Source code for MVDataProcessing.Util
import pandas
import numpy
import datetime
import random
from datetime import datetime
import datetime as dt
[docs]
def TimeProfile(time_stopper: list, name: str = '', show: bool = False, estimate_for: int = 0):
"""
Simple code profiler.
How to use:
Create a list -> time_stopper = []
Put a -> time_stopper.append(['time_init',time.perf_counter()]) at the beginning.
Put time_stopper.append(['Func_01',time.perf_counter()]) after the code block with the first parameter being
a name and the second being the time.
Call this function at the end.
Example:
time_stopper.append(['time_init',time.perf_counter()])
func1()
time_stopper.append(['func1',time.perf_counter()])
func2()
time_stopper.append(['func2',time.perf_counter()])
func3()
time_stopper.append(['func3',time.perf_counter()])
func4()
time_stopper.append(['func4',time.perf_counter()])
TimeProfile(time_stopper,'My Profiler',show=True,estimate_for=500)
The estimate_for parameter makes the calculation as if you would run x times the code analyzed.
:param time_stopper: A List that will hold all the stop times.
:type time_stopper: list
:param name: A name for this instance of time profile. Defaults to empty.
:type name: str, optional
:param show: If True shows the data on the console. Defaults to False.
:type show: bool, optional
:param estimate_for: A multiplier to be applied at the end. Takes the whole
time analyzed and multiplies by "estimate_for".
:type estimate_for: int
:return: None
:rtype: None
"""
if(show):
print("Profile: " + name)
time_stopper = pandas.DataFrame(time_stopper, columns=['Type', 'time'])
# time_stopper['time'] = time_stopper['time']-time_stopper['time'].min()
time_stopper['Delta'] = time_stopper['time'] - time_stopper['time'].shift(periods=1, fill_value=0)
time_stopper = time_stopper.iloc[1:, :]
time_stopper['%'] = numpy.round(100 * time_stopper['Delta'] / time_stopper['Delta'].sum(), 2)
total_estimate = time_stopper['Delta'].sum()
time_stopper = pandas.concat((time_stopper,
pandas.DataFrame([['Total', numpy.nan, time_stopper['Delta'].sum(), 100]],
columns=['Type', 'time', 'Delta', '%'])))
print(time_stopper)
if estimate_for != 0:
print(
f"Estimation for {estimate_for} "
f"runs: {numpy.round(total_estimate * estimate_for / (60 * 60), 2)} hours.")
return
# BUG Some sample_freq have trouble lol.
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def DataSynchronization(x_in: pandas.core.frame.DataFrame,
start_date_dt: datetime,
end_date_dt: datetime,
sample_freq: int = 5,
sample_time_base: str = 'm') -> pandas.core.frame.DataFrame:
"""
Makes the Data Synchronization between the columns (time series) of the data provided.
Theory background.:
The time series synchronization is the first step in processing the dataset. The synchronization is vital
since the alignment between phases (φa, φb, φv) of the same quantity, between quantities (V, I, pf) of the
same feeder, and between feeders, provides many advantages. The first one being the ability to combine all
nine time series, the three-phase voltage, current, and power factor of each feeder to calculate the secondary
quantities (Pactive/Preactive, Eactive/Ereactive).
Furthermore, the synchronization between feeders provides the capability to analyze the iteration between them,
for instance, in load transfers for scheduled maintenance and to estimate substation’s transformers quantities
by the sum of all feeders.
Most of the functions in this module assumes that the time series are "Clean" to a certain sample_freq. Therefore,
this function must be executed first on the dataset.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param start_date_dt: The start date where the synchronization should start.
:type start_date_dt: datetime
:param end_date_dt: The end date where the synchronization will consider samples.
:type end_date_dt: datetime
:param sample_freq: The sample frequency of the time series. Defaults to 5.
:type sample_freq: int,optional
:param sample_time_base: The base time of the sample frequency. Specify if the sample frequency is in (D)ay,
(M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. Defaults to (m)inutes.
:type sample_time_base: srt,optional
:raises Exception: if x_in has no DatetimeIndex.
:raises Exception: if start_date_dt not in datetime format.
:raises Exception: if end_date_dt not in datetime format.
:raises Exception: if sample_time_base is not in (D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds.
:return: Y: The synchronized pandas.core.frame.DataFrame
:rtype: Y: pandas.core.frame.DataFrame
"""
# BASIC INPUT CHECK
if not (isinstance(x_in.index, pandas.DatetimeIndex)):
raise Exception("x_in DataFrame has no DatetimeIndex.")
if not (isinstance(start_date_dt, datetime)):
raise Exception("start_date_dt Date not in datetime format.")
if not (isinstance(end_date_dt, datetime)):
raise Exception("end_date_dt Date not in datetime format.")
if sample_time_base not in ['s', 'm', 'h', 'D', 'M', 'Y']:
raise Exception("sample_time_base not valid. Ex. ['s','m','h','D','M','Y'] ")
added_dic = {'s': 'ms', 'm': 's', 'h': 'm', 'D': 'h', 'M': 'D', 'Y': 'M'}
floor_dic = {'s': 's', 'm': 'min', 'h': 'h', 'D': 'D', 'M': 'ME', 'Y': 'YE'}
x_in.index = x_in.index.tz_localize(None) # Makes the datetimeIndex naive (no time zone)
'''
Creates a base vector that contains all the samples
between start_date_dt and end_date_dt filled timestamp and with nan
'''
qty_data = len(x_in.columns)
time_array = numpy.arange(start_date_dt, end_date_dt, numpy.timedelta64(sample_freq, sample_time_base),
dtype='datetime64')
time_array = time_array + numpy.timedelta64(1, added_dic[
sample_time_base]) # ADD a second/Minute/Hour/Day/Month to the end so during the sort
# this samples will be at last (HH:MM:01)
vet_samples = pandas.DataFrame(index=time_array, columns=range(qty_data), dtype=object)
vet_samples.index.name = 'timestamp'
# Creates the output dataframe which is the same but without the added second.
df_y = vet_samples.copy(deep=True)
df_y.index = df_y.index.floor(floor_dic[sample_time_base]) # Flush the seconds
# Saves the name of the columns
save_columns_name = x_in.columns.values
# Start to process each column
phase_list = numpy.arange(0, x_in.shape[1])
for phase in phase_list:
x = x_in.copy(deep=True)
x.columns = df_y.columns
x = x.loc[~x.iloc[:, phase].isnull(), phase] # Gets only samples on the phase of interest
x = x[numpy.logical_and(x.index < end_date_dt,
x.index >= start_date_dt)]
if x.shape[0] != 0:
# Process samples that are multiple of sample_freq
df_x = x.copy(deep=True)
df_vet_samples = vet_samples[phase]
# remove seconds (00:00:00) to put this specific samples at the beginning during sort
df_x = df_x.sort_index(ascending=True) # Ensures the sequence of timestamps
df_x.index = df_x.index.round(
'1' + floor_dic[sample_time_base]) # Remove seconds, rounding to the nearest minute
df_x = df_x[
df_x.index.minute % sample_freq == 0] # Samples that are multiple of sample_freq have preference
if not df_x.empty:
df_x = df_x[~df_x.index.duplicated(keep='first')] # Remove unnecessary duplicates
# joins both vectors
df_aux = pandas.concat([df_x, df_vet_samples])
df_aux = df_aux.sort_index(ascending=True) # Ensures the sequence of timestamps
'''
Remove sec. (00:00:00), and remove duplicates leaving X when there is data
and vet amostra where its empty
'''
df_aux.index = df_aux.index.floor(floor_dic[sample_time_base])
df_aux = df_aux[~df_aux.index.duplicated(keep='first')] # Remove unnecessary duplicates
# Make sure that any round up that ended up out of the period of study is removed
df_aux = df_aux[numpy.logical_and(df_aux.index < end_date_dt, df_aux.index >= start_date_dt)]
df_y.loc[:, phase] = df_aux
# Process samples that are NOT multiple of sample_freq
df_x = x.copy(deep=True)
df_vet_samples = vet_samples[phase]
# remove seconds (00:00:00) to put this specific samples at the beginning during sort
df_x = df_x.sort_index(ascending=True) # Ensures the sequence of timestamps
df_x.index = df_x.index.round(
'1' + floor_dic[sample_time_base]) # Remove seconds, rounding to the nearest minute
df_x = df_x[
df_x.index.minute % sample_freq != 0] # Samples that are NOT multiple of sample_freq have preference
if not df_x.empty:
df_x.index = df_x.index.round(str(sample_freq) + floor_dic[
sample_time_base]) # Approximate sample to the closest multiple of sample_freq
df_x = df_x[~df_x.index.duplicated(keep='first')] # Remove unnecessary duplicates
# joins both vectors
df_aux = pandas.concat([df_x, df_vet_samples])
df_aux = df_aux.sort_index(ascending=True) # Ensures the sequence of timestamps
'''
Remove sec. (00:00:00), and remove duplicates leaving X when there is data
and vet amostra where its empty
'''
df_aux.index = df_aux.index.floor(floor_dic[sample_time_base])
df_aux = df_aux[~df_aux.index.duplicated(keep='first')] # Remove unnecessary duplicates
# Make sure that any round up that ended up out of the period of study is removed
df_aux = df_aux[numpy.logical_and(df_aux.index < end_date_dt, df_aux.index >= start_date_dt)]
# Copy data to the output vector only if there is no data there yet.
df_y.loc[df_y.iloc[:, phase].isnull(), phase] = df_aux.loc[df_y.iloc[:, phase].isnull()]
# Last operations before the return of Y
df_y = df_y.astype(float)
df_y.columns = save_columns_name # Gives back the original name of the columns in x_in
return df_y
[docs]
def IntegrateHour(x_in: pandas.DataFrame, sample_freq: int = 5,
sample_time_base: str = 'm') -> pandas.core.frame.DataFrame:
"""
Integrates the input pandas.core.frame.DataFrame to an hour samples.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetimes.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param sample_freq: The sample frequency of the time series. Defaults to 5.
:type sample_freq: int,optional
:param sample_time_base: The base time of the sample frequency. Specify if the sample frequency is in (m)inutes
or (s)econds. Defaults to (m)inutes.
:type sample_time_base: srt,optional
:raises Exception: if x_in has no DatetimeIndex.
:return: df_y: The pandas.core.frame.DataFrame integrated by hour.
