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<div style='background-image: url("../../share/images/header.svg") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>
<div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px">
<div style="position: relative ; ... | 234a4c3ae7c66228ce84555a68a11484d4aeb447 | 58,009 | ipynb | Jupyter Notebook | notebooks/Earthquake Physics/DiscontinuousGalerkin/dg_elastic_physicalfluxes.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | 3 | 2020-07-11T10:01:39.000Z | 2020-12-16T14:26:03.000Z | notebooks/Earthquake Physics/DiscontinuousGalerkin/dg_elastic_physicalfluxes.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | null | null | null | notebooks/Earthquake Physics/DiscontinuousGalerkin/dg_elastic_physicalfluxes.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | 3 | 2020-11-11T05:05:41.000Z | 2022-03-12T09:36:24.000Z | 122.900424 | 37,152 | 0.808288 | true | 5,053 | Qwen/Qwen-72B | 1. YES
2. YES | 0.879147 | 0.795658 | 0.6995 | __label__eng_Latn | 0.785711 | 0.463505 |
**Notebook Outline:**
- [Setup with libraries](#Set-up-Cells)
- [Fundamental equations for Poisson MGWR](#Fundamental-equations-for-Binomial-MGWR)
- [Example Dataset](#Example-Dataset)
- [Helper functions](#Helper-functions)
- [Univariate example](#Univariate-example)
- [Parameter check](#Parameter-check)
... | 998d672a62ef7522ab9f51475e459f43a6d5cb80 | 15,527 | ipynb | Jupyter Notebook | Notebooks/.ipynb_checkpoints/Binomial_MGWR_univariate_check-checkpoint.ipynb | TaylorOshan/MGWR_workshop_book | 4c0be5cb08dfc669c8da0d1c074f3c5052a81c0a | [
"MIT-0"
] | 6 | 2021-01-21T08:30:01.000Z | 2021-07-24T05:40:43.000Z | Notebooks/Binomial_MGWR_univariate_check.ipynb | TaylorOshan/MGWR_book | c59db902b34d625af4d0e1b90fbc95018a3de579 | [
"MIT-0"
] | null | null | null | Notebooks/Binomial_MGWR_univariate_check.ipynb | TaylorOshan/MGWR_book | c59db902b34d625af4d0e1b90fbc95018a3de579 | [
"MIT-0"
] | 4 | 2020-07-20T19:43:36.000Z | 2021-06-07T23:41:08.000Z | 28.179673 | 176 | 0.503188 | true | 2,642 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.771843 | 0.662063 | __label__eng_Latn | 0.59159 | 0.376524 |
# Subspace
**Subspace.** The set of vectors $V$ is a linear subspace of $\mathbb{R}^n \iff$ null vector $\in V$ and $V$ is closed under scalar multiplication and addition.
⚠️ *Union of subspaces is not a subspace*
> The reason why this can happen is that all vector spaces, and hence subspaces too, must be closed und... | a3398163ff03b7c95028982f28b01a7b3d173e4a | 6,023 | ipynb | Jupyter Notebook | H3_vector_spaces.ipynb | jppgks/linear-algebra-notebooks | 08ce0ad8e7a8fb65ddd872d3084d1f8946b65fe0 | [
"MIT"
] | 1 | 2016-11-30T09:55:17.000Z | 2016-11-30T09:55:17.000Z | H3_vector_spaces.ipynb | jppgks/linear-algebra-notebooks | 08ce0ad8e7a8fb65ddd872d3084d1f8946b65fe0 | [
"MIT"
] | null | null | null | H3_vector_spaces.ipynb | jppgks/linear-algebra-notebooks | 08ce0ad8e7a8fb65ddd872d3084d1f8946b65fe0 | [
"MIT"
] | null | null | null | 38.363057 | 420 | 0.58343 | true | 1,285 | Qwen/Qwen-72B | 1. YES
2. YES | 0.923039 | 0.874077 | 0.806808 | __label__eng_Latn | 0.995481 | 0.712817 |
# Haverly's Pooling Problem
## Objective and Prerequisites
One of the new features of Gurobi 9.0 is the addition of a bilinear solver, which enables finding the optimal solution of non-convex quadratic programming problems (i.e. QPs, QCQPs, MIQPs, and MIQCQPs). This notebook will show you how to use this feature by ... | 63ec521c2c160a2316e904a0ac8ff705b095bf9d | 29,255 | ipynb | Jupyter Notebook | haverly/haverly.ipynb | Zhong-HY/modeling-examples | 66355938dde764a320f0a6a70a9e5a69c696a18f | [
"Apache-2.0"
] | null | null | null | haverly/haverly.ipynb | Zhong-HY/modeling-examples | 66355938dde764a320f0a6a70a9e5a69c696a18f | [
"Apache-2.0"
] | null | null | null | haverly/haverly.ipynb | Zhong-HY/modeling-examples | 66355938dde764a320f0a6a70a9e5a69c696a18f | [
"Apache-2.0"
] | null | null | null | 43.664179 | 939 | 0.553375 | true | 7,146 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.843895 | 0.782597 | __label__eng_Latn | 0.935615 | 0.656568 |
```python
import numpy as np
from scipy import optimize
```
Starting with an initial flow over a horizontal surface where $M_1 = 2.2$, an oblique shock forms at an angle of $\theta = 35^{\circ}$, which deflects the flow by the angle $\delta$. The flow now has Mach number $M_2$.
To satisfy the boundary condition of ... | f43ce2b9dded71a17c27009fd4a2c533689b90f3 | 5,742 | ipynb | Jupyter Notebook | oblique_shock.ipynb | kyleniemeyer/gasdynamics | 50dce7a030daa2757aa55cf6c9a5ae66d079ab72 | [
"MIT"
] | 4 | 2019-10-17T18:21:23.000Z | 2021-08-17T19:30:07.000Z | oblique_shock.ipynb | kyleniemeyer/gasdynamics | 50dce7a030daa2757aa55cf6c9a5ae66d079ab72 | [
"MIT"
] | null | null | null | oblique_shock.ipynb | kyleniemeyer/gasdynamics | 50dce7a030daa2757aa55cf6c9a5ae66d079ab72 | [
"MIT"
] | null | null | null | 26.219178 | 230 | 0.512887 | true | 977 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92079 | 0.875787 | 0.806416 | __label__eng_Latn | 0.951164 | 0.711906 |
# Week 10 of Introduction to Biological System Design
## Compiling Chemical Reaction Network Models for Biological Systems
### Ayush Pandey
Pre-requisite: To get the best out of this notebook, make sure that you have basic understanding of chemical reaction networks and ordinary differential equations (ODE). Further, ... | 8efdca74ef0870d08f6de6dfcbc58c5707093197 | 31,459 | ipynb | Jupyter Notebook | reading/week10_compiling_crn_models.ipynb | BioSysDesign/E164 | 69f6236de2d8172e541a5b56f7807d4767f20979 | [
"BSD-3-Clause"
] | null | null | null | reading/week10_compiling_crn_models.ipynb | BioSysDesign/E164 | 69f6236de2d8172e541a5b56f7807d4767f20979 | [
"BSD-3-Clause"
] | null | null | null | reading/week10_compiling_crn_models.ipynb | BioSysDesign/E164 | 69f6236de2d8172e541a5b56f7807d4767f20979 | [
"BSD-3-Clause"
] | null | null | null | 58.911985 | 15,260 | 0.745097 | true | 2,936 | Qwen/Qwen-72B | 1. YES
2. YES | 0.73412 | 0.721743 | 0.529846 | __label__eng_Latn | 0.934364 | 0.069339 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, C.D. Cooper, G.F. Forsyth.
# Riding the wave
## Numerical schemes for hyperbolic PDEs
Welcome back! This is the second notebook of *Riding the wave: Convection problems*, the third module of ["Practical N... | a54310ae786ab6c5c8c9a4e7404d9e6a23efc791 | 231,202 | ipynb | Jupyter Notebook | lessons/03_wave/03_02_convectionSchemes.ipynb | Fluidentity/numerical-mooc | 083bbe9dc923b0ada6db2ebfbe13392fb66c6fbc | [
"CC-BY-3.0"
] | null | null | null | lessons/03_wave/03_02_convectionSchemes.ipynb | Fluidentity/numerical-mooc | 083bbe9dc923b0ada6db2ebfbe13392fb66c6fbc | [
"CC-BY-3.0"
] | null | null | null | lessons/03_wave/03_02_convectionSchemes.ipynb | Fluidentity/numerical-mooc | 083bbe9dc923b0ada6db2ebfbe13392fb66c6fbc | [
"CC-BY-3.0"
] | null | null | null | 69.576286 | 6,552 | 0.778367 | true | 8,569 | Qwen/Qwen-72B | 1. YES
2. YES | 0.835484 | 0.718594 | 0.600374 | __label__eng_Latn | 0.965553 | 0.2332 |
# The One-Dimensional Particle in a Box
## 🥅 Learning Objectives
- Determine the energies and eigenfunctions of the particle-in-a-box.
- Learn how to normalize a wavefunction.
- Learn how to compute expectation values for quantum-mechanical operators.
- Learn the postulates of quantum mechanics
## Cyanine D... | 10d5fed85c4fc358fc01c1bc77d5d0741ec61380 | 126,397 | ipynb | Jupyter Notebook | book/ParticleIn1DBox.ipynb | RichRick1/IntroQM2022 | 91a37b630b9b83c76c972ee2e958a13640b1a37f | [
"CC0-1.0"
] | 5 | 2022-02-08T18:42:37.000Z | 2022-02-21T19:33:46.000Z | book/ParticleIn1DBox.ipynb | RichRick1/IntroQM2022 | 91a37b630b9b83c76c972ee2e958a13640b1a37f | [
"CC0-1.0"
] | 2 | 2022-01-26T18:45:29.000Z | 2022-03-04T20:32:52.000Z | book/ParticleIn1DBox.ipynb | RichRick1/IntroQM2022 | 91a37b630b9b83c76c972ee2e958a13640b1a37f | [
"CC0-1.0"
] | 2 | 2022-02-08T17:56:55.000Z | 2022-03-03T08:30:53.000Z | 125.021761 | 77,594 | 0.819165 | true | 11,282 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.76908 | 0.58059 | __label__eng_Latn | 0.970802 | 0.187236 |
# Projet EDP : Ecoulement de gel hydroalcoolique avec l'équation de Stockes
( En cas de problème sur l'exécution du code ou avec les images, merci de me contacter : matthieu.briet@student-cs.fr
### Introduction
La crise sanitaire actuelle nous pousse à utiliser de plus en plus des gels hydroalcoolique parfois sous... | 56c77bcb1f87164f79a3342d3d02b6eb0ab7e820 | 280,487 | ipynb | Jupyter Notebook | PJT_EDP_Briet.ipynb | MatthBriet/Projet_EDP | 3628d18a8357a01b58c879b1adfbcdcee9e2764d | [
"MIT"
] | null | null | null | PJT_EDP_Briet.ipynb | MatthBriet/Projet_EDP | 3628d18a8357a01b58c879b1adfbcdcee9e2764d | [
"MIT"
] | null | null | null | PJT_EDP_Briet.ipynb | MatthBriet/Projet_EDP | 3628d18a8357a01b58c879b1adfbcdcee9e2764d | [
"MIT"
] | null | null | null | 436.216174 | 81,628 | 0.940995 | true | 3,344 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.7773 | 0.689624 | __label__fra_Latn | 0.883592 | 0.440559 |
# Derivación numérica
Aunque la derivada de una función se puede obtener algorítmicamente de manera analítica, los algoritmos numéricos que usemos pueden depender de muchas derivadas o no pueden acceder a otra cosa más que la función original.
Así, comúnmente utilizaremos una aproximación numérica de la derivada en l... | 61cfdfccc40c472872d5105808fbd9a2e5b1bb82 | 108,830 | ipynb | Jupyter Notebook | files/fiscomp_2020-4/material/clase06.ipynb | sayeg84/sayeg84.github.io | 18f2e36dd7252603fad8f7093dc5aa00fc721be4 | [
"MIT"
] | null | null | null | files/fiscomp_2020-4/material/clase06.ipynb | sayeg84/sayeg84.github.io | 18f2e36dd7252603fad8f7093dc5aa00fc721be4 | [
"MIT"
] | null | null | null | files/fiscomp_2020-4/material/clase06.ipynb | sayeg84/sayeg84.github.io | 18f2e36dd7252603fad8f7093dc5aa00fc721be4 | [
"MIT"
] | null | null | null | 119.33114 | 26,476 | 0.666452 | true | 3,783 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.924142 | 0.796183 | __label__spa_Latn | 0.959468 | 0.688134 |
```python
%reset -f
```
```python
from sympy import *
```
```python
init_printing()
```
## Define variables
```python
x,y,z = symbols('x y z')
```
```python
f = sin(x)
```
## Differentiate
```python
diff(f,x)
```
## Integrate
```python
integrate(f, x)
```
```python
integrate(f, [x,0,pi])
```
```pyth... | 84ddd304ab58bf6e24c61f8e59f38a8b7d20ae60 | 4,624 | ipynb | Jupyter Notebook | sympy/Untitled.ipynb | krajit/krajit.github.io | 221c8bcdf0612b3ae28c827809aa309ea6a7b0c2 | [
"MIT"
] | 2 | 2018-09-29T07:40:07.000Z | 2022-02-28T22:17:04.000Z | sympy/Untitled.ipynb | krajit/krajit.github.io | 221c8bcdf0612b3ae28c827809aa309ea6a7b0c2 | [
"MIT"
] | 2 | 2019-08-26T08:42:47.000Z | 2019-08-26T09:48:21.000Z | sympy/Untitled.ipynb | krajit/krajit.github.io | 221c8bcdf0612b3ae28c827809aa309ea6a7b0c2 | [
"MIT"
] | 8 | 2017-09-09T23:32:09.000Z | 2020-01-28T21:11:39.000Z | 24.465608 | 708 | 0.63019 | true | 108 | Qwen/Qwen-72B | 1. YES
2. YES | 0.939025 | 0.72487 | 0.680671 | __label__yue_Hant | 0.257874 | 0.419758 |
# Lecture 4
## Differentiation II:
### Product, Chain and Quotient Rules
```python
import numpy as np
import sympy as sp
sp.init_printing()
##################################################
##### Matplotlib boilerplate for consistency #####
##################################################
from ipywidgets import... | 2f0015316c698bbc0b7543c8931e81b185ebc02d | 13,423 | ipynb | Jupyter Notebook | lectures/lecture-04-differentiation-02.ipynb | SABS-R3/2020-essential-maths | 5a53d60f1e8fdc04b7bb097ec15800a89f67a047 | [
"Apache-2.0"
] | 1 | 2021-11-27T12:07:13.000Z | 2021-11-27T12:07:13.000Z | lectures/lecture-04-differentiation-02.ipynb | SABS-R3/2021-essential-maths | 8a81449928e602b51a4a4172afbcd70a02e468b8 | [
"Apache-2.0"
] | null | null | null | lectures/lecture-04-differentiation-02.ipynb | SABS-R3/2021-essential-maths | 8a81449928e602b51a4a4172afbcd70a02e468b8 | [
"Apache-2.0"
] | 1 | 2020-10-30T17:34:52.000Z | 2020-10-30T17:34:52.000Z | 25.616412 | 184 | 0.487447 | true | 2,572 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.867036 | 0.654538 | __label__eng_Latn | 0.92317 | 0.359043 |
# Introduction to orthogonal coordinates
In $\mathbb{R}^3$, we can think that each point is given by the
intersection of three surfaces. Thus, we have three families of curved
surfaces that intersect each other at right angles. These surfaces are
orthogonal locally, but not (necessarily) globally, and are
defined by
... | 37ad4501c97e4936a62f91748c39070b383beb66 | 28,739 | ipynb | Jupyter Notebook | docs/tutorials/curvilinear_coordinates.ipynb | nicoguaro/continuum_mechanics | f8149b69b8461784f6ed721294cd1a49ffdfa3d7 | [
"MIT"
] | 21 | 2018-12-09T15:02:51.000Z | 2022-02-16T09:28:38.000Z | docs/tutorials/curvilinear_coordinates.ipynb | nicoguaro/continuum_mechanics | f8149b69b8461784f6ed721294cd1a49ffdfa3d7 | [
"MIT"
] | 223 | 2019-05-06T16:31:50.000Z | 2022-03-31T21:21:03.000Z | docs/tutorials/curvilinear_coordinates.ipynb | nicoguaro/continuum_mechanics | f8149b69b8461784f6ed721294cd1a49ffdfa3d7 | [
"MIT"
] | 7 | 2020-01-29T10:03:52.000Z | 2022-02-25T19:34:37.000Z | 81.644886 | 8,296 | 0.782317 | true | 1,375 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.896251 | 0.84622 | __label__eng_Latn | 0.988746 | 0.804385 |
## 1. A Numerical Solution to The Heat Equation
*By Parnian Kassraie*
***
***
*For solving this problem you don't need to know anything outside the course's syllabus. But if you are interested and you haven't passed Engineering Mathematics yet, you can read about the Heat Equation from [here.](https://en.wikipedia.org... | 4854c52a575babc8fd894768ba2f71d8d0876844 | 5,324 | ipynb | Jupyter Notebook | PyHW.ipynb | svarthafnyra/Fun-with-Data | 3a7b49f49f1e7aad6587bdac47b6bb315b54dc20 | [
"MIT"
] | null | null | null | PyHW.ipynb | svarthafnyra/Fun-with-Data | 3a7b49f49f1e7aad6587bdac47b6bb315b54dc20 | [
"MIT"
] | null | null | null | PyHW.ipynb | svarthafnyra/Fun-with-Data | 3a7b49f49f1e7aad6587bdac47b6bb315b54dc20 | [
"MIT"
] | null | null | null | 23.662222 | 268 | 0.529489 | true | 756 | Qwen/Qwen-72B | 1. YES
2. YES | 0.951142 | 0.831143 | 0.790535 | __label__eng_Latn | 0.961259 | 0.675011 |
```python
import numpy as np
from scipy.integrate import odeint
import numpy as np
from sympy import symbols,sqrt,sech,Rational,lambdify,Matrix,exp,cosh,cse,simplify,cos,sin
from sympy.vector import CoordSysCartesian
from theano.scalar.basic_sympy import SymPyCCode
from theano import function
from theano.scalar impor... | d9812952884bd5d32fb2e44880e5d20c760d9d61 | 13,931 | ipynb | Jupyter Notebook | src/ionotomo/notebooks/FermatPrincipleSpherical.ipynb | Joshuaalbert/IonoTomo | 9f50fbac698d43a824dd098d76dce93504c7b879 | [
"Apache-2.0"
] | 7 | 2017-06-22T08:47:07.000Z | 2021-07-01T12:33:02.000Z | src/ionotomo/notebooks/FermatPrincipleSpherical.ipynb | Joshuaalbert/IonoTomo | 9f50fbac698d43a824dd098d76dce93504c7b879 | [
"Apache-2.0"
] | 1 | 2019-04-03T15:21:19.000Z | 2019-04-03T15:48:31.000Z | src/ionotomo/notebooks/FermatPrincipleSpherical.ipynb | Joshuaalbert/IonoTomo | 9f50fbac698d43a824dd098d76dce93504c7b879 | [
"Apache-2.0"
] | 2 | 2020-03-01T16:20:00.000Z | 2020-07-07T15:09:02.000Z | 58.288703 | 3,622 | 0.565932 | true | 2,132 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.718594 | 0.658311 | __label__kor_Hang | 0.211049 | 0.367808 |
# Tutorial rápido de Python para Matemáticos
© Ricardo Miranda Martins, 2022 - http://www.ime.unicamp.br/~rmiranda/
## Índice
1. [Introdução](1-intro.html)
2. [Python é uma boa calculadora!](2-calculadora.html) [(código fonte)](2-calculadora.ipynb)
3. [Resolvendo equações](3-resolvendo-eqs.html) [(código font... | 7cf27dbb03ddb8e8765f4ea307e44e1259d2296d | 141,394 | ipynb | Jupyter Notebook | 6-limites-derivadas-integrais.ipynb | rmiranda99/tutorial-math-python | 6fe211f9cd0b8b93d4a0543a690ca124fee6a8b2 | [
"CC-BY-4.0"
] | null | null | null | 6-limites-derivadas-integrais.ipynb | rmiranda99/tutorial-math-python | 6fe211f9cd0b8b93d4a0543a690ca124fee6a8b2 | [
"CC-BY-4.0"
] | null | null | null | 6-limites-derivadas-integrais.ipynb | rmiranda99/tutorial-math-python | 6fe211f9cd0b8b93d4a0543a690ca124fee6a8b2 | [
"CC-BY-4.0"
] | null | null | null | 105.439224 | 23,320 | 0.852038 | true | 3,058 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.839734 | 0.69042 | __label__por_Latn | 0.997348 | 0.442409 |
# Variablen
Wenn Sie ein neues Jupyter Notebook erstellen, wählen Sie `Python 3.6` als Typ des Notebooks aus.
