exercise 2 - module 2

parent d4c7533e
#+TITLE: Your title #+TITLE: Exercise 2 - Simple calculation
#+AUTHOR: Your name #+AUTHOR: Miguel Felipe Silva Vasconcelos
#+DATE: Today's date #+DATE: 26-02-2021
#+LANGUAGE: en #+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export # #+PROPERTY: header-args :eval never-export
...@@ -11,84 +11,82 @@ ...@@ -11,84 +11,82 @@
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script> #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script> #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Some explanations
This is an org-mode document with code examples in R. Once opened in * Reading input
Emacs, this document can easily be exported to HTML, PDF, and Office Creating the data structure to store the following numbers:
formats. For more information on org-mode, see #+begin_src
https://orgmode.org/guide/.
14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9,
18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1,
13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9,
12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,
13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1,
13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1,
14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8,
8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4,
16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0
#+end_src
#+begin_src python :results value :session *python* :exports both
import numpy as np
array = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9,
18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1,
13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9,
12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,
13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1,
13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1,
14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8,
8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4,
16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])
#+end_src
#+RESULTS:
When you type the shortcut =C-c C-e h o=, this document will be
exported as HTML. All the code in it will be re-executed, and the
results will be retrieved and included into the exported document. If
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 * Calculating the average/mean
is exxecuted by typing ~C-c C-c~): We can use numpy's [[https://numpy.org/doc/stable/reference/generated/numpy.average.html#numpy.average][average method]]
#+begin_src python :results output :exports both #+begin_src python :results value :session *python* :exports both
print("Hello world!") np.average(array)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: Hello world! : 14.113000000000001
And now the same but in an Python session. With a session, Python's * Calculating the standard deviation
state, i.e. the values of all the variables, remains persistent from We can use numpy's [[https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std][std method]]
one code block to the next. The code is still executed using ~C-c #+begin_src python :results value :session *python* :exports both
C-c~. np.std(array, ddof=1)
#+end_src
#+RESULTS:
: 4.334094455301447
* Calculating the median
We can use numpy's [[https://numpy.org/doc/stable/reference/generated/numpy.median.html#numpy.median][median method]]
#+begin_src python :results value :session *python* :exports both
np.median(array)
#+end_src
#+RESULTS:
: 14.5
#+begin_src python :results output :session :exports both * Finding the minimum value
import numpy We can use numpy's min method
x=numpy.linspace(-15,15) #+begin_src python :results value :session *python* :exports both
print(x) np.min(array)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
#+begin_example : 2.8
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592 * Finding the max value
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 We can use numpy's max method
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694 #+begin_src python :results value :session *python* :exports both
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245 np.max(array)
0.30612245 0.91836735 1.53061224 2.14285714 2.75510204
3.36734694 3.97959184 4.59183673 5.20408163 5.81632653
6.42857143 7.04081633 7.65306122 8.26530612 8.87755102
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
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
[[file:./cosxsx.png]] : 23.4
Note the parameter ~:exports results~, which indicates that the code
will not appear in the exported document. We recommend that in the
context of this MOOC, you always leave this parameter setting as
~:exports both~, because we want your analyses to be perfectly
transparent and reproducible.
Watch out: the figure generated by the code block is /not/ stored in
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
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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