:rtype: df_y: pandas.core.frame.DataFrame
"""
hour_divider = {'s': 60 * 60, 'm': 60}
# -------------------#
# BASIC INPUT CHECK #
# -------------------#
if not (isinstance(x_in.index, pandas.DatetimeIndex)):
raise Exception("x_in DataFrame has no DatetimeIndex.")
df_y = x_in.copy(deep=True)
time_vet_stamp = df_y.index[numpy.arange(0, len(df_y.index), int(hour_divider[sample_time_base] / sample_freq))]
df_y = df_y.groupby([df_y.index.year, df_y.index.month, df_y.index.day, df_y.index.hour]).mean()
df_y = df_y.reset_index(drop=True)
df_y.insert(0, 'timestamp', time_vet_stamp)
df_y.set_index('timestamp', inplace=True)
return df_y
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def Correlation(x_in: pandas.DataFrame) -> float:
"""
Calculates the correlation between each column of the DataFrame and outputs the average of all.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:return: corr_value: Value of the correlation
:rtype: corr_value: float
"""
corr_value = x_in.corr()[x_in.corr() != 1].mean().mean()
return corr_value
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def DayPeriodMapper(hour: int) -> int:
"""
Maps a given hour to one of four periods of a day.
For 0 to 5 (hour) -> 0 night
For 6 to 11 (hour) -> 1 moorning
For 12 to 17 (hour) -> 2 afternoon
For 18 to 23 (hour) -> 3 evening
:param hour: an hour of the day between 0 and 23.
:type hour: int
:return: mapped: Period of the day
:rtype: mapped: int
"""
return (
0 if 0 <= hour < 6
else
1 if 6 <= hour < 12
else
2 if 12 <= hour < 18
else
3
)
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def DayPeriodMapperVet(hour: pandas.core.series.Series) -> pandas.core.series.Series:
"""
Maps a given hour to one of four periods of a day.
For 0 to 5 (hour) -> 0 night
For 6 to 11 (hour) -> 1 moorning
For 12 to 17 (hour) -> 2 afternoon
For 18 to 23 (hour) -> 3 evening
:param hour: A pandas.core.series.Series with values between 0 and 23 to map each hour in the series to a period
of the day. this is a "vector" format for DayPeriodMapper function.
:type hour: pandas.core.series.Series
:return: period_day: The hour pandas.core.series.Series mapped to periods of the day
:rtype: period_day: pandas.core.series.Series
"""
map_dict = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0,
6: 1, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1,
12: 2, 13: 2, 14: 2, 15: 2, 16: 2, 17: 2,
18: 3, 19: 3, 20: 3, 21: 3, 22: 3, 23: 3}
period_day = hour.map(map_dict)
return period_day
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def YearPeriodMapperVet(month: pandas.core.series.Series) -> pandas.core.series.Series:
"""
Maps a given month to one of two periods of a year, being dry and humid .
For october to march (month) -> 0 humid
For april to september (month) -> 1 dry
:param month: A pandas.core.series.Series with values between 0 and 12 to map each month
in the series to dry or humid.
:return: season: The months pandas.core.series.Series mapped to dry or humid.
:rtype: season: pandas.core.series.Series
"""
map_dict = {10: 0, 11: 0, 12: 0, 1: 0, 2: 0, 3: 0,
4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1}
season = month.map(map_dict)
return season
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def CountMissingData(x_in: pandas.core.frame.DataFrame, remove_from_process: list = [], show=False) -> float:
"""
Calculates the number of vacacies on the dataset.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param remove_from_process: Columns to be kept off the process.
:type remove_from_process: list,optional
:param show: Specify if the function should print or not the value that is also returned.
:type show: bool,optional
:return: Y: Returns the amount of vacancies.
:rtype: Y: float
"""
Y = x_in.loc[:, x_in.columns.difference(remove_from_process)].isnull().sum().sum()
if show:
print(f"Total number of missing samples {Y}")
return Y
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def CalcUnbalance(x_in: pandas.core.frame.DataFrame, remove_from_process: list = []) -> pandas.core.frame.DataFrame:
"""
Calculates the unbalance between phases for every timestamp.
Equation:
Y = (MAX-MEAN)/MEAN
Ref.: Derating of induction motors operating with a combination of unbalanced voltages and over or under-voltages
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param remove_from_process: Columns to be kept off the process.
:type remove_from_process: list,optional
:return: Y: A pandas.core.frame.DataFrame with the % of unbalance between columns (phases).
:rtype: Y: pandas.core.frame.DataFrame
"""
X = x_in.loc[:, x_in.columns.difference(remove_from_process)]
Y = pandas.DataFrame([], index=x_in.index)
Y['Unbalance'] = 100 * (X.max(axis=1) - X.mean(axis=1)) / X.mean(axis=1)
return Y
[docs]
def SavePeriod(x_in: pandas.core.frame.DataFrame,
df_save: pandas.core.frame.DataFrame) -> tuple:
"""
For a given set of periods (Start->End) returns the data. It also returns the indexes.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param df_save: The first column with the start and the second column with the end date.
:type df_save: pandas.core.frame.DataFrame
:return: df_values,index_return: The input pandas.core.frame.DataFrame sliced by the df_save periods. it also returns
the indexes
:rtype: df_values,index_return: tuple
"""
df_values = pandas.DataFrame([])
index_return = pandas.DataFrame([])
for _, row in df_save.iterrows():
if(df_values.size==0):
df_values = x_in.loc[numpy.logical_and(x_in.index >= row.iloc[0], x_in.index <= row.iloc[1]), :]
else:
df_values = pandas.concat((df_values,x_in.loc[numpy.logical_and(x_in.index >= row.iloc[0], x_in.index <= row.iloc[1]), :]),axis=0)
if(index_return.size==0):
index_return = pandas.Series(x_in.index[numpy.logical_and(x_in.index >= row.iloc[0], x_in.index <= row.iloc[1])].values)
else:
index_return = pandas.concat((index_return,pandas.Series(x_in.index[numpy.logical_and(x_in.index >= row.iloc[0], x_in.index <= row.iloc[1])].values)),axis=0)
return df_values, index_return
[docs]
def MarkNanPeriod(x_in: pandas.core.frame.DataFrame,
df_remove: pandas.core.frame.DataFrame,
remove_from_process: list = []) -> pandas.core.frame.DataFrame:
"""
Marks as nan all specified timestamps
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param df_remove: List of periods to mark as nan. The first column with the start and the second column with
the end date all in datetime.
:type df_remove: pandas.core.frame.DataFrame
:param remove_from_process: Columns to be kept off the process;
:type remove_from_process: list,optional
:return: Y: The input pandas.core.frame.DataFrame with samples filled based on the proportion between time series.
:rtype: Y: pandas.core.frame.DataFrame
"""
Y = x_in.copy(deep=True)
# Remove the keep out columns
if len(remove_from_process) > 0:
Y = Y.drop(remove_from_process, axis=1)
for index, row in df_remove.iterrows():
Y.loc[numpy.logical_and(Y.index >= row.iloc[0], Y.index <= row.iloc[1]), Y.columns.difference(
remove_from_process)] = numpy.nan
# return the keep out columns
if len(remove_from_process) > 0:
Y = pandas.concat([Y, x_in.loc[:, remove_from_process]], axis=1)
return Y
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def ReturnOnlyValidDays(x_in: pandas.core.frame.DataFrame,
sample_freq: int = 5,
threshold_accept: float = 1.0,
sample_time_base: str = 'm',
remove_from_process=[]) -> tuple:
"""
Returns all valid days. A valid day is one with no missing values for any
of the timeseries on each column.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param sample_freq: The sample frequency of the time series. Defaults to 5.
:type sample_freq: int,optional
:param threshold_accept: The amount of samples that is required to consider a valid day. Defaults to 1 (100%).
:type threshold_accept: float,optional
:param sample_time_base: The base time of the sample frequency. Specify if the sample frequency is in (h)ours,
(m)inutes, or (s)econds. Defaults to (m)inutes.
:type sample_time_base: srt,optional
:param remove_from_process: Columns to be kept off the process;
:type remove_from_process: list,optional
:raises Exception: if x_in has no DatetimeIndex.
:raises Exception: if sample_time_base is not in seconds, minutes or hours.
:return: Y: A tupole with the pandas.core.frame.DataFrame with samples filled based on the proportion
between time series and the number of valid days
:rtype: Y: tuple
"""
# BASIC INPUT CHECK
if not(isinstance(x_in.index, pandas.core.frame.DatetimeIndex)):
raise Exception("DataFrame has no DatetimeIndex.")
if sample_time_base not in ['s', 'm', 'h']:
raise Exception("The sample_time_base is not in seconds, minutes or hours.")
X = x_in.copy(deep=True)
if len(remove_from_process) > 0:
X = X.drop(remove_from_process, axis=1)
qty_sample_dic = {'s': 24 * 60 * 60, 'm': 24 * 60, 'h': 24}
df_count = X.groupby([X.index.year, X.index.month, X.index.day]).count() / (
qty_sample_dic[sample_time_base] / sample_freq)
time_vet_stamp = X.index[numpy.arange(0, len(X.index), int((qty_sample_dic[sample_time_base] / sample_freq)))]
df_count = df_count.reset_index(drop=True)
df_count.insert(0, 'timestamp_day', time_vet_stamp)
df_count.set_index('timestamp_day', inplace=True)
df_count = df_count >= threshold_accept
df_count = df_count.sum(axis=1) == df_count.shape[1]
df_count.name = 'isValid'
df_count = df_count.reset_index()
X['timestamp_day'] = X.index.floor("D").values
keep_X_index = X.index
X = pandas.merge(X, df_count, on='timestamp_day', how='left')
X.index = keep_X_index
X = X.loc[X['isValid'] == True, :]
X.drop(columns=['isValid', 'timestamp_day'], inplace=True)
df_count.set_index('timestamp_day', inplace=True)
return X, df_count
[docs]
def GetDayMaxMin(x_in: pandas.core.frame.DataFrame, start_date_dt: datetime, end_date_dt:datetime, sample_freq: int =5, threshold_accept:float=1.0, exe_param:str='max'):
"""
Returns a tuple of pandas.core.frame.DataFrame containing the values of maximum or minimum of each day
and the timestamp of each occurrence. For each weekday that is not a valid day the maximum or minimum
is interpolated->ffill->bff. The interpolation is made regarding each weekday.
:param x_in: A pandas.core.frame.DataFrame where the index is of type "pandas.core.indexes.datetime.DatetimeIndex"
and each column contain an electrical quantity time series.
