| <UPF version="2.0.1"> | |
| <PP_INFO> | |
| Generated using Vanderbilt code, version 7 3 6 | |
| Author: kfg Generation date: 31 3 15 | |
| Automatically converted from original format | |
| 1 The Pseudo was generated with a Scalar-Relativistic Calculation | |
| 2.23000000000E+00 Local Potential cutoff radius | |
| nl pn l occ Rcut Rcut US E pseu | |
| 4P 4 1 6.00 0.00000000000 1.53000000000 -4.15068716400 | |
| 4D 4 2 9.50 0.00000000000 1.63000000000 -0.66209811300 |
As of May 4th, 2021, erlang is not available from macports for the ARM (Apple Silicon) platform.
Docker containers make it super easy to install postgresql and rabbitmq.
The M1 chips have great parallelism, so I'd hope to be able to run docker without noticing the overhead too much.
I could use conda as well, but docker provides the neat docker-compose.yaml that makes it possible to start up and shut down all the services required with a single command.
| # -*- coding: utf-8 -*- | |
| ########################################################################### | |
| # Copyright (c), The AiiDA team. All rights reserved. # | |
| # This file is part of the AiiDA code. # | |
| # # | |
| # The code is hosted on GitHub at https://github.com/aiidateam/aiida-core # | |
| # For further information on the license, see the LICENSE.txt file # | |
| # For further information please visit http://www.aiida.net # | |
| ########################################################################### | |
| # pylint: disable=no-name-in-module |
| diff --git a/Dockerfile b/Dockerfile | |
| index 433c334..fcb1d09 100644 | |
| --- a/Dockerfile | |
| +++ b/Dockerfile | |
| @@ -38,12 +38,17 @@ RUN /usr/bin/pip3 install \ | |
| # TODO, remove when https://github.com/aiidateam/aiida-sssp/pull/25 is merged | |
| # and installed on AiiDA lab | |
| WORKDIR /opt/pseudos | |
| -RUN base_url=http://legacy-archive.materialscloud.org/file/2018.0001/v3; \ | |
| -wget ${base_url}/SSSP_efficiency_pseudos.aiida; \ |
| @pytest.mark.asyncio | |
| async def test_wait_pause_play_resume(): | |
| """ | |
| Test that if you pause a process that and its awaitable finishes that it | |
| completes correctly when played again. | |
| """ | |
| proc = utils.WaitForSignalProcess() | |
| asyncio.ensure_future(proc.step_until_terminated()) | |
| await utils.run_until_waiting(proc) |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # fitting equation: y = (a-d)/(1+(x/c)^b)+d | |
| # genrate the data set | |
| a,b,c,d = 1,2,3,4 | |
| x = np.arange(0,10,0.01) | |
| y = (a-d)/(1+(x/c)**b)+d + 0.1*np.random.random(len(x)) |
| ENV["EDITOR"] = "atom" | |
| const PLOTS_DEFAULTS = Dict(:theme => :wong) | |
| try | |
| @eval using Revise | |
| # Turn on Revise's automatic-evaluation behavior | |
| Revise.async_steal_repl_backend() | |
| @eval using Rebugger | |
| # Activate Rebugger's key bindings |
| // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt | |
| /* | |
| This is an example illustrating the use the general purpose non-linear | |
| least squares optimization routines from the dlib C++ Library. | |
| This example program will demonstrate how these routines can be used for data fitting. | |
| In particular, we will generate a set of data and then use the least squares | |
| routines to infer the parameters of the model which generated the data. | |
| */ |
| PACKAGE="kiwipy" | |
| VERSION_FILE=${PACKAGE}/version.py | |
| version=$1 | |
| while true; do | |
| read -p "Release version ${version}? " yn | |
| case $yn in | |
| [Yy]* ) break;; | |
| [Nn]* ) exit;; | |
| * ) echo "Please answer yes or no.";; |
| // keepDoingSomething will keep trying to doSomething() until either | |
| // we get a result from doSomething() or the timeout expires | |
| func keepDoingSomething() (bool, error) { | |
| timeout := time.After(5 * time.Second) | |
| tick := time.Tick(500 * time.Millisecond) | |
| // Keep trying until we're timed out or got a result or got an error | |
| for { | |
| select { | |
| // Got a timeout! fail with a timeout error | |
| case <-timeout: |