| from scipy.signal import decimate, resample_poly | |
| def resample_ecg(trace, input_freq, output_freq, axis=-1): | |
| """Resample ecg. | |
| `trace` should be a multidimensional numpy array. `axis` gives which axis will be resampled (be default the last | |
| one). `input_freq` and `output_freq` are the input and output frequency (in heartz). Use the most appropriate SciPy | |
| function vailable: first try decimate (with bidirectional filtering), if the input and output frequencies are not | |
| multiple, it resamples using polyphase filtering. |
Authors: Antônio H. Ribeiro and Manoel H. Ribeiro
This is a guide containing practical steps to install nvidia gpu drivers and tensorflow which combines successful steps
from previous (troublesome) experiences. We currently use nvidia cuda toolkit v9.0 and cudnn v7.0.5. This
methodology has worked for Ubuntu v16.04 and for graphic cards GTX 980Tiand GTX 1080Ti.
| /* CUTEst2 interface to Python and NumPy */ | |
| /* (c)2011 Arpad Buermen */ | |
| /* Licensed under LGPL V2.1 */ | |
| /* Note that in Windows we do not use Debug compile because we don't have the debug version | |
| of Python libraries and interpreter. We use Release version instead where optimizations | |
| are disabled. Such a Release version can be debugged. | |
| */ | |
| /* Unused CUTEst tools - sparse finite element matrices and banded matrices |
| # (C)2011 Arpad Buermen | |
| # Licensed under LGPL V2.1 | |
| """PyCUTEst problem manager | |
| Currently works only under Linux. | |
| Requres CUTEst installation for double precision built with gcc and gfortran. | |
| CUTEst library must be built with -fPIC (as position independent code). |
| using DifferentialEquations | |
| using Plots | |
| plotlyjs() | |
| function kuramoto_pair_aux(t, θ, params, dθ) | |
| w1, w2, k = params | |
| dθ[1] = w1 - k*sin(θ[2] - θ[1]) | |
| dθ[2] = w2 - k*sin(θ[1] - θ[2]) | |
| end |
| using DifferentialEquations | |
| using Plots | |
| pyplot() | |
| f = @ode_def_noinvjac KuramotoPair begin | |
| dθ1 = w1 - k*sin(θ2 - θ1) | |
| dθ2 = w2 - k*sin(θ1 - θ2) | |
| end w1=>1.0 w2=>2.0 k=>1.0 | |
| # Simulation parameters |
| # Example | |
| from __future__ import division, print_function, absolute_import | |
| import numpy as np | |
| from ipsolver import minimize_constrained, NonlinearConstraint, BoxConstraint | |
| # Define objective function and derivatives | |
| fun = lambda x: 1/2*(x[0] - 2)**2 + 1/2*(x[1] - 1/2)**2 | |
| grad = lambda x: np.array([x[0] - 2, x[1] - 1/2]) | |
| hess = lambda x: np.eye(2) | |
| # Define nonlinear constraint |