rewrite base_assumptions to generate YAML parameters

energy_demand/basic/basic_functions.py

@@ -115,3 +115,43 @@ def get_month_from_string(month_string):
         month = 12
 
     return int(month)
+
+def get_month_from_int(month_int):
+    """Convert string month to int month with Jan == 1
+
+    Argument
+    --------
+    month_string : str
+        Month given as a string
+
+    Returns
+    --------
+    month : int
+        Month as an integer (jan = 1, dez = 12)
+    """
+    if month_int == 1:
+        month_str = 'Jan'
+    elif month_int == 2:
+        month_str = 'Feb'
+    elif month_int == 3:
+        month_str = 'Mar'
+    elif month_int == 4:
+        month_str = 'Apr'
+    elif month_int == 5:
+        month_str = 'May'
+    elif month_int == 6:
+        month_str = 'Jun'
+    elif month_int == 7:
+        month_str = 'Jul'
+    elif month_int == 8:
+        month_str = 'Aug'
+    elif month_int == 9:
+        month_str = 'Sep'
+    elif month_int == 10:
+        month_str = 'Oct'
+    elif month_int == 11:
+        month_str = 'Nov'
+    elif month_int == 12:
+        month_str = 'Dec'
+
+    return str(month_str)

energy_demand/cli/__init__.py

@@ -53,10 +53,13 @@ def run_model(args):
     data['sim_param']['base_yr'] = 2015
     data['sim_param']['simulated_yrs'] = [2015, 2018, 2025, 2050] #[2015, 2020, 2025]
 
-    data['assumptions'] = base_assumptions.load_assumptions(
+    base_assumptions.load_assumptions(
         data['paths'], data['enduses'], data['lookups'], data['fuels'], data['sim_param'])
+
+    data['assumptions'] = read_data.read_param_yaml(data['paths']['yaml_parameters'])
+
     data['assumptions']['seasons'] = date_prop.read_season(year_to_model=2015)
-    data['assumptions'] = base_assumptions.update_assumptions(data['assumptions'])
+    data['assumptions']['technologies'] = base_assumptions.update_assumptions(data['assumptions']['technologies'], data['assumptions']['eff_achieving_factor']['factor_achieved'])
     data['tech_lp'] = data_loader.load_data_profiles(data['paths'], data['local_paths'], data['assumptions'])
     data['weather_stations'], data['temp_data'] = data_loader.load_temp_data(data['local_paths'])
 

energy_demand/enduse_func.py

@@ -162,9 +162,8 @@ def __init__(
             self.fuel_new_y = apply_smart_metering(
                 enduse,
                 self.fuel_new_y,
-                assumptions,
-                sim_param,
-                assumptions['enduse_overall_change_ey']['year_until_changed'])
+                assumptions['smart_meter_assump'],
+                sim_param)
             logging.debug("... Fuel train C: " + str(np.sum(self.fuel_new_y)))
 
             # --Enduse specific consumption change in %
@@ -335,8 +334,8 @@ def __init__(
                         enduse,
                         sim_param,
                         loadfactor_yd_cy,
-                        assumptions['demand_management'],
-                        assumptions['demand_management']['year_until_changed'])
+                        assumptions['demand_management']['enduses_demand_managent'],
+                        assumptions['demand_management']['demand_management_year_until_changed'])
 
                     if not peak_shift_crit:
                         self.fuel_yh = fuel_yh
@@ -1014,17 +1013,14 @@ def apply_heat_recovery(enduse, assumptions, service, crit_dict, base_sim_param)
         # Fraction of heat recovered until end_year
         heat_recovered_p_by = assumptions['heat_recovered'][enduse]
 
-        # Year until recovered
-        year_until_recovered =  assumptions['heat_recovered']['year_until_recovered']
-
         if heat_recovered_p_by == 0:
             return service
         else:
             # Fraction of heat recovered in current year
             sig_diff_factor = diffusion_technologies.sigmoid_diffusion(
                 base_sim_param['base_yr'],
                 base_sim_param['curr_yr'],
-                year_until_recovered,
+                assumptions['heat_recovered']['heat_recovered_year_until_changed'],
                 assumptions['other_enduse_mode_info']['sigmoid']['sig_midpoint'],
                 assumptions['other_enduse_mode_info']['sigmoid']['sig_steeppness'])
 