:type x_in: pandas.core.frame.DataFrame
:param start_date_dt:
:param end_date_dt:
:param sample_freq: The sample frequency of the time series. Defaults to 5.
:type sample_freq: int,optional
:param threshold_accept: The amount of samples that is required to consider a valid day. Defaults to 1 (100%).
:type threshold_accept: float,optional
:param exe_param: 'max' return the maximum and min return the minimum value of each valid day
(Default value = 'max')
:type exe_param: srt,optional
:return: Y: The first parameter is a pandas.core.frame.DataFrame with maximum value for each day
and the second parameter pandas.core.frame.DataFrame with the timestamps.
:rtype: Y: tuple
"""
# BASIC INPUT CHECK
if not(isinstance(x_in.index, pandas.core.frame.DatetimeIndex)):
raise Exception("DataFrame has no DatetimeIndex.")
X = x_in.copy(deep=True)
X, _ = ReturnOnlyValidDays(X, sample_freq, threshold_accept)
if exe_param == 'max':
Y = X.groupby([X.index.year, X.index.month, X.index.day]).max()
vet_idx = X.groupby([X.index.year, X.index.month, X.index.day]).idxmax()
else:
Y = X.groupby([X.index.year, X.index.month, X.index.day]).min()
vet_idx = X.groupby([X.index.year, X.index.month, X.index.day]).idxmin()
# redo the timestamp index
vet_idx.index.rename(['Year', 'Month', 'Day'], inplace=True)
vet_idx = vet_idx.reset_index(drop=False)
time_vet_stamp = pandas.to_datetime(
vet_idx['Year'].astype(str) + '-' + vet_idx['Month'].astype(str) + '-' + vet_idx['Day'].astype(str))
vet_idx.drop(columns=['Year', 'Month', 'Day'], inplace=True)
vet_idx = vet_idx.reset_index(drop=True)
vet_idx.insert(0, 'timestamp_day', time_vet_stamp)
vet_idx.set_index('timestamp_day', inplace=True)
# redo the timestamp index
Y.index.rename(['Year', 'Month', 'Day'], inplace=True)
Y = Y.reset_index(drop=False)
time_vet_stamp = pandas.to_datetime(Y['Year'].astype(str) + '-' + Y['Month'].astype(str) + '-' + Y['Day'].astype(str))
Y.drop(columns=['Year', 'Month', 'Day'], inplace=True)
Y = Y.reset_index(drop=True)
Y.insert(0, 'timestamp_day', time_vet_stamp)
Y.set_index('timestamp_day', inplace=True)
Y = DataSynchronization(Y, start_date_dt, end_date_dt, sample_freq=1, sample_time_base='D')
vet_idx = pandas.merge(vet_idx, Y, left_index=True, right_index=True, how='right', suffixes=('', '_remove'))
vet_idx.drop(columns=vet_idx.columns[vet_idx.columns.str.contains('_remove')], inplace=True)
# Missing days get midnight as the hour of max and min
for col in vet_idx.columns.values:
vet_idx.loc[vet_idx[col].isna(), col] = vet_idx.index[vet_idx[col].isna()]
# Interpolate by day of the week
Y = Y.groupby(Y.index.weekday, group_keys=False).apply(lambda x: x.interpolate())
Y = Y.groupby(Y.index.weekday, group_keys=False).apply(lambda x: x.ffill())
Y = Y.groupby(Y.index.weekday, group_keys=False).apply(lambda x: x.bfill())
return Y, vet_idx
[docs]
def GetWeekDayCurve(x_in: pandas.core.frame.DataFrame, sample_freq:int=5, threshold_accept:float=1.0, min_sample_per_day:int=3, min_sample_per_workday:int=9):
"""
Analyzes and normalizes time series data in a DataFrame to compute average curves for each weekday,
considering various sampling and validity thresholds.
:param x_in: Input DataFrame with a DatetimeIndex.
:type: pandas.core.frame.DataFrame
:param sample_freq: Sampling frequency in minutes, default is 5.
:type: int
:param threshold_accept: Threshold for accepting valid data, default is 1.0.
:type: float
:param min_sample_per_day: Minimum samples required per day to consider the data valid, default is 3.
:type: int
:param min_sample_per_workday: Minimum samples required per workday (Monday to Friday) to consider the data valid, default is 9.
:type: int
:raises Exception: If the DataFrame does not have a DatetimeIndex.
:return: A DataFrame containing the normalized data for each weekday.
:rtype: pandas.core.frame.DataFrame
"""
# BASIC INPUT CHECK
if not (isinstance(x_in.index, pandas.core.frame.DatetimeIndex)):
raise Exception("DataFrame has no DatetimeIndex.")
X = x_in.copy(deep=True)
Y, df_count = ReturnOnlyValidDays(X, sample_freq, threshold_accept)
# Get valid data statistics
df_count = df_count.loc[df_count['isValid'], :]
df_stats = df_count.groupby(df_count.index.weekday).count()
# fill days that does not exist with count zero.
for i_day in range(0,7):
if i_day not in df_stats.index.values:
print(f'Weekday {i_day} does not exist.')
df_stats.loc[i_day] = 0
# Has enough data do use ?
if numpy.min(df_stats['isValid'].values) >= min_sample_per_day:
print('Can calculate a curve for every weekday')
Y = Y.groupby([Y.index.weekday, Y.index.hour, Y.index.minute]).mean()
Y.index.names = ['WeekDay', 'Hour', 'Min']
Y = Y.reset_index()
# Normalization max min each day
grouper = Y.groupby([Y.WeekDay])
maxes = grouper.transform('max')
mins = grouper.transform('min')
Y.iloc[:, 3:] = (Y.iloc[:, 3:] - mins.iloc[:, 2:]) / (maxes.iloc[:, 2:] - mins.iloc[:, 2:])
else:
work_days = df_stats.loc[df_stats.index <= 4, 'isValid'].sum()
sat_qty = df_stats.loc[df_stats.index == 5, 'isValid'].sum()
sun_qty = df_stats.loc[df_stats.index == 6, 'isValid'].sum()
if (work_days >= min_sample_per_workday) and sun_qty >= min_sample_per_day and sat_qty >= min_sample_per_day:
print('Can calculate a curve for every weekday and use Sat. and Sun.')
Y['WeekDay'] = Y.index.weekday.values
Y['Hour'] = Y.index.hour.values
Y['Min'] = Y.index.minute.values
Y = Y.reset_index(drop=True)
Y.loc[Y['WeekDay'] <= 4, 'WeekDay'] = 0
Y = Y.groupby([Y.WeekDay, Y.Hour, Y.Min]).mean()
Y.index.names = ['WeekDay', 'Hour', 'Min']
Y = Y.reset_index()
# Normalization max min each day
grouper = Y.groupby([Y.WeekDay])
maxes = grouper.transform('max')
mins = grouper.transform('min')
Y.iloc[:, 3:] = (Y.iloc[:, 3:] - mins.iloc[:, 2:]) / (maxes.iloc[:, 2:] - mins.iloc[:, 2:])
for i_day in [1, 2, 3, 4]:
Y_day_aux = Y.loc[Y.WeekDay == 0, :].copy(deep=True)
Y_day_aux.WeekDay = i_day
Y = pandas.concat((Y, Y_day_aux))
Y = Y.reset_index(drop=True)
else:
print('Not enough data using default curve.')
Y = DefaultWeekDayCurve()
return Y
[docs]
def CurrentDummyData(qty_weeks:int = 12*4,start_date_dt:datetime = datetime(2023,1,1)):
"""
Generates a DataFrame containing dummy time series data.
This function creates a pandas DataFrame representing time series data over a specified number of weeks, starting from a given date. The data includes artificial variations to simulate different patterns, including seasonal variations and random noise. The DataFrame includes columns 'IA', 'IB', 'IV', and 'IN', each containing modified time series data. The index of the DataFrame is set to timestamps at 5-minute intervals, starting from the specified start date.
Parameters
----------
qty_weeks : int, optional
The number of weeks to generate data for, by default 48 weeks (12*4).
start_date_dt : datetime, optional
The start date for the time series data, by default datetime(2023,1,1).
Returns
-------
pandas.DataFrame
A DataFrame containing the generated time series data with columns 'IA', 'IB', 'IV', and 'IN', and a timestamp index.
