Fix pandas issues

demandlib/bdew/__init__.py

@@ -0,0 +1,11 @@
+# -*- coding: utf-8 -*-
+"""
+Load profiles
+
+SPDX-FileCopyrightText: Patrik Schönfeldt
+
+SPDX-License-Identifier: MIT
+"""
+
+from .elec_slp import ElecSlp
+from .heat_building import HeatBuilding

demandlib/bdew/elec_slp.py

@@ -0,0 +1,173 @@
+# -*- coding: utf-8 -*-
+"""
+Implementation of the standard load profiles
+
+SPDX-FileCopyrightText: Birgit Schachler
+SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
+SPDX-FileCopyrightText: jnnr
+SPDX-FileCopyrightText: Stephen Bosch
+SPDX-FileCopyrightText: Patrik Schönfeldt
+
+SPDX-License-Identifier: MIT
+"""
+import datetime
+import pandas as pd
+import os
+import calendar
+
+from demandlib.tools import add_weekdays2df
+
+
+class ElecSlp:
+    """Generate electrical standardized load profiles based on the BDEW method.
+
+    Attributes
+    ----------
+    datapath : string
+        Path to the csv files containing the load profile data.
+    date_time_index : pandas.DateTimeIndex
+        Time range for and frequency for the profile.
+
+    Parameters
+    ----------
+    year : integer
+        Year of the demand series.
+
+    Optional Parameters
+    -------------------
+    seasons : dictionary
+        Describing the time ranges for summer, winter and transition periods.
+    holidays : dictionary or list
+        The keys of the dictionary or the items of the list should be datetime
+        objects of the days that are holidays.
+    """
+
+    def __init__(self, year, seasons=None, holidays=None):
+        if calendar.isleap(year):
+            hoy = 8784
+        else:
+            hoy = 8760
+        self.datapath = os.path.join(os.path.dirname(__file__), 'bdew_data')
+        self.date_time_index = pd.date_range(
+            datetime.datetime(year, 1, 1, 0), periods=hoy * 4, freq='15Min')
+        if seasons is None:
+            self.seasons = {
+                'summer1': [5, 15, 9, 14],  # summer: 15.05. to 14.09
+                'transition1': [3, 21, 5, 14],  # transition1 :21.03. to 14.05
+                'transition2': [9, 15, 10, 31],  # transition2 :15.09. to 31.10
+                'winter1': [1, 1, 3, 20],  # winter1:  01.01. to 20.03
+                'winter2': [11, 1, 12, 31],  # winter2: 01.11. to 31.12
+            }
+        else:
+            self.seasons = seasons
+        self.year = year
+        self.slp_frame = self.all_load_profiles(self.date_time_index,
+                                                holidays=holidays)
+
+    def all_load_profiles(self, time_df, holidays=None):
+        slp_types = ['h0', 'g0', 'g1', 'g2', 'g3', 'g4', 'g5', 'g6', 'l0',
+                     'l1', 'l2']
+        new_df = self.create_bdew_load_profiles(time_df, slp_types,
+                                                holidays=holidays)
+
+        new_df.drop(['hour', 'weekday'], 1, inplace=True)
+        return new_df
+
+    def create_bdew_load_profiles(self, dt_index, slp_types, holidays=None):
+        """Calculates the hourly electricity load profile in MWh/h of a region.
+        """
+
+        # define file path of slp csv data
+        file_path = os.path.join(self.datapath, 'selp_series.csv')
+
+        # Read standard load profile series from csv file
+        selp_series = pd.read_csv(file_path)
+        tmp_df = selp_series
+        # Create an index to merge. The year and month will be ignored only the
+        # time index is necessary.
+        index = pd.date_range(
+            datetime.datetime(2007, 1, 1, 0), periods=2016, freq='15Min')
+        tmp_df.set_index(index, inplace=True)
+
+        # Create empty DataFrame to take the results.
+        new_df = pd.DataFrame(index=dt_index, columns=slp_types).fillna(0)