Innerhalb des Notebooks arbeiten Sie dann mit Python in der Version 3.6. Um zu verstehen, welche Bedeutung Variablen haben, müssen Sie also Variablen in Python 3.6 verstehen.
In Python ist eine Variable ein ... | 677a1e10b2fc6f7c511a776fde7a147943fc807d | 5,675 | ipynb | Jupyter Notebook | src/03-Variablen.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | src/03-Variablen.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | src/03-Variablen.ipynb | w-meiners/anb-first-steps | 6cb3583f77ae853922acd86fa9e48e9cf5188596 | [
"MIT"
] | null | null | null | 42.350746 | 621 | 0.595595 | true | 964 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.936285 | 0.815127 | __label__deu_Latn | 0.999312 | 0.732146 |
# Laboratório 2: Mofo e Fungicida
### Referente ao capítulo 6
Nesse laboratório, seja $x(t)$ a concentração de mofo que queremos reduzir em um período de tempo fixo. Assumiremos que $x$ tenha crescimento com taxa $r$ e capacidade de carga $M.$ Seja $u(t)$ o fungicida que reduz a população em $u(t)x(t).$ Assim
$$
x'(... | a98189579c32fa33c9f981f6b3bfeab8fc22fb12 | 106,567 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/Laboratory2-checkpoint.ipynb | lucasmoschen/optimal-control-biological | 642a12b6a3cb351429018120e564b31c320c44c5 | [
"MIT"
] | 1 | 2021-11-03T16:27:39.000Z | 2021-11-03T16:27:39.000Z | notebooks/.ipynb_checkpoints/Laboratory2-checkpoint.ipynb | lucasmoschen/optimal-control-biological | 642a12b6a3cb351429018120e564b31c320c44c5 | [
"MIT"
] | null | null | null | notebooks/.ipynb_checkpoints/Laboratory2-checkpoint.ipynb | lucasmoschen/optimal-control-biological | 642a12b6a3cb351429018120e564b31c320c44c5 | [
"MIT"
] | null | null | null | 260.555012 | 42,656 | 0.924517 | true | 1,900 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.894789 | 0.764072 | __label__por_Latn | 0.996749 | 0.613528 |
# Symbolic Regression
This example combines neural differential equations with regularised evolution to discover the equations
$\frac{\mathrm{d} x}{\mathrm{d} t}(t) = \frac{y(t)}{1 + y(t)}$
$\frac{\mathrm{d} y}{\mathrm{d} t}(t) = \frac{-x(t)}{1 + x(t)}$
directly from data.
**References:**
This example appears as ... | 85b477be66dee5b2c2b83bb2e7b5ae3fadbf4d4e | 59,376 | ipynb | Jupyter Notebook | examples/symbolic_regression.ipynb | FedericoV/diffrax | 98b010242394491fea832e77dc94f456b48495fa | [
"Apache-2.0"
] | null | null | null | examples/symbolic_regression.ipynb | FedericoV/diffrax | 98b010242394491fea832e77dc94f456b48495fa | [
"Apache-2.0"
] | null | null | null | examples/symbolic_regression.ipynb | FedericoV/diffrax | 98b010242394491fea832e77dc94f456b48495fa | [
"Apache-2.0"
] | null | null | null | 164.933333 | 46,340 | 0.878082 | true | 1,932 | Qwen/Qwen-72B | 1. YES
2. YES | 0.737158 | 0.771844 | 0.568971 | __label__eng_Latn | 0.839384 | 0.16024 |
```python
from __future__ import print_function
import sisl
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
In this analysis example we will show how to plot the wavefunction for a periodic system (the same scheme may be used to plot molecular orbitals).
The basic principle of plotting the r... | 6fd15e919fe13f51ca4b4d17dca31df7717756f6 | 6,077 | ipynb | Jupyter Notebook | ts-tbt-sisl-tutorial-master/S_03/run.ipynb | rwiuff/QuantumTransport | 5367ca2130b7cf82fefd4e2e7c1565e25ba68093 | [
"MIT"
] | 1 | 2021-09-25T14:05:45.000Z | 2021-09-25T14:05:45.000Z | ts-tbt-sisl-tutorial-master/S_03/run.ipynb | rwiuff/QuantumTransport | 5367ca2130b7cf82fefd4e2e7c1565e25ba68093 | [
"MIT"
] | 1 | 2020-03-31T03:17:38.000Z | 2020-03-31T03:17:38.000Z | ts-tbt-sisl-tutorial-master/S_03/run.ipynb | rwiuff/QuantumTransport | 5367ca2130b7cf82fefd4e2e7c1565e25ba68093 | [
"MIT"
] | 2 | 2020-01-27T10:27:51.000Z | 2020-06-17T10:18:18.000Z | 45.014815 | 294 | 0.59898 | true | 1,253 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.70253 | 0.557181 | __label__eng_Latn | 0.984799 | 0.132847 |
# Eisntein Tensor calculations using Symbolic module
```python
import numpy as np
import pytest
import sympy
from sympy import cos, simplify, sin, sinh, tensorcontraction
from einsteinpy.symbolic import EinsteinTensor, MetricTensor, RicciScalar
sympy.init_printing()
```
### Defining the Anti-de Sitter spacetime Met... | c722970826c908ce91ed41926c7dc64c5f9e02e7 | 21,485 | ipynb | Jupyter Notebook | docs/source/examples/Einstein_Tensor_symbolic_calculation.ipynb | Varunvaruns9/einsteinpy | befc0879c65a53b811e7d6a9ec47675ae28a08c5 | [
"MIT"
] | 1 | 2019-03-08T16:13:56.000Z | 2019-03-08T16:13:56.000Z | docs/source/examples/Einstein_Tensor_symbolic_calculation.ipynb | Varunvaruns9/einsteinpy | befc0879c65a53b811e7d6a9ec47675ae28a08c5 | [
"MIT"
] | null | null | null | docs/source/examples/Einstein_Tensor_symbolic_calculation.ipynb | Varunvaruns9/einsteinpy | befc0879c65a53b811e7d6a9ec47675ae28a08c5 | [
"MIT"
] | 1 | 2022-03-19T18:46:13.000Z | 2022-03-19T18:46:13.000Z | 180.546218 | 17,424 | 0.847708 | true | 201 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944995 | 0.849971 | 0.803218 | __label__eng_Latn | 0.485465 | 0.704478 |
<h1><center> Computation of the modified equation of a numerical scheme (univariate PDE evolution equation)</center></h1>
<center>
Olivier Pannekoucke <br> 2020
# Introduction
In this illustration we compute the modified equation assowiated with the Euler discretization and the centered discretization of the adve... | b7a5068b3b2b37714893bc47f872d9628bd04b1e | 4,914 | ipynb | Jupyter Notebook | euler-centered-modified-equation.ipynb | opannekoucke/modified-equation | 11a0f18b24e3142a65976048e385e9def54628fd | [
"CECILL-B"
] | null | null | null | euler-centered-modified-equation.ipynb | opannekoucke/modified-equation | 11a0f18b24e3142a65976048e385e9def54628fd | [
"CECILL-B"
] | null | null | null | euler-centered-modified-equation.ipynb | opannekoucke/modified-equation | 11a0f18b24e3142a65976048e385e9def54628fd | [
"CECILL-B"
] | null | null | null | 24.326733 | 357 | 0.524013 | true | 649 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.839734 | 0.778738 | __label__eng_Latn | 0.777021 | 0.647603 |
<center>
<h1> ILI285 - Computación Científica I / INF285 - Computación Científica </h1>
<h2> Roots of 1D equations </h2>
<h2> <a href="#acknowledgements"> [S]cientific [C]omputing [T]eam </a> </h2>
<h2> Version: 1.32</h2>
</center>
## Table of Contents
* [Introduction](#intro)
* [Bisection Method](#bi... | d4700a7da8ab0ff2e34cbace88bd018ccbb33691 | 267,638 | ipynb | Jupyter Notebook | SC1/04_roots_of_1D_equations.ipynb | maxaubel/Scientific-Computing | 57a04b5d3e3f7be2fe9b06127f7e569659698656 | [
"BSD-3-Clause"
] | 37 | 2017-06-05T21:01:15.000Z | 2022-03-17T12:51:55.000Z | SC1/04_roots_of_1D_equations.ipynb | maxaubel/Scientific-Computing | 57a04b5d3e3f7be2fe9b06127f7e569659698656 | [
"BSD-3-Clause"
] | null | null | null | SC1/04_roots_of_1D_equations.ipynb | maxaubel/Scientific-Computing | 57a04b5d3e3f7be2fe9b06127f7e569659698656 | [
"BSD-3-Clause"
] | 63 | 2017-10-02T21:21:30.000Z | 2022-03-23T02:23:22.000Z | 200.929429 | 37,024 | 0.886974 | true | 4,922 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.793106 | 0.594106 | __label__eng_Latn | 0.845086 | 0.218636 |
<a href="https://colab.research.google.com/github/Jun-629/20MA573/blob/master/src/Hw4_Monotonicity_in_volatility.ipynb" target="_parent"></a>
- __Suppose $f$ is convex and $X$ is submartingale, prove that
$g(t) = \mathbb E[f(X_t)]$ is increasing.__
__Pf:__
Assuming that $X_n$ is a submartingale with respect to the ... | d645512c3fa2fe1f975ef21f6d146f4b43b1bcb5 | 6,057 | ipynb | Jupyter Notebook | src/Hw4_Monotonicity_in_volatility.ipynb | Jun-629/20MA573 | addad663d2dede0422ae690e49b230815aea4c70 | [
"MIT"
] | null | null | null | src/Hw4_Monotonicity_in_volatility.ipynb | Jun-629/20MA573 | addad663d2dede0422ae690e49b230815aea4c70 | [
"MIT"
] | null | null | null | src/Hw4_Monotonicity_in_volatility.ipynb | Jun-629/20MA573 | addad663d2dede0422ae690e49b230815aea4c70 | [
"MIT"
] | 1 | 2020-02-05T21:42:08.000Z | 2020-02-05T21:42:08.000Z | 44.536765 | 248 | 0.483077 | true | 1,474 | Qwen/Qwen-72B | 1. YES
2. YES | 0.882428 | 0.877477 | 0.77431 | __label__eng_Latn | 0.878974 | 0.637314 |
<table>
<tr align=left><td>
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Kyle T. Mandli</td>
</table>
```python
from __future__ import print_function
%matplotlib inline
import numpy
import matplotlib.pyplot as plt
import w... | cea1f6eb2c7e763d93a45dfadc40fd7b90ac3c39 | 108,891 | ipynb | Jupyter Notebook | 05_root_finding_optimization.ipynb | mspieg/intro-numerical-methods | d267a075c95acfed6bbcbe91951a05539be61311 | [
"CC-BY-4.0"
] | 6 | 2020-09-10T13:01:06.000Z | 2022-01-20T15:05:30.000Z | 05_root_finding_optimization.ipynb | AinsleyChen/intro-numerical-methods | 2eda74cccbed5c0d4c57e24c3f4c96a1aa741f08 | [
"CC-BY-4.0"
] | null | null | null | 05_root_finding_optimization.ipynb | AinsleyChen/intro-numerical-methods | 2eda74cccbed5c0d4c57e24c3f4c96a1aa741f08 | [
"CC-BY-4.0"
] | 35 | 2020-01-21T16:08:37.000Z | 2022-01-21T12:46:56.000Z | 27.094053 | 303 | 0.472013 | true | 22,778 | Qwen/Qwen-72B | 1. YES
2. YES | 0.737158 | 0.841826 | 0.620559 | __label__eng_Latn | 0.757459 | 0.280096 |
# Series
```python
import pandas as pd
from oeis.sequence import OEIS_Sequence
from matplotlib import pyplot as plt
```
```python
plt.plot(Sequence.terms)
plt.title(Sequence.description)
plt.show()
```
```python
def formula_latex(k, floor=True):
latex = r"$$\left\lfloor\frac{n^2}{" + str(k) + r"}\right\rfloor... | d3f4991d356c73a7439e08e3a39cd9062f7e5544 | 56,700 | ipynb | Jupyter Notebook | code/01-Intro/oeis.ipynb | EnriquePH/Libro_Bestiario_Mates | 77347cbf5fd9e4c6f7d52c671e29c8d6781b0bb7 | [
"CC0-1.0"
] | null | null | null | code/01-Intro/oeis.ipynb | EnriquePH/Libro_Bestiario_Mates | 77347cbf5fd9e4c6f7d52c671e29c8d6781b0bb7 | [
"CC0-1.0"
] | null | null | null | code/01-Intro/oeis.ipynb | EnriquePH/Libro_Bestiario_Mates | 77347cbf5fd9e4c6f7d52c671e29c8d6781b0bb7 | [
"CC0-1.0"
] | null | null | null | 110.526316 | 38,180 | 0.781041 | true | 3,968 | Qwen/Qwen-72B | 1. YES
2. YES | 0.896251 | 0.826712 | 0.740942 | __label__krc_Cyrl | 0.315338 | 0.559787 |
---
author: Nathan Carter (ncarter@bentley.edu)
---
This answer assumes you have imported SymPy as follows.
