Just came across this video while trying to derivate some earthquake records for an assignment. Thank you very much for this tutorial. I particulary appriciate that you explain the codes with specific examples, making them easy to relate to other problems. You speak in clear and pragmatic ('practical') terms. Keep up the good Work. Regards from Norway
Thanks to you, I have my first working introduction to Sympy. This was definitely worth my time. (I worked this in PyCharm & Jupyter. Definitely cooler in Jupyter!)
Awesome video about this man! Love this. Highly informative and motivating at the same time. For the numpy gradient function, you can actually specify the edge_order to be equal to 2: edge_order=2. This way, the left and right endpoint derivatives will be calculated using three-point forward and backward differentiation formulas which are order O(h^2) (same as the derivatives for the interior points computed using the CFD formula) as opposed to just O(h). This doesn't make too much difference if you just need the first derivative, but if you will take the second derivative or more, you might see sharp straight lines connecting your first and second points; and your penultimate and last points. Then, it between, it will be a way smoother curve. That was my experience when I would apply the gradient function recursively. For the last part in the Quasi-Symbolic case, I loved learning about the vectorize function and it's use case! It's perfectly applicable when solving a boundary-value problem ordinary differential equation and you want to solve it using the shooting method: you define the shooting function whose root you can solve for using scipy.optimize's root_scalar function. One can also plot the shooting function, but, without "vectorizing" the shooting function, it would be tedious and inefficient to plot the function since one would be forced to use for loops and Python for loops are slow. But with numpy's vectorize function, plotting it would be nice and simple. (The same could also be actually achieved using Python's map function: y = np.array( list( map(function, iterable) ) ). This may have similar performance to np.vectorize since I read that the map function uses C code underneath.) Lastly, for SciPy's derivative function, an alternative to getting more accurate derivatives with is using higher order formulas by specifying the order of it with an odd number greater than the derivative order you're taking: derivative (f, x dx, n=2, order=5). I find this more preferable than using very low values of dx like 1e-6 since while it could yield very accurate results for the first derivative, higher derivatives starting from the second derivative are way less accurate due to round off error, since from the finite difference formulas, the h term in the denominator gets a higher degree with each higher derivative. Anyway, I got too carried away. I just love sharing my experiences with Python and additional learnings from this channel ❤️
I used to really confused when they taught me calculus in school. And look now here I am performing the same thing in Python. And tbh, I am having fun. Thank you!
Thank you for this video. I am curious to know, whether you have an idea how to solve the drift when trying to get the velocity and position of after calculating the 1st and 2nd integration of acceleration data captured by an IMU?
Mr P. Solver, how about defining a function that calculates the derivative of f with respect to x without using any libraries, do you have any videos for that?
This was fantastic and is exactly what I want to learn coding for - solving mathematics and engineering problems. Do you have any videos you can point me towards to start learning python for this purpose?
how to calculate the derivative, if the data have 4 dimention (time, high level,longitude,and latitude), i want calculate derivative in each dimention ?
How to inputs a polynomial in standard algebraic notation and outputs the first derivative of that polynomial?(python) If both the inputted polynomial and its derivative should be represented as strings.
Thank you so much for the amazing explanation. Sir, I have one problem and I am unable to fix it. Can you please give me a hint as I need that? How to code this =>( Y = d^n/ dx^n * ( x^2- 1)^n) in python.🙏🙏🙏🙏🙏
Right here my dudes. ruclips.net/video/Teb28OFMVFc/видео.html Not so much visualization per se, but lots of stuff with gradient, curl, finding line integrals, etc.
Lol I just had to add this when I found out you were from Canada… Me: ‘Dude, you know the girl across the hall from me is an Endocrinologist?’ Building manager: ‘(pauses to think)… oh yea! And she’s actually really nice too… she’s Canadian.’ That was our conversation on the elevator like a week ago
filename = "data/test.txt" xx, yy = np.loadtxt(filename) For this to work, I had to create arrays `x`, `y` first, then save them like this np.savetxt('data/test.txt', (x, y)) to then marvel at the way NumPy presents the information in a text file. Frankly, I don't think I would ever want to enter test data by hand into a file, that on loading, demands that type of format. The format may be suitable for an automatic data logger, presenting floating-point numbers in scientific notation without delimiters for all values of `x`, followed by all `y` values directly beneath, making the file contents not only look very compressed but any changes made by hand deviating from that numeric format might not be read correctly at all as was found when trying to construct common floating-point data by hand inside the text editor.
Just came across this video while trying to derivate some earthquake records for an assignment. Thank you very much for this tutorial. I particulary appriciate that you explain the codes with specific examples, making them easy to relate to other problems. You speak in clear and pragmatic ('practical') terms. Keep up the good Work. Regards from Norway
Your tutorials are fantastic! I'm working on economic models and this is saving me a ton of time.
found you on reddit, amazing content thanks man!
I felt my (work)life changing while watching this, thanks very much for bringing this package to my attention
Always great videos. Keep it up!
Thanks to you, I have my first working introduction to Sympy. This was definitely worth my time. (I worked this in PyCharm & Jupyter. Definitely cooler in Jupyter!)
Amazing! Thanks for making these videos.
Awesome video about this man! Love this. Highly informative and motivating at the same time. For the numpy gradient function, you can actually specify the edge_order to be equal to 2: edge_order=2. This way, the left and right endpoint derivatives will be calculated using three-point forward and backward differentiation formulas which are order O(h^2) (same as the derivatives for the interior points computed using the CFD formula) as opposed to just O(h). This doesn't make too much difference if you just need the first derivative, but if you will take the second derivative or more, you might see sharp straight lines connecting your first and second points; and your penultimate and last points. Then, it between, it will be a way smoother curve. That was my experience when I would apply the gradient function recursively.
