dnnagy

import numpy as np
from matplotlib import pyplot as plt

# Set up dark color theme
plt.rcParams['axes.facecolor'] = '#323A48'
plt.rcParams['axes.edgecolor'] = '#92A2BD'
plt.rcParams['figure.facecolor'] = '#323A48'
plt.rcParams['savefig.facecolor'] = '#323A48'
plt.rcParams['text.color'] = '#DBE1EA'
plt.rcParams['axes.labelcolor'] = '#DBE1EA'

dnnagy

from datetime import datetime
xx = '2018-01-01 00:30'
dt = datetime.strptime(xx, "%Y-%m-%d %H:%M")
dt.timestamp() # 1514766600.0

dnnagy

def generate_batches(X, y, batch_size, shuffle=False, random_state=None, return_dropped=True):
  
  X_batches = X
  y_batches = y
  
  nDrop = len(X)%batch_size 
  
  # This function only shuffles the array along the first axis 
  # of a multi-dimensional array. The order of sub-arrays is 
  # changed but their contents remains the same.

dnnagy

"{:,}".format(1234567890) # => '1,234,567,890'
int('1,234,567,890'.replace(',',"")) # => 1234567890

dnnagy

import numpy as np
import keras
from keras import Sequential
from keras.models import Sequential
from keras.models import Model
from keras.layers import Input, Dense, LSTM, Concatenate, Flatten
from keras.optimizers import SGD
from keras.layers.normalization import BatchNormalization

np.random.seed(42)

dnnagy

import time

## 24 hour format ##
def print_t(str_):
  print( "[" + time.strftime("%Y-%m-%d %H:%M:%S") + "] " + str(str_))

dnnagy

def split_scientific_float(n_prec, _f):
  v=('{:.'+str(n_prec)+'g}').format(_f).split('e')
  if len(v)==1: v.append(0)
  return v

split_scientific_float(5, 4.311237638482733e-91) # Output: ['4.3112', '-91']

dnnagy

def find_nearest_index(array, value):
  array = np.asarray(array)
  idx = (np.abs(array - value)).argmin()
  return idx

dnnagy

def arrayToLatexRow(a):
  ns = []
  for n in a:
    ns.append("${0}$".format(n))
    
  ns = ' & '.join(ns)
  ns = ns + " \\ \hline"
  return ns

tauarr = np.array([0.00325, 0.00228, 0.00253, 0.00284, 0.00272, 0.00272])

dnnagy

def latexRowToArray(s):
  nums = s.split("&")
  for k in range(len(nums)):
    nums[k] = ''.join([i for i in nums[k] if i.isdigit() or i in ['.']])
    nums[k] = float(nums[k])
  return np.array(nums)
  
taus = '$0.00325$ & $0.00228$ & $0.00253$ & $0.00284$ & $0.00272$ & $0.00272$ \\ \hline'
latexRowToArray(taus)
#output: array([0.00325, 0.00228, 0.00253, 0.00284, 0.00272, 0.00272])