```python
from sympy import * # load all math functions
init_printing( use_latex='mathjax' ) # use pretty math output
```
Sequences are typically written in terms of an independent variable... | 55b5054f556e227c9801fc25b6839e806570a76e | 3,778 | ipynb | Jupyter Notebook | database/tasks/How to define a mathematical sequence/Python, using SymPy.ipynb | nathancarter/how2data | 7d4f2838661f7ce98deb1b8081470cec5671b03a | [
"MIT"
] | null | null | null | database/tasks/How to define a mathematical sequence/Python, using SymPy.ipynb | nathancarter/how2data | 7d4f2838661f7ce98deb1b8081470cec5671b03a | [
"MIT"
] | null | null | null | database/tasks/How to define a mathematical sequence/Python, using SymPy.ipynb | nathancarter/how2data | 7d4f2838661f7ce98deb1b8081470cec5671b03a | [
"MIT"
] | 2 | 2021-07-18T19:01:29.000Z | 2022-03-29T06:47:11.000Z | 23.177914 | 219 | 0.489677 | true | 464 | Qwen/Qwen-72B | 1. YES
2. YES | 0.957912 | 0.798187 | 0.764593 | __label__eng_Latn | 0.967639 | 0.614738 |
# The standard deb model
The standard DEB model, in the energy formulation, contains four dynamic state variables: reserve energy $E$, structure volume $V$, maturity energy $E_M$ and reproduction buffer energy $E_R$:
\begin{eqnarray}
\frac{dE}{dt} &=& \dot{p}_A - \dot{p}_C\\
\frac{dV}{dt} &=& \frac{\dot{p}_G}... | fef446b2f82d26db4e82b206b669fffa2bbfc4f7 | 336,663 | ipynb | Jupyter Notebook | my-first-deb.ipynb | nepstad/pydebtest | e1409d0c5cd19d72a045a81eaa6ac1bcba77acc9 | [
"MIT"
] | 1 | 2017-05-30T18:27:47.000Z | 2017-05-30T18:27:47.000Z | my-first-deb.ipynb | nepstad/pydebtest | e1409d0c5cd19d72a045a81eaa6ac1bcba77acc9 | [
"MIT"
] | null | null | null | my-first-deb.ipynb | nepstad/pydebtest | e1409d0c5cd19d72a045a81eaa6ac1bcba77acc9 | [
"MIT"
] | null | null | null | 551.906557 | 74,636 | 0.924625 | true | 3,254 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.743168 | 0.659342 | __label__eng_Latn | 0.268495 | 0.370203 |
```python
import sympy as sym
import numpy as np
```
```python
def rotationGlobalX(alpha):
return np.array([[1,0,0],[0,np.cos(alpha),-np.sin(alpha)],[0,np.sin(alpha),np.cos(alpha)]])
def rotationGlobalY(beta):
return np.array([[np.cos(beta),0,np.sin(beta)], [0,1,0],[-np.sin(beta),0,np.cos(beta)]])
def rotat... | 77cfccd29f47940dc64b804f9797123856ad0568 | 484,016 | ipynb | Jupyter Notebook | notebooks/elipsoid3DRotMatrix1.ipynb | tallesmedeiros/BMC | 2f5ccad4a58ffc00d5372970a605352f18cfe1b9 | [
"CC-BY-4.0"
] | 1 | 2022-03-15T14:50:42.000Z | 2022-03-15T14:50:42.000Z | notebooks/elipsoid3DRotMatrix1.ipynb | tallesmedeiros/BMC | 2f5ccad4a58ffc00d5372970a605352f18cfe1b9 | [
"CC-BY-4.0"
] | null | null | null | notebooks/elipsoid3DRotMatrix1.ipynb | tallesmedeiros/BMC | 2f5ccad4a58ffc00d5372970a605352f18cfe1b9 | [
"CC-BY-4.0"
] | null | null | null | 203.453552 | 223,168 | 0.868969 | true | 2,474 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.815232 | 0.723278 | __label__kor_Hang | 0.123706 | 0.518749 |
This file is part of the pyMOR project (http://www.pymor.org).
Copyright 2013-2020 pyMOR developers and contributors. All rights reserved.
License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
# Heat equation example
## Analytic problem formulation
We consider the heat equation on the segment $[... | 381c188e320a24f0c783dd4e6040ed84ae2ec908 | 22,159 | ipynb | Jupyter Notebook | notebooks/heat.ipynb | weslowrie/pymor | badb5078b2394162d04a1ebfefe9034b889dac64 | [
"Unlicense"
] | null | null | null | notebooks/heat.ipynb | weslowrie/pymor | badb5078b2394162d04a1ebfefe9034b889dac64 | [
"Unlicense"
] | null | null | null | notebooks/heat.ipynb | weslowrie/pymor | badb5078b2394162d04a1ebfefe9034b889dac64 | [
"Unlicense"
] | null | null | null | 25.238041 | 180 | 0.531297 | true | 4,040 | Qwen/Qwen-72B | 1. YES
2. YES | 0.822189 | 0.672332 | 0.552784 | __label__eng_Latn | 0.410803 | 0.122632 |
# Composition
A deep neural network is simply a composition of (parametrized) processing nodes. Composing two nodes $g$ and $f$ gives yet another node $h = f \cdot g$, or $h(x) = f(g(x))$. We can also evaluate two nodes in parallel and express the result as the concatenation of the two outputs, $h(x) = (f(x), g(x))$. ... | e6874217f4a010af89bcd04ebf607f1d01fdf9b2 | 27,126 | ipynb | Jupyter Notebook | tutorials/06_composition.ipynb | pmorerio/ddn | 68e44e3ccfbeed285a78bf75cc778802dd15890e | [
"MIT"
] | 161 | 2019-09-08T05:22:43.000Z | 2022-03-31T06:13:43.000Z | tutorials/06_composition.ipynb | pmorerio/ddn | 68e44e3ccfbeed285a78bf75cc778802dd15890e | [
"MIT"
] | 11 | 2020-09-15T06:59:23.000Z | 2021-12-27T04:15:19.000Z | tutorials/06_composition.ipynb | pmorerio/ddn | 68e44e3ccfbeed285a78bf75cc778802dd15890e | [
"MIT"
] | 29 | 2019-09-15T08:34:45.000Z | 2022-01-04T04:48:54.000Z | 69.375959 | 12,152 | 0.706997 | true | 3,016 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91118 | 0.843895 | 0.76894 | __label__eng_Latn | 0.961738 | 0.624838 |
# Pg. 332 #29, 41, 53, 71, 73, 83, 87, 89, 93, 101, 102
---
Eric Nguyen
20 Dec 2018
```
import numpy as np
import matplotlib.pyplot as plt
```
### *Find each logarithm*. *Round to six decimal places.*
#### 29. $\ln{5894}$
```
ans29 = round(np.log(5894), 6)
ans29
```
#### Answer 29:
> $8.68169$
### *Solve for ... | a22cb2f3ff24a984f5196cd771571186513e8167 | 41,106 | ipynb | Jupyter Notebook | 2018-12/2018-12-20.ipynb | airicbear/calculus-homework | a765d3ba35b2b3794b9b2cce038152682eeb2cb8 | [
"MIT"
] | null | null | null | 2018-12/2018-12-20.ipynb | airicbear/calculus-homework | a765d3ba35b2b3794b9b2cce038152682eeb2cb8 | [
"MIT"
] | 1 | 2019-02-04T07:00:05.000Z | 2019-02-09T01:17:25.000Z | 2018-12/2018-12-20.ipynb | airicbear/calculus-homework | a765d3ba35b2b3794b9b2cce038152682eeb2cb8 | [
"MIT"
] | null | null | null | 43.544492 | 17,974 | 0.635868 | true | 2,930 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.843895 | 0.752595 | __label__eng_Latn | 0.356014 | 0.586862 |
# Expectation–maximization algorithm
# Purpose
* Understand how the EM-algorithm works to estimate parameters
# Methodology
* Implement a simple EM-algorithm
# Setup
```python
# %load imports.py
## Local packages:
%matplotlib inline
%load_ext autoreload
%autoreload 2
%config Completer.use_jedi = False ## (To fix... | f4567bee0391585574891c6ab3dba4296ae25a19 | 4,792 | ipynb | Jupyter Notebook | notebooks/15.20_EM-algorithm.ipynb | martinlarsalbert/wPCC | 16e0d4cc850d503247916c9f5bd9f0ddb07f8930 | [
"MIT"
] | null | null | null | notebooks/15.20_EM-algorithm.ipynb | martinlarsalbert/wPCC | 16e0d4cc850d503247916c9f5bd9f0ddb07f8930 | [
"MIT"
] | null | null | null | notebooks/15.20_EM-algorithm.ipynb | martinlarsalbert/wPCC | 16e0d4cc850d503247916c9f5bd9f0ddb07f8930 | [
"MIT"
] | null | null | null | 22.92823 | 94 | 0.545492 | true | 610 | Qwen/Qwen-72B | 1. YES
2. YES | 0.766294 | 0.7773 | 0.59564 | __label__eng_Latn | 0.68778 | 0.222201 |
## Classical Mechanics - Week 9
### Last Week:
- We saw how a potential can be used to analyze a system
- Gained experience with plotting and integrating in Python
### This Week:
- We will study harmonic oscillations using packages
- Further develope our analysis skills
- Gain more experience wtih sympy
```python... | a73a1c51df951f48c17577fb37b8e487ad74b29b | 529,879 | ipynb | Jupyter Notebook | doc/AdminBackground/PHY321/CM_Jupyter_Notebooks/Answers/CM_Notebook9_Answers.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 20 | 2020-01-09T17:41:16.000Z | 2022-03-09T00:48:58.000Z | doc/AdminBackground/PHY321/CM_Jupyter_Notebooks/Answers/CM_Notebook9_Answers.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 6 | 2020-01-08T03:47:53.000Z | 2020-12-15T15:02:57.000Z | doc/AdminBackground/PHY321/CM_Jupyter_Notebooks/Answers/CM_Notebook9_Answers.ipynb | Shield94/Physics321 | 9875a3bf840b0fa164b865a3cb13073aff9094ca | [
"CC0-1.0"
] | 33 | 2020-01-10T20:40:55.000Z | 2022-02-11T20:28:41.000Z | 524.632673 | 129,732 | 0.945386 | true | 3,784 | Qwen/Qwen-72B | 1. YES
2. YES | 0.908618 | 0.903294 | 0.820749 | __label__eng_Latn | 0.946832 | 0.745208 |
We are answering questions in <cite data-cite="bibtex_lane2019online">(Lane, 2019)</cite>
The first question we answer is on how to find the smallest absolute difference for the set of numbers $S=\left\{2,3,4,9,16\right\}$
```python
s=[2,3,4,9,16]
result=[]
for i in range(10,1,-1):
sum=0
for j in s:
... | 078fd1a35bbc73b87465f579d1fd9f2d3a1d32e2 | 3,997 | ipynb | Jupyter Notebook | ch-3/smallest-absolute-difference.ipynb | jhancock1975/online-status-book-exercises | 70059beffc7f8b2ce84a4bb5c6bcbaf8eda339fa | [
"Apache-2.0"
] | null | null | null | ch-3/smallest-absolute-difference.ipynb | jhancock1975/online-status-book-exercises | 70059beffc7f8b2ce84a4bb5c6bcbaf8eda339fa | [
"Apache-2.0"
] | null | null | null | ch-3/smallest-absolute-difference.ipynb | jhancock1975/online-status-book-exercises | 70059beffc7f8b2ce84a4bb5c6bcbaf8eda339fa | [
"Apache-2.0"
] | null | null | null | 34.162393 | 493 | 0.563172 | true | 756 | Qwen/Qwen-72B | 1. YES
2. YES | 0.942507 | 0.934395 | 0.880674 | __label__eng_Latn | 0.992957 | 0.884434 |
# Monte Carlo Markov Chain
## Christina Lee
## Category: Numerics
### Monte Carlo Physics Series
* [Monte Carlo: Calculation of Pi](../Numerics_Prog/Monte-Carlo-Pi.ipynb)
* [Monte Carlo Markov Chain](../Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb)
* [Monte Carlo Ferromagnet](../Prerequisites/Monte-Carlo-Ferromagnet... | 20df515a7c6d9b11f21021158d72eb3a8bda002a | 347,860 | ipynb | Jupyter Notebook | Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 22 | 2015-11-15T08:47:04.000Z | 2022-02-25T10:47:12.000Z | Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 11 | 2016-02-23T12:18:26.000Z | 2019-09-14T07:14:26.000Z | Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 6 | 2016-02-24T03:08:22.000Z | 2022-03-10T18:57:19.000Z | 115.376451 | 343 | 0.599333 | true | 1,934 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.800692 | 0.63089 | __label__eng_Latn | 0.991225 | 0.3041 |
```julia
] activate .
```
[Vandermonde matrix:](https://en.wikipedia.org/wiki/Vandermonde_matrix)
\begin{align}V=\begin{bmatrix}1&\alpha _{1}&\alpha _{1}^{2}&\dots &\alpha _{1}^{n-1}\\1&\alpha _{2}&\alpha _{2}^{2}&\dots &\alpha _{2}^{n-1}\\1&\alpha _{3}&\alpha _{3}^{2}&\dots &\alpha _{3}^{n-1}\\\vdots &\vdots &\vdots ... | f914ce46e8f7b810739c0e8b28f512dca498c2a1 | 26,340 | ipynb | Jupyter Notebook | playground/vandermonde/vandermonde.ipynb | crstnbr/JuliaWorkshop19 | 17a19bd100fcaf1c20b577af7af943061b8a157c | [
"MIT"
] | 98 | 2019-07-26T20:02:31.000Z | 2021-08-06T08:12:15.000Z | playground/vandermonde/vandermonde.ipynb | mattborghi/JuliaWorkshop19 | ae4fc28e52e8fc0fd9abdf6359a72b0bb5fe61f3 | [
"MIT"
] | 5 | 2019-07-25T14:24:54.000Z | 2019-10-25T17:37:37.000Z | playground/vandermonde/vandermonde.ipynb | mattborghi/JuliaWorkshop19 | ae4fc28e52e8fc0fd9abdf6359a72b0bb5fe61f3 | [
"MIT"
] | 25 | 2019-08-09T18:26:12.000Z | 2021-08-08T00:05:50.000Z | 50.947776 | 685 | 0.550683 | true | 1,348 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.919643 | 0.810018 | __label__eng_Latn | 0.569428 | 0.720277 |
# Physics 256
## Physics of Baseball
```python
import style
style._set_css_style('../include/bootstrap.css')
```
## Last Time
### [Notebook Link: 14_ProjectileMotion.ipynb](./14_ProjectileMotion.ipynb)
- projectile motion for a cannon shell with air resistance
- building a simple targetting algorithm
## Today
... | a7c709182b98d0b83313b58f02203a2a9aa96f4a | 8,617 | ipynb | Jupyter Notebook | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/15_Baseball.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | null | null | null | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/15_Baseball.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | null | null | null | 4-assets/BOOKS/Jupyter-Notebooks/Overflow/15_Baseball.ipynb | impastasyndrome/Lambda-Resource-Static-Assets | 7070672038620d29844991250f2476d0f1a60b0a | [
"MIT"
] | 1 | 2021-11-05T07:48:26.000Z | 2021-11-05T07:48:26.000Z | 25.195906 | 197 | 0.500754 | true | 1,586 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.727975 | 0.618758 | __label__eng_Latn | 0.701954 | 0.275913 |
# Casing design affects frac geometry and economics
_Ohm Devani_
NOTE: This project will focus on relative economic uplift between 2 casing designs in similar geology.