For the last part in the Quasi-Symbolic case, I loved learning about the vectorize function and it's use case! It's perfectly applicable when solving a boundary-value problem ordinary differential equation and you want to solve it using the shooting method: you define the shooting function whose root you can solve for using scipy.optimize's root_scalar function. One can also plot the shooting function, but, without "vectorizing" the shooting function, it would be tedious and inefficient to plot the function since one would be forced to use for loops and Python for loops are slow. But with numpy's vectorize function, plotting it would be nice and simple.
(The same could also be actually achieved using Python's map function: y = np.array( list( map(function, iterable) ) ). This may have similar performance to np.vectorize since I read that the map function uses C code underneath.)
Lastly, for SciPy's derivative function, an alternative to getting more accurate derivatives with is using higher order formulas by specifying the order of it with an odd number greater than the derivative order you're taking: derivative (f, x dx, n=2, order=5). I find this more preferable than using very low values of dx like 1e-6 since while it could yield very accurate results for the first derivative, higher derivatives starting from the second derivative are way less accurate due to round off error, since from the finite difference formulas, the h term in the denominator gets a higher degree with each higher derivative.
Anyway, I got too carried away. I just love sharing my experiences with Python and additional learnings from this channel ❤️
Thank you for such a fanstastic video!
I used to really confused when they taught me calculus in school. And look now here I am performing the same thing in Python. And tbh, I am having fun. Thank you!
Fantastic video, clear explanations, and sample code to demo important points.
Thank you for this video. I am curious to know, whether you have an idea how to solve the drift when trying to get the velocity and position of after calculating the 1st and 2nd integration of acceleration data captured by an IMU?
Great videos. I received my engineering degree long before Python was created.
Awesome! keep up the good work
To filter the noisy data use a low pass filter it's much more simple to what you did, thanks for the information I did enjoy the video.
thanks bro...too much helpful 🙏🏻
Nice video. By the way, what environment are you using to display the math formulas below the code ?
Awesome dude! just missing a tutorial for linear algebra and you are golden.
Hmmmm good idea
This video was a lifesaver!
your vids amaze me haha thanks for sharing all this
Mr P. Solver, how about defining a function that calculates the derivative of f with respect to x without using any libraries, do you have any videos for that?
Very useful content. Like it so much. Thank you :)
You the best G really helped me out earn a sub!
thanks a lot for this excellent and very useful video.
You are a master piece, love you so much
Does all this get replaced by AutoGrad or Automatic Differentiation, like that implemented in modern Deep Learning libraries or packages like CasADi?
Would you be so kind to make a Math Series in Python for topics like Calculus, Linear Algebra, etc. ?
Very useful. Thank you so much. Regards.
Thanks you so much!! So well explained!! ^_^
dear mentor , I have write an program that have a problem to use nsolve , can you recommend something faster method instead of nsolve in sciepy
how to print the mathematical formula like in the video in pycharm or colab?
Seems only jupyter can performence like this
This was fantastic and is exactly what I want to learn coding for - solving mathematics and engineering problems. Do you have any videos you can point me towards to start learning python for this purpose?
Idk, maybe subscribe to him? He got plenty of these kinds of video
Excellent, thanks.
Thank you!
how to calculate the derivative, if the data have 4 dimention (time, high level,longitude,and latitude), i want calculate derivative in each dimention ?
Good video, thanks
can I get the example data?
How to inputs a polynomial in standard algebraic notation and outputs
the first derivative of that polynomial?(python)
If both the inputted polynomial and its derivative
should be represented as strings.
Subscribed!
Thank you so much for the amazing explanation. Sir, I have one problem and I am unable to fix it. Can you please give me a hint as I need that? How to code this =>( Y = d^n/ dx^n * ( x^2- 1)^n) in python.🙏🙏🙏🙏🙏
best guy
Can you make a video on visualizing gradient , divergence and curl with python. Thanks!
That would be awesome, maybe also a video about line integrals and all that stuff
@@andresderudder9950 yeah, solving vector calculus symbolically, maybe we get to see the geometry of the problem.
Right here my dudes.
ruclips.net/video/Teb28OFMVFc/видео.html
Not so much visualization per se, but lots of stuff with gradient, curl, finding line integrals, etc.
@@MrPSolver already watched it, its a great video and helped a lot. But cannot plot the gradient function. Thanks for your content man🙌🏼
@@utkarsh4784 Thank you for the ideas for new videos! This will likely come in the future :)
Great video
cool video
Lol I just had to add this when I found out you were from Canada…
Me: ‘Dude, you know the girl across the hall from me is an Endocrinologist?’
Building manager: ‘(pauses to think)… oh yea! And she’s actually really nice too… she’s Canadian.’
That was our conversation on the elevator like a week ago
Are you on slack or discord? I need help PLEASE PLEASE
filename = "data/test.txt"
xx, yy = np.loadtxt(filename)
For this to work, I had to create arrays `x`, `y` first, then save them like this
np.savetxt('data/test.txt', (x, y))
to then marvel at the way NumPy presents the information in a text file.
Frankly, I don't think I would ever want to enter test data by hand into a file, that on loading, demands that type of format.
The format may be suitable for an automatic data logger, presenting floating-point numbers in scientific notation without delimiters for all values of `x`, followed by all `y` values directly beneath, making the file contents not only look very compressed but any changes made by hand deviating from that numeric format might not be read correctly at all as was found when trying to construct common floating-point data by hand inside the text editor.
Did not get 7, -7 part
Discord link not working for me? Is it moved or what?
@Amit Sehrawat - Discord link worked for me. Perhaps try again.
Leibniz wants to know your location.
Exactly 😂
There is a technique in data science called standarization.
another qualtity video
9:30 🔑 👉 👈
Hey, do you have a community discord or something??