Examples
--------
>>> dummy_data = CurrentDummyData(24, datetime(2023,1,1))
>>> dummy_data.head()
"""
dummy_week = pandas.DataFrame([[133.4,128.7,122.3,5.7],
[131.3,129.2,120.9,4.7],
[126.5,124.7,120.9,4.7],
[129.7,126.6,121.1,4.7],
[128.1,130.2,121.6,5.6],
[128.1,130.2,121.6,5.6],
[126.6,124.5,119.1,5.3],
[126.6,124.5,119.1,5.3],
[127.3,125.0,121.3,4.9],
[125.0,126.0,120.1,5.0],
[125.0,126.0,120.1,5.0],
[125.8,123.9,120.2,5.0],
[125.8,123.9,120.2,5.0],
[121.5,119.9,114.5,4.7],
[122.2,120.9,115.8,4.8],
[126.6,125.1,114.5,4.8],
[121.6,120.1,115.3,4.6],
[121.6,120.1,115.3,4.6],
[119.8,120.3,114.5,5.0],
[121.4,118.8,112.7,4.7],
[121.4,118.8,112.7,4.7],
[125.2,117.9,111.9,5.0],
[125.2,117.9,114.2,5.0],
[120.2,119.4,112.0,5.3],
[120.8,118.4,113.1,5.4],
[120.8,118.4,113.1,5.4],
[122.4,118.5,113.3,5.1],
[122.4,118.5,113.3,5.1],
[121.7,117.0,113.2,4.9],
[120.0,117.2,111.5,4.9],
[120.0,117.2,111.5,4.9],
[120.0,115.2,111.5,4.6],
[120.0,115.2,111.5,4.6],
[118.1,115.4,110.9,4.7],
[118.5,116.9,112.3,4.7],
[118.5,116.9,109.2,4.7],
[116.6,113.1,109.3,4.8],
[116.6,113.1,109.3,4.8],
[118.9,114.0,109.9,4.7],
[119.0,115.4,110.9,5.0],
[119.0,115.4,110.9,5.0],
[118.6,115.0,110.0,5.0],
[118.6,115.0,110.0,5.0],
[117.6,113.3,109.6,5.0],
[116.7,113.0,108.4,4.9],
[116.7,113.0,108.4,4.9],
[117.4,115.5,110.5,4.8],
[117.4,115.5,110.5,4.8],
[116.6,113.4,109.3,4.6],
[115.3,113.8,108.4,4.9],
[115.3,113.8,108.4,4.9],
[116.2,112.9,108.1,4.9],
[116.2,114.6,108.1,4.9],
[117.9,113.9,109.7,4.8],
[115.8,110.3,106.4,4.4],
[115.8,110.3,106.4,4.4],
[116.2,113.0,106.6,4.7],
[116.2,113.0,106.6,4.7],
[115.5,112.0,108.3,4.9],
[118.8,112.8,111.6,4.5],
[118.8,112.8,114.3,4.5],
[115.1,112.5,108.1,4.3],
[115.1,109.5,104.2,4.3],
[113.2,111.7,107.3,4.2],
[109.3,109.2,105.0,4.4],
[109.3,109.2,105.0,4.4],
[108.7,108.1,104.1,5.1],
[110.1,108.1,104.1,5.1],
[108.5,108.0,105.2,4.7],
[108.0,108.4,103.6,4.7],
[108.0,109.5,103.6,4.7],
[109.0,105.0,103.6,4.4],
[105.0,105.0,99.0,4.4],
[110.1,109.6,104.3,4.5],
[112.1,113.0,108.0,4.5],
[112.1,113.0,108.0,4.9],
[115.2,112.5,107.0,4.4],
[110.2,110.4,104.5,4.4],
[113.4,111.8,107.0,4.5],
[112.9,111.6,105.7,4.5],
[112.9,111.6,105.7,4.5],
[112.1,108.4,106.8,4.4],
[115.9,115.4,110.2,3.9],
[115.4,114.9,112.0,4.9],
[115.2,115.8,111.9,4.2],
[115.2,115.8,115.3,4.2],
[115.5,113.2,111.3,4.3],
[115.5,113.2,115.4,4.3],
[117.2,115.7,111.0,3.9],
[117.0,117.4,111.4,4.1],
[117.0,115.7,111.4,4.1],
[117.7,117.4,110.1,3.9],
[119.6,119.3,116.8,3.9],
[122.2,123.9,116.7,4.0],
[125.2,123.5,115.8,4.7],
[127.7,126.8,118.3,4.7],
[128.5,125.3,117.9,4.4],
[124.9,121.8,122.8,4.4],
[131.3,127.3,121.0,4.9],
[132.0,127.0,125.9,4.3],
[128.2,127.2,123.4,4.3],
[131.8,126.2,118.7,4.5],
[136.5,130.0,124.6,4.0],
[136.1,143.1,123.3,5.1],
[136.4,132.0,128.2,4.0],
[136.2,132.6,130.0,4.0],
[135.1,131.4,131.3,4.2],
[129.6,128.2,125.8,4.2],
[130.6,129.8,126.7,4.3],
[133.8,132.9,129.0,4.2],
[132.1,130.4,127.4,4.8],
[133.7,129.8,127.4,4.3],
[131.5,131.9,128.5,4.6],
[140.9,139.7,131.7,4.1],
[142.6,137.0,132.5,3.6],
[135.7,137.5,132.3,3.6],
[139.0,141.3,132.8,3.9],
[140.3,141.4,132.9,3.9],
[142.3,141.7,133.4,4.4],
[137.3,135.4,133.0,3.7],
[144.3,136.7,133.9,3.7],
[152.7,155.2,133.4,3.7],
[145.1,138.6,138.2,3.7],
[146.4,142.0,133.7,3.9],
[153.3,146.7,141.2,3.9],
[146.2,145.4,140.9,4.1],
[143.1,142.3,135.1,3.7],
[151.5,149.1,141.2,3.7],
[151.4,144.0,144.3,4.0],
[149.1,147.3,144.9,4.0],
[145.7,143.0,139.3,4.1],
[142.1,141.8,137.7,3.9],
[142.5,142.5,139.5,3.9],
[144.3,144.2,137.3,3.6],
[155.1,149.2,142.5,3.6],
[149.6,150.1,148.1,4.2],
[153.3,151.5,143.3,4.1],
[146.5,142.8,142.6,3.9],
[149.2,147.0,140.5,4.0],
[153.0,149.9,146.0,4.2],
[155.8,155.2,147.7,3.9],
[153.1,148.7,144.2,3.5],
[153.4,154.3,143.0,3.5],
[159.1,150.3,149.7,4.0],
[155.2,153.0,146.3,4.0],
[156.7,154.6,147.5,3.9],
[158.5,153.1,147.1,4.1],
[156.8,156.6,148.2,4.1],
[152.8,153.7,147.2,3.9],
[155.1,150.2,147.2,3.9],
[153.2,152.1,150.5,5.0],
[155.4,153.4,147.2,4.5],
[155.6,148.4,147.2,4.0],
[156.2,151.6,147.4,4.1],
[151.7,151.6,147.4,4.1],
[151.5,152.8,144.9,4.1],
[152.1,148.2,143.6,4.1],
[162.6,158.9,153.8,4.1],
[156.2,153.4,150.2,4.0],
[152.5,149.0,148.9,4.0],
[157.6,154.0,148.9,4.2],
[159.9,155.4,152.0,4.2],
[159.0,157.2,152.6,4.4],
[157.2,155.9,150.8,4.5],
[161.0,154.8,152.6,4.3],
[165.5,161.8,158.2,4.3],
[163.3,160.6,152.8,4.3],
[161.6,155.4,153.0,4.3],
[166.9,160.6,156.3,4.2],
[170.9,168.5,155.1,4.2],
[162.7,156.5,151.1,4.5],
[162.8,160.2,156.9,4.5],
[163.3,157.5,156.2,4.5],
[162.6,158.5,152.6,5.3],
[162.5,157.8,153.4,4.3],
[159.0,158.3,156.3,4.6],
[162.0,160.3,156.5,4.7],
[166.0,161.3,156.5,4.7],
[162.9,159.9,151.3,4.2],
[167.1,159.8,160.2,4.2],
[161.9,161.3,155.1,4.3],
[161.9,160.2,155.4,4.1],
[163.5,158.2,154.5,4.1],
[164.7,158.3,152.1,4.3],
[165.5,162.9,155.6,4.3],
[164.7,161.6,155.5,4.2],
[168.4,167.9,159.3,5.5],
[163.4,158.1,154.9,4.5],
[164.5,162.7,156.0,4.7],
[166.4,166.9,157.4,4.7],
[165.2,163.0,153.4,4.3],
[161.6,160.8,152.4,4.3],
[162.0,161.0,154.2,4.3],
[166.7,164.9,156.7,4.2],
[162.0,159.5,149.4,4.2],
[163.9,162.6,155.2,4.1],
[165.2,161.5,155.2,4.3],
[161.3,164.1,155.3,4.3],
[161.3,161.9,157.0,3.8],
[161.7,162.6,155.1,3.5],
[160.0,161.1,152.2,4.0],
[158.8,156.1,152.1,3.6],
[162.8,161.2,157.4,4.5],
[155.2,155.2,148.4,4.1],
[157.2,153.3,149.1,4.1],
[156.2,153.6,147.9,4.0],
[155.5,152.4,148.2,4.2],
[154.9,150.9,148.5,4.2],
[150.4,151.6,144.6,3.8],
[155.3,150.6,145.1,3.8],
[150.3,151.2,146.5,4.2],
[156.6,152.9,146.2,4.7],
[156.0,151.9,143.8,4.5],
[156.0,153.1,144.9,4.8],
[156.2,154.2,145.4,4.8],
[151.1,153.8,145.9,4.1],
[151.2,149.2,144.0,4.1],
[145.9,144.1,139.0,4.3],
[151.1,149.1,137.4,5.1],
[146.0,149.1,137.4,5.1],
[145.8,144.9,139.8,4.8],
[152.1,146.4,139.7,5.5],
[153.0,153.3,142.5,5.2],
[154.2,151.3,144.4,5.0],
[158.1,158.0,152.7,5.0],
[163.1,158.2,147.6,5.3],
[165.7,160.1,152.7,5.3],
[166.0,162.8,155.3,5.1],
[165.4,162.8,155.1,6.2],
[167.1,160.7,155.7,6.2],
[164.5,164.5,156.7,5.8],
[174.3,164.8,164.5,5.2],
[171.9,170.6,162.3,5.1],
[173.7,170.6,160.7,5.3],
[174.4,165.6,160.7,6.2],
[172.2,171.9,162.4,5.4],
[173.3,171.9,166.3,5.4],
[178.4,178.1,164.1,5.5],
[173.9,168.6,161.8,5.9],
[169.1,168.9,162.2,5.9],
[169.4,166.8,160.6,5.1],
[175.7,167.1,164.0,5.1],
[171.0,168.7,163.7,4.9],
[172.3,167.1,163.9,5.0],
[166.9,164.5,161.7,5.0],
[172.0,167.5,162.2,4.8],
[171.7,162.0,159.6,4.8],
[172.4,167.1,164.8,5.4],
[168.4,166.8,158.5,5.8],
[172.5,168.8,161.5,5.8],
[167.5,165.1,162.7,5.3],
[169.9,163.5,156.4,5.3],
[169.8,161.5,161.4,6.3],
[164.7,163.6,156.9,5.8],
[171.8,166.5,161.5,5.3],
[170.6,162.4,157.3,5.7],
[170.6,162.4,157.3,5.7],
[166.6,165.0,154.9,6.3],
[166.9,160.5,155.7,5.4],
[166.9,162.1,157.6,6.3],
[169.2,167.3,156.3,5.7],
[169.2,167.3,156.3,5.3],
[164.2,162.3,152.9,5.5],
[170.8,166.3,158.7,6.3],
[165.7,165.9,158.7,6.3],
[165.6,162.7,155.9,5.9],
[166.4,166.2,158.3,5.9],
[166.9,161.2,153.1,5.7],
[166.3,161.5,156.0,5.6],
[164.0,161.5,156.0,6.4],
[162.6,159.4,154.9,5.8],
[162.6,159.4,149.9,5.8],
[162.6,161.5,154.9,6.0],
[164.8,163.7,153.3,5.9],