## Contents
1. Model inputs
2. Base case
- define per-frac endpoints (min rate defined by dfit initiation rate, max rate defined by hhp of fleet, ... | 64339c3d399c4dd1cc39bf395ac8a57a3b76de17 | 170,864 | ipynb | Jupyter Notebook | comp-cost.ipynb | energydevohm/completion-cost-study | d836390c4eb4f19781882feefd94e5ad79ec32af | [
"MIT"
] | null | null | null | comp-cost.ipynb | energydevohm/completion-cost-study | d836390c4eb4f19781882feefd94e5ad79ec32af | [
"MIT"
] | null | null | null | comp-cost.ipynb | energydevohm/completion-cost-study | d836390c4eb4f19781882feefd94e5ad79ec32af | [
"MIT"
] | null | null | null | 298.191972 | 55,772 | 0.915693 | true | 3,418 | Qwen/Qwen-72B | 1. YES
2. YES | 0.805632 | 0.763484 | 0.615087 | __label__eng_Latn | 0.44538 | 0.267384 |
# Sinais exponenciais
Neste notebook avaliaremos os sinais exponenciais do tipo
\begin{equation}
x(t) = A \ \mathrm{e}^{a \ t}
\end{equation}
Estamos interessados em 3 casos:
1. $A \ \in \ \mathbb{R}$ e $a \ \in \ \mathbb{R}$ - As exponenciais reais.
2. $A \ \in \ \mathbb{C}$ e $a \ \in \ \mathbb{C}, \ \mathrm{Re... | 2403e82a16f5776c818907980d72a15399ede1da | 182,961 | ipynb | Jupyter Notebook | Aula 6 - Sinais exponenciais/sinais exponenciais.ipynb | RicardoGMSilveira/codes_proc_de_sinais | e6a44d6322f95be3ac288c6f1bc4f7cfeb481ac0 | [
"CC0-1.0"
] | 8 | 2020-10-01T20:59:33.000Z | 2021-07-27T22:46:58.000Z | Aula 6 - Sinais exponenciais/sinais exponenciais.ipynb | RicardoGMSilveira/codes_proc_de_sinais | e6a44d6322f95be3ac288c6f1bc4f7cfeb481ac0 | [
"CC0-1.0"
] | null | null | null | Aula 6 - Sinais exponenciais/sinais exponenciais.ipynb | RicardoGMSilveira/codes_proc_de_sinais | e6a44d6322f95be3ac288c6f1bc4f7cfeb481ac0 | [
"CC0-1.0"
] | 9 | 2020-10-15T12:08:22.000Z | 2021-04-12T12:26:53.000Z | 639.723776 | 88,804 | 0.93984 | true | 1,727 | Qwen/Qwen-72B | 1. YES
2. YES | 0.924142 | 0.90053 | 0.832217 | __label__por_Latn | 0.690031 | 0.771853 |
# Implementation details: deriving expected moment dynamics
$$
\def\n{\mathbf{n}}
\def\x{\mathbf{x}}
\def\N{\mathbb{\mathbb{N}}}
\def\X{\mathbb{X}}
\def\NX{\mathbb{\N_0^\X}}
\def\C{\mathcal{C}}
\def\Jc{\mathcal{J}_c}
\def\DM{\Delta M_{c,j}}
\newcommand\diff{\mathop{}\!\mathrm{d}}
\def\Xc{\mathbf{X}_c}
\newcommand{\mu... | 81bc79131d91c87554f1d99056a9a66de96d9141 | 33,386 | ipynb | Jupyter Notebook | (EXTRA) Implementation details.ipynb | zechnerlab/Compartor | 93c1b0752b6fdfffddd4f1ac6b9631729eae9a95 | [
"BSD-2-Clause"
] | 1 | 2021-02-10T15:56:02.000Z | 2021-02-10T15:56:02.000Z | (EXTRA) Implementation details.ipynb | zechnerlab/Compartor | 93c1b0752b6fdfffddd4f1ac6b9631729eae9a95 | [
"BSD-2-Clause"
] | null | null | null | (EXTRA) Implementation details.ipynb | zechnerlab/Compartor | 93c1b0752b6fdfffddd4f1ac6b9631729eae9a95 | [
"BSD-2-Clause"
] | 1 | 2021-12-05T11:24:22.000Z | 2021-12-05T11:24:22.000Z | 55.092409 | 4,224 | 0.70236 | true | 3,178 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.740174 | 0.587037 | __label__eng_Latn | 0.683596 | 0.202213 |
# Linear regression
Linear regression is the simplest linear method used for modelling the relationship between the independent variables and the dependent ones. It tries to estimate it by finding a line which is as close as possible to all the data points.
\begin{equation}
y=ax+b
\end{equation}
#### Boston housing... | 8370582d7a523ae4b2d22edcf7bd4110bc2d6ff0 | 196,995 | ipynb | Jupyter Notebook | ML1/linear/021_Linear_regression.ipynb | DevilWillReign/ML2022 | cb4cc692e9f0e178977fb5e1d272e581b30f998d | [
"MIT"
] | null | null | null | ML1/linear/021_Linear_regression.ipynb | DevilWillReign/ML2022 | cb4cc692e9f0e178977fb5e1d272e581b30f998d | [
"MIT"
] | null | null | null | ML1/linear/021_Linear_regression.ipynb | DevilWillReign/ML2022 | cb4cc692e9f0e178977fb5e1d272e581b30f998d | [
"MIT"
] | null | null | null | 175.73149 | 40,060 | 0.895378 | true | 4,567 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.863392 | 0.682537 | __label__eng_Latn | 0.819026 | 0.424094 |
# Find worst cases
\begin{equation}
\begin{array}{rl}
\mathcal{F}_L =& \dfrac{4 K I H r}{Q_{in}(1+f)}\\
u_{c} =& \dfrac{-KI\mathcal{F}_L}{\theta \left(\mathcal{F}_L+1\right)}\\
\tau =& -\dfrac{r}{|u_{c}|}\\
C_{\tau,{\rm decay}}=& C_0 \exp{\left(-\lambda \tau \right)}\\
C_{\tau,{\rm filtr}}=& C_0 \... | a177476a37fedabc31c1305b99307bc4db604baf | 125,060 | ipynb | Jupyter Notebook | notebooks/Concepts/Find worst case (1).ipynb | edsaac/bioparticle | 67e191329ef191fc539b290069524b42fbaf7e21 | [
"MIT"
] | null | null | null | notebooks/Concepts/Find worst case (1).ipynb | edsaac/bioparticle | 67e191329ef191fc539b290069524b42fbaf7e21 | [
"MIT"
] | 1 | 2020-09-25T23:31:21.000Z | 2020-09-25T23:31:21.000Z | notebooks/Concepts/Find worst case (1).ipynb | edsaac/VirusTransport_RxSandbox | 67e191329ef191fc539b290069524b42fbaf7e21 | [
"MIT"
] | 1 | 2021-09-30T05:00:58.000Z | 2021-09-30T05:00:58.000Z | 108.842472 | 46,524 | 0.795274 | true | 10,424 | Qwen/Qwen-72B | 1. YES
2. YES | 0.859664 | 0.699254 | 0.601124 | __label__yue_Hant | 0.120933 | 0.234942 |
# Population coding (Pouget et al., 2010)
A response of a cell can be characterized by an "encoding model" of the stimulus ($s$):
\begin{align}
r_{i} = f_{i}(s) + n_{i}
\end{align}
in which $n$ represents a noise term assumed to follow a normal distribution with a variance proportional to the mean value, $f_{i}(s)$.... | 0b48949c9c68e41163bb566f7d458b7915b27938 | 26,873 | ipynb | Jupyter Notebook | population_coding.ipynb | lukassnoek/random_notebooks | d7df507ce2b6949726c29de0022aae2d0dc583ac | [
"MIT"
] | 3 | 2018-05-28T13:45:11.000Z | 2021-08-31T11:41:34.000Z | population_coding.ipynb | lukassnoek/random_notebooks | d7df507ce2b6949726c29de0022aae2d0dc583ac | [
"MIT"
] | null | null | null | population_coding.ipynb | lukassnoek/random_notebooks | d7df507ce2b6949726c29de0022aae2d0dc583ac | [
"MIT"
] | 2 | 2018-05-28T13:46:05.000Z | 2018-06-11T15:25:59.000Z | 193.330935 | 12,964 | 0.910058 | true | 439 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.798187 | 0.744272 | __label__eng_Latn | 0.937467 | 0.567525 |
# Chapter 3: Linear Regression
- **Chapter 3 from the book [An Introduction to Statistical Learning](https://www.statlearning.com/).**
- **By Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani.**
- **Pages from $120$ to $121$**
- **By [Mosta Ashour](https://www.linkedin.com/in/mosta-ashour/)**
**Exercises... | c6ebbb18afb794aaf3ca5d40e74dd5810524fe50 | 14,237 | ipynb | Jupyter Notebook | Notebooks/3_7_0_Linear_Regression_Conceptual.ipynb | MostaAshour/ISL-in-python | 87255625066f88d5d4625d045bdc6427a4ad9193 | [
"MIT"
] | null | null | null | Notebooks/3_7_0_Linear_Regression_Conceptual.ipynb | MostaAshour/ISL-in-python | 87255625066f88d5d4625d045bdc6427a4ad9193 | [
"MIT"
] | null | null | null | Notebooks/3_7_0_Linear_Regression_Conceptual.ipynb | MostaAshour/ISL-in-python | 87255625066f88d5d4625d045bdc6427a4ad9193 | [
"MIT"
] | null | null | null | 40.793696 | 486 | 0.531081 | true | 3,351 | Qwen/Qwen-72B | 1. YES
2. YES | 0.611382 | 0.847968 | 0.518432 | __label__eng_Latn | 0.985755 | 0.042821 |
# Fractal drum
Nori Parelius
This project was a part of an exam in Computational Physics that I took in May 2015. At the time I used Fortran to solve it, but I have since rewritten it in Matlab and now Python.
The project is about finding the eigenvalues and eigenvectors of a "fractal drum" - a thin membrane stretc... | 1898c47ea4930716f400fc8a643fd3be6227d52a | 377,754 | ipynb | Jupyter Notebook | fractal drum.ipynb | nori-parelius/fractal-drum | 92c3c816a0d7a4dc6634e4bc82621e05052cce9b | [
"MIT"
] | null | null | null | fractal drum.ipynb | nori-parelius/fractal-drum | 92c3c816a0d7a4dc6634e4bc82621e05052cce9b | [
"MIT"
] | null | null | null | fractal drum.ipynb | nori-parelius/fractal-drum | 92c3c816a0d7a4dc6634e4bc82621e05052cce9b | [
"MIT"
] | null | null | null | 460.67561 | 299,340 | 0.937766 | true | 4,891 | Qwen/Qwen-72B | 1. YES
2. YES | 0.934395 | 0.845942 | 0.790445 | __label__eng_Latn | 0.993846 | 0.6748 |
# Нотация Денавита-Хартенберга
```python
from sympy import *
def rz(a):
return Matrix([
[cos(a), -sin(a), 0, 0],
[sin(a), cos(a), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
])
def ry(a):
return Matrix([
[cos(a), 0, sin(a), 0],
[0, 1, 0, 0],
[-sin(a), 0, cos(... | 35dff3d0c15a3fd12a87713c721c51843dc68a5b | 3,242 | ipynb | Jupyter Notebook | 3 - DH notation.ipynb | red-hara/jupyter-dh-notation | 0ffd305b3e67ce7dd3c20f2d1c719b53251dbf58 | [
"MIT"
] | null | null | null | 3 - DH notation.ipynb | red-hara/jupyter-dh-notation | 0ffd305b3e67ce7dd3c20f2d1c719b53251dbf58 | [
"MIT"
] | null | null | null | 3 - DH notation.ipynb | red-hara/jupyter-dh-notation | 0ffd305b3e67ce7dd3c20f2d1c719b53251dbf58 | [
"MIT"
] | null | null | null | 23.492754 | 111 | 0.442011 | true | 631 | Qwen/Qwen-72B | 1. YES
2. YES | 0.96378 | 0.859664 | 0.828527 | __label__krc_Cyrl | 0.820881 | 0.763278 |
# Improving predictive models using non-spherical Gaussian priors
Based on the CNN abstract of [Nunez-Elizalde, Huth, & Gallant](https://www2.securecms.com/CCNeuro/docs-0/5928d71e68ed3f844e8a256f.pdf).
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr
from s... | c3b7b450bd447680958cad0684a5c64baeeab36f | 289,260 | ipynb | Jupyter Notebook | tikhonov_regression_with_non_sphrerical_prior.ipynb | lukassnoek/random_notebooks | d7df507ce2b6949726c29de0022aae2d0dc583ac | [
"MIT"
] | 3 | 2018-05-28T13:45:11.000Z | 2021-08-31T11:41:34.000Z | tikhonov_regression_with_non_sphrerical_prior.ipynb | lukassnoek/random_notebooks | d7df507ce2b6949726c29de0022aae2d0dc583ac | [
"MIT"
] | null | null | null | tikhonov_regression_with_non_sphrerical_prior.ipynb | lukassnoek/random_notebooks | d7df507ce2b6949726c29de0022aae2d0dc583ac | [
"MIT"
] | 2 | 2018-05-28T13:46:05.000Z | 2018-06-11T15:25:59.000Z | 375.662338 | 47,584 | 0.933143 | true | 3,100 | Qwen/Qwen-72B | 1. YES
2. YES | 0.907312 | 0.795658 | 0.72191 | __label__eng_Latn | 0.277933 | 0.515571 |
# Direct Inversion of the Iterative Subspace
When solving systems of linear (or nonlinear) equations, iterative methods are often employed. Unfortunately, such methods often suffer from convergence issues such as numerical instability, slow convergence, and significant computational expense when applied to difficult ... | 59715c8a9bedc5c87e8692a20987713397774e4e | 19,896 | ipynb | Jupyter Notebook | Tutorials/03_Hartree-Fock/3b_rhf-diis.ipynb | zyth0s/psi4julia | beb0384028f1a3654b8a2f8690b7db5bd9c24b86 | [
"BSD-3-Clause"
] | 4 | 2021-02-13T22:14:21.000Z | 2021-04-17T07:34:10.000Z | Tutorials/03_Hartree-Fock/3b_rhf-diis.ipynb | zyth0s/psi4julia | beb0384028f1a3654b8a2f8690b7db5bd9c24b86 | [
"BSD-3-Clause"
] | null | null | null | Tutorials/03_Hartree-Fock/3b_rhf-diis.ipynb | zyth0s/psi4julia | beb0384028f1a3654b8a2f8690b7db5bd9c24b86 | [
"BSD-3-Clause"
] | null | null | null | 41.798319 | 938 | 0.559208 | true | 4,443 | Qwen/Qwen-72B | 1. YES
2. YES | 0.843895 | 0.841826 | 0.710413 | __label__eng_Latn | 0.932634 | 0.488858 |
```python
# import Python libraries
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import sympy as sym
from sympy.plotting import plot
import pandas as pd
from IPython.display import display
from IPython.core.display import Math
```
```python
# time elbow_flexion BIClong BICsh... | 6203a281dc4f596ec8ae6d15534f7f5271f4320c | 68,935 | ipynb | Jupyter Notebook | courses/modsim2018/ahmadhassan/Ahmad_Task20.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | courses/modsim2018/ahmadhassan/Ahmad_Task20.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | courses/modsim2018/ahmadhassan/Ahmad_Task20.ipynb | ahmadhassan01/bmc | 3114b7d3ecd1f7c678fac0c04e8e139ac2898992 | [
"MIT"
] | null | null | null | 134.376218 | 53,584 | 0.835149 | true | 3,085 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.757794 | 0.659734 | __label__eng_Latn | 0.319614 | 0.371113 |
```python
import sys
import numpy as np
print(sys.version)
np.__version__
```
3.9.7 (default, Sep 16 2021, 16:59:28) [MSC v.1916 64 bit (AMD64)]
'1.20.3'
```python
#Criação de matriz com numpy matrix
matriz = np.matrix("1, 2, 3;4, 5, 6")
print(matriz)
```
[[1 2 3]
[4 5 6]]
```python
matri... | 49555fb7debfe6bbd49bdfc39b0ef947d8baa0e8 | 28,079 | ipynb | Jupyter Notebook | NumPy/ArrayEmatrizNumpy.ipynb | DjCod3r/Jupyter | bbf8e0eb5ae766ef509968be79541eba9389c544 | [
"MIT"
] | 1 | 2022-03-03T14:40:51.000Z | 2022-03-03T14:40:51.000Z | NumPy/ArrayEmatrizNumpy.ipynb | DjCod3r/Jupyter | bbf8e0eb5ae766ef509968be79541eba9389c544 | [
"MIT"
] | null | null | null | NumPy/ArrayEmatrizNumpy.ipynb | DjCod3r/Jupyter | bbf8e0eb5ae766ef509968be79541eba9389c544 | [
"MIT"
] | null | null | null | 60.126338 | 19,138 | 0.803412 | true | 1,116 | Qwen/Qwen-72B | 1. YES
2. YES | 0.812867 | 0.865224 | 0.703312 | __label__por_Latn | 0.944887 | 0.472362 |
# Laplace transform
This notebook is a short tutorial of Laplace transform using SymPy.
