EverythingSmartHome

esphome:
  friendly_name: flexispot-e7q
  name: flexispot-e7q
  platformio_options:
    board_build.flash_mode: dio
  on_boot:
   - priority: -10
     then:
      - button.press: button_m
   - priority: 600

MindPatch

key_bindings:
  - { key: V,        mods: Command,       action: Paste                        }
  - { key: C,        mods: Command,       action: Copy                         }
  - { key: Q,        mods: Command,       action: Quit                         }
  - { key: N,        mods: Command,       action: SpawnNewInstance             }
  - { key: Return,   mods: Command,       action: ToggleFullscreen             }

  - { key: Home,                          chars: "\x1bOH",   mode: AppCursor   }
  - { key: Home,                          chars: "\x1b[H",   mode: ~AppCursor  }
  - { key: End,                           chars: "\x1bOF",   mode: AppCursor   }

andersx

#!/usr/bin/env python2
import numpy as np
import sys

elements = dict()
elements["H"] = 1
elements["C"] = 6
elements["N"] = 7
elements["O"] = 8
elements["F"] = 9

ptosco

jerriclynsjohn

import svelte from 'rollup-plugin-svelte';
import resolve from 'rollup-plugin-node-resolve';
import commonjs from 'rollup-plugin-commonjs';
import livereload from 'rollup-plugin-livereload';
import { terser } from 'rollup-plugin-terser';
import postcss from 'rollup-plugin-postcss';
import autoPreprocess from 'svelte-preprocess';

const production = !process.env.ROLLUP_WATCH;

larsbratholm

"""
Calculate 95% confidence interval for rapported MAE. 
The data is assumed to follow a laplacian distribution.
See https://waset.org/publications/8809/confidence-intervals-for-double-exponential-distribution-a-simulation-approach
for derivation.
"""

import numpy as np
import scipy.stats as ss

andersx

from __future__ import print_function

import os

import csv
import ast

from copy import deepcopy

import scipy

andersx

def multiprocessing_func(pids, compounds_list, rd):

    for i in pids:
       rep_out = generate_fchl_acsf(
           compounds[i].nuclear_charges,
           compounds[i].coordinates,
           **rep_params)
       rd[i] = rep_out

manager = mp.Manager()

andersx

import numpy as np
import scipy


def kpca(K, n=2, centering=True):
    
    assert K.shape[0] == K.shape[1], "Square matrix required for Kernel PCA."
    assert np.allclose(K, K.T, atol=1e-8), "Symmetric matrix required for Kernel PCA."

    # First center kernel.

ptosco