+        new_df = add_weekdays2df(new_df, holidays=holidays,
+                                 holiday_is_sunday=True)
+
+        new_df['hour'] = dt_index.hour
+        new_df['minute'] = dt_index.minute
+        time_df = new_df[['date', 'hour', 'minute', 'weekday']].copy()
+        tmp_df[slp_types] = tmp_df[slp_types].astype(float)
+
+        # Inner join the slps on the time_df to the slp's for a whole year
+        tmp_df['hour_of_day'] = tmp_df.index.hour
+        tmp_df['minute_of_hour'] = tmp_df.index.minute
+        left_cols = ['hour_of_day', 'minute_of_hour', 'weekday']
+        right_cols = ['hour', 'minute', 'weekday']
+        tmp_df = tmp_df.reset_index()
+        tmp_df.pop('index')
+
+        for p in self.seasons.keys():
+            a = datetime.datetime(self.year, self.seasons[p][0],
+                                  self.seasons[p][1], 0, 0)
+            b = datetime.datetime(self.year, self.seasons[p][2],
+                                  self.seasons[p][3], 23, 59)
+            merged_df = pd.DataFrame.merge(
+                tmp_df[tmp_df['period'] == p[:-1]], time_df[a:b],
+                left_on=left_cols, right_on=right_cols,
+                how='inner').drop(
+                ['hour_of_day'], 1)
+
+            merged_df.index = (
+                    pd.to_datetime(merged_df['date'])
+                    + pd.to_timedelta(merged_df['hour'], unit='h')
+                    + pd.to_timedelta(merged_df['minute'], unit='m'))
+            merged_df.sort_index(inplace=True)
+
+            new_df.update(merged_df)
+
+        new_df.drop('date', axis=1, inplace=True)
+        return new_df.div(new_df.sum(axis=0), axis=1)
+
+    def get_profile(self, ann_el_demand_per_sector,
+                    dyn_function_h0: bool = None):
+        """ Get the profiles for the given annual demand
+
+        Parameters
+        ----------
+        ann_el_demand_per_sector : dictionary
+            Key: sector, value: annual value
+        dyn_function_h0: bool, default None
+            (None is interpreted as False but also raises a FutureWarning.)
+            Use the dynamisation function of the BDEW to smoothen the
+            seasonal edges. Functions resolution is daily.
+            f(x) = -3.916649251 * 10^-10 * x^4 + 3,2 * 10^-7 * x³ - 7,02
+            * 10^-5 * x²+0,0021 * x +1,24
+            Adjustment of accuracy: from -3,92 to -3.916649251
+
+        Returns
+        -------
+        pandas.DataFrame : Table with all profiles
+
+        """
+        if dyn_function_h0 is None:
+            warning_message = ("Current default for 'dyn_function_h0' is"
+                               + "'False'. This is about to change to 'True'."
+                               + "Set 'False' explicitly to retain the current"
+                               + "behaviour.")
+            raise FutureWarning(warning_message)
+        elif dyn_function_h0:
+            quartersinyear = len(self.slp_frame)
+            for quarter in range(quartersinyear):
+                quarterhour_to_day = (quarter + 1) / (24 * 4)
+                smoothing_factor = (
+                        -3.916649251 * 10 ** -10
+                        * quarterhour_to_day ** 4 + 3.2 * 10 ** -7
+                        * quarterhour_to_day ** 3 - 7.02 * 10 ** -5
+                        * quarterhour_to_day ** 2 + 0.0021
+                        * quarterhour_to_day + 1.24)
+
+                self.slp_frame['h0'][quarter] = self.slp_frame['h0'][
+                                                    quarter] * smoothing_factor
+        return self.slp_frame.multiply(pd.Series(ann_el_demand_per_sector),
+                                       axis=1).dropna(how='all', axis=1) * 4