The main functions to use are ``laplace_transform`` and ``inverse_laplace_transform``.
```python
from sympy import *
```
```python
init_session()
```
IPython console for SymPy 1.0 (Python 2.7.13-64-bit) (ground types: py... | 4e471cf00c8cd5f6dab3069a42dd4d659d08098f | 40,477 | ipynb | Jupyter Notebook | notebooks/sympy/laplace_transform.ipynb | nicoguaro/AdvancedMath | 2749068de442f67b89d3f57827367193ce61a09c | [
"MIT"
] | 26 | 2017-06-29T17:45:20.000Z | 2022-02-06T20:14:29.000Z | notebooks/sympy/laplace_transform.ipynb | nicoguaro/AdvancedMath | 2749068de442f67b89d3f57827367193ce61a09c | [
"MIT"
] | null | null | null | notebooks/sympy/laplace_transform.ipynb | nicoguaro/AdvancedMath | 2749068de442f67b89d3f57827367193ce61a09c | [
"MIT"
] | 13 | 2019-04-22T08:08:56.000Z | 2022-01-27T08:15:53.000Z | 58.073171 | 3,414 | 0.748499 | true | 1,479 | Qwen/Qwen-72B | 1. YES
2. YES | 0.718594 | 0.822189 | 0.590821 | __label__eng_Latn | 0.635664 | 0.211004 |
# 13 - Panel Data and Fixed Effects
## Controlling What you Cannot See
Methods like propensity score, linear regression and matching are very good at controlling for confounding in non-random data, but they rely on a key assumption: conditional unconfoundedness
$
(Y_0, Y_1) \perp T | X
$
To put it in words, they re... | b102577c086703ffadf6e2868dc2a679b3caec7a | 108,086 | ipynb | Jupyter Notebook | causal-inference-for-the-brave-and-true/13-Panel-Data-and-Fixed-Effects.ipynb | qiringji/python-causality-handbook | add5ab57a8e755242bdbc3d4d0ee00867f6a1e55 | [
"MIT"
] | 1 | 2021-07-07T03:57:54.000Z | 2021-07-07T03:57:54.000Z | causal-inference-for-the-brave-and-true/13-Panel-Data-and-Fixed-Effects.ipynb | qiringji/python-causality-handbook | add5ab57a8e755242bdbc3d4d0ee00867f6a1e55 | [
"MIT"
] | null | null | null | causal-inference-for-the-brave-and-true/13-Panel-Data-and-Fixed-Effects.ipynb | qiringji/python-causality-handbook | add5ab57a8e755242bdbc3d4d0ee00867f6a1e55 | [
"MIT"
] | null | null | null | 82.382622 | 23,676 | 0.710046 | true | 9,971 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.870597 | 0.741708 | __label__eng_Latn | 0.986828 | 0.561568 |
# astGl - Uebung 4
## Aufgabe 1
```python
%matplotlib notebook
from sympy import *
import matplotlib.pyplot as plt
from IPython.display import display, Math, Latex
def disp(str):
display(Latex(str))
```
```python
G1,G2,G = symbols('G1,G2,G')
eqg = [
(G1, G),
(G2, G)
]
eqg
```
[(G1, G), (G2, G)]... | 1169d571cc14161bb6416badf02915ef171e096f | 3,455 | ipynb | Jupyter Notebook | astGl/astGl_Uebung4_.ipynb | mnemocron/FHNW | e43c298cb9c8f617fa19b77dd6630a342c78bda7 | [
"Unlicense"
] | 1 | 2020-10-07T07:28:33.000Z | 2020-10-07T07:28:33.000Z | astGl/astGl_Uebung4_.ipynb | mnemocron/FHNW | e43c298cb9c8f617fa19b77dd6630a342c78bda7 | [
"Unlicense"
] | null | null | null | astGl/astGl_Uebung4_.ipynb | mnemocron/FHNW | e43c298cb9c8f617fa19b77dd6630a342c78bda7 | [
"Unlicense"
] | 1 | 2021-01-17T16:38:58.000Z | 2021-01-17T16:38:58.000Z | 19.088398 | 162 | 0.438205 | true | 407 | Qwen/Qwen-72B | 1. YES
2. YES | 0.914901 | 0.766294 | 0.701083 | __label__yue_Hant | 0.386654 | 0.467182 |
# Quadcopter
## Summary
This notebook outlines a the design of a motion controller for a quadcopter.
## Goals
The ultimate goal is to apply the designed control system to a simulated environment - for this I have chosen Python and specifially [pybullet](https://pybullet.org/) as the 3D physics simulator and [pyglet](h... | 89283f93c9f9c7b5e9bdaf815738c52ecb9cc357 | 562,357 | ipynb | Jupyter Notebook | notebooks/quadcopter-3d.ipynb | tristeng/control | dbf99de467e92d998f4fd078057476cdf98537c7 | [
"MIT"
] | 3 | 2020-11-27T10:49:46.000Z | 2021-04-04T03:41:19.000Z | notebooks/quadcopter-3d.ipynb | tristeng/control | dbf99de467e92d998f4fd078057476cdf98537c7 | [
"MIT"
] | null | null | null | notebooks/quadcopter-3d.ipynb | tristeng/control | dbf99de467e92d998f4fd078057476cdf98537c7 | [
"MIT"
] | 2 | 2021-09-23T16:07:38.000Z | 2022-02-08T04:23:46.000Z | 509.381341 | 151,176 | 0.93359 | true | 10,063 | Qwen/Qwen-72B | 1. YES
2. YES | 0.912436 | 0.870597 | 0.794364 | __label__eng_Latn | 0.8875 | 0.683907 |
# Discriminative Classification
G. Richards (2016,2018), based on materials from Connolly, VanderPlas, and Ivezic.
Last time we talked about how to do classification by mapping the full pdf of your parameter space. This time we will concentrate on methods that seek only to determine the **decision boundary**, so cal... | 7cda1f52207e774687eeebfaccd06942f37651a9 | 72,034 | ipynb | Jupyter Notebook | notebooks/Classification2.ipynb | gtrichards/PHYS_T480_F18 | b3ffcd9effb427a67ac0ed50695328f6a91b3f64 | [
"MIT"
] | 12 | 2018-12-26T20:19:42.000Z | 2022-02-10T04:10:00.000Z | notebooks/Classification2.ipynb | gtrichards/PHYS_T480_F18 | b3ffcd9effb427a67ac0ed50695328f6a91b3f64 | [
"MIT"
] | 1 | 2019-07-17T11:46:25.000Z | 2019-07-19T11:41:45.000Z | notebooks/Classification2.ipynb | gtrichards/PHYS_T480_F18 | b3ffcd9effb427a67ac0ed50695328f6a91b3f64 | [
"MIT"
] | 6 | 2018-09-24T00:44:04.000Z | 2020-05-24T02:07:01.000Z | 37.054527 | 660 | 0.548755 | true | 14,230 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.901921 | 0.778711 | __label__eng_Latn | 0.868131 | 0.647538 |
## First Assignment
#### 1) Apply the appropriate string methods to the **x** variable (as '.upper') to change it exactly to: "$Dichlorodiphenyltrichloroethane$".
```python
x = "DiClOrod IFeNi lTRicLOr oETaNo DiChlorod iPHeny lTrichL oroEThaNe"
```
```python
y = x.replace(' ','')
print(y[27:].capitalize())
```
... | 19d84d6fbd2ad03cb3a9fdda6cc7f921dd902567 | 25,673 | ipynb | Jupyter Notebook | Assigments/Assignment_1.ipynb | stkiesling/Python_Course | 57e953677c9d5913da6a7744ca82e2eaf66c2638 | [
"Apache-2.0"
] | null | null | null | Assigments/Assignment_1.ipynb | stkiesling/Python_Course | 57e953677c9d5913da6a7744ca82e2eaf66c2638 | [
"Apache-2.0"
] | null | null | null | Assigments/Assignment_1.ipynb | stkiesling/Python_Course | 57e953677c9d5913da6a7744ca82e2eaf66c2638 | [
"Apache-2.0"
] | null | null | null | 31.773515 | 1,482 | 0.500682 | true | 3,528 | Qwen/Qwen-72B | 1. YES
2. YES | 0.857768 | 0.888759 | 0.762349 | __label__eng_Latn | 0.970853 | 0.609524 |
```python
import sympy as sp
```
```python
v0,l,x,y = sp.symbols('V_0 L x y')
```
```python
eq1 = sp.Eq(x**2/(l/(2*v0))**2+(y-v0)**2,v0**2)
eq1
```
$\displaystyle \left(- V_{0} + y\right)^{2} + \frac{4 V_{0}^{2} x^{2}}{L^{2}} = V_{0}^{2}$
```python
eq = sp.solve(eq1,y)[0]
sp.simplify(eq)
```
$\displayst... | 0db3e54a757fb9eb8dcf4975998083cb2009b2a0 | 1,845 | ipynb | Jupyter Notebook | Potentials/semiellipticalpotential.ipynb | ethank5149/Quantum-Mechanics | 71e1c2a47b8a399bf0ba7e07bb0dcbaa4a2068bd | [
"MIT"
] | null | null | null | Potentials/semiellipticalpotential.ipynb | ethank5149/Quantum-Mechanics | 71e1c2a47b8a399bf0ba7e07bb0dcbaa4a2068bd | [
"MIT"
] | null | null | null | Potentials/semiellipticalpotential.ipynb | ethank5149/Quantum-Mechanics | 71e1c2a47b8a399bf0ba7e07bb0dcbaa4a2068bd | [
"MIT"
] | null | null | null | 20.5 | 116 | 0.478049 | true | 178 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92523 | 0.757794 | 0.701134 | __label__yue_Hant | 0.428198 | 0.467301 |
# <center> Neutrino-Driven Wind Transsonic Velocity Solver </center>
<center>By Brian Nevins</center>
Image from: <a href="https://www.newsweek.com/weird-neutron-star-shouldnt-exist-discovered-scientists-1140445"> Newsweek </a>
---
# Authors
Brian Nevins<br>
Dr. Luke Roberts, Michigan State University
---
# Abstra... | 1bb22b181d717149799235646a281d82ab3ee00f | 91,604 | ipynb | Jupyter Notebook | neutrino-winds/Final Project Report.ipynb | colbrydi/neutrino-winds | 0088a0568841cda00ee8303b797d05be9feab844 | [
"BSD-3-Clause"
] | null | null | null | neutrino-winds/Final Project Report.ipynb | colbrydi/neutrino-winds | 0088a0568841cda00ee8303b797d05be9feab844 | [
"BSD-3-Clause"
] | null | null | null | neutrino-winds/Final Project Report.ipynb | colbrydi/neutrino-winds | 0088a0568841cda00ee8303b797d05be9feab844 | [
"BSD-3-Clause"
] | null | null | null | 243.62766 | 42,804 | 0.91103 | true | 2,354 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.824462 | 0.681592 | __label__eng_Latn | 0.997967 | 0.421899 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi
# Bird's-eye view of mechanical vibrations
Welcome to **Lesson 4** of the third module in _Engineering Computations_. This course module is dedicated to studying the dynamics of change... | cd4dfd5390e927e8d46fd18f35e5060aa1e2c9ef | 337,284 | ipynb | Jupyter Notebook | notebooks_en/4_Birdseye_Vibrations.ipynb | engineersCode/EngCom3_flyatchange | ced7377e7a79e6a82da1249254013faccbd6763a | [
"BSD-3-Clause"
] | 6 | 2019-06-26T17:56:09.000Z | 2019-12-14T17:04:37.000Z | notebooks_en/4_Birdseye_Vibrations.ipynb | engineersCode/EngCom3_flyatchange | ced7377e7a79e6a82da1249254013faccbd6763a | [
"BSD-3-Clause"
] | 1 | 2018-05-18T13:25:58.000Z | 2018-05-19T03:27:05.000Z | notebooks_en/4_Birdseye_Vibrations.ipynb | engineersCode/EngCom3_flyatchange | ced7377e7a79e6a82da1249254013faccbd6763a | [
"BSD-3-Clause"
] | 7 | 2019-10-28T15:53:48.000Z | 2021-09-12T21:43:16.000Z | 308.303473 | 86,364 | 0.910867 | true | 6,953 | Qwen/Qwen-72B | 1. YES
2. YES | 0.76908 | 0.774583 | 0.595717 | __label__eng_Latn | 0.984952 | 0.22238 |
```python
from estado import *
from sympy import *
init_printing(use_unicode=True)
import numpy as np
```
```python
```
```python
estado_inicial_de_busca = estado('water','gas',200,120.46850585938)
estado_finalB = busca_estado('specific_enthalpy',2802.88935988,'T',estado_inicial_de_busca, precision=0.9)
```
... | 2d48b510740da1fb31c922d670d66a9befcc68a3 | 15,475 | ipynb | Jupyter Notebook | Thermo/Estudo.ipynb | victorathanasio/Personal-projects | 94c870179cec32aa733a612a6faeb047df16d977 | [
"MIT"
] | null | null | null | Thermo/Estudo.ipynb | victorathanasio/Personal-projects | 94c870179cec32aa733a612a6faeb047df16d977 | [
"MIT"
] | null | null | null | Thermo/Estudo.ipynb | victorathanasio/Personal-projects | 94c870179cec32aa733a612a6faeb047df16d977 | [
"MIT"
] | null | null | null | 33.936404 | 139 | 0.549208 | true | 3,648 | Qwen/Qwen-72B | 1. YES
2. YES | 0.793106 | 0.805632 | 0.638952 | __label__por_Latn | 0.855513 | 0.32283 |
# "Symbolic Euler's Method"
> "Applying Euler's Method to ODE , but with a twist: we're going to call method with Symbolic variables"
- toc: true
- badges: true
- comments: true
- categories: [jupyter, math, calculus, symbolics, julialang]
```julia
#collapse-show
# load dependacies
using MyCalculus
using Plots
usin... | ca76e14ca7d2e0fcb32e1dc68f1f4f5437a2666f | 80,043 | ipynb | Jupyter Notebook | src/2021-12-28-EulersMethod.ipynb | gjunqueira-sys/MyCalculus.jl | 9a1dee9be36b805e9523ca6d047d827f58c29a62 | [
"MIT"
] | null | null | null | src/2021-12-28-EulersMethod.ipynb | gjunqueira-sys/MyCalculus.jl | 9a1dee9be36b805e9523ca6d047d827f58c29a62 | [
"MIT"
] | null | null | null | src/2021-12-28-EulersMethod.ipynb | gjunqueira-sys/MyCalculus.jl | 9a1dee9be36b805e9523ca6d047d827f58c29a62 | [
"MIT"
] | null | null | null | 236.813609 | 26,720 | 0.741102 | true | 1,750 | Qwen/Qwen-72B | 1. YES
2. YES | 0.880797 | 0.857768 | 0.75552 | __label__eng_Latn | 0.618529 | 0.593657 |
---
## 30. Integración Numérica
Eduard Larrañaga (ealarranaga@unal.edu.co)
---
### Resumen
En este cuaderno se presentan algunas técnicas de integración numérica.
