fprime | F ' - A flight software and embedded systems framework
kandi X-RAY | fprime Summary
kandi X-RAY | fprime Summary
F' - A flight software and embedded systems framework
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fprime Examples and Code Snippets
Lines of Code : 198License : Permissive (Apache-2.0)
Copy// ======================================================================
// \title GpsComponentImpl.cpp
// \author lestarch
// \brief cpp implementation of the F' sample GPS receiver for a
// NMEA GPS receiver device.
//
// \copyright
// C private:
// ----------------------------------------------------------------------
// Types
// ----------------------------------------------------------------------
enum class ThrottleState {
THROTTLED,
NOT_THROTTLED
};
F32 Test private:
// ----------------------------------------------------------------------
// Types
// ----------------------------------------------------------------------
enum class ThrottleState {
THROTTLED,
NOT_THROTTLED
};
F32 Test """ Purge HTML directory of files with _ prefix
Script to purge a directory of files starting with _ as this does not host well with GH pages and
thus these files need to be rewritten. This file is designed for speed in these replacements.
"""
from # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup -------------------- def f_prime(x):
r = np.sqrt((x[0] - 3)**2 + (x[0] - 2)**2)
return np.array(((x[0] - 3)/r, (x[0] - 2)/r)) >>> nsolve(k1, -.5)
-0.427127686132521
def height(design_var):
return fsolve(height_equation, x0=10000, args=tuple(design_var))[0]
fun: 4.363615312479626e-05
hess_inv: array([[6.05302726e+04, 6.37807548e+01, 5.21903776e+04],
[6.37807548def f(x):
return x*(np.sin(x)/np.cos(x))-1000
def fprime(x):
# first derivative of f
# do this by hand or use derivative-calculator.net or wolframalpha
return np.tan(x)+ x*(1/np.cos(x))**2
def fprime2(x):
# second de # Secant method
if x1 is not None:
if x1 == x0:
raise ValueError("x1 and x0 must be different")
p1 = x1
else:
eps = 1e-4
p1 = x0 * (1 + eps)
Community Discussions
Trending Discussions on fprime
QUESTION
I'm currently trying to implement a mathematic method to approximate f(x) = 0. I've already implemented it in some languages and I want to do it in ruby now just for training. But I have this error that I really does'nt understand Here is my code
...ANSWER
Answered 2022-Feb-16 at 10:42i think there is space on newton method call
QUESTION
I'm trying to find the maximum points of curvature of a tanh(x) function, but the mathematical definition for curvature contains an absolute value function. I've specified that x must be real-valued which resolves sympy returning an imaginary function for the derivative of the curvature. However when trying to solve for k'(x)=0 using the sym.solve() method python will simply continue running and not return an answer at all.
ANSWER
Answered 2022-Feb-09 at 22:42It's a pretty high-order nonlinear equation. Try nsolve:
QUESTION
I'm facing this question while trying to minimize pretty straight forward code.
...ANSWER
Answered 2022-Jan-28 at 10:43You deisgn_var argument in height() gets passed as an array, not a tuple, when called through minimize. I am not familiar enough with the inner workings of minimize to understand why. But the following small modification, explictly converting the argument to a tuple, should fix it
QUESTION
I'm trying to solve the following equation:
Where a list of A_e/A* values are given, and gamma=1.2, how should I solve this equation such that a list of M_e is returned corresponding to a list of A_e/A* values?
I thought about using scipy.optimize.newton, but it seems like this is not the right approach
ANSWER
Answered 2021-Oct-19 at 19:44newton works for scalar functions, and you are turning it into a vector function. Since you want the zero for different Ae values, include the Ae parameter in the function definition, then call newton several times (you can use the args keyword):
QUESTION
The scipy.optimize.newton documentation reads: "The Newton-Raphson method is used if the derivative fprime of func is provided, otherwise the secant method is used." The secant method requires two estimates of the zero of func, x0 and x1. But the examples provided show that optimize.newton works with the secant method when ONLY x0 is input. So I assume optimize.newton is somehow assigning x1 based off our input of x0, but I'm not sure and am curious to know what's going on. Any clarification would be immensely appreciated!