demandlib/bdew.py → demandlib/bdew/heat_building.py

@@ -3,160 +3,21 @@
 Implementation of the bdew heat load profiles
 
 
+SPDX-FileCopyrightText: Birgit Schachler
+SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
+SPDX-FileCopyrightText: jnnr
+SPDX-FileCopyrightText: Stephen Bosch
+SPDX-FileCopyrightText: Patrik Schönfeldt
+
+SPDX-License-Identifier: MIT
 """
-import datetime
 from math import ceil
 import numpy as np
 import pandas as pd
 import os
-from .tools import add_weekdays2df
-import calendar
 
 
-class ElecSlp:
-    """Generate electrical standardized load profiles based on the BDEW method.
-
-    Attributes
-    ----------
-    datapath : string
-        Path to the csv files containing the load profile data.
-    date_time_index : pandas.DateTimeIndex
-        Time range for and frequency for the profile.
-
-    Parameters
-    ----------
-    year : integer
-        Year of the demand series.
-
-    Optional Parameters
-    -------------------
-    seasons : dictionary
-        Describing the time ranges for summer, winter and transition periods.
-    holidays : dictionary or list
-        The keys of the dictionary or the items of the list should be datetime
-        objects of the days that are holidays.
-    """
-
-    def __init__(self, year, seasons=None, holidays=None):
-        if calendar.isleap(year):
-            hoy = 8784
-        else:
-            hoy = 8760
-        self.datapath = os.path.join(os.path.dirname(__file__), 'bdew_data')
-        self.date_time_index = pd.date_range(
-            datetime.datetime(year, 1, 1, 0), periods=hoy * 4, freq='15Min')
-        if seasons is None:
-            self.seasons = {
-                'summer1': [5, 15, 9, 14],  # summer: 15.05. to 14.09
-                'transition1': [3, 21, 5, 14],  # transition1 :21.03. to 14.05
-                'transition2': [9, 15, 10, 31],  # transition2 :15.09. to 31.10
-                'winter1': [1, 1, 3, 20],  # winter1:  01.01. to 20.03
-                'winter2': [11, 1, 12, 31],  # winter2: 01.11. to 31.12
-            }
-        else:
-            self.seasons = seasons
-        self.year = year
-        self.slp_frame = self.all_load_profiles(self.date_time_index,
-                                                holidays=holidays)
-
-    def all_load_profiles(self, time_df, holidays=None):
-        slp_types = ['h0', 'g0', 'g1', 'g2', 'g3', 'g4', 'g5', 'g6', 'l0',
-                     'l1', 'l2']
-        new_df = self.create_bdew_load_profiles(time_df, slp_types,
-                                                holidays=holidays)
-
-        new_df.drop(['hour', 'weekday'], 1, inplace=True)
-        return new_df
-
-    def create_bdew_load_profiles(self, dt_index, slp_types, holidays=None):
-        """Calculates the hourly electricity load profile in MWh/h of a region.
-        """
-
-        # define file path of slp csv data
-        file_path = os.path.join(self.datapath, 'selp_series.csv')
-
-        # Read standard load profile series from csv file
-        selp_series = pd.read_csv(file_path)
-        tmp_df = selp_series
-        # Create an index to merge. The year and month will be ignored only the
-        # time index is necessary.
-        index = pd.date_range(
-            datetime.datetime(2007, 1, 1, 0), periods=2016, freq='15Min')
-        tmp_df.set_index(index, inplace=True)
-
-        # Create empty DataFrame to take the results.
-        new_df = pd.DataFrame(index=dt_index, columns=slp_types).fillna(0)
-        new_df = add_weekdays2df(new_df, holidays=holidays,
-                                 holiday_is_sunday=True)
-
-        new_df['hour'] = dt_index.hour + 1
-        new_df['minute'] = dt_index.minute
-        time_df = new_df[['date', 'hour', 'minute', 'weekday']].copy()
-        tmp_df[slp_types] = tmp_df[slp_types].astype(float)
-
-        # Inner join the slps on the time_df to the slp's for a whole year
-        tmp_df['hour_of_day'] = tmp_df.index.hour + 1
-        tmp_df['minute_of_hour'] = tmp_df.index.minute
-        left_cols = ['hour_of_day', 'minute_of_hour', 'weekday']
-        right_cols = ['hour', 'minute', 'weekday']
-        tmp_df = tmp_df.reset_index()
-        tmp_df.pop('index')
-
-        for p in self.seasons.keys():
-            a = datetime.datetime(self.year, self.seasons[p][0],
-                                  self.seasons[p][1], 0, 0)
-            b = datetime.datetime(self.year, self.seasons[p][2],
-                                  self.seasons[p][3], 23, 59)
-            new_df.update(pd.DataFrame.merge(
-                tmp_df[tmp_df['period'] == p[:-1]], time_df[a:b],
-                left_on=left_cols, right_on=right_cols,
-                how='inner', left_index=True).sort_index().drop(
-                ['hour_of_day'], 1))
-
-        new_df.drop('date', axis=1, inplace=True)
-        return new_df.div(new_df.sum(axis=0), axis=1)
-
-    def get_profile(self, ann_el_demand_per_sector,
-                    dyn_function_h0: bool = None):
-        """ Get the profiles for the given annual demand
-
-        Parameters
-        ----------
-        ann_el_demand_per_sector : dictionary
-            Key: sector, value: annual value
-        dyn_function_h0: bool, default None
-            (None is interpreted as False but also raises a FutureWarning.)
-            Use the dynamisation function of the BDEW to smoothen the
-            seasonal edges. Functions resolution is daily.
-            f(x) = -3.916649251 * 10^-10 * x^4 + 3,2 * 10^-7 * x³ - 7,02
-            * 10^-5 * x²+0,0021 * x +1,24
-            Adjustment of accuracy: from -3,92 to -3.916649251
-
-        Returns
-        -------
-        pandas.DataFrame : Table with all profiles
-
-        """
-        if dyn_function_h0 is None:
-            warning_message = ("Current default for 'dyn_function_h0' is"
-                               + "'False'. This is about to change to 'True'."
-                               + "Set 'False' explicitly to retain the current"
-                               + "behaviour.")
-            raise FutureWarning(warning_message)
-        elif dyn_function_h0:
-            quartersinyear = len(self.slp_frame)
-            for quarter in range(quartersinyear):
-                quarterhour_to_day = (quarter + 1) / (24 * 4)
-                smoothing_factor = -3.916649251 * 10 ** -10 \
-                                   * quarterhour_to_day ** 4 + 3.2 * 10 ** -7 \
-                                   * quarterhour_to_day ** 3 - 7.02 * 10 ** -5 \
-                                   * quarterhour_to_day ** 2 + 0.0021 \
-                                   * quarterhour_to_day + 1.24
-
-                self.slp_frame['h0'][quarter] = self.slp_frame['h0'][
-                                                    quarter] * smoothing_factor
-        return self.slp_frame.multiply(pd.Series(ann_el_demand_per_sector),
-                                       axis=1).dropna(how='all', axis=1) * 4
+from demandlib.tools import add_weekdays2df
 