---
Una de las tareas más comunes en astrofísica es evaluar integrales como
\begin{equation}
I = \int_a^b f(x) dx ,
\end{equation}
y, en muchos c... | 5d640532850390f62842f65b21a1b6d4e87f7cc4 | 130,619 | ipynb | Jupyter Notebook | 03. Integracion/01. Integracion.ipynb | jegonzalezba/AstrofisicaComputacional2022 | eeacf21b2b2cf1605149fd57ba39f8e14aa7309e | [
"MIT"
] | 1 | 2022-03-26T21:47:31.000Z | 2022-03-26T21:47:31.000Z | 03. Integracion/01. Integracion.ipynb | jegonzalezba/AstrofisicaComputacional2022 | eeacf21b2b2cf1605149fd57ba39f8e14aa7309e | [
"MIT"
] | null | null | null | 03. Integracion/01. Integracion.ipynb | jegonzalezba/AstrofisicaComputacional2022 | eeacf21b2b2cf1605149fd57ba39f8e14aa7309e | [
"MIT"
] | null | null | null | 178.685363 | 21,120 | 0.887329 | true | 4,443 | Qwen/Qwen-72B | 1. YES
2. YES | 0.901921 | 0.822189 | 0.741549 | __label__spa_Latn | 0.737932 | 0.5612 |
# Scenario A - Noise Level Variation (multiple runs for init mode)
In this scenario the noise level on a generated dataset is varied in three steps: low/medium/high,
the rest of the parameters in the dataset is kept constant.
The model used in the inference of the parameters is formulated as follows:
\begin{equati... | 81985c20076858b3685c35e442f094780782a5e5 | 8,954 | ipynb | Jupyter Notebook | code/scenarios/scenario_a/scenario_noise_mruns.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | 1 | 2021-01-07T02:22:25.000Z | 2021-01-07T02:22:25.000Z | code/scenarios/scenario_a/scenario_noise_mruns.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | null | null | null | code/scenarios/scenario_a/scenario_noise_mruns.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | null | null | null | 25.65616 | 148 | 0.527362 | true | 1,363 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.685949 | 0.540481 | __label__eng_Latn | 0.697599 | 0.094048 |
$$ \newcommand{\pd}[2]{ \frac{\partial #1}{\partial #2} }
\newcommand{\od}[2]{\frac{d #1}{d #2}}
\newcommand{\td}[2]{\frac{D #1}{D #2}}
\newcommand{\ab}[1]{\langle #1 \rangle}
\newcommand{\bss}[1]{\textsf{\textbf{#1}}}
\newcommand{\ol}{\overline}
\newcommand{\olx}[1]{\overline{#1}^x}
$$
# Hydrostatic and Geostrophic B... | 0a9c457bb77eebf8099c4d7c518e6c3c82494c99 | 291,727 | ipynb | Jupyter Notebook | book/06_hydrostatic_geostrophic.ipynb | monocilindro/intro_to_physical_oceanography | 1cd76829d94dcbd13e5e81c923db924ff0798c1b | [
"MIT"
] | 82 | 2015-09-18T02:01:53.000Z | 2022-02-28T01:43:48.000Z | book/06_hydrostatic_geostrophic.ipynb | monocilindro/intro_to_physical_oceanography | 1cd76829d94dcbd13e5e81c923db924ff0798c1b | [
"MIT"
] | 5 | 2015-09-19T01:35:28.000Z | 2022-02-28T17:23:53.000Z | book/06_hydrostatic_geostrophic.ipynb | monocilindro/intro_to_physical_oceanography | 1cd76829d94dcbd13e5e81c923db924ff0798c1b | [
"MIT"
] | 51 | 2015-09-12T00:30:33.000Z | 2022-02-08T19:37:51.000Z | 427.752199 | 261,932 | 0.923346 | true | 7,081 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.835484 | 0.706771 | __label__eng_Latn | 0.529117 | 0.480397 |
# R(2,2) playground
Ted Corcovilos, 2021-01-08
Playing around with the 2d "mother algebra" $R(2,2)$, as described in C. Doran, et al., "Lie Groups as Spin Groups," *Journal of Mathematical Physics 34*, 3642 (1993). doi:[10.1063/1.530050](http://doi.org/10.1063/1.530050)
I'll name the basis vectors $p_1, p_2, m_1, m_2... | db9f4202f01771b3c4b8ecf99512a5878be1504f | 16,934 | ipynb | Jupyter Notebook | R22.ipynb | corcoted/GA-scratch | a7ba5da5fa758f52330c4e1218d56c2e193a3091 | [
"MIT"
] | null | null | null | R22.ipynb | corcoted/GA-scratch | a7ba5da5fa758f52330c4e1218d56c2e193a3091 | [
"MIT"
] | null | null | null | R22.ipynb | corcoted/GA-scratch | a7ba5da5fa758f52330c4e1218d56c2e193a3091 | [
"MIT"
] | null | null | null | 25.050296 | 450 | 0.483879 | true | 2,897 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.810479 | 0.7332 | __label__eng_Latn | 0.783089 | 0.541801 |
# Principle of least action
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
The [principle of least action](https://en.wikipedia.org/wiki/Principle_of_least_action) applied to the movement of a mechanical system states t... | cadf6c94eba05b34b3da8073e212031e753205ae | 3,823 | ipynb | Jupyter Notebook | notebooks/principle_of_least_action.ipynb | gbiomech/BMC | fec9413b17a54f00ba6818438f7a50b132353e42 | [
"CC-BY-4.0"
] | 1 | 2022-01-07T22:30:39.000Z | 2022-01-07T22:30:39.000Z | notebooks/principle_of_least_action.ipynb | gbiomech/BMC | fec9413b17a54f00ba6818438f7a50b132353e42 | [
"CC-BY-4.0"
] | null | null | null | notebooks/principle_of_least_action.ipynb | gbiomech/BMC | fec9413b17a54f00ba6818438f7a50b132353e42 | [
"CC-BY-4.0"
] | null | null | null | 36.409524 | 419 | 0.608423 | true | 569 | Qwen/Qwen-72B | 1. YES
2. YES | 0.79053 | 0.822189 | 0.649965 | __label__eng_Latn | 0.978064 | 0.348418 |
## Performance Indicator
It is fundamental for any algorithm to measure the performance. In a multi-objective scenario, we can not calculate the distance to the true global optimum but must consider a set of solutions. Moreover, sometimes the optimum is not even known, and other techniques must be used.
First, let u... | 8b9050be9a8b0d98a1138eec121c860bb92828b4 | 8,988 | ipynb | Jupyter Notebook | doc/source/misc/performance_indicator.ipynb | renansantosmendes/benchmark_tests | 106f842b304a7fc9fa348ea0b6d50f448e46538b | [
"Apache-2.0"
] | null | null | null | doc/source/misc/performance_indicator.ipynb | renansantosmendes/benchmark_tests | 106f842b304a7fc9fa348ea0b6d50f448e46538b | [
"Apache-2.0"
] | null | null | null | doc/source/misc/performance_indicator.ipynb | renansantosmendes/benchmark_tests | 106f842b304a7fc9fa348ea0b6d50f448e46538b | [
"Apache-2.0"
] | null | null | null | 26.91018 | 395 | 0.556965 | true | 1,358 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.877477 | 0.79381 | __label__eng_Latn | 0.97428 | 0.682619 |
## bayestestimation basis
The bayestestimation module uses a hierachical Bayesian model to estimate the posterior distributions of two samples, the parameters of these samples can be approximated by simulation, as can the difference in the paramters.
#### Sections
- Specifying the hierachial model
- Estimating the p... | 85ea9aee95acae26b4e3ff748ac959ce50dff91e | 3,407 | ipynb | Jupyter Notebook | docs/bayestestimation_basis.ipynb | oli-chipperfield/bayestestimation | aace7949fff01af2b574334a1a1fd2ce93fe10f4 | [
"MIT"
] | 1 | 2021-01-29T01:33:52.000Z | 2021-01-29T01:33:52.000Z | docs/bayestestimation_basis.ipynb | oli-chipperfield/bayestestimation | aace7949fff01af2b574334a1a1fd2ce93fe10f4 | [
"MIT"
] | null | null | null | docs/bayestestimation_basis.ipynb | oli-chipperfield/bayestestimation | aace7949fff01af2b574334a1a1fd2ce93fe10f4 | [
"MIT"
] | null | null | null | 34.07 | 285 | 0.59319 | true | 624 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.787931 | 0.707322 | __label__eng_Latn | 0.977696 | 0.481678 |
# Solutions to Exercises (not Activities) in the Bohemian Unit
1: Write down as many questions as you can for this unit.
Maybe this is the most important one of these in this book (OER). Your questions are as likely as ours to be productive. But, here are some of ours. Most have no answers that we know of. In no ... | 5ac3944988c175044bf22e2d1a09eb4cebd863de | 112,685 | ipynb | Jupyter Notebook | book/Solutions/Solutions to Exercises (not Activities) in the Bohemian Unit.ipynb | jameshughes89/Computational-Discovery-on-Jupyter | 614eaaae126082106e1573675599e6895d09d96d | [
"MIT"
] | 14 | 2022-02-21T23:50:22.000Z | 2022-03-23T22:21:55.000Z | book/Solutions/Solutions to Exercises (not Activities) in the Bohemian Unit.ipynb | jameshughes89/Computational-Discovery-on-Jupyter | 614eaaae126082106e1573675599e6895d09d96d | [
"MIT"
] | null | null | null | book/Solutions/Solutions to Exercises (not Activities) in the Bohemian Unit.ipynb | jameshughes89/Computational-Discovery-on-Jupyter | 614eaaae126082106e1573675599e6895d09d96d | [
"MIT"
] | 2 | 2022-02-22T02:43:44.000Z | 2022-02-23T14:27:31.000Z | 629.52514 | 103,600 | 0.942787 | true | 1,681 | Qwen/Qwen-72B | 1. YES
2. YES | 0.699254 | 0.828939 | 0.579639 | __label__eng_Latn | 0.998974 | 0.185026 |
# Getting started with TensorFlow (Eager Mode)
**Learning Objectives**
- Understand difference between Tensorflow's two modes: Eager Execution and Graph Execution
- Practice defining and performing basic operations on constant Tensors
- Use Tensorflow's automatic differentiation capability
## Introduction
**Ea... | 4403e592643fa21744f90f2c71020bacadd9315b | 64,233 | ipynb | Jupyter Notebook | courses/machine_learning/deepdive/02_tensorflow/a_tfstart_eager.ipynb | kamalaboulhosn/training-data-analyst | 41b2464a562b8d1d2699e4a6acc01ca3bb083d90 | [
"Apache-2.0"
] | 3 | 2019-06-27T16:32:45.000Z | 2019-08-09T17:37:22.000Z | courses/machine_learning/deepdive/02_tensorflow/a_tfstart_eager.ipynb | yungshenglu/training-data-analyst | 6cf69648400705298a88c2feeb69de1c593e245a | [
"Apache-2.0"
] | 6 | 2020-01-28T22:55:06.000Z | 2022-02-10T00:32:23.000Z | courses/machine_learning/deepdive/02_tensorflow/a_tfstart_eager.ipynb | yungshenglu/training-data-analyst | 6cf69648400705298a88c2feeb69de1c593e245a | [
"Apache-2.0"
] | 4 | 2020-05-15T06:23:05.000Z | 2021-12-20T06:00:15.000Z | 114.701786 | 22,368 | 0.854841 | true | 2,543 | Qwen/Qwen-72B | 1. YES
2. YES | 0.903294 | 0.896251 | 0.809579 | __label__eng_Latn | 0.918508 | 0.719255 |
_Lambda School Data Science_
# Ordinary Least Squares Regression
## What is Linear Regression?
Linear Regression is a statistical model that seeks to describe the relationship between some y variable and one or more x variables.
In the simplest case, linear regression seeks to fit a straight line through a cloud... | be0eda1a091b44accb5ead828cd32329eee621bb | 265,801 | ipynb | Jupyter Notebook | module1-ols-regression/ols-regression.ipynb | Jaavion/DS-Unit-2-Sprint-2-Regression | 42dfba88be9d346a31b017f15893b697ede2b185 | [
"MIT"
] | null | null | null | module1-ols-regression/ols-regression.ipynb | Jaavion/DS-Unit-2-Sprint-2-Regression | 42dfba88be9d346a31b017f15893b697ede2b185 | [
"MIT"
] | null | null | null | module1-ols-regression/ols-regression.ipynb | Jaavion/DS-Unit-2-Sprint-2-Regression | 42dfba88be9d346a31b017f15893b697ede2b185 | [
"MIT"
] | null | null | null | 71.509551 | 25,006 | 0.648718 | true | 17,519 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771843 | 0.651355 | 0.502744 | __label__eng_Latn | 0.661891 | 0.006372 |
# Model Project
***
_In this model project we will present a simple Robinson Crusoe production economy. We will solve the model analytically using sympy, evaluate the markets in different parameterizations of price and wage and visualize one solution_
## The theoretical model:
Imagine that Crusoe is schizophenic an... | cdaba8bdaad60deb04573dacea255ed432411996 | 75,219 | ipynb | Jupyter Notebook | modelproject/ModelProject4.ipynb | NumEconCopenhagen/projects-2019-cl | 39de2cd51b04af07852cd2f3e614809373c6fb82 | [
"MIT"
] | null | null | null | modelproject/ModelProject4.ipynb | NumEconCopenhagen/projects-2019-cl | 39de2cd51b04af07852cd2f3e614809373c6fb82 | [
"MIT"
] | 8 | 2019-04-14T15:53:56.000Z | 2019-05-14T21:53:36.000Z | modelproject/ModelProject4.ipynb | NumEconCopenhagen/projects-2019-cl | 39de2cd51b04af07852cd2f3e614809373c6fb82 | [
"MIT"
] | null | null | null | 149.244048 | 41,052 | 0.858374 | true | 2,247 | Qwen/Qwen-72B | 1. YES
2. YES | 0.851953 | 0.752013 | 0.640679 | __label__eng_Latn | 0.989257 | 0.326843 |
# Cariberation (TOP LEFT -> Bottom Right)
```python
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
# text cv2 puttext
font = cv2.FONT_HERSHEY_SIMPLEX
location = (100,50)
fontScale = 1
fontColor = (255,255,255)
line... | ff2e912cc762621473b876349d89a8f6b0c62e0c | 12,065 | ipynb | Jupyter Notebook | Hackverse/Cursor/.ipynb_checkpoints/Cursor-checkpoint.ipynb | princesinghr1/team_Light | e015f9517e5347fe6fd731928e99697621d70a81 | [
"MIT"
] | null | null | null | Hackverse/Cursor/.ipynb_checkpoints/Cursor-checkpoint.ipynb | princesinghr1/team_Light | e015f9517e5347fe6fd731928e99697621d70a81 | [
"MIT"
] | null | null | null | Hackverse/Cursor/.ipynb_checkpoints/Cursor-checkpoint.ipynb | princesinghr1/team_Light | e015f9517e5347fe6fd731928e99697621d70a81 | [
"MIT"
] | 2 | 2021-02-27T07:53:30.000Z | 2021-02-27T07:54:04.000Z | 30.31407 | 98 | 0.458185 | true | 2,169 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.692642 | 0.566286 | __label__eng_Latn | 0.373711 | 0.154003 |
# Radar FMCW and CSM algorithms
This notebook shows a naive (didactically intuitive) implementation of following algorithms:
- FMCW (Frequency Modulated Continuous Wave)
- CSM (Chirp Sequence Modulation)
Both algorithms are used for range and velocity measurements in automated/assisted driving domain.
