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ca693b8bd1eb5f2789da9233f56f5d25
mooc-rr
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b985c45a
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b985c45a
authored
Apr 10, 2024
by
Tommy Rushton
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Do exercise 3.
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module2/exo2/exercice_python_en.org
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@@ -92,3 +92,61 @@ followed by ~Tab~.
...
@@ -92,3 +92,61 @@ followed by ~Tab~.
Now it's your turn! You can delete all this information and replace it
Now it's your turn! You can delete all this information and replace it
by your computational document.
by your computational document.
* Exercise 02-2
Load the data:
#+begin_src python :results value :session :exports both
import numpy as np
x = 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:
Calculate the mean:
#+begin_src python :results value :session :exports both
np.mean(x)
#+end_src
#+RESULTS:
: 14.113000000000001
Calculate the minimum:
#+begin_src python :results value :session :exports both
np.min(x)
#+end_src
#+RESULTS:
: 2.8
Calculate the maximum:
#+begin_src python :results value :session :exports both
np.max(x)
#+end_src
#+RESULTS:
: 23.4
Calclurate the median:
#+begin_src python :results value :session :exports both
np.median(x)
#+end_src
#+RESULTS:
: 14.5
Calculate the standard deviation:
#+begin_src python :results value :session :exports both
np.std(x, ddof=1)
#+end_src
#+RESULTS:
: 4.334094455301447
module2/exo3/exercice_python_en.org
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#+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
*
Exercise 02-3
This is an org-mode document with code examples in R. Once opened in
Load the data:
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 :session :exports both
exported as HTML. All the code in it will be re-executed, and the
import numpy as np
results will be retrieved and included into the exported document. If
x = np.array([
you do not want to re-execute all code each time, you can delete the #
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
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
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
C-c~.
#+begin_src python :results output :session :exports both
** Sequence plot:
import numpy
x=numpy.linspace(-15,15)
print(x)
#+end_src
#+RESULTS:
#+begin_example
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245
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 :session :results output file :var matplot_lib_filename="./sequence_plot.png" :exports both
#+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))
plt.figure(figsize=(
6,4.
5))
plt.plot(x
,numpy.cos(x)/x
)
plt.plot(x)
plt.tight_layout()
plt.tight_layout()
plt.grid(color="lightgrey", linestyle="--")
plt.xlim([0,100])
plt.ylim([0,25])
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:./
sequence_plot
.png]]
Note the parameter ~:exports results~, which indicates that the code
** Histogram Plot
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
#+begin_src python :session :results output file :var matplot_lib_filename="./histogram.png" :exports both
the org document. It's a plain file, here named ~cosxsx.png~. You have
plt.figure(figsize=(6,4.5))
to commit it explicitly if you want your analysis to be legible and
plt.hist(x, edgecolor="black", alpha=.75, zorder=2)
understandable on GitLab.
plt.grid(color="lightgrey", linestyle="--")
plt.xlim([0,25])
plt.ylim([0,25])
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
Finally, don't forget that we provide in the resource section of this
#+RESULTS:
MOOC a configuration with a few keyboard shortcuts that allow you to
[[file:./histogram.png]]
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.
module2/exo3/histogram.png
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module2/exo3/sequence_plot.png
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