Paths corrected

energy_demand/_scrap.py

@@ -28,3 +28,57 @@
 fig, ax = plt.subplots()
 ax.stackplot(x, y)
 plt.show()
+
+    """A one-line summary that does not use variable names or the
+    function name.
+    Several sentences providing an extended description. Refer to
+    variables using back-ticks, e.g. `var`.
+
+    Parameters
+    ----------
+    var1 : array_like
+        Array_like means all those objects -- lists, nested lists, etc. --
+        that can be converted to an array.  We can also refer to
+        variables like `var1`.
+    var2 : int
+        The type above can either refer to an actual Python type
+        (e.g. ``int``), or describe the type of the variable in more
+        detail, e.g. ``(N,) ndarray`` or ``array_like``.
+    long_var_name : {'hi', 'ho'}, optional
+        Choices in brackets, default first when optional.
+
+    Returns
+    -------
+    type
+        Explanation of anonymous return value of type ``type``.
+    describe : type
+        Explanation of return value named `describe`.
+    out : type
+        Explanation of `out`.
+
+    Other Parameters
+    ----------------
+    only_seldom_used_keywords : type
+        Explanation
+    common_parameters_listed_above : type
+        Explanation
+
+    Raises
+    ------
+    BadException
+        Because you shouldn't have done that.
+
+    See Also
+    --------
+    otherfunc : relationship (optional)
+    newfunc : Relationship (optional), which could be fairly long, in which
+              case the line wraps here.
+    thirdfunc, fourthfunc, fifthfunc
+
+    Notes
+    -----
+    Notes about the implementation algorithm (if needed).
+    This can have multiple paragraphs.
+    You may include some math:
+
+"""

energy_demand/data_loader.py

@@ -1,13 +1,132 @@
-""" new load profile generator """
-
+"""Loads all necessary data"""
 import os
 import csv
 import unittest
 import matplotlib.pyplot as plt
+import numpy as np
 import energy_demand.data_loader_functions as df
-import main_functions as mf
+import energy_demand.main_functions as mf
 # pylint: disable=I0011,C0321,C0301,C0103, C0325
 
