python tutorial should now look like something

python/02_intro_to_packages.md

+## intro to packages
+There are A LOT of them. I will cover only the popular ones which I personally used
+that proved to be of high quality and usability.  
+Unfortunatelly, managing Python packages can be very messy.
+However, most of the problems can be solved via compartmentalisation.
+
+### Installing packages
+We will cover 3 ways to install packages:
+1. through your distributions package manager
+2. pip
+3. anaconda
+There are also virtual environments, but I found conda + pip 
+to be the easiest way to get random python projects to work,
+so I'm not covering it here. 
+The best way changes from year to year, but this has not failed me yet.

python/03_math_and_vizualization.md

+### numpy
+Fast matrix operations.
+**examples**: Show basic usage of what's below in ipython.
+Content:
+ - wrappers around fast c code
+ - vectorization into real arrays
+ - arange
+ - linspace
+ - basic math (addition, powers, exponentials)
+ - matrix multiplication
+ - matrix inversion
+ - so much more in [numpy documentation](https://numpy.org/) 
+
+### scipy
+Ready to use advanced math.
+Content:
+- linear algebra
+- optimization
+- regression
+- so much more in [scipy documentation](https://scipy.org/)
+**examples**
+- show chalmers nonlin lab optimization example (just code and plot is ok)
+- Bonus: a more detailed IK example (math first, then code, but very quickly)
+
+### matplotlib 
+**examples**: 
+- plot a sin wave
+- download and run something from the gallery
+Content:
+ - plotting numpy arrays
+ - completely intuitive for basics, animations and GUI are a bit weird
+ - [matplotlib gallery] (https://matplotlib.org/stable/gallery/index.html)
+Bonus: going far with matplotlib: own projects:
+ - basic animation with celluloid: ~/chalmers/SCS hw1 and hw2
+ - AR car: ~/chalmers/AR2/hw3/guberina_hw3
+ - manipulability_visualizer from ur_simple_control/visualize
+Matplotlib animation is a bit involved. I will provide tutorial
+only if there is demand for it.
+The same goes for tkinter GUIs with embedded matplotlib.

python/04_opencv.md

+### openCV
+- open up the laptop camera: ~/programs/camera_no_lag.py
+- show some basic transformations like grascale, ~/programs/camera_no_lag.py
+- open up ip_webcam, and open then open it in opencv
+- show classic object detection example in action ~/chalmers/HUMRO/HUMROgroup5/classic_cv_test
+- show yolov4 in action HUMRO project, mention [yolo-v8](https://docs.ultralytics.com/)
+- much more at [opencv documentation](https://docs.opencv.org/4.x/index.html)

python/05_other_noteworthy_packages.md

+### other noteworthy packages
+- GUI - [tkinter]( https://tkdocs.com/), ex. previously shown in matplotlib manipulator_visualizer
+- simbolic math - sympy, ex. calculate hessian: ~/chalmers/NONLIN/lab/simpy_lab1.py 
+- classic ML - [scikit-learn](https://scikit-learn.org/stable/index.html)
+- deep learning - [pytorch](https://pytorch.org/) 
+- tables - [pandas](https://pandas.pydata.org/)
+- deep learning object detection and more - [yolo-v8](https://github.com/ultralytics/ultralytics)
+- classic control - [control](https://python-control.readthedocs.io/en/0.10.1/)

python/python_common_packages.md

-2.10. numpy - 1 video - do everything in the shell
-     - wrappers around fast c code
-     - vectorization into real arrays
-     - arange
-     - linspace
-     - basic math (addition, powers, exponentials)
-     - matrix multiplication
-     - matrix inversion
-     - many many more
-2.11. matplotlib - plot a sin wave
-     - plotting numpy arrays
-     - completely intuitive for basics, can get weird otherwise
-     - show matplotlib gallery
-     - show chalmers SCS labs
-     - show manipulability_visualizer
-     - show AR car
-2.12. openCV - 1 video just for this
-     - open up the laptop camera
-     - open up ip_webcam over LAN
-     - show some basic transformations like negative
-     - show classic object detection example in action
-     - show yolov4 in action HUMRO project, mention openpose
-2.13. scipy - go over - 1 video and other libraries
-     - linear algebra
-     - optimization
-     - regression
-     - ...
-     - show chalmers nonlin lab optimization example (just code and plot is ok)
-     - show a more detailed IK example (math first, then code, but very quickly)
-2.14. other noteworthy packages - just show websites
-     - tkinter - mention its shown use in manipulator_visualizer and hand detection
-     - sympy - example: calculate hessian: /home/gospodar/chalmers/NONLIN/lab/simpy_lab1.py 
-     - scikit-learn for classic ML
-     - pytorch for neural networks
-     - pandas for tables (eats SQL, Excell, csv no problem)
-     - yolo-v8 - the end all be all of CNN based object detection
-     - control - classic control