[165.9,163.6,157.9,5.9],
[166.0,164.5,157.3,5.4],
[166.0,159.2,154.6,5.4],
[162.5,165.4,153.5,5.8],
[161.9,162.2,152.5,6.4],
[164.2,159.9,153.9,6.4],
[160.2,160.2,151.5,5.9],
[165.4,159.9,153.0,5.9],
[160.5,159.4,148.9,6.0],
[158.1,155.7,148.6,5.8],
[158.1,155.1,146.4,5.8],
[155.7,150.6,146.9,5.3],
[157.3,155.7,146.9,6.6],
[157.0,155.7,148.3,5.8],
[157.0,156.0,146.7,5.8],
[157.0,156.0,146.7,6.5],
[154.6,154.7,145.4,6.2],
[154.6,150.8,145.4,5.4],
[152.7,152.6,143.8,5.6],
[151.7,153.7,141.5,6.5],
[151.7,148.6,140.3,5.5],
[151.4,149.4,141.1,5.6],
[152.6,151.9,138.9,5.6],
[150.6,146.9,140.1,5.6],
[152.3,148.7,140.2,5.2],
[147.3,148.7,140.2,5.2],
[147.2,146.9,137.2,5.6],
[147.7,143.6,133.7,5.6],
[146.9,143.1,133.6,5.2],
[148.0,145.2,136.4,5.4],
[148.0,145.2,135.7,5.5],
[144.6,141.3,133.8,5.5],
[142.3,140.5,132.0,5.5],
[143.7,141.0,134.0,5.6],
[142.3,140.4,131.3,5.7],
[143.3,140.4,132.9,6.7],
[140.8,138.8,131.9,6.1],
[140.8,136.0,131.9,5.7],
[141.3,137.1,127.9,5.6],
[142.3,137.8,130.1,5.6],
[139.1,138.6,130.3,5.6],
[141.1,139.8,132.6,5.8],
[141.1,139.8,127.6,5.8],
[139.7,134.8,130.8,5.4],
[139.3,135.1,128.6,5.4],
[139.3,136.9,130.9,5.4],
[138.7,136.7,129.5,5.3],
[138.7,136.7,129.5,5.3],
[136.0,131.7,126.8,5.6],
[137.7,133.5,126.1,5.6],
[137.0,134.9,128.4,5.6],
[137.5,133.3,128.5,5.4],
[137.5,133.3,123.5,5.4],
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[140.0,142.0,138.5,4.8],
[141.7,144.1,133.5,4.1],
[140.1,141.3,131.4,4.2],
[142.6,144.7,135.2,4.4],
[144.0,143.8,135.6,4.3],
[144.0,143.8,135.6,4.3],
[142.4,142.9,135.9,4.5],
[143.5,145.0,136.5,4.1],
[142.8,149.8,134.9,4.1],
[148.0,145.7,140.6,4.6],
[142.8,145.7,135.6,4.6],
[143.0,144.9,135.5,4.6],
[141.7,141.7,134.2,4.2],
[138.0,139.5,130.5,4.2],
[139.8,139.7,133.3,4.6],
[139.8,144.6,133.3,4.6],
[141.1,139.5,134.9,4.1],
[142.8,144.6,136.2,4.0],
[142.8,139.4,136.2,4.0],
[141.1,138.4,133.8,4.2],
[143.2,138.4,133.8,4.2],
[138.8,138.5,131.6,4.3],
[141.9,144.5,135.2,4.4],
[141.9,139.5,135.2,4.4],
[140.1,139.7,134.6,4.5],
[140.1,139.7,134.6,4.5],
[143.4,139.8,134.4,4.5],
[142.3,139.8,135.0,4.0],
[138.0,140.3,130.5,4.0],
[142.9,140.2,130.4,4.2],
[138.3,135.2,135.5,4.2],
[140.2,137.0,130.5,4.2],
[140.2,140.6,131.6,4.7],
[140.2,137.3,130.9,4.7],
[137.7,138.7,131.1,4.4],
[137.7,138.7,131.1,4.4],
[139.8,137.5,131.1,4.5],
[141.2,140.5,133.8,4.5],
[141.2,137.5,131.4,4.5],
[142.7,141.4,131.2,4.4],
[142.7,141.4,136.4,4.4],
[143.8,138.1,131.2,4.4],
[142.9,140.5,132.0,4.4],
[142.9,140.5,132.0,4.4],
[138.7,143.7,130.6,4.3],
[143.0,143.9,136.4,4.3],
[141.8,138.7,134.7,4.6],
[144.4,145.3,137.5,5.1],
[148.3,149.2,136.4,5.6],
[153.3,147.3,139.7,5.3],
[158.6,154.3,152.2,5.3],
[157.8,155.3,148.4,5.4],
[162.9,158.1,150.8,5.2],
[162.9,158.1,150.8,5.2],
[163.8,158.8,152.3,5.3],
[163.8,159.6,156.7,5.3],
[166.1,162.5,156.0,5.3],
[165.9,158.9,151.2,5.2],
[169.0,160.4,156.0,5.2],
[166.0,165.5,153.7,5.3],
[163.6,160.4,150.6,5.3],
[164.5,160.2,150.7,5.2],
[165.6,162.8,155.7,5.3],
[169.1,165.6,161.0,5.3],
[168.2,161.7,156.8,5.3],
[169.0,162.0,156.8,5.3],
[164.4,157.0,154.3,5.2],
[167.2,164.3,158.8,5.8],
[167.2,164.3,158.8,5.8],
[169.0,163.0,156.4,5.9],
[163.9,163.0,156.9,5.9],
[168.9,162.1,156.0,5.7],
[165.3,161.5,153.9,5.8],
[165.3,161.5,157.0,5.8],
[168.9,162.9,154.9,5.9],
[163.9,162.9,152.0,5.6],
[168.2,164.6,157.8,5.5],
[164.0,164.6,157.8,5.5],
[169.0,160.2,156.5,5.5],
[166.3,159.8,151.9,5.6],
[158.7,159.8,152.0,5.6],
[163.8,156.7,152.5,5.7],
[163.8,156.7,152.5,5.7],
[162.7,157.1,151.7,5.4],
[161.9,157.6,152.4,5.5],
[161.9,157.6,147.4,5.5],
[163.7,157.9,152.8,5.7],
[163.7,157.9,147.8,5.7],
[162.5,158.3,149.3,5.8],
[162.9,158.1,152.6,5.4],
[162.9,158.1,152.6,5.4],
[162.3,162.5,151.9,5.6],
[163.8,157.4,152.8,5.6],
[162.0,162.8,152.1,5.6],
[165.2,162.9,151.6,6.3],
[165.2,162.6,152.8,6.3],
[163.6,157.5,152.8,5.7],
[163.6,162.6,152.8,5.7],
[164.2,160.0,150.7,5.6],
[164.0,162.3,152.0,5.9],
[164.0,162.3,152.0,5.9],
[162.6,162.7,153.2,6.0],
[162.6,162.7,153.2,6.0],
[162.1,163.2,155.5,6.0],
[163.1,163.2,153.3,5.9],
[163.1,163.2,153.3,5.9],
[164.2,162.9,153.9,6.4],
[164.2,162.9,153.9,6.4],
[158.4,163.8,154.2,6.4],
[160.8,157.4,150.3,5.7],
[160.8,162.6,150.3,6.6],
[162.5,163.1,154.1,6.3],
[158.4,162.5,152.6,7.7],
[158.5,162.3,147.5,6.7],
[160.9,160.6,152.6,6.1],
[158.7,160.6,152.6,6.1],
[158.5,159.2,148.8,6.4],
[158.5,157.3,148.8,6.4],
[160.5,157.3,148.5,6.3],
[157.0,154.8,146.5,6.6],
[157.0,154.8,146.5,6.6],
[156.9,156.7,148.1,6.8],
[153.6,156.7,148.1,6.8],
[155.7,152.2,142.4,6.3],
[153.4,152.7,142.0,6.7],
[153.4,152.7,142.0,6.7],
[154.5,153.7,145.2,6.1],
[154.5,153.7,145.2,6.1],
[155.9,152.5,142.4,6.1],
[154.1,153.5,142.4,6.2],
[154.1,153.5,142.4,6.2],
[153.2,153.4,142.8,6.3],
[153.2,147.4,137.4,6.3],
[148.2,149.9,137.7,6.4],
[149.6,148.5,138.1,6.2],
[149.6,148.5,138.1,6.2],
[148.6,146.4,136.2,6.1],
[148.6,146.4,136.2,6.1],
[147.9,146.5,135.6,6.2],
[148.0,145.5,137.3,6.1],
[148.0,145.5,137.3,6.1],
[147.0,146.8,136.2,6.2],
[147.0,146.8,136.2,6.2],
[147.5,146.0,135.0,6.4],
[147.1,145.2,135.9,6.2],
[143.2,145.2,132.5,6.2],
[145.4,143.8,133.5,6.2],
[145.4,143.8,133.5,6.2],
[144.6,143.0,131.7,6.1],
[143.6,142.1,132.9,6.1],
[143.6,142.1,132.9,6.1],
[143.6,142.2,131.6,6.2],
[143.6,142.2,131.6,6.2],
[143.6,142.3,132.2,6.2],
[141.4,141.0,131.4,6.0],
[141.4,141.0,131.4,6.0],
[143.7,141.8,132.8,6.2],
[143.7,141.8,132.8,6.2],
[141.8,139.4,130.7,6.1],
[139.6,138.0,130.6,6.0],
[143.1,142.4,130.6,6.7],
[140.4,139.1,131.2,5.6],
[140.4,137.4,131.2,5.6],
[138.0,138.9,127.4,5.8],
[143.2,138.8,132.4,5.6],
[138.1,138.8,132.4,5.6],
[141.2,137.5,130.3,5.6],
[137.7,137.5,127.3,5.6],
[138.1,138.4,128.2,5.7],
[136.7,135.2,127.3,5.8],
[136.7,135.2,127.3,5.6],
[137.8,136.5,128.6,5.5],
[137.8,136.5,128.6,5.5],
[137.7,135.1,127.5,5.6],
[136.1,134.5,126.8,5.7],
[132.7,134.5,122.3,5.7],
[127.8,127.2,119.3,5.7],
[127.8,127.2,117.2,5.7],
[122.5,127.2,117.9,5.8],
[125.9,125.7,116.4,5.6],
[125.9,122.1,116.4,5.6],
[121.3,122.1,114.8,5.3],
[121.3,122.1,114.8,5.3],
[123.8,123.2,115.6,5.2],
[120.7,120.3,113.3,5.1],
[120.7,120.3,113.3,5.1],
[117.5,117.0,109.8,5.3],
[117.5,117.0,109.8,5.6],
[116.8,119.5,111.1,5.4],
[116.0,117.6,109.7,5.5],
[116.0,117.7,109.7,5.5],
[118.1,117.7,110.5,5.1],
[118.1,117.7,110.5,5.1],
[121.2,120.5,114.9,5.3],
[118.4,119.1,113.6,5.0],
[118.4,119.1,113.6,5.0],
[116.2,117.0,110.1,5.4],
[116.2,117.0,110.1,5.4],
[115.7,117.5,110.8,5.3],
[115.7,117.5,110.8,5.3],
[117.3,118.5,110.4,5.2],
[116.9,123.0,113.2,5.3],
[122.8,123.0,117.8,5.3],
[117.3,120.8,113.8,5.1],
[117.2,120.8,112.7,5.1],
[122.3,123.2,117.8,4.6],
[120.9,122.3,112.7,4.6],
[120.9,123.0,117.9,4.6],
[120.4,121.6,118.0,4.4],
[122.1,121.6,118.0,4.4],
[121.2,123.0,112.9,4.5],
[123.0,123.6,116.7,4.5],
[123.0,123.6,116.7,4.5],
[121.7,122.9,116.8,4.9],
[121.7,122.9,113.0,4.9],
[122.1,122.9,118.1,4.7],
[121.6,121.9,117.8,4.8],
[121.6,121.9,117.8,4.8],
[124.5,127.3,117.6,4.7],
[124.5,127.3,117.6,4.7],
[122.0,122.8,118.0,4.7],
[123.2,126.3,114.2,4.9],
[127.4,122.5,117.8,4.7],
[126.2,126.6,117.2,4.8],
