dcbark01

# Pseudocode for Simulated Annealing Algorithm

while t_new > stopping temp:
     for number of runs at each temp:
          x_new, y_new = random neighboring value of x_old, y_old
          calculate the cost c_new of this neighboring solution
          if   c_new – c_old >= 0:
               This is a better solution, so move to it
          else:
               Calculate the probability of moving to the new worse solution

dcbark01

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Module for implementing Simulated Annealing algorithm for single objective optimization.

author := "Daniel Barker"

"""

import random

dcbark01

dcbark01

# Import the FloodScraper Module
from FloodScraper import FloodScraper

# User inputs
gage_number = 520
reported_from = '09/02/2017 07:31:00 AM'
last = '6 Hours'

# Create scraper interest and run a query
scraper = FloodScraper()

dcbark01

# Import the FloodScraper Module
from FloodScraper import FloodScraper

# User inputs
gage_number = 520
reported_from = '09/02/2017 07:31:00 AM'
last = '7 Days'
filename = 'gage_data.json'

# Create scraper interest and run a query

dcbark01

import pandas as pd
import matplotlib.pyplot as plt
from FloodScraper import FloodScraper

# User Inputs
gage_number = 475
reported_from = '09/02/2017 07:31:00 AM'
last = '7 Days'

# Create scraper interest and run a query

dcbark01

#!/usr/bin/env bash

# ---------------------------- INSTALL NOTES ----------------------------
# To properly setup the AWS CLI, make sure that this shell script is run with the two required positional args:
#   $1 :: (string) AWS Access Key ID
#   $2 :: (string) AWS Secret Key
#
# Also make sure to give the script permission to run!:
#    chmod 755 /home/ec2-user/install.sh
#

dcbark01

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# if your dates are strings you need this step
df.Date = pd.to_datetime(df.Date)

fig,ax = plt.subplots()
ax.plot_date(df.Date,df.A)
ax.plot_date(df.Date,df.B)

dcbark01

"""
@author:    dcbark01
@date:      Nov 2019
@license:   MIT
"""

import os
import json
import shlex
import subprocess

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def pretty_size(size):
    """Pretty prints a torch.Size object"""
    assert(isinstance(size, torch.Size))
    return " × ".join(map(str, size))


def bytes2human(n, format='%(value).1f %(symbol)s', symbols='customary'):
    """
    Convert n bytes into a human readable string based on format.
    symbols can be either "customary", "customary_ext", "iec" or "iec_ext",