danielwatson6

# Calculate the weekday of any given date
# Author: djwatt5

# We use the floor method from the math module
# to calculate the year offset.
import math

# Format: day: 1-31, month: 1-12, year: 1-
# String option: if not specified, the result
# will be an integer (Sunday: 0, Monday: 1, ...,

danielwatson6

###
### This is an example on how to use blobs with the webapp_enhanced project
### Author: djwatt5
###
### Most of the content here can be automatically generated
### using the console tool from webapp_enhanced. The only
### piece of code below that is custom can be found in the
### `self.create()` method (see line 43).
###

danielwatson6

from lib import db, utils
from lib.db import validators

BOOK_EXTENSIONS = ['azw', 'azw1', 'azw4', 'epub', 'kf8', 'mobi', 'pdb', 'pdf', 'prc', 'tpz']


class BookModel(db.BlobModel):
	
	### Data:
	

danielwatson6

### Code for passing through edit pages

# Handle a PUT request.
def put(self, *args):
	BaseController.put(self, *args)
	
	# Form is a dictionary with elements like
	#     property_name: list_of_validators.
	form = self.model.form
	

danielwatson6

# Biology IA Simulations
# By Daniel Watson

import numpy
import matplotlib.pyplot as plt

# Controlled variables
h = .1 # days (step size)
population = 3.9e6 # remains constant
mosquitoes = 1e8 # remains constant

danielwatson6

// Copy paste this into the JS console of any window that has loaded
// https://coinmarketcap.com/

var CURRENCY = 'bitcoin';


var w = window.open('https://coinmarketcap.com/currencies/' + CURRENCY + '/historical-data/?start=20000101&end=20170806');

w.addEventListener('load', function () {
  var rows = $(w.document.getElementById('historical-data')).find('tr');

danielwatson6

"""Automatically build a vanilla or Cudnn RNN."""

import numpy as np
import tensorflow as tf


def gru(inputs,
        num_layers,
        num_units,
        direction='unidirectional',

danielwatson6

def split_by_delimiter(ts, delimiter):
    """Split a numeric tensor similarly to python's `str.split` method."""
    ts_str = tf.reshape(tf.reduce_join(tf.as_string(ts), separator=' '), [-1])
    ts_slices = tf.string_split(
        tf.string_split(ts_str, delimiter=str(delimiter)).values)

    result = tf.SparseTensor(
        ts_slices.indices,
        tf.string_to_number(ts_slices.values, out_type=ts.dtype),
        ts_slices.dense_shape)

danielwatson6

"""Optimizer to find the closest sigmoidal function to the identity."""

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf


LEARNING_RATE = 1e-3  # make this small to make optimization converge.
BATCH_SIZE = 10000  # make this big to make optimization more accurate.
STEPS = 1000  # increase this if the learning rate is too small.