sin
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694 function is based on the fact that if X∼U(0,1) and Y∼U(0,1), then
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245 P[X2+Y2≤1]=π/4 (see "Monte Carlo method" on Wikipedia). The following
0.30612245 0.91836735 1.53061224 2.14285714 2.75510204 code uses this approach:
3.36734694 3.97959184 4.59183673 5.20408163 5.81632653
6.42857143 7.04081633 7.65306122 8.26530612 8.87755102 #+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
9.48979592 10.10204082 10.71428571 11.32653061 11.93877551
12.55102041 13.16326531 13.7755102 14.3877551 15. ]
#+end_example
Finally, an example for graphical output:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
plt.figure(figsize=(10,5)) np.random.seed(seed=42)
plt.plot(x,numpy.cos(x)/x) N = 1000
plt.tight_layout() x = np.random.uniform(size=N, low=0, high=1)
y = np.random.uniform(size=N, low=0, high=1)
accept = (x*x+y*y) <= 1
reject = np.logical_not(accept)
fig, ax = plt.subplots(1)
ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)
ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)
ax.set_aspect('equal')
plt.savefig(matplot_lib_filename) plt.savefig(matplot_lib_filename)
print(matplot_lib_filename) print(matplot_lib_filename)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
[[file:./cosxsx.png]] [[file:None]]
Note the parameter ~:exports results~, which indicates that the code It is then straightforward to obtain a (not really good) approximation to π
will not appear in the exported document. We recommend that in the by counting how many times, on average, X2+Y2
context of this MOOC, you always leave this parameter setting as is smaller than 1:
~:exports both~, because we want your analyses to be perfectly
transparent and reproducible. #+begin_src python :results output :session :exports both
4*np.mean(accept)
Watch out: the figure generated by the code block is /not/ stored in #+end_src
the org document. It's a plain file, here named ~cosxsx.png~. You have
to commit it explicitly if you want your analysis to be legible and #+RESULTS:
understandable on GitLab.
Finally, don't forget that we provide in the resource section of this
MOOC a configuration with a few keyboard shortcuts that allow you to
quickly create code blocks in Python by typing ~<p~, ~<P~ or ~<PP~
followed by ~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
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