ANSWER
Answered 2021-Sep-27 at 19:48Short answer: a perturbed version of x0 is used.
It looks like the actual behavior should be documented better. The source code for the function starts at this line in the file zeros.py; the code relevant to your question is:
QUESTION
Hello I'm Andrea and I'm a beginner to programming,
I' m trying to solve the Project Euler - problem 3, but I can't understand why the "j" doesn't exit from the while loop ( It generates an infinite loop ).
When the "j" enters in the else (inside the while) the "$prime" variable value is set to true, so I suppose the program has to exit from the while loop (because the second condition: while ( $j < $i || $prime != true || $noPrime != true )), but the program continues to go on.
I know the code is not complete for the problem and not beautiful too, but I'm really stuck on this problem.
Code below:
...ANSWER
Answered 2021-Apr-02 at 17:51Your while loop condition is written listing all the things that must be true in order for it to keep looping. If any one of those becomes false it is done and the condition should evaluate to false.
Using || doesn’t evaluate to false until all 3 things are false. True || false || true is still true so it keeps looping.
Using && evaluates to true if any one of these things is false.
Also be on the lookout for redundancy. The two booleans are two ways of expressing the same thing.
QUESTION
I am working on a dataset that is a collection of several medical predictor variables and one target variable, used to classify whether a patient has diabetes or not. I am building my model without using scikit learn / sklearn library. I have attached the link to dataset below.
https://www.kaggle.com/uciml/pima-indians-diabetes-database
I have trained and tested mode but I keep getting over 100% accuracy. I am very beginner in this field, therefore I apologize if I have made silly mistakes. Below is my code ( and I only use Glucose and DiabetesPedigreeFunction) to classify.
...ANSWER
Answered 2021-Mar-25 at 12:12You used mod instead of division.
Accuracy should be computed like this:
QUESTION
I want to solve a system of nonlinear equations using scipy.root. For performance reason, I want to provide the jacobian of the system using a LinearOperator. However, I cannot get it to work. Here is a minimal example using the gradient of the Rosenbrock function, where I first define the Jacobian (i.e. the Hessian of the Rosenbrock function) as a LinearOperator.
...ANSWER
Answered 2021-Mar-11 at 10:57Partial answer :
I was able to input my "exact" jacobian into scipy.optimize.nonlin.nonlin_solve . This really felt hacky. Long story short, I defined a class inheriting from scipy.optimize.nonlin.Jacobian, where I defined "update" and "solve" method so that my exact jacobian would be used by the solver.
I expect performance results to greatly vary from problem to problem. Let me detail my experience for a ~10k dimensional critial point solve of an "almost" coercive function (i.e. the problem would be coercive if I had taken the time to remove a 4-dimensional symmetry generator), with many many local minima (and thus presumably many many critical points).
Long story short, this gave terrible results far from the optimum, but local convergence was achieved in fewer optimization cycles. The cost of each of those optimization cycle was (for my personal problem at hand) far greater than the "standard" krylov lgmres, so in the end even close to the optimum, I cannot really say it was worth the trouble.
To be honest, I am very impressed with the Jacobian finite difference approximation of the 'krylov' method of scipy.optimize.root.
QUESTION
first, sorry about my poor English skill.
I make BDT-model, but something problem here.
this function is change code for column 4
...ANSWER
Answered 2021-Jan-06 at 17:10Here's how to read the error: focus on not iterable. Something in the line is attempting to iterate. The only part that makes sense for is sum(map(float, final)). So we know that final is None.
final is the return value of calculate. Looking at the code, calculate does indeed return nothing, rather than the expected iterable.
At one point, calculate states
QUESTION
I'm trying to calculate an integral and I don't understand the error output, what does it mean, what's the problem and how do I fix it? the output is "ValueError: The array returned by a function changed size between calls", I added the full output. pleas help, thank you
...ANSWER
Answered 2020-Nov-06 at 15:07I changed the variables of
optical_depth_fixed_source_integrand
so that they include the functions y_min and y_max instead of their vaviabes and now it works
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Install fprime
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Python 3.6+ with pip
Java
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