## Introducti... | 7bb173bd02ae1356b1a8b4a37319026f2e918dd1 | 355,928 | ipynb | Jupyter Notebook | RADAR.ipynb | kopytjuk/fmcw | ceba9f71e41d54c3b339c7e40a840a3d8db542d8 | [
"MIT"
] | 31 | 2019-12-23T05:06:19.000Z | 2022-02-22T17:19:01.000Z | RADAR.ipynb | kopytjuk/fmcw | ceba9f71e41d54c3b339c7e40a840a3d8db542d8 | [
"MIT"
] | null | null | null | RADAR.ipynb | kopytjuk/fmcw | ceba9f71e41d54c3b339c7e40a840a3d8db542d8 | [
"MIT"
] | 9 | 2020-05-06T20:54:58.000Z | 2022-02-13T09:42:35.000Z | 215.322444 | 65,232 | 0.902295 | true | 8,585 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.810479 | 0.722794 | __label__eng_Latn | 0.511098 | 0.517624 |
# Introduction:
Une sonde spatiale est un véhicule spatial sans équipage lancé dans l'espace pour étudier à plus ou moins grande distance différents objets célestes et elle est amenée à franchir de grandes distances et à fonctionner loin de la Terre et du Soleil. Le facteur principale qui doit être mis en jeu afin de ... | 3a7dd694ce46b423fde21a4818bb5e69fd2122fd | 639,424 | ipynb | Jupyter Notebook | Solar System in 2D.ipynb | mhibatallah/Simulating-the-New-Horizon-Space-Probe-Trajectory | c90558a1b82b6c8d738f06dc1a0dd341657e5f9f | [
"MIT"
] | null | null | null | Solar System in 2D.ipynb | mhibatallah/Simulating-the-New-Horizon-Space-Probe-Trajectory | c90558a1b82b6c8d738f06dc1a0dd341657e5f9f | [
"MIT"
] | null | null | null | Solar System in 2D.ipynb | mhibatallah/Simulating-the-New-Horizon-Space-Probe-Trajectory | c90558a1b82b6c8d738f06dc1a0dd341657e5f9f | [
"MIT"
] | null | null | null | 505.07425 | 129,848 | 0.928847 | true | 9,388 | Qwen/Qwen-72B | 1. YES
2. YES | 0.760651 | 0.685949 | 0.521768 | __label__fra_Latn | 0.865877 | 0.050571 |
```python
import numpy as np
import numpy.linalg as nl
import numpy.random as nr
import sympy as sy
import IPython.display as disp
sy.init_printing()
```
# 역행렬<br>Inverse matrix
## 2x2
다음 비디오는 역행열 찾는 가우스 조단법을 소개한다.<br>
Following video introduces Gauss Jordan method finding the inverse matrix. (36:23 ~ 42:20)
... | db9e25653564fec6f10a9e8dfc127cd7bb4f51bb | 18,290 | ipynb | Jupyter Notebook | 60_linear_algebra_2/150_Inverse_matrix.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 60_linear_algebra_2/150_Inverse_matrix.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 60_linear_algebra_2/150_Inverse_matrix.ipynb | kangwonlee/2009eca-nmisp-template | 46a09c988c5e0c4efd493afa965d4a17d32985e8 | [
"BSD-3-Clause"
] | null | null | null | 18.456105 | 219 | 0.470585 | true | 2,455 | Qwen/Qwen-72B | 1. YES
2. YES | 0.817574 | 0.839734 | 0.686545 | __label__kor_Hang | 0.890633 | 0.433405 |
```python
from sympy import *
init_printing()
```
```python
K,L,r,w,p,T = symbols('K L r w p T',
real=True,
positive=True,
finite=True)
production = T * K * L
cost = r*K + w*L**2
profit = p * production - cost
profit
```
```python
DK = profit.diff(K)
DL = profi... | faa1a83889e1a9b508086f497b6df7ad34b05361 | 15,389 | ipynb | Jupyter Notebook | assets/pdfs/math_bootcamp/final2017/problem_2.ipynb | joepatten/joepatten.github.io | 4b9acc8720f3a33337368fee719902b54a6f2f68 | [
"MIT"
] | null | null | null | assets/pdfs/math_bootcamp/final2017/problem_2.ipynb | joepatten/joepatten.github.io | 4b9acc8720f3a33337368fee719902b54a6f2f68 | [
"MIT"
] | 5 | 2020-08-09T16:28:31.000Z | 2020-08-10T14:48:57.000Z | assets/pdfs/math_bootcamp/final2017/problem_2.ipynb | joepatten/joepatten.github.io | 4b9acc8720f3a33337368fee719902b54a6f2f68 | [
"MIT"
] | null | null | null | 49.964286 | 1,920 | 0.742673 | true | 254 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92944 | 0.815232 | 0.75771 | __label__eng_Latn | 0.563114 | 0.598746 |
Consider the standard incomplete markets model and answer the following:
Write a python program that returns the recursive competitive equilibrium for a economy with
the following parameters:
* intertemporal discount factor($\beta$) = 0.98;
* CRRA utility function with $\sigma$ = 2;
* depreciation rate $\del... | 0764087b10f6cf068544d46eaaa8085247b42c3c | 19,471 | ipynb | Jupyter Notebook | Recursive Equilibrium.ipynb | valcareggi/Macroeconomics | e6b1165aeacf2369b70f2d710a198962c3390864 | [
"MIT"
] | null | null | null | Recursive Equilibrium.ipynb | valcareggi/Macroeconomics | e6b1165aeacf2369b70f2d710a198962c3390864 | [
"MIT"
] | null | null | null | Recursive Equilibrium.ipynb | valcareggi/Macroeconomics | e6b1165aeacf2369b70f2d710a198962c3390864 | [
"MIT"
] | null | null | null | 29.90937 | 488 | 0.50963 | true | 4,080 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.746139 | 0.661978 | __label__eng_Latn | 0.872525 | 0.376328 |
<a href="https://colab.research.google.com/github/mohd-faizy/Probabilistic-Deep-Learning-with-TensorFlow/blob/main/Week_3_Programming_Assignment.ipynb" target="_parent"></a>
# Programming Assignment
## RealNVP for the LSUN bedroom dataset
### Instructions
In this notebook, you will develop the RealNVP normalising f... | 603d7060cefbc6df27c52fb8522d12338273c4dd | 206,634 | ipynb | Jupyter Notebook | 03_Bijectors_and_Normalising_Flows/Week_3_Programming_Assignment.ipynb | mohd-faizy/07T_Probabilistic-Deep-Learning-with-TensorFlow- | 3cf6719c55c1744f820c2a437ce986cc0e71ba61 | [
"MIT"
] | 28 | 2020-12-21T16:28:38.000Z | 2022-03-25T16:12:43.000Z | 03_Bijectors_and_Normalising_Flows/Week_3_Programming_Assignment.ipynb | mohd-faizy/07T_Probabilistic-Deep-Learning-with-TensorFlow- | 3cf6719c55c1744f820c2a437ce986cc0e71ba61 | [
"MIT"
] | null | null | null | 03_Bijectors_and_Normalising_Flows/Week_3_Programming_Assignment.ipynb | mohd-faizy/07T_Probabilistic-Deep-Learning-with-TensorFlow- | 3cf6719c55c1744f820c2a437ce986cc0e71ba61 | [
"MIT"
] | 20 | 2021-01-08T10:55:46.000Z | 2022-03-31T23:00:45.000Z | 114.099393 | 96,590 | 0.801494 | true | 12,174 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.795658 | 0.622732 | __label__eng_Latn | 0.921716 | 0.285145 |
# Modelproject - Cournot competition
## Introduction
In this project, we find the optimal production quantity for each of two firms in a Cournot - competition. We compare the situation with two identical firms and two non-identical firms.
### We apply following assumptions for the model:
* There are two firms in the... | 66f3610e2b9de6e0dc757fbe2f37bb812bc5ae89 | 24,393 | ipynb | Jupyter Notebook | Modelproject-Cournot99.ipynb | NumEconCopenhagen/projects-2020-the-group | 2c408fc5541265277373db5f37e1cf7b42af775f | [
"MIT"
] | null | null | null | Modelproject-Cournot99.ipynb | NumEconCopenhagen/projects-2020-the-group | 2c408fc5541265277373db5f37e1cf7b42af775f | [
"MIT"
] | 12 | 2020-04-13T08:35:03.000Z | 2020-05-13T11:02:36.000Z | Modelproject-Cournot99.ipynb | NumEconCopenhagen/projects-2020-the-group | 2c408fc5541265277373db5f37e1cf7b42af775f | [
"MIT"
] | 1 | 2020-03-16T12:36:53.000Z | 2020-03-16T12:36:53.000Z | 28.463244 | 195 | 0.537449 | true | 3,860 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.766294 | 0.660191 | __label__eng_Latn | 0.959608 | 0.372176 |
```python
%matplotlib inline
import warnings
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import gridspec
warnings.filterwarnings('ignore')
```
<style type="text/css">
.input, .output_prompt {
display:none !important;
}
</style>
# Introduction to Pulsar Timing
[](http://mybinder.org/repo/ma... | cbb27b8b5e2935e50639f57079efcfe47d6a0916 | 37,815 | ipynb | Jupyter Notebook | lectures/Day4-FourierMethods/1_Introduction_to_pulsar_timing.ipynb | carmensg/IAA_School2019 | e07274d0b3437ccedc5d306b7f86f4a12535b1a2 | [
"BSD-2-Clause"
] | null | null | null | lectures/Day4-FourierMethods/1_Introduction_to_pulsar_timing.ipynb | carmensg/IAA_School2019 | e07274d0b3437ccedc5d306b7f86f4a12535b1a2 | [
"BSD-2-Clause"
] | null | null | null | lectures/Day4-FourierMethods/1_Introduction_to_pulsar_timing.ipynb | carmensg/IAA_School2019 | e07274d0b3437ccedc5d306b7f86f4a12535b1a2 | [
"BSD-2-Clause"
] | 4 | 2019-10-18T05:11:00.000Z | 2021-11-23T13:42:04.000Z | 33.793566 | 331 | 0.513606 | true | 7,253 | Qwen/Qwen-72B | 1. YES
2. YES | 0.885631 | 0.805632 | 0.713493 | __label__eng_Latn | 0.82114 | 0.496015 |
<!-- dom:TITLE: Computational Physics Lectures: Partial differential equations -->
# Computational Physics Lectures: Partial differential equations
<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory... | 3a635af85d9f4de1e1809cda3e15ebd2d060faf9 | 133,206 | ipynb | Jupyter Notebook | doc/pub/pde/ipynb/pde.ipynb | halvarsu/ComputationalPhysics | a300693de89968f3d976aa68121a4e1c135ffab3 | [
"CC0-1.0"
] | 1 | 2019-04-12T12:30:48.000Z | 2019-04-12T12:30:48.000Z | doc/pub/pde/ipynb/pde.ipynb | cosmologist10/ComputationalPhysics | c6642becb1036e2faaf4f1da78a31785b2033fe7 | [
"CC0-1.0"
] | null | null | null | doc/pub/pde/ipynb/pde.ipynb | cosmologist10/ComputationalPhysics | c6642becb1036e2faaf4f1da78a31785b2033fe7 | [
"CC0-1.0"
] | null | null | null | 29.263181 | 284 | 0.491006 | true | 25,997 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.782662 | 0.828939 | 0.648779 | __label__eng_Latn | 0.963299 | 0.345662 |
```python
import sys
print("Using Python {}.{}.".format(sys.version_info.major, sys.version_info.minor))
```
Using Python 3.9.
## Importing packages
```python
from sympy import *
from scipy.optimize import toms748
from scipy.integrate import solve_ivp
from scipy.integrate import quad
import numpy as np
init_p... | 08e3fd1a5fa2188dd96219afafa17f591723be91 | 40,664 | ipynb | Jupyter Notebook | PythonCodes/Exercises/Class-SEAS/.ipynb_checkpoints/BEM-checkpoint.ipynb | Nicolucas/C-Scripts | 2608df5c2e635ad16f422877ff440af69f98f960 | [
"MIT"
] | 1 | 2020-02-25T08:05:13.000Z | 2020-02-25T08:05:13.000Z | PythonCodes/Exercises/Class-SEAS/.ipynb_checkpoints/BEM-checkpoint.ipynb | Nicolucas/C-Scripts | 2608df5c2e635ad16f422877ff440af69f98f960 | [
"MIT"
] | null | null | null | PythonCodes/Exercises/Class-SEAS/.ipynb_checkpoints/BEM-checkpoint.ipynb | Nicolucas/C-Scripts | 2608df5c2e635ad16f422877ff440af69f98f960 | [
"MIT"
] | null | null | null | 87.074946 | 5,800 | 0.824611 | true | 1,087 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.651355 | 0.585647 | __label__eng_Latn | 0.310517 | 0.198984 |
# Linear models with CNN features
```python
# Rather than importing everything manually, we'll make things easy
# and load them all in utils.py, and just import them from there.
%matplotlib inline
import utils; reload(utils)
from utils import *
```
## Introduction
We need to find a way to convert the imagenet pre... | fdc05f00441f36e4a2d82812e2ea9b1ee967fb1e | 42,942 | ipynb | Jupyter Notebook | deeplearning1/nbs/lesson2.ipynb | mribbons/fastaicourses | 0c854faca4f107058668858878668d2cc5fadd35 | [
"Apache-2.0"
] | null | null | null | deeplearning1/nbs/lesson2.ipynb | mribbons/fastaicourses | 0c854faca4f107058668858878668d2cc5fadd35 | [
"Apache-2.0"
] | null | null | null | deeplearning1/nbs/lesson2.ipynb | mribbons/fastaicourses | 0c854faca4f107058668858878668d2cc5fadd35 | [
"Apache-2.0"
] | null | null | null | 28.103403 | 770 | 0.605235 | true | 5,701 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91611 | 0.815232 | 0.746842 | __label__eng_Latn | 0.996663 | 0.573497 |
<a href="https://colab.research.google.com/github/Alro10/PyTorch1.xTutorials/blob/master/04-Neural-Network/04_NeuralNets_mnist.ipynb" target="_parent"></a>
# Neural Networks
This is a tutorial for using shallow neural networks (NNets). The magic ReLU activation is a part of NNets arquitecture:
\begin{equation}
f(x)=... | 27d4dc87aa2a88c7628ba4b984565f2baca27e8a | 10,698 | ipynb | Jupyter Notebook | lesson04-Neural-Network/04_NeuralNets_mnist.ipynb | Alro10/PyTorch1.0Tutorials | f37ac6e4ed877a0e8f69d986db3a18c1ba571975 | [
"MIT"
] | 1 | 2019-08-16T01:40:16.000Z | 2019-08-16T01:40:16.000Z | lesson04-Neural-Network/04_NeuralNets_mnist.ipynb | Alro10/PyTorch1.0Tutorials | f37ac6e4ed877a0e8f69d986db3a18c1ba571975 | [
"MIT"
] | null | null | null | lesson04-Neural-Network/04_NeuralNets_mnist.ipynb | Alro10/PyTorch1.0Tutorials | f37ac6e4ed877a0e8f69d986db3a18c1ba571975 | [
"MIT"
] | null | null | null | 32.222892 | 262 | 0.439615 | true | 1,501 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.640636 | 0.537964 | __label__yue_Hant | 0.24917 | 0.088199 |
# Time dependent tensile response
```python
%matplotlib widget
import matplotlib.pylab as plt
from bmcs_beam.tension.time_dependent_cracking import TimeDependentCracking
```
```python
import sympy as sp
sp.init_printing()
import numpy as np
```
# Single material point
## Time dependent function
```python
TimeD... | c0fe65f552589e931cc19437d7f4ea4f2bc500ef | 72,293 | ipynb | Jupyter Notebook | bmcs_beam/tension/time_dependent_cracking.ipynb | bmcs-group/bmcs_beam | b53967d0d0461657ec914a3256ec40f9dcff80d5 | [
"MIT"
] | 1 | 2021-05-07T11:10:27.000Z | 2021-05-07T11:10:27.000Z | bmcs_beam/tension/time_dependent_cracking.ipynb | bmcs-group/bmcs_beam | b53967d0d0461657ec914a3256ec40f9dcff80d5 | [
"MIT"
] | null | null | null | bmcs_beam/tension/time_dependent_cracking.ipynb | bmcs-group/bmcs_beam | b53967d0d0461657ec914a3256ec40f9dcff80d5 | [
"MIT"
] | null | null | null | 76.662778 | 23,612 | 0.800562 | true | 2,424 | Qwen/Qwen-72B | 1. YES
2. YES | 0.90599 | 0.746139 | 0.675994 | __label__eng_Latn | 0.635677 | 0.408892 |
# Analytical problem
Defining a problem with an explicit mathematical representation is straightforwars.