[122.3,122.5,112.8,4.8],
[120.8,121.3,117.9,4.6],
[122.3,120.7,114.2,4.6],
[122.3,117.5,114.2,4.6],
[122.8,120.1,113.1,4.4],
[122.8,122.6,113.1,4.4],
[119.8,122.5,111.5,4.0],
[122.9,121.3,112.7,4.8],
[122.9,116.5,112.7,4.8],
[122.3,116.4,114.0,4.3],
[122.3,116.4,114.0,4.3],
[121.9,121.5,112.9,4.2],
[125.0,121.8,116.8,4.3],
[121.8,121.8,113.4,4.3],
[126.9,121.5,116.9,4.4],
[126.9,121.5,113.4,4.4],
[121.9,121.8,113.4,4.3],
[119.5,120.0,111.6,4.3],
[119.5,120.0,108.3,4.3],
[121.7,119.4,114.3,4.2],
[121.7,117.0,114.3,4.2],
[121.7,122.1,114.8,4.9],
[119.8,119.2,111.4,4.9],
[116.8,117.1,111.4,4.9],
[119.4,119.8,112.2,4.7],
[116.6,116.9,112.2,4.7],
[119.3,118.5,113.7,4.7],
[123.7,122.4,114.3,4.7],
[123.7,122.4,114.3,4.7],
[121.0,119.8,113.4,5.0],
[121.0,119.8,113.4,5.0],
[121.9,120.0,111.7,4.4],
[121.8,119.7,113.3,4.5],
[121.8,119.7,113.3,4.5],
[122.9,121.2,114.0,4.2],
[122.9,121.2,114.0,4.2],
[122.2,121.6,114.4,4.4],
[124.0,122.5,115.9,5.2],
[121.9,122.5,115.9,5.2],
[123.1,122.2,113.1,4.8],
[123.1,122.2,113.1,4.8],
[120.9,121.7,111.8,4.6],
[119.5,120.4,112.0,4.8],
[122.0,120.4,112.0,4.8],
[122.8,121.6,113.0,4.8],
[127.0,121.6,113.0,4.8],
[125.7,124.6,115.3,5.0],
[124.3,123.0,116.2,4.7],
[124.3,123.0,116.2,4.7],
[124.2,121.3,114.4,4.7],
[121.9,121.3,113.7,4.7],
[123.0,119.6,114.2,4.7],
[125.3,122.1,116.4,4.7],
[125.3,122.1,116.4,4.7],
[122.8,121.7,113.9,4.3],
[122.8,121.7,114.2,4.3],
[125.3,122.6,116.7,4.5],
[125.3,123.0,118.0,5.1],
[122.3,123.0,114.5,5.1],
[122.3,121.5,118.0,4.6],
[122.3,121.5,119.0,4.6],
[124.4,122.2,117.9,5.7],
[122.2,122.2,117.9,5.7],
[123.5,122.4,117.7,5.0],
[124.0,121.9,117.1,4.8],
[124.0,121.9,117.1,4.8],
[124.2,122.3,119.0,4.9],
[127.2,122.3,119.0,4.9],
[124.0,120.6,117.5,4.9],
[129.1,127.4,118.7,5.0],
[129.1,121.8,118.7,5.0],
[132.5,126.7,124.0,4.9],
[132.5,126.7,124.0,4.9],
[127.5,127.1,118.9,4.5],
[127.4,124.9,121.5,4.9],
[127.4,121.6,121.5,4.9],
[126.8,121.3,119.7,4.7],
[126.8,126.4,119.7,4.7],
[131.8,124.2,124.3,4.9],
[126.6,125.3,119.9,4.8],
[131.7,125.3,124.1,4.8],
[130.5,126.6,124.0,4.6],
[126.7,126.6,119.1,4.6],
[126.8,126.4,118.7,4.6],
[127.6,123.3,118.8,4.7],
[127.6,126.8,118.8,4.7],
[126.9,126.8,124.2,4.7],
[131.7,126.8,124.2,4.7],
[131.6,125.6,121.9,4.7],
[129.1,126.5,121.8,4.4],
[129.1,126.5,121.8,4.4],
[129.0,126.4,122.7,4.7],
[129.0,126.4,122.7,4.7],
[129.8,126.9,122.7,4.5],
[132.0,130.5,126.9,4.6],
[135.8,132.0,129.4,4.6],
[136.0,129.7,129.3,4.2],
[136.0,129.7,129.3,4.2],
[136.1,130.2,123.6,4.3],
[140.8,136.5,128.4,4.1],
[140.8,135.5,128.4,4.1],
[141.4,135.3,128.9,4.5],
[141.4,135.7,134.0,4.5],
[141.3,140.3,135.4,4.5],
[141.3,142.7,132.0,4.7],
[141.4,142.7,132.0,4.7],
[146.9,144.4,139.1,4.7],
[146.9,151.1,139.1,4.7],
[146.0,149.2,137.6,4.8],
[149.5,149.3,139.2,4.7],
[149.5,149.3,139.2,4.7],
[151.4,151.1,144.2,4.8],
[155.9,157.5,150.0,4.8],
[161.1,158.2,150.0,4.4],
[166.1,162.5,155.1,4.7],
[166.1,162.5,155.1,4.7],
[167.5,166.4,155.8,4.7],
[168.1,163.9,157.0,4.7],
[167.1,163.4,155.6,4.7],
[168.6,165.5,157.0,4.7],
[173.1,165.5,162.0,4.7],
[173.0,168.1,160.8,4.9],
[173.0,168.1,160.8,4.9],
[168.1,168.4,159.0,5.1],
[167.0,163.3,157.3,5.2],
[167.0,168.4,157.3,5.2],
[173.5,167.8,162.2,5.2],
[173.6,167.8,162.2,5.2],
[173.8,168.5,160.8,5.2],
[171.7,168.2,160.6,5.3],
[169.2,168.2,157.1,5.3],
[172.9,169.9,163.1,5.2],
[168.4,163.3,158.4,5.2],
[169.9,168.4,158.8,5.4],
[170.2,167.0,162.4,5.4],
[168.4,167.0,157.2,5.4],
[172.0,169.0,159.5,5.5],
[172.0,169.0,162.5,5.5],
[169.9,163.4,157.4,5.6],
[173.2,169.3,162.0,5.6],
[173.2,168.4,162.0,5.6],
[169.1,168.6,157.3,5.7],
[169.1,163.4,157.3,5.7],
[168.7,169.4,156.2,5.7],
[171.0,166.8,156.2,5.5],
[171.0,166.8,156.2,5.5],
[169.7,165.8,157.7,5.9],
[163.7,165.8,151.8,5.9],
[168.6,162.0,156.9,5.9],
[168.6,162.0,156.9,5.9],
[167.3,164.4,156.2,6.1],
[166.0,163.7,156.1,6.0],
[166.0,163.7,156.9,6.0],
[168.9,163.9,157.1,5.9],
[169.1,166.9,158.3,6.1],
[169.1,166.9,158.3,6.1],
[169.4,164.6,158.2,6.2],
[169.4,169.6,158.2,6.2],
[170.4,165.5,158.4,6.1],
[170.4,165.5,158.4,6.1],
[168.4,164.5,156.9,6.3],
[168.6,166.8,160.6,5.9],
[168.6,164.7,156.2,5.9],
[170.9,166.1,160.8,5.9],
[170.9,166.1,156.2,6.3],
[167.9,164.9,156.4,6.3],
[166.2,164.7,157.6,6.4],
[166.2,164.7,161.5,6.4],
[166.2,163.7,157.5,7.3],
[166.2,163.7,157.5,6.3],
[163.5,162.5,155.5,6.3],
[167.0,163.2,157.5,6.6],
[163.7,163.2,157.5,6.6],
[163.4,164.8,154.7,6.5],
[163.4,159.8,154.7,6.5],
[163.2,159.8,153.5,6.3],
[163.1,158.1,152.5,6.4],
[163.1,158.1,151.2,6.4],], columns=['IA', 'IB', 'IV', 'IN'])
dummy= pandas.concat([dummy_week] * qty_weeks, ignore_index=True)
end_date_dt = start_date_dt + dt.timedelta(days=qty_weeks*7)
dummy_idx = numpy.arange(start_date_dt, end_date_dt, numpy.timedelta64(5, 'm'), dtype='datetime64')
dummy['timestamp'] = dummy_idx
dummy.set_index('timestamp', inplace=True)
dummy.index = pandas.to_datetime(dummy.index)
cycles = 0.7 * dummy.shape[0] / (365 * 24 * 12) # how many sine cycles
resolution = dummy.shape[0] # how many datapoints to generate
length = numpy.pi * 2 * cycles
season_year = numpy.sin(numpy.arange(0, length, length / resolution))
cycles = 12 * 4 * dummy.shape[0] / (365 * 24 * 12) # how many sine cycles
resolution = dummy.shape[0] # how many datapoints to generate
length = numpy.pi * 2 * cycles
season_week = numpy.sin(numpy.arange(0, length, length / resolution))
cycles = 12 * dummy.shape[0] / (365 * 24 * 12) # how many sine cycles
resolution = dummy.shape[0] # how many datapoints to generate
length = numpy.pi * 2 * cycles
season_month = numpy.sin(numpy.arange(0, length, length / resolution))
rand_year = random.randint(5, 10)
rand_month = random.randint(1, 5)
rand_week = random.randint(1, 3)
rand_vet = numpy.random.randint(5, 10, size=dummy.shape[0])
step_vet = numpy.zeros(dummy.shape[0])
# Load transfer
for i in range(0, random.randint(1, 4)):
start = random.randint(0, dummy.shape[0])
end = start + random.randint(1, 60) * 24 * 12
if end >= len(step_vet):
end = len(step_vet)
step_vet[start:end] = random.randint(-50, -20)
# Noise
for i in range(0, random.randint(1, 40)):
start = random.randint(0, dummy.shape[0])
end = start + random.randint(1, 12 * 3)
if end >= len(step_vet):
end = len(step_vet)
step_vet[start:end] = random.randint(-300, 300)
dummy['IA'] = dummy['IA'].values + rand_year * season_year + rand_month * season_month \
+ rand_week * season_week + rand_vet + step_vet
dummy['IB'] = dummy['IB'].values + rand_year * season_year + rand_month * season_month \
+ rand_week * season_week + rand_vet + step_vet
dummy['IV'] = dummy['IV'].values + rand_year * season_year + rand_month * season_month \
+ rand_week * season_week + rand_vet + step_vet
dummy['IN'] = dummy['IN'].values + (rand_year / 10) * season_year \
+ (rand_month / 10) * season_month + (rand_week / 10) \
* season_week + rand_vet / 10
return dummy
[docs]
def VoltageDummyData(qty_weeks:int = 12*4,start_date_dt:datetime = datetime(2023,1,1)):
"""
Generate a DataFrame containing dummy voltage data over a specified number of weeks.