@@ -1248,7 +1244,7 @@ def apply_climate_change(enduse, fuel_new_y, cooling_factor_y, heating_factor_y,
 
     return fuel_new_y
 
-def apply_smart_metering(enduse, fuel_y, assumptions, base_sim_param, year_until_changed):
+def apply_smart_metering(enduse, fuel_y, smart_meter_assump, base_sim_param):
     """Calculate fuel savings depending on smart meter penetration
 
     Arguments
@@ -1257,8 +1253,8 @@ def apply_smart_metering(enduse, fuel_y, assumptions, base_sim_param, year_until
         Enduse
     fuel_y : array
         Yearly fuel per fueltype
-    assumptions : dict
-        assumptions
+    smart_meter_assump : dict
+        smart meter assumptions
     base_sim_param : dict
         Base simulation parameters
 
@@ -1274,25 +1270,22 @@ def apply_smart_metering(enduse, fuel_y, assumptions, base_sim_param, year_until
     - In the assumptions the maximum penetration and also the
         generally fuel savings for each enduse can be defined.
     """
-    if enduse in assumptions['savings_smart_meter']:
-
-        # Year until changed
-        year_until_changed = year_until_changed
+    if enduse in smart_meter_assump['savings_smart_meter']:
 
         # Sigmoid diffusion up to current year
         sigm_factor = diffusion_technologies.sigmoid_diffusion(
             base_sim_param['base_yr'],
             base_sim_param['curr_yr'],
-            year_until_changed,
-            assumptions['smart_meter_diff_params']['sig_midpoint'],
-            assumptions['smart_meter_diff_params']['sig_steeppness'])
+            smart_meter_assump['smart_meter_year_until_changed'],
+            smart_meter_assump['smart_meter_diff_params']['sig_midpoint'],
+            smart_meter_assump['smart_meter_diff_params']['sig_steeppness'])
 
         # Smart Meter penetration (percentage of people having smart meters)
-        penetration_by = assumptions['smart_meter_p_by']
-        penetration_cy = assumptions['smart_meter_p_by'] + (
-            sigm_factor * (assumptions['smart_meter_p_ey'] - assumptions['smart_meter_p_by']))
+        penetration_by = smart_meter_assump['smart_meter_p_by']
+        penetration_cy = smart_meter_assump['smart_meter_p_by'] + (
+            sigm_factor * (smart_meter_assump['smart_meter_p_future'] - smart_meter_assump['smart_meter_p_by']))
 
-        savings = assumptions['savings_smart_meter'][enduse]
+        savings = smart_meter_assump['savings_smart_meter'][enduse]
         saved_fuel = fuel_y * (penetration_by - penetration_cy) * savings
         fuel_y = fuel_y - saved_fuel
 

energy_demand/geography/weather_region.py

@@ -60,9 +60,9 @@ def __init__(
         temp_cy = change_temp_climate(
             temp_by,
             assumptions['yeardays_month_days'],
-            assumptions['climate_change_temp_diff_month'],
+            assumptions['climate_change_temp_diff_month']['temps'],
             sim_param,
-            assumptions['climate_change_temp_diff_year_until_changed'])
+            assumptions['climate_change_temp_diff_month']['climate_change_temp_diff_year_until_changed'])
 
         #Change temp_cy depending on climate assumptions
         rs_t_base_heating_cy = hdd_cdd.sigm_temp(

energy_demand/main.py

@@ -8,7 +8,7 @@
 https://nismod.github.io/docs/smif-prerequisites.html#sector-modeller
 
 # TODOEnd year diffusion assumption
-'''
+5'''
 import os
 import sys
 import logging
@@ -17,12 +17,11 @@
 from energy_demand import energy_model
 from energy_demand.assumptions import base_assumptions
 from energy_demand.read_write import data_loader
-from energy_demand.read_write import read_data
 from energy_demand.basic import testing_functions as testing
 from energy_demand.validation import lad_validation
 from energy_demand.plotting import plotting_results
 from energy_demand.basic import logger_setup
-from energy_demand.read_write import write_data
+from energy_demand.read_write import write_data, read_data
 from energy_demand.basic import basic_functions
 from energy_demand.basic import date_prop
 