+def load_data(data, path_main, data_ext):
+    """All base data no provided externally are loaded
+
+    All necessary data to run energy demand model is loaded.
+    This data is loaded in the wrapper.
+
+    Parameters
+    ----------
+    data : dict
+        Dict with own data
+    path_main : str
+        Path to all data of model run which are not provided externally by wrapper
+
+    Returns
+    -------
+    data : list
+        Returns a list where storing all data
+
+    """
+    path_dict = {
+
+        # Residential
+        # -----------
+        'path_main': path_main,
+        'path_pop_reg_lu': os.path.join(path_main, 'scenario_and_base_data/lookup_nr_regions.csv'),
+        'path_dwtype_lu': os.path.join(path_main, 'residential_model/lookup_dwelling_type.csv'),
+        'path_lookup_appliances':os.path.join(path_main, 'residential_model/lookup_appliances_HES.csv'),
+        'path_fuel_type_lu': os.path.join(path_main, 'scenario_and_base_data/lookup_fuel_types.csv'),
+        'path_day_type_lu': os.path.join(path_main, 'residential_model/lookup_day_type.csv'),
+        'path_bd_e_load_profiles': os.path.join(path_main, 'residential_model/HES_base_appliances_eletricity_load_profiles.csv'),
+        'path_temp_2015': os.path.join(path_main, 'residential_model/SNCWV_YEAR_2015.csv'),
+        'path_hourly_gas_shape_resid': os.path.join(path_main, 'residential_model/SANSOM_residential_gas_hourly_shape.csv'),
+        'path_dwtype_dist': os.path.join(path_main, 'residential_model/data_residential_model_dwtype_distribution.csv'),
+        'path_dwtype_age': os.path.join(path_main, 'residential_model/data_residential_model_dwtype_age.csv'),
+        'path_dwtype_floorarea_dw_type': os.path.join(path_main, 'residential_model/data_residential_model_dwtype_floorarea.csv'),
+        'path_reg_floorarea': os.path.join(path_main, 'residential_model/data_residential_model_floorarea.csv'),
+        'path_reg_dw_nr': os.path.join(path_main, 'residential_model/data_residential_model_nr_dwellings.csv'),
+        'path_data_residential_by_fuel_end_uses': os.path.join(path_main, 'residential_model/data_residential_by_fuel_end_uses.csv'),
+        'path_lu_appliances_HES_matched': os.path.join(path_main, 'residential_model/lookup_appliances_HES_matched.csv'),
+        'path_txt_shapes_resid': os.path.join(path_main, 'residential_model/txt_load_shapes')
+
+        # Service
+        # -----------
+        #'path_temp_2015_service': os.path.join(path_main, 'service_model/CSV_YEAR_2015_service.csv')
+        }
+
+    data['path_dict'] = path_dict
+
+    # -- Reads in all csv files and store them in a dictionary
+    data['path_main'] = path_main
+
+    # Lookup data
+    data['reg_lu'] = mf.read_csv_dict_no_header(path_dict['path_pop_reg_lu'])                # Region lookup table
+    data['dwtype_lu'] = mf.read_csv_dict_no_header(path_dict['path_dwtype_lu'])              # Dwelling types lookup table
+    data['app_type_lu'] = mf.read_csv(path_dict['path_lookup_appliances'])                   # Appliances types lookup table
+    data['fuel_type_lu'] = mf.read_csv_dict_no_header(path_dict['path_fuel_type_lu'])        # Fuel type lookup
+    data['day_type_lu'] = mf.read_csv(path_dict['path_day_type_lu'])                         # Day type lookup
+
+    #fuel_bd_data = read_csv_float(path_dict['path_base_data_fuel'])               # All disaggregated fuels for different regions
+    data['path_temp_2015'] = mf.read_csv(path_dict['path_temp_2015'])                         # csv_temp_2015 #TODO: Delete because loaded in read_shp_heating_gas
+    data['hourly_gas_shape'] = mf.read_csv_float(path_dict['path_hourly_gas_shape_resid'])         # Load hourly shape for gas from Robert Sansom #TODO: REmove because in read_shp_heating_gas
+
+    #path_dwtype_age = read_csv_float(['path_dwtype_age'])
+    data['dwtype_distr'] = mf.read_csv_nested_dict(path_dict['path_dwtype_dist'])      # dISTRIBUTION of dwelligns base year #TODO: REMOVE AND ONLY LOAD YEAR 2015
+    data['dwtype_age_distr'] = mf.read_csv_nested_dict(path_dict['path_dwtype_age'])
+    data['dwtype_floorarea']  = mf.read_csv_dict(path_dict['path_dwtype_floorarea_dw_type'])
+    data['reg_floorarea'] = mf.read_csv_dict_no_header(path_dict['path_reg_floorarea'])
+    data['reg_dw_nr'] = mf.read_csv_dict_no_header(path_dict['path_reg_dw_nr'])
+
+    # load shapes
+    data['dict_shp_enduse_h_resid'] = {}
+    data['dict_shp_enduse_d_resid'] = {}
+
+    # Data new approach
+    data_residential_by_fuel_end_uses = mf.read_csv_base_data_resid(path_dict['path_data_residential_by_fuel_end_uses']) # Yearly end use data
+
+    scrap = 0
+    for enduse in data_residential_by_fuel_end_uses:
+        scrap += np.sum(data_residential_by_fuel_end_uses[enduse])
+    print("scrap FUELS READ IN FROM EXCEL: " + str(scrap))
+
+    # Add the yearly fuel data of the external Wrapper to the enduses (RESIDENTIAL HERE)
+    ###data = add_yearly_external_fuel_data(data, data_ext, data_residential_by_fuel_end_uses) #TODO: ALSO IMPORT ALL OTHER END USE RELATED THINS SUCH AS SHAPE
+
+    data['lu_appliances_HES_matched'] = mf.read_csv(path_dict['path_lu_appliances_HES_matched'])
+
+    # SERVICE SECTOR
+    #data['csv_temp_2015_service'] = mf.read_csv(path_dict['path_temp_2015_service']) # csv_temp_2015 #TODO: Dele
+    data['dict_shp_enduse_h_service'] = {}
+    data['dict_shp_enduse_d_service'] = {}
+
+    # ----------------------------------------
+    # --Convert loaded data into correct units
+    # ----------------------------------------
+
+    # Fuel residential
+    for enduse in data_residential_by_fuel_end_uses:
+        data_residential_by_fuel_end_uses[enduse] = mf.conversion_ktoe_gwh(data_residential_by_fuel_end_uses[enduse])
+
+    data['data_residential_by_fuel_end_uses'] = data_residential_by_fuel_end_uses
+
+
+    # --- Generate load_shapes ##TODO
+    data = generate_data(data) # Otherwise already read out files are read in from txt files
+
+    # -- Read in load shapes from files
+    data = collect_shapes_from_txts(data)
+
+    # ---TESTS
+    # Test if numer of fuel types is identical (Fuel lookup needs to have same dimension as end-use fuels)
+    for end_use in data['data_residential_by_fuel_end_uses']:
+        assert len(data['fuel_type_lu']) == len(data['data_residential_by_fuel_end_uses'][end_use]) # Fuel in fuel distionary does not correspond to len of input fuels
+
+    scrap = 0
+    for enduse in data['data_residential_by_fuel_end_uses']:
+        scrap += np.sum(data_residential_by_fuel_end_uses[enduse])
+    print("scrap FUELS FINAL FOR OUT: " + str(scrap))
+
+    return data
 