 
 class HeatBuilding:
@@ -193,7 +54,7 @@ def __init__(self, df_index, **kwargs):
         self.df = pd.DataFrame(index=df_index)
         self.df = add_weekdays2df(self.df, holiday_is_sunday=True,
                                   holidays=kwargs.get('holidays'))
-        self.df['hour'] = self.df.index.hour + 1
+        self.df['hour'] = self.df.index.hour + 1  # hour of the day
         self.temperature = kwargs.get('temperature')
         self.annual_heat_demand = kwargs.get('annual_heat_demand')
         self.shlp_type = kwargs.get('shlp_type').upper()
@@ -286,7 +147,12 @@ def get_sf_values(self, filename='shlp_hour_factors.csv'):
         right_cols = ['hour'] + (['weekday'] if not residential else [])
         sf_mat = pd.DataFrame.merge(
             hour_factors, self.df, left_on=left_cols, right_on=right_cols,
-            how='outer', left_index=True).sort_index()
+            how='outer')
+
+        sf_mat.index = (
+                pd.to_datetime(sf_mat['date'])
+                + pd.to_timedelta(sf_mat['hour_of_day'] - 1, unit='h'))
+        sf_mat.sort_index(inplace=True)
 
         # drop unnecessary columns
         drop_cols = (
@@ -345,9 +211,15 @@ def get_weekday_parameters(self, filename='shlp_weekday_factors.csv'):
 
         tmp_df['weekdays'] = np.array(list(range(7))) + 1
 
-        return np.array(list(map(float, pd.DataFrame.merge(
+        merged_df = pd.DataFrame.merge(
             tmp_df, self.df, left_on='weekdays', right_on='weekday',
-            how='inner', left_index=True).sort_index()['wochentagsfaktor'])))
+            how='inner')
+        merged_df.index = (
+                pd.to_datetime(merged_df['date'])
+                + pd.to_timedelta(merged_df['hour'] - 1, unit='h'))
+        merged_df.sort_index(inplace=True)
+
+        return np.array(list(map(float, merged_df['wochentagsfaktor'])))
 
     def get_bdew_profile(self):
         """ Calculation of the hourly heat demand using the bdew-equations