@@ -105,14 +104,24 @@ def energy_demand_model(data, fuel_in=0, fuel_in_elec=0):
     data['sim_param']['base_yr'] = 2015
     data['sim_param']['simulated_yrs'] = [2015, 2018, 2025, 2050]
 
-    data['assumptions'] = base_assumptions.load_assumptions(
+    # TODO: MOVE TO SCENARIO INIT
+    base_assumptions.load_assumptions(
         data['paths'], data['enduses'], data['lookups'], data['fuels'], data['sim_param'])
+
+    # Read parameters from yaml file
+    # ------------------------------
+    print("Read assumptions from file: " + str(data['paths']['yaml_parameters']))
+    data['assumptions'] = read_data.read_param_yaml(data['paths']['yaml_parameters'])
+
     data['assumptions']['seasons'] = date_prop.read_season(year_to_model=2015)
     data['assumptions']['model_yeardays_daytype'], data['assumptions']['yeardays_month'], data['assumptions']['yeardays_month_days'] = date_prop.get_model_yeardays_datype(year_to_model=2015)
     data['tech_lp'] = data_loader.load_data_profiles(data['paths'], data['local_paths'], data['assumptions'])
-    data['assumptions'] = base_assumptions.update_assumptions(data['assumptions'])
+    data['assumptions']['technologies'] = base_assumptions.update_assumptions(data['assumptions']['technologies'], data['assumptions']['eff_achieving_factor']['factor_achieved'])
     data['weather_stations'], data['temp_data'] = data_loader.load_temp_data(data['local_paths'])
 
+    # ------------------------------
+
+
     # ==========
     data['lu_reg'] = data_loader.load_LAC_geocodes_info(data['local_paths']['path_dummy_regions'])
     data = data_loader.dummy_data_generation(data)

energy_demand/read_write/data_loader.py

@@ -357,7 +357,10 @@ def load_paths(path):
         'lp_elec_primary_heating': os.path.join(
             path, 'config_data', 'submodel_residential', 'lp_elec_primary_heating_HES.csv'),
         'lp_elec_secondary_heating': os.path.join(
-            path, 'config_data', 'submodel_residential', 'lp_elec_secondary_heating_HES.csv')
+            path, 'config_data', 'submodel_residential', 'lp_elec_secondary_heating_HES.csv'),
+
+        'yaml_parameters': os.path.join(path, 'yaml_parameters.yml'),
+        'yaml_parameters_complete': os.path.join(path, 'yaml_parameters_complete.yml')
         }
 
     return paths

energy_demand/read_write/read_data.py

@@ -5,12 +5,21 @@
 import csv
 import json
 import logging
+import yaml
 from collections import defaultdict
 import numpy as np
 from energy_demand.technologies import tech_related
 from energy_demand.read_write import read_weather_data
 from energy_demand.profiles import load_profile
 
+def read_param_yaml(path):
+    """
+    """
+    with open(path, 'r') as ymlfile:
+        parameter_dict = yaml.load(ymlfile)
+
+    return parameter_dict
+
 def read_in_results(path_runs, lookups, seasons, model_yeardays_daytype):
     """Read and post calculate
     results from txt files

energy_demand/read_write/write_data.py

@@ -4,6 +4,56 @@
 import numpy as np
 import configparser
 from energy_demand.basic import basic_functions
+import yaml
+from yaml import Loader, Dumper
+import collections
+
+def dump(data, file_path):
+    """Write plain data to a file as yaml
+
+    Parameters
+    ----------
+    file_path : str
+        The path of the configuration file to write
+    data
+        Data to write (should be lists, dicts and simple values)
+    """
+    with open(file_path, 'w') as file_handle:
+        return yaml.dump(data, file_handle, Dumper=Dumper, default_flow_style=False)
+
+def write_yaml_param_complete(path_yaml, dict_to_dump):
+    """Write all assumption parameters to YAML
+    #TODO :ORDER
+    """
+    list_to_dump_complete = []
+
+    for dict_key, dict_values in dict_to_dump.items():
+        try:
+            parameter_infos = dict_values['param_infos']
+
+            for paramter_info in parameter_infos:
+                dict_to_dump_complete = {} #collections.OrderedDict()
+                dict_to_dump_complete['suggested_range'] = paramter_info['suggested_range']
+                dict_to_dump_complete['absolute_range'] = paramter_info['absolute_range']
+                dict_to_dump_complete['description'] = paramter_info['description']
+                dict_to_dump_complete['name'] = paramter_info['name']
+                dict_to_dump_complete['default_value'] = paramter_info['default_value']
+                dict_to_dump_complete['units'] = paramter_info['units']
+                list_to_dump_complete.append(dict_to_dump_complete)
+        except:
+            pass #not correctly formated assumption
+
+    # Dump list
+    dump(list_to_dump_complete, path_yaml)
+    return
+
+def write_yaml_param(path_yaml, dict_to_dump):
+    """Write all assumption parameters to YAML
+
+    """
+    with open(path_yaml, 'w') as file_handle:
+        yaml.dump(dict_to_dump, file_handle)
+    return
 