This function creates a time series of voltage data, simulating variations in voltage values
over a given time period. The data includes random noise and step changes to mimic real-world
fluctuations in voltage readings.
Parameters
----------
qty_weeks : int, optional
The number of weeks over which to generate the data (default is 48 weeks).
start_date_dt : datetime, optional
The start date for the data generation (default is January 1, 2023).
Returns
-------
pandas.DataFrame
A DataFrame with timestamps as index and columns 'VA', 'VB', and 'VV' representing
simulated voltage readings for three different phases or measurements. Each column
contains voltage values that are affected by random noise and step changes.
Notes
-----
- The voltage values are simulated around a base value of 13.8, adjusted by a random noise
factor and step changes.
- The step changes in voltage are randomly introduced at various points in the time series.
- The timestamps are spaced 5 minutes apart.
Examples
--------
>>> dummy_data = VoltageDummyData()
>>> dummy_data.head()
"""
end_date_dt = start_date_dt + dt.timedelta(days=qty_weeks*7)
dummy = numpy.arange(start_date_dt, end_date_dt, numpy.timedelta64(5, 'm'), dtype='datetime64')
dummy = pandas.DataFrame(dummy, columns=['timestamp'])
dummy.set_index('timestamp', inplace=True)
rand_vet = 0.05 * 13.8 * numpy.random.rand(dummy.shape[0], 1) - 0.025 * 13.8
step_vet = numpy.zeros((dummy.shape[0], 1))
# Noise
for i in range(0, random.randint(1, 40)):
start = random.randint(0, dummy.shape[0])
end = start + random.randint(1, 12 * 3)
if end >= len(step_vet):
end = len(step_vet)
step_vet[start:end] = random.randint(-1, 1)
dummy['VA'] = 1.03 * 13.8 + rand_vet + step_vet
dummy['VB'] = 1.03 * 13.8 + rand_vet + step_vet
dummy['VV'] = 1.03 * 13.8 + rand_vet + step_vet
return dummy
[docs]
def PowerFactorDummyData(qty_weeks:int = 12*4,start_date_dt:datetime = datetime(2023,1,1)):
"""
Generates dummy power factor data for a specified number of weeks starting from a given date.
This function creates a pandas DataFrame containing simulated power factor data across three columns: 'FPA', 'FPB', and 'FPV'.
Each row represents a 5-minute interval within the specified time frame. The data includes base values with added random
load transfer and noise effects to simulate real-world fluctuations in power factor measurements.
Parameters
----------
qty_weeks : int, optional
The quantity of weeks to generate data for, defaults to 48 weeks (approximately one year).
start_date_dt : datetime, optional
The start date for the data generation, defaults to January 1, 2023.
Returns
-------
pandas.DataFrame
A DataFrame with a datetime index representing 5-minute intervals and columns 'FPA', 'FPB', and 'FPV' for power factor values.
The data includes random variations to simulate realistic power factor changes over time.
Notes
-----
- The function internally generates a dummy week of data and replicates it for the number of weeks specified.
- Random load transfers and noise are added to the base values to create variability in the data.
- The DataFrame's index is set to the timestamp of each record, making it suitable for time series analysis.
Examples
--------
>>> import pandas
>>> from datetime import datetime
>>> dummy_data = PowerFactorDummyData(qty_weeks=12, start_date_dt=datetime(2023, 1, 1))
>>> dummy_data.head()
"""
dummy_week = pandas.DataFrame([[0.99,1.0,0.99],
[0.99,1.0,0.99],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,1.0,0.98],
[0.99,0.99,0.98],
[0.99,0.99,0.98],
[0.98,1.0,0.98],
[0.98,0.99,0.98],
[0.98,0.99,0.98],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.98],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.98],
[0.98,0.99,0.98],
[0.99,1.0,0.98],
[0.98,0.99,0.97],
[0.98,0.99,0.97],
[0.98,0.99,0.98],
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[0.98,0.99,0.98],
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[0.98,1.0,0.98],
[0.98,1.0,0.98],
[0.99,0.99,0.98]], columns=['FPA', 'FPB', 'FPV'])
dummy= pandas.concat([dummy_week] * qty_weeks, ignore_index=True)
end_date_dt = start_date_dt + dt.timedelta(days=qty_weeks*7)
dummy_idx = numpy.arange(start_date_dt, end_date_dt, numpy.timedelta64(5, 'm'), dtype='datetime64')
dummy['timestamp'] = dummy_idx
dummy.set_index('timestamp', inplace=True)
dummy.index = pandas.to_datetime(dummy.index)
step_vet = numpy.zeros(dummy.shape[0])
# Load transfer
for i in range(0, random.randint(1, 4)):
start = random.randint(0, dummy.shape[0])
end = start + random.randint(1, 60) * 24 * 12
if end >= len(step_vet):
end = len(step_vet)
step_vet[start:end] = -0.2 * random.random()
# Noise
for i in range(0, random.randint(1, 40)):
start = random.randint(0, dummy.shape[0])
end = start + random.randint(1, 12 * 3)
if end >= len(step_vet):
end = len(step_vet)
step_vet[start:end] = random.randint(-10,2)*0.07
dummy['FPA'] = dummy['FPA'].values + step_vet
dummy['FPB'] = dummy['FPB'].values + step_vet
dummy['FPV'] = dummy['FPV'].values + step_vet
return dummy
[docs]
def PowerDummyData(qty_weeks:int = 12*4,start_date_dt:datetime = datetime(2023,1,1)):
"""
Generates dummy power data for a specified number of weeks from a start date.
This function calculates the apparent power (S), active power (P), and reactive power (Q)
for a given number of weeks starting from a specified date. It uses the CurrentDummyData,
VoltageDummyData, and PowerFactorDummyData functions to generate current (I), voltage (V),
and power factor (pf) data, respectively. The final DataFrame includes columns for S, P, and Q.
Parameters:
qty_weeks (int): The quantity of weeks for which to generate data. Default is 48 weeks.
start_date_dt (datetime): The start date for data generation. Default is January 1, 2023.
Returns:
pandas.DataFrame: A DataFrame containing the columns 'S' (apparent power),
'P' (active power), and 'Q' (reactive power).
Example:
>>> PowerDummyData(4, datetime(2023, 1, 1))
[Output will be a DataFrame with the calculated power data for 4 weeks starting from January 1, 2023]
"""
end_date_dt = start_date_dt + dt.timedelta(days=qty_weeks*7)
I = CurrentDummyData(qty_weeks, start_date_dt)
V = VoltageDummyData(qty_weeks, start_date_dt)
pf = PowerFactorDummyData(qty_weeks, start_date_dt)
I = I.iloc[:, :-1]
dummy = pandas.DataFrame([])
dummy['S'] = V['VA'] / numpy.sqrt(3) * I['IA'] + V['VB'] / numpy.sqrt(3) * I['IB'] \
+ V['VV'] / numpy.sqrt(3) * I['IV']
dummy['P'] = V['VA'] / numpy.sqrt(3) * I['IA'] * pf['FPA'] + V['VB'] / numpy.sqrt(3) * I['IB'] * pf['FPB'] \
+ V['VV'] / numpy.sqrt(3) * I['IV'] * pf['FPV']
dummy['Q'] = dummy['S'].pow(2) - dummy['P'].pow(2)
dummy['Q'] = numpy.sqrt(dummy['Q'].abs())
return dummy
[docs]
def EnergyDummyData(qty_weeks:int = 12*4,start_date_dt:datetime = datetime(2023,1,1)):
"""
Generate a dummy pandas DataFrame containing cumulative energy data.
This function creates a DataFrame with two columns: 'Eactive' and 'Ereactive'.
'Eactive' is the cumulative sum of the 'P' column from the PowerDummyData function,
and 'Ereactive' is the absolute cumulative sum of the 'Q' column from the same function.
Parameters
----------
qty_weeks : int, optional
The quantity of weeks for which to generate the data, default is 48 weeks (12*4).
start_date_dt : datetime, optional
The starting date for the data generation, default is January 1, 2023.
Returns
-------
pandas.DataFrame
A DataFrame with two columns 'Eactive' and 'Ereactive' representing the cumulative
active and reactive energy data respectively.
Examples
--------
>>> EnergyDummyData(4, datetime(2023, 1, 1))
DataFrame with the cumulative energy data for 4 weeks starting from January 1, 2023.
Notes
-----
The function relies on PowerDummyData function to generate initial power data
which is then cumulatively summed to generate energy data.
