Idf parser and standardization is done (correct version)_need to be updated

imports/schedules/DOE_IDF.py

@@ -1,65 +1,105 @@
-
 import pandas as pd
 from imports.schedules.helpers.schedules_helper import SchedulesHelper
 import parseidf
 
 
-class IdfParser:
-    _OCCUPANCY_SCHEDULE= ("BLDG_OCC_SCH_wo_SB","Occupancy")
-    _Activity_Schedule= "ACTIVITY_SCH"
-    _Electrical_Equipent_Schedule= "BLDG_EQUIP_SCH"
-    _Lighting_Schedule="BLDG_LIGHT_SCH"
-    _Infiltration_Ventilation_Schedule="INFIL_QUARTER_ON_SCH"
 
-  def __init__(self, city, base_path, type):
+class IDFParser:
-    self._city = city
+    _OCCUPANCY_SCHEDULE = ("BLDG_OCC_SCH_wo_SB", "Occupancy")
-    self._idf_schedules_path = base_path / 'ASHRAE901_OfficeSmall_STD2019_Buffalo.idf'
+    _Activity_Schedule = "ACTIVITY_SCH"
-    with open(self._idf_schedules_path, 'r') as f:
+    _Electrical_Equipent_Schedule = "BLDG_EQUIP_SCH"
-        idf = parseidf.parse(f.read())
+    _Lighting_Schedule = "BLDG_LIGHT_SCH"
+    _Infiltration_Ventilation_Schedule = "INFIL_QUARTER_ON_SCH"
 
-    for building in city.buildings:
+    def __init__(self, city, base_path):
-      schedules = dict()
+        self._city = city
-      for usage_zone in building.usage_zones:
+        self._idf_schedules_path = base_path / 'ASHRAE901_OfficeSmall_STD2019_Buffalo.idf'
-        usage_schedules = pd.Datafram(idf['SCHEDULE:COMPACT'])
+        with open(self._idf_schedules_path, 'r') as f:
+            idf = parseidf.parse(f.read())
+        print(idf.keys())  # lists the object types in the idf file
+        print(idf['SCHEDULE:COMPACT'])  # lists all the Output:Variable objects in the idf file
+        Holiday = []
+        Winterdesignday = []
+        Summerdesignday = []
+        Customday1 = []
+        Customday2 = []
+        Weekday = []
+        Weekend = []
+        dayType = []
+        compactKeywords = ['Weekdays', 'Weekends', 'Alldays', 'AllOtherDays', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
+                           'Thursday', 'Friday', 'Saturday', 'Holiday', 'Winterdesignday', 'Summerdesignday', 'Customday1',
+                           'Customday2']
+        for val in idf['SCHEDULE:COMPACT']:
+            if val[1] == 'BLDG_LIGHT_SCH':
+                count = 0
+                for words in val:
+                    dayType.append(words.title().split('For: ')[-1])
+                    index = []
+                    for count, word_dayType in enumerate(dayType):
+                        if word_dayType in compactKeywords:
+                            index.append(count)
+                index.append(len(dayType))
+                for i in range(len(index) - 1):
+                    numberOfDaySch = list(dayType[index[i] + 1:index[i + 1]])
+                    hourlyValues = list(range(24))
+                    startHour = 0
+                    for num in range(int(len(numberOfDaySch) / 2)):
+                        untilTime = list(map(int, (numberOfDaySch[2 * num].split('Until: ')[-1]).split(":")[0:]))
+                        endHour = int(untilTime[0] + untilTime[1] / 60)
+                        value = float(numberOfDaySch[2 * num + 1])
+                        for hour in range(startHour, endHour):
+                            hourlyValues[hour] = value
+                            print(hour, value)
+                        startHour = endHour
+                    if dayType[index[i]] == 'Weekdays':
+                        Weekday.append(hourlyValues)
+                    elif dayType[index[i]] == 'Weekends':
+                        Weekend.append(hourlyValues)
+                    elif dayType[index[i]] == 'Holiday':
+                        Holiday.append(hourlyValues)
+                    elif dayType[index[i]] == 'Winterdesignday':
+                        Winterdesignday.append(hourlyValues)
+                    elif dayType[index[i]] == 'Summerdesignday':
+                        Summerdesignday.append(hourlyValues)
+                    elif dayType[index[i]] == 'Customday1':
+                        Customday1.append(hourlyValues)
+                    elif dayType[index[i]] == 'Customday2':
+                        Customday2.append(hourlyValues)
+                    else:
+                        print('its none of them')
+        idf_schedules = pd.DataFrame(
+            {'week_day': Weekday,
+             'Weekends': Weekend,
+             'Holiday': Holiday,
+             'Winterdesignday': Winterdesignday,
+             'Summerdesignday': Summerdesignday,
+             'Customday1': Customday1,
+             'Customday2': Customday2,
+             })
+        print(idf_schedules)
 
-        Small_Office_Occupancy = []
+        for building in city.buildings:
-        Small_Office_OccupancyActivity = []
+          schedules = dict()
-        Small_Office_ElectricalEquipment = []
+          for usage_zone in building.usage_zones:
-        Small_Office_Lighting = []
+            usage_schedules = idf_schedules
-        Small_Office_Infiltration = []
 
-        for i in usage_schedules:
+          # todo: should we save the data type? How?
-            if i[1] == self._Occupancy_Schedule:
+            number_of_schedule_types = 5
-                return Small_Office_Occupancy.append(i)
+            schedules_per_schedule_type = 6
-            elif i[1] == 'ACTIVITY_SCH':
+            idf_day_types = dict({'week_day': 0, 'Weekends': 1, 'Holiday': 2, 'Winterdesignday': 3, 'Summerdesignday': 4, 'Customday1': 5, 'Customday2': 6})
-                Small_Office_OccupancyActivity.append(i)
+            for schedule_types in range(0, number_of_schedule_types):
-            elif i[1] == 'BLDG_EQUIP_SCH':
+              data = pd.DataFrame()
-                Small_Office_ElectricalEquipment.append(i)
+              columns_names = []
-            elif i[1] == 'BLDG_LIGHT_SCH':
+              name = ''
-                Small_Office_Lighting.append(i)
+              for schedule_day in range(0, len(idf_schedules)):
-            elif i[1] == 'INFIL_QUARTER_ON_SCH':
+                row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day]
-                Small_Office_Infiltration.append(i)
+                if schedule_day == idf_day_types['week_day']:
-            else:
+                  name = row_cells[0]
-                print("The schedule name is not standard")
+                columns_names.append(row_cells[2])
+                data1 = row_cells[schedules_per_schedule_type:]
+                data = pd.concat([data, data1],  axis=1)
+              data.columns = columns_names
+              schedules[name] = data
+            usage_zone.schedules = schedules
 
 
-'''
-      # todo: should we save the data type? How?
-        number_of_schedule_types = 13
-        schedules_per_schedule_type = 3
-        day_types = dict({'week_day': 0, 'saturday': 1, 'sunday': 2})
-        for schedule_types in range(0, number_of_schedule_types):
-          data = pd.DataFrame()
-          columns_names = []
-          name = ''
-          for schedule_day in range(0, schedules_per_schedule_type):
-            row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day]
-            if schedule_day == day_types['week_day']:
-              name = row_cells[0]
-            columns_names.append(row_cells[2])
-            data1 = row_cells[schedules_per_schedule_type:]
-            data = pd.concat([data, data1],  axis=1)
-          data.columns = columns_names
-          schedules[name] = data
-        usage_zone.schedules = schedules
-'''