As an example, consider the following multiobjective optimization problem
\begin{equation}
\begin{aligned}
& \underset{\mathbf x}{\text{min}}
& & x_1^2 - x_2; x_2^2 - 3x_1 \\
& \text{s.t.} & & x_1 + x_2 \leq 10 \... | 62f4b411b18df3ff4d60773c795725dc6ec5e25e | 4,737 | ipynb | Jupyter Notebook | notebooks/analytical_problem.ipynb | gialmisi/DESDEOv2 | 0eeb4687d2e539845ab86a5018ff99b92e4ca5cf | [
"MIT"
] | 1 | 2019-08-08T05:11:21.000Z | 2019-08-08T05:11:21.000Z | notebooks/analytical_problem.ipynb | gialmisi/DESDEOv2 | 0eeb4687d2e539845ab86a5018ff99b92e4ca5cf | [
"MIT"
] | 3 | 2019-08-25T08:49:33.000Z | 2019-09-06T08:06:46.000Z | notebooks/analytical_problem.ipynb | gialmisi/DESDEOv2 | 0eeb4687d2e539845ab86a5018ff99b92e4ca5cf | [
"MIT"
] | 1 | 2019-11-07T14:42:29.000Z | 2019-11-07T14:42:29.000Z | 28.709091 | 420 | 0.568503 | true | 751 | Qwen/Qwen-72B | 1. YES
2. YES | 0.972415 | 0.897695 | 0.872932 | __label__eng_Latn | 0.954038 | 0.866447 |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, C.D. Cooper, G.F. Forsyth.
# Reaction-diffusion model
This IPython Notebook presents the context and set-up for the coding assignment of Module 4: _Spreading out: Diffusion problems_, of the course [**"Prac... | be0d7f2dddf8fb43d17b72548ca3d1157e2189f9 | 287,907 | ipynb | Jupyter Notebook | lessons/04_spreadout/06_Reaction_Diffusion.ipynb | SrLobo1/numerical-mooc | 202c3859c5545099cbe8e69702c45475eadf5329 | [
"CC-BY-3.0"
] | 1 | 2017-02-10T12:09:09.000Z | 2017-02-10T12:09:09.000Z | lessons/04_spreadout/06_Reaction_Diffusion.ipynb | albertonogueira/numerical-mooc | dd95e650310502b5cdfe6e405ed7ab7e1496d233 | [
"CC-BY-3.0"
] | null | null | null | lessons/04_spreadout/06_Reaction_Diffusion.ipynb | albertonogueira/numerical-mooc | dd95e650310502b5cdfe6e405ed7ab7e1496d233 | [
"CC-BY-3.0"
] | null | null | null | 531.193727 | 268,055 | 0.92524 | true | 3,351 | Qwen/Qwen-72B | 1. YES
2. YES | 0.870597 | 0.808067 | 0.703501 | __label__eng_Latn | 0.964743 | 0.4728 |
#Commutator and expansion based computations with Python & Sympy
```
from sympy.physics.quantum import Commutator, Dagger, Operator
from sympy import simplify, exp, series
init_printing()
t = Symbol("t")
```
Here's a quick demo on how to do computations with commutators and expansions involving operators with Python... | daf0767276fc78e3114e41a809195bc72f160bb7 | 12,143 | ipynb | Jupyter Notebook | ipythonNotebooks/commutators_and_sympy.ipynb | kgourgou/blog | c9da56dc87a2b349efe06972a59706bfb181b197 | [
"MIT"
] | 2 | 2015-12-02T06:18:58.000Z | 2016-10-07T20:21:04.000Z | ipythonNotebooks/commutators_and_sympy.ipynb | kgourgou/blog | c9da56dc87a2b349efe06972a59706bfb181b197 | [
"MIT"
] | null | null | null | ipythonNotebooks/commutators_and_sympy.ipynb | kgourgou/blog | c9da56dc87a2b349efe06972a59706bfb181b197 | [
"MIT"
] | null | null | null | 50.807531 | 1,032 | 0.550688 | true | 1,573 | Qwen/Qwen-72B | 1. YES
2. YES | 0.887205 | 0.847968 | 0.752321 | __label__eng_Latn | 0.952906 | 0.586226 |
```python
from IPython.display import Image
Image('../../../python_for_probability_statistics_and_machine_learning.jpg')
```
[Python for Probability, Statistics, and Machine Learning](https://www.springer.com/fr/book/9783319307152)
```python
from __future__ import division
%pylab inline
```
Po... | 2d8aeab96216ebc9c6179fe5bb4c7cc02645db59 | 171,769 | ipynb | Jupyter Notebook | chapters/statistics/notebooks/Bootstrap.ipynb | rajkubp020/helloword | 4bd22691de24b30a0f5b73821c35a7ac0666b034 | [
"MIT"
] | null | null | null | chapters/statistics/notebooks/Bootstrap.ipynb | rajkubp020/helloword | 4bd22691de24b30a0f5b73821c35a7ac0666b034 | [
"MIT"
] | null | null | null | chapters/statistics/notebooks/Bootstrap.ipynb | rajkubp020/helloword | 4bd22691de24b30a0f5b73821c35a7ac0666b034 | [
"MIT"
] | null | null | null | 216.606557 | 114,721 | 0.908115 | true | 3,702 | Qwen/Qwen-72B | 1. YES
2. YES | 0.754915 | 0.843895 | 0.637069 | __label__eng_Latn | 0.991351 | 0.318456 |
# Optimization
- [Least squares](#Least-squares)
- [Gradient descent](#Gradient-descent)
- [Constraint optimization](#Constraint-optimization)
- [Global optimization](#Global-optimization)
## Intro
Biological research uses optimization when performing many types of machine learning, or when it interfaces with engine... | 1234964b99c6a467c013fafe683c6bff01e1dd88 | 126,104 | ipynb | Jupyter Notebook | day2/scicomp_optimization.ipynb | grokkaine/biopycourse | cb8b554abb987e6f657c5e522c7e28ecbc9fb4d5 | [
"CC0-1.0"
] | 9 | 2017-05-16T06:07:22.000Z | 2021-08-06T14:58:28.000Z | day2/scicomp_optimization.ipynb | grokkaine/biopycourse | cb8b554abb987e6f657c5e522c7e28ecbc9fb4d5 | [
"CC0-1.0"
] | null | null | null | day2/scicomp_optimization.ipynb | grokkaine/biopycourse | cb8b554abb987e6f657c5e522c7e28ecbc9fb4d5 | [
"CC0-1.0"
] | 18 | 2017-05-16T07:25:08.000Z | 2021-04-22T19:22:53.000Z | 213.373942 | 38,768 | 0.902105 | true | 3,605 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.843895 | 0.708647 | __label__eng_Latn | 0.985462 | 0.484757 |
# Taylor problem 2.20 Template
last revised: 08-Jan-2019 by Dick Furnstahl [furnstahl.1@osu.edu]
**This is a template for solving problem 2.20. Go through and fill in the blanks where ### appears.**
The goal of this problem is to plot and comment on the trajectory of a projectile subject to linear air resistance, c... | e6b0dacc568b13837728fc1acbed74ec48cf9f99 | 72,783 | ipynb | Jupyter Notebook | 2020_week_1/Taylor_problem_2.20_template.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_1/Taylor_problem_2.20_template.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_1/Taylor_problem_2.20_template.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 219.888218 | 34,000 | 0.906393 | true | 1,879 | Qwen/Qwen-72B | 1. YES
2. YES | 0.839734 | 0.757794 | 0.636346 | __label__eng_Latn | 0.934838 | 0.316775 |
# Understanding the FFT Algorithm
*This notebook first appeared as a post by Jake Vanderplas on [Pythonic Perambulations](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/). The notebook content is BSD-licensed.*
<!-- PELICAN_BEGIN_SUMMARY -->
The Fast Fourier Transform (FFT) is one of the most importa... | c8bd85d482f44e5b4141d30ddc73154d99dec2c9 | 20,214 | ipynb | Jupyter Notebook | 10. Fast_Fourier_Transform/FFT.ipynb | mriosrivas/DSP_Student_2021 | 7d978d5a538e2eb198dfbe073b4d8dcbf1aa756f | [
"MIT"
] | 2 | 2022-01-25T04:58:58.000Z | 2022-03-24T23:00:13.000Z | 10. Fast_Fourier_Transform/FFT.ipynb | mriosrivas/DSP_Student_2021 | 7d978d5a538e2eb198dfbe073b4d8dcbf1aa756f | [
"MIT"
] | 1 | 2021-11-25T00:39:40.000Z | 2021-11-25T00:39:40.000Z | 10. Fast_Fourier_Transform/FFT.ipynb | mriosrivas/DSP_Student_2021 | 7d978d5a538e2eb198dfbe073b4d8dcbf1aa756f | [
"MIT"
] | null | null | null | 38.429658 | 851 | 0.605719 | true | 3,839 | Qwen/Qwen-72B | 1. YES
2. YES | 0.774583 | 0.874077 | 0.677046 | __label__eng_Latn | 0.994309 | 0.411335 |
# Simulate Euclid Images Using HST Ones
In this notebook, we are going to simulate step by a Euclid space telescope image using a HST one.
First things first, we start by preparing the worksapce.
```python
# to correctly show figures
%matplotlib inline
# import libraries here
import galsim
import numpy as np
impor... | 9b62475ed5c5b72a1a3872bdadd15863dca1183d | 109,757 | ipynb | Jupyter Notebook | data/euclid_generation_example/HST2Euclid.ipynb | CosmoStat/ShapeDeconv | 3869cb6b9870ff1060498eedcb99e8f95908f01a | [
"MIT"
] | 4 | 2020-12-17T14:58:28.000Z | 2022-01-22T06:03:55.000Z | data/euclid_generation_example/HST2Euclid.ipynb | CosmoStat/ShapeDeconv | 3869cb6b9870ff1060498eedcb99e8f95908f01a | [
"MIT"
] | 9 | 2021-01-13T10:38:28.000Z | 2021-07-06T23:37:08.000Z | data/euclid_generation_example/HST2Euclid.ipynb | CosmoStat/ShapeDeconv | 3869cb6b9870ff1060498eedcb99e8f95908f01a | [
"MIT"
] | null | null | null | 374.59727 | 58,460 | 0.91135 | true | 1,457 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.743168 | 0.628677 | __label__eng_Latn | 0.944272 | 0.298959 |
# <center> Single compartment model using double exponentials</center>
## Summary
### 1. Setup and testing
The model is trying to simulate a single compartment,
$$ C_m \frac{dV_m}{dt} = g_{leak}(V_m - E_{leak}) + g_{exc}(V_m - E_{AMPA}) + g_{inh}(V_m - E_{GABA})$$
Here $E$'s are reversal potentials, $V_m$ is membran... | 55d53b42817f1c7a202a85d50ac1430af7c24e8b | 924,178 | ipynb | Jupyter Notebook | model/Single_comp_conductance_model.ipynb | elifesciences-publications/linearity | 777769212ac43d854d23d5b967c6323747c56c09 | [
"MIT"
] | 1 | 2019-04-22T17:07:37.000Z | 2019-04-22T17:07:37.000Z | model/Single_comp_conductance_model.ipynb | elifesciences-publications/linearity | 777769212ac43d854d23d5b967c6323747c56c09 | [
"MIT"
] | null | null | null | model/Single_comp_conductance_model.ipynb | elifesciences-publications/linearity | 777769212ac43d854d23d5b967c6323747c56c09 | [
"MIT"
] | 3 | 2019-04-25T13:10:24.000Z | 2021-09-05T03:45:36.000Z | 293.669527 | 126,634 | 0.891809 | true | 11,695 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.833325 | 0.688919 | __label__eng_Latn | 0.256521 | 0.438922 |
# Example usage of rdsolver
(c) 2018 Justin Bois. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT).
`rdsolver` solves the following system of P... | 5c3b6e9dbc07863d634681f16e5c6a8f4928926b | 256,820 | ipynb | Jupyter Notebook | notebooks/asdm_example.ipynb | emorisse/rdsolver | 89ef35eeadc50bf3618e10fd7e3f1ed0250ead30 | [
"MIT"
] | 2 | 2021-04-27T03:47:17.000Z | 2022-01-17T19:30:06.000Z | notebooks/asdm_example.ipynb | emorisse/rdsolver | 89ef35eeadc50bf3618e10fd7e3f1ed0250ead30 | [
"MIT"
] | 4 | 2017-07-14T22:52:20.000Z | 2017-08-31T22:55:32.000Z | notebooks/asdm_example.ipynb | emorisse/rdsolver | 89ef35eeadc50bf3618e10fd7e3f1ed0250ead30 | [
"MIT"
] | 2 | 2021-08-16T14:59:00.000Z | 2021-10-14T04:55:48.000Z | 279.760349 | 217,076 | 0.766767 | true | 2,932 | Qwen/Qwen-72B | 1. YES
2. YES | 0.891811 | 0.859664 | 0.766658 | __label__eng_Latn | 0.986723 | 0.619535 |
# Mish Derivatves
```python
import torch
from torch.nn import functional as F
```
```python
inp = torch.randn(100) + (torch.arange(0, 1000, 10, dtype=torch.float)-500.)
inp
```
tensor([-500.3069, -490.6361, -480.3858, -471.2755, -459.0872, -451.1570,
-440.2400, -429.6230, -419.9467, -408.3055, -... | 53d868ef3912cc35ae9fdbb2c8085b7ab875169f | 12,605 | ipynb | Jupyter Notebook | extra/Derivatives.ipynb | hiyyg/mish-cuda | b389b9f84433d8b9b4129d3e879ba746d248d8f2 | [
"MIT"
] | 145 | 2019-09-25T17:43:54.000Z | 2022-03-09T08:17:44.000Z | extra/Derivatives.ipynb | hiyyg/mish-cuda | b389b9f84433d8b9b4129d3e879ba746d248d8f2 | [
"MIT"
] | 20 | 2019-11-18T22:20:02.000Z | 2022-02-16T03:04:30.000Z | extra/Derivatives.ipynb | hiyyg/mish-cuda | b389b9f84433d8b9b4129d3e879ba746d248d8f2 | [
"MIT"
] | 51 | 2019-10-10T03:52:05.000Z | 2022-03-24T07:14:01.000Z | 23.918406 | 218 | 0.471083 | true | 1,950 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.897695 | 0.83249 | __label__yue_Hant | 0.15861 | 0.772485 |
<a href="https://colab.research.google.com/github/liadmagen/MedicalImageProcessingCourse/blob/main/medImgproc_00_working_with_images.ipynb" target="_parent"></a>
In this notebook, we'll explore how images are represented by the computer. We'll learn how to load, examine and manipulate images, and to perform basic pre-... | 251f7f5bf2472f202443baa684180711e7fad165 | 666,610 | ipynb | Jupyter Notebook | medImgproc_00_working_with_images.ipynb | liadmagen/MedicalImageProcessingCourse | 64b73269740a636255a3a0626f6e63a574f3248b | [
"CC0-1.0"
] | null | null | null | medImgproc_00_working_with_images.ipynb | liadmagen/MedicalImageProcessingCourse | 64b73269740a636255a3a0626f6e63a574f3248b | [
"CC0-1.0"
] | null | null | null | medImgproc_00_working_with_images.ipynb | liadmagen/MedicalImageProcessingCourse | 64b73269740a636255a3a0626f6e63a574f3248b | [
"CC0-1.0"
] | null | null | null | 709.914803 | 123,861 | 0.944788 | true | 3,899 | Qwen/Qwen-72B | 1. YES
2. YES | 0.890294 | 0.833325 | 0.741904 | __label__eng_Latn | 0.951741 | 0.562024 |
# Inertial Brownian motion simulation
The Inertial Langevin equation for a particle of mass $m$ and some damping $\gamma$ writes:
\begin{equation}
m\ddot{x} = -\gamma \dot{x} + \sqrt{2k_\mathrm{B}T \gamma} \mathrm{d}B_t
\end{equation}
Integrating the latter equation using the Euler method, one can replace $\dot{x}$ ... | be32e71bbcec574a7ec139f5ff4e06eb52789e19 | 606,134 | ipynb | Jupyter Notebook | 03_tail/inertial_sim/inertial_Brownian_motion.ipynb | eXpensia/Confined-Brownian-Motion | bd0eb6dea929727ea081dae060a7d1aa32efafd1 | [
"MIT"
] | null | null | null | 03_tail/inertial_sim/inertial_Brownian_motion.ipynb | eXpensia/Confined-Brownian-Motion | bd0eb6dea929727ea081dae060a7d1aa32efafd1 | [
"MIT"
] | null | null | null | 03_tail/inertial_sim/inertial_Brownian_motion.ipynb | eXpensia/Confined-Brownian-Motion | bd0eb6dea929727ea081dae060a7d1aa32efafd1 | [
"MIT"
] | null | null | null | 914.229261 | 369,168 | 0.9503 | true | 3,724 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.870597 | 0.821998 | __label__eng_Latn | 0.748395 | 0.748109 |
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