def decay_growth_df(growth_type, growth_rate):
vintages, years = np.asarray(cfgfile.get('case', 'vintages')), np.asarray(cfgfile.get('case', 'years'))
ages = np.zeros((len(years), len(vintages)))
for i, year in enumerate(years):
ages[i] = vintages - year
if growth_type == 'linear':
fill = 1 + (growth_rate * ages)
fill = np.triu(fill, k=(min(years) - min(vintages)))
elif growth_type == 'exponential':
fill = (1 + growth_rate) ** ages
fill = np.triu(fill, k=(min(years) - min(vintages)))
return pd.DataFrame(fill, index=vintages, columns=years)
"""
multiprocessing.log_to_stderr().setLevel(logging.DEBUG)
if __name__ == "__main__":
_start_time = datetime.datetime.now()
print "Running dispatch..."
multiprocessing.freeze_support(
) # can be omitted if we won't be producing a Windows executable
# ############# SETTINGS ############# #
# Currently using all available CPUs in multiprocessing function below
available_cpus = multiprocessing.cpu_count()
# Solver info
solver_name = cfgfile.get('case', 'solver')
solver_settings = None
# Pyomo solve keyword arguments
# Set 'keepfiles' to True if you want the problem and solution files
# Set 'tee' to True if you want to stream the solver output
solve_kwargs = {"keepfiles": False, "tee": False}
# How much to print to standard output; set to 1 for more detailed output, 0 for limited detail
stdout_detail = 1
# The directory structure will have to change, but I'm not sure how yet
current_directory = os.getcwd()
results_directory = current_directory + "/results"
inputs_directory = current_directory + "/test_inputs"
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 02 11:19:36 2015
@author: Ben
"""
import numpy as np
from config import cfgfile
import pandas as pd
vintage_start, vintage_stop, vintage_step = 1950, 2050, 1
vintages = np.arange(vintage_start, vintage_stop + 1, vintage_step)
year_start, year_stop, year_step = 1950, 2050, 1
years = np.asarray(cfgfile.get('case', 'years'))
vintages = np.asarray(cfgfile.get('case', 'vintages'))
# vintage_age creates an array of numbers for each vintage v that represents age in year y.
def vintage_age(years, vintages):
ages = np.zeros((len(years), len(vintages)))
# vintage_exist creates a binary array that is 1 if year is greater than vintage (i.e. vintage v exists in year y)
def vintage_exist(years, vintages):
# Built in function to create a lower triangular distribution, k gives the shift from the diagonal
age = vintage_age(years, vintages)
return np.triu(age, k=(min(years) - min(vintages)))
test = np.triu(len(years), len(vintages), k=min(years) - min(vintages))
:return:
"""
multiprocessing.log_to_stderr().setLevel(logging.DEBUG)
if __name__ == "__main__":
_start_time = datetime.datetime.now()
print "Running dispatch..."
multiprocessing.freeze_support() # can be omitted if we won't be producing a Windows executable
# ############# SETTINGS ############# #
# Currently using all available CPUs in multiprocessing function below
available_cpus = multiprocessing.cpu_count()
# Solver info
solver_name = cfgfile.get('case','solver')
solver_settings = None
# Pyomo solve keyword arguments
# Set 'keepfiles' to True if you want the problem and solution files
# Set 'tee' to True if you want to stream the solver output
solve_kwargs = {"keepfiles": False, "tee": False}
# How much to print to standard output; set to 1 for more detailed output, 0 for limited detail
stdout_detail = 1
# The directory structure will have to change, but I'm not sure how yet
current_directory = os.getcwd()
results_directory = current_directory + "/results"
inputs_directory = current_directory + "/test_inputs"