 def write_simulation_inifile(path, sim_param, enduses, assumptions, reg_nrs):
     """Write .ini file with simulation parameters

energy_demand/scripts/init_scripts.py

@@ -17,6 +17,7 @@
 from energy_demand.basic import basic_functions
 from energy_demand.basic import logger_setup
 from energy_demand.basic import date_prop
+from energy_demand.read_write import read_data, write_data
 
 def post_install_setup(args):
     """Run initialisation scripts
@@ -54,10 +55,11 @@ def post_install_setup(args):
     data['sim_param']['base_yr'] = 2015
     data['sim_param']['simulated_yrs'] = [2015, 2020, 2025]
 
-    data['assumptions'] = base_assumptions.load_assumptions(
+    base_assumptions.load_assumptions(
         data['paths'], data['enduses'], data['lookups'], data['fuels'], data['sim_param'])
+    data['assumptions'] = read_data.read_param_yaml(data['paths']['yaml_parameters'])
     data['assumptions']['seasons'] = date_prop.read_season(year_to_model=2015)
-    data['assumptions'] = base_assumptions.update_assumptions(data['assumptions'])
+    data['assumptions']['technologies'] = base_assumptions.update_assumptions(data['assumptions']['technologies'], data['assumptions']['eff_achieving_factor']['factor_achieved'])
 
     # Delete all previous data from previous model runs
     basic_functions.del_previous_setup(data['local_paths']['data_processed'])
@@ -119,10 +121,11 @@ def scenario_initalisation(path_data_energy_demand, data=False):
         data['sim_param']['simulated_yrs'] = [2015, 2020, 2025]
         data['sim_param']['curr_yr'] = 2015 #NEEDED
 
-        data['assumptions'] = base_assumptions.load_assumptions(
+        base_assumptions.load_assumptions(
             data['paths'], data['enduses'], data['lookups'], data['fuels'], data['sim_param'])
+        data['assumptions'] = read_data.read_param_yaml(data['paths']['yaml_parameters'])
         data['assumptions']['seasons'] = date_prop.read_season(year_to_model=2015)
-        data['assumptions'] = base_assumptions.update_assumptions(data['assumptions'])
+        data['assumptions']['technologies'] = base_assumptions.update_assumptions(data['assumptions']['technologies'], data['assumptions']['eff_achieving_factor']['factor_achieved'])
 
         data['lu_reg'] = data_loader.load_LAC_geocodes_info(data['local_paths']['path_dummy_regions'])
         data = data_loader.dummy_data_generation(data)

energy_demand/scripts/s_disaggregation.py

@@ -283,7 +283,7 @@ def rs_disaggregate(lu_reg, sim_param, data, rs_national_fuel):
         sim_param,
         lu_reg,
         data['temp_data'],
-        data['assumptions']['smart_meter_diff_params'],
+        data['assumptions']['smart_meter_assump']['smart_meter_diff_params'],
         data['assumptions']['rs_t_base_heating'],
         data['reg_coord'],
         data['weather_stations'])

kernel-b2106b65-0cb8-4928-8ad9-8a3cc7ed39ef.json

@@ -0,0 +1,12 @@
+{
+  "shell_port": 63583,
+  "iopub_port": 63584,
+  "stdin_port": 63585,
+  "control_port": 63586,
+  "hb_port": 63587,
+  "ip": "127.0.0.1",
+  "key": "ac60daed-38c2cb2e371228d413597b1c",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}