 #run_data_collection = False #Scrap
 # ---------------------------------------------------

energy_demand/data_loader_functions.py

@@ -6,7 +6,7 @@
 import unittest
 import json
 from datetime import date
-import main_functions as mf
+import energy_demand.main_functions as mf
 
 
 # HES-----------------------------------

energy_demand/main.py

@@ -43,7 +43,7 @@
 import energy_demand.residential_model as rm
 import energy_demand.plot_functions as pf
 import energy_demand.national_dissaggregation as nd
-#import energy_demand.data_loader as dl
+import energy_demand.data_loader as dl
 
 print("Start Energy Demand Model with python version: " + str(sys.version))
 import numpy as np
@@ -109,10 +109,6 @@ def energy_demand_model(data, data_ext):
     #pf.plot_x_days(result_dict[2], 0, 2)
     return result_dict
 
-
-
-
-
 # Run
 if __name__ == "__main__":
 
@@ -145,7 +141,7 @@ def energy_demand_model(data, data_ext):
 
     # Model calculations outside main function
     path_main = r'C:/Users/cenv0553/GIT/NISMODII/data/' #path_main = '../data'
-    base_data = mf.load_data(base_data, path_main, data_external) # Load and generate data
+    base_data = dl.load_data(base_data, path_main, data_external) # Load and generate data
 
     # Load assumptions
     print("Load Assumptions")
@@ -163,61 +159,3 @@ def energy_demand_model(data, data_ext):
     # Run main function
     energy_demand_model(base_data, data_external)
     print("Finished running Energy Demand Model")
-
-
-
-
-
-    """A one-line summary that does not use variable names or the
-    function name.
-    Several sentences providing an extended description. Refer to
-    variables using back-ticks, e.g. `var`.
-
-    Parameters
-    ----------
-    var1 : array_like
-        Array_like means all those objects -- lists, nested lists, etc. --
-        that can be converted to an array.  We can also refer to
-        variables like `var1`.
-    var2 : int
-        The type above can either refer to an actual Python type
-        (e.g. ``int``), or describe the type of the variable in more
-        detail, e.g. ``(N,) ndarray`` or ``array_like``.
-    long_var_name : {'hi', 'ho'}, optional
-        Choices in brackets, default first when optional.
-
-    Returns
-    -------
-    type
-        Explanation of anonymous return value of type ``type``.
-    describe : type
-        Explanation of return value named `describe`.
-    out : type
-        Explanation of `out`.
-
-    Other Parameters
-    ----------------
-    only_seldom_used_keywords : type
-        Explanation
-    common_parameters_listed_above : type
-        Explanation
-
-    Raises
-    ------
-    BadException
-        Because you shouldn't have done that.
-
-    See Also
-    --------
-    otherfunc : relationship (optional)
-    newfunc : Relationship (optional), which could be fairly long, in which
-              case the line wraps here.
-    thirdfunc, fourthfunc, fifthfunc
-
-    Notes
-    -----
-    Notes about the implementation algorithm (if needed).
-    This can have multiple paragraphs.
-    You may include some math:
-
-"""