"""
dummy_s = PowerDummyData(qty_weeks, start_date_dt)
dummy = pandas.DataFrame([])
dummy['Eactive'] = dummy_s['P'].cumsum(skipna=True)
dummy['Ereactive'] = dummy_s['Q'].abs().cumsum(skipna=True)
return dummy
[docs]
def DefaultWeekDayCurve():
default = pandas.DataFrame([[0,0,0,0.452,0.446,0.44,0.815],
[0,0,5,0.454,0.444,0.443,0.904],
[0,0,10,0.436,0.431,0.431,0.868],
[0,0,15,0.433,0.424,0.417,0.893],
[0,0,20,0.416,0.408,0.412,0.858],
[0,0,25,0.4,0.389,0.398,0.859],
[0,0,30,0.393,0.38,0.384,0.899],
[0,0,35,0.377,0.369,0.37,0.919],
[0,0,40,0.366,0.354,0.357,0.861],
[0,0,45,0.357,0.344,0.35,0.829],
[0,0,50,0.342,0.33,0.34,0.836],
[0,0,55,0.337,0.32,0.33,0.822],
[0,1,0,0.323,0.305,0.314,0.857],
[0,1,5,0.312,0.292,0.301,0.84],
[0,1,10,0.302,0.286,0.291,0.838],
[0,1,15,0.294,0.28,0.287,0.861],
[0,1,20,0.286,0.276,0.273,0.905],
[0,1,25,0.271,0.259,0.266,0.819],
[0,1,30,0.262,0.245,0.254,0.799],
[0,1,35,0.248,0.233,0.245,0.787],
[0,1,40,0.248,0.232,0.241,0.769],
[0,1,45,0.245,0.229,0.234,0.794],
[0,1,50,0.234,0.213,0.229,0.834],
[0,1,55,0.227,0.203,0.226,0.822],
[0,2,0,0.224,0.207,0.218,0.773],
[0,2,5,0.221,0.211,0.212,0.748],
[0,2,10,0.206,0.192,0.2,0.777],
[0,2,15,0.202,0.194,0.204,0.764],
[0,2,20,0.199,0.182,0.195,0.731],
[0,2,25,0.185,0.174,0.182,0.752],
[0,2,30,0.177,0.173,0.172,0.75],
[0,2,35,0.172,0.164,0.162,0.755],
[0,2,40,0.174,0.155,0.158,0.73],
[0,2,45,0.168,0.148,0.152,0.7],
[0,2,50,0.163,0.142,0.153,0.702],
[0,2,55,0.155,0.138,0.147,0.69],
[0,3,0,0.153,0.135,0.144,0.728],
[0,3,5,0.149,0.128,0.137,0.709],
[0,3,10,0.141,0.12,0.13,0.697],
[0,3,15,0.14,0.117,0.129,0.7],
[0,3,20,0.133,0.114,0.129,0.717],
[0,3,25,0.132,0.112,0.121,0.71],
[0,3,30,0.128,0.108,0.118,0.686],
[0,3,35,0.118,0.1,0.116,0.699],
[0,3,40,0.118,0.097,0.112,0.713],
[0,3,45,0.114,0.091,0.108,0.713],
[0,3,50,0.108,0.087,0.103,0.694],
[0,3,55,0.105,0.081,0.093,0.704],
[0,4,0,0.102,0.079,0.093,0.637],
[0,4,5,0.097,0.078,0.09,0.63],
[0,4,10,0.101,0.072,0.093,0.673],
[0,4,15,0.1,0.074,0.093,0.69],
[0,4,20,0.098,0.068,0.089,0.684],
[0,4,25,0.094,0.068,0.081,0.659],
[0,4,30,0.097,0.071,0.085,0.632],
[0,4,35,0.099,0.076,0.089,0.686],
[0,4,40,0.095,0.071,0.081,0.66],
[0,4,45,0.095,0.069,0.077,0.632],
[0,4,50,0.101,0.076,0.092,0.627],
[0,4,55,0.11,0.089,0.104,0.651],
[0,5,0,0.106,0.087,0.103,0.606],
[0,5,5,0.121,0.109,0.124,0.602],
[0,5,10,0.122,0.116,0.133,0.555],
[0,5,15,0.107,0.108,0.111,0.575],
[0,5,20,0.102,0.096,0.111,0.562],
[0,5,25,0.097,0.099,0.1,0.548],
[0,5,30,0.082,0.084,0.079,0.526],
[0,5,35,0.054,0.049,0.056,0.489],
[0,5,40,0.035,0.027,0.041,0.539],
[0,5,45,0.017,0.015,0.025,0.492],
[0,5,50,0.003,0.002,0.006,0.53],
[0,5,55,0.003,0.004,0.0,0.478],
[0,6,0,0.0,0.0,0.008,0.414],
[0,6,5,0.058,0.073,0.058,0.39],
[0,6,10,0.079,0.093,0.097,0.43],
[0,6,15,0.072,0.086,0.092,0.402],
[0,6,20,0.075,0.086,0.08,0.41],
[0,6,25,0.062,0.063,0.071,0.425],
[0,6,30,0.064,0.056,0.072,0.327],
[0,6,35,0.046,0.042,0.057,0.302],
[0,6,40,0.027,0.034,0.043,0.373],
[0,6,45,0.036,0.03,0.045,0.361],
[0,6,50,0.029,0.033,0.051,0.334],
[0,6,55,0.029,0.035,0.054,0.378],
[0,7,0,0.023,0.048,0.066,0.297],
[0,7,5,0.045,0.068,0.101,0.236],
[0,7,10,0.068,0.096,0.12,0.222],
[0,7,15,0.09,0.113,0.142,0.226],
[0,7,20,0.117,0.142,0.163,0.222],
[0,7,25,0.147,0.168,0.188,0.279],
[0,7,30,0.148,0.18,0.186,0.269],
[0,7,35,0.161,0.188,0.193,0.273],
[0,7,40,0.174,0.196,0.198,0.242],
[0,7,45,0.176,0.197,0.209,0.25],
[0,7,50,0.181,0.198,0.208,0.189],
[0,7,55,0.2,0.216,0.23,0.172],
[0,8,0,0.182,0.188,0.208,0.17],
[0,8,5,0.223,0.233,0.247,0.101],
[0,8,10,0.23,0.234,0.265,0.131],
[0,8,15,0.24,0.253,0.285,0.141],
[0,8,20,0.256,0.263,0.286,0.138],
[0,8,25,0.267,0.261,0.289,0.153],
[0,8,30,0.275,0.264,0.3,0.085],
[0,8,35,0.268,0.264,0.282,0.113],
[0,8,40,0.268,0.267,0.291,0.121],
[0,8,45,0.258,0.25,0.291,0.1],
[0,8,50,0.263,0.269,0.299,0.06],
[0,8,55,0.263,0.261,0.302,0.04],
[0,9,0,0.275,0.266,0.3,0.034],
[0,9,5,0.309,0.298,0.328,0.023],
[0,9,10,0.324,0.309,0.352,0.076],
[0,9,15,0.328,0.32,0.353,0.1],
[0,9,20,0.358,0.345,0.373,0.004],
[0,9,25,0.37,0.363,0.388,0.0],
[0,9,30,0.379,0.365,0.392,0.085],
[0,9,35,0.377,0.375,0.402,0.101],
[0,9,40,0.38,0.381,0.411,0.051],
[0,9,45,0.36,0.363,0.392,0.068],
[0,9,50,0.381,0.381,0.41,0.071],
[0,9,55,0.375,0.374,0.412,0.035],
[0,10,0,0.398,0.398,0.426,0.085],
[0,10,5,0.381,0.386,0.416,0.003],
[0,10,10,0.393,0.404,0.432,0.01],
[0,10,15,0.417,0.414,0.443,0.03],
[0,10,20,0.424,0.431,0.457,0.089],
[0,10,25,0.426,0.434,0.461,0.051],
[0,10,30,0.417,0.431,0.455,0.084],
[0,10,35,0.438,0.44,0.469,0.054],
[0,10,40,0.447,0.451,0.468,0.021],
[0,10,45,0.47,0.484,0.509,0.025],
[0,10,50,0.465,0.474,0.51,0.044],
[0,10,55,0.487,0.487,0.526,0.093],
[0,11,0,0.484,0.482,0.512,0.063],
[0,11,5,0.527,0.533,0.554,0.099],
[0,11,10,0.528,0.529,0.557,0.143],
[0,11,15,0.526,0.527,0.557,0.121],
[0,11,20,0.527,0.531,0.55,0.118],
[0,11,25,0.534,0.535,0.554,0.136],
[0,11,30,0.532,0.522,0.555,0.131],
[0,11,35,0.571,0.566,0.585,0.149],
[0,11,40,0.577,0.575,0.596,0.145],
[0,11,45,0.588,0.587,0.614,0.142],
[0,11,50,0.615,0.621,0.634,0.171],
[0,11,55,0.639,0.64,0.669,0.202],
[0,12,0,0.63,0.646,0.67,0.23],
[0,12,5,0.639,0.639,0.664,0.199],
[0,12,10,0.633,0.638,0.663,0.211],
[0,12,15,0.627,0.638,0.669,0.184],
[0,12,20,0.587,0.59,0.625,0.211],
[0,12,25,0.586,0.585,0.618,0.232],
[0,12,30,0.543,0.541,0.583,0.174],
[0,12,35,0.534,0.534,0.568,0.192],
[0,12,40,0.493,0.498,0.543,0.193],
[0,12,45,0.489,0.488,0.532,0.224],
[0,12,50,0.449,0.446,0.484,0.12],
[0,12,55,0.494,0.484,0.528,0.09],
[0,13,0,0.487,0.479,0.522,0.114],
[0,13,5,0.508,0.498,0.536,0.081],
[0,13,10,0.516,0.518,0.552,0.103],
[0,13,15,0.546,0.553,0.578,0.07],
[0,13,20,0.563,0.569,0.607,0.067],
[0,13,25,0.565,0.574,0.603,0.061],
[0,13,30,0.56,0.566,0.601,0.07],
[0,13,35,0.592,0.592,0.62,0.067],
[0,13,40,0.573,0.577,0.613,0.107],
[0,13,45,0.597,0.609,0.637,0.145],
[0,13,50,0.576,0.587,0.607,0.115],
[0,13,55,0.616,0.62,0.637,0.074],
[0,14,0,0.621,0.625,0.665,0.088],
[0,14,5,0.644,0.643,0.681,0.049],
[0,14,10,0.582,0.581,0.626,0.091],
[0,14,15,0.594,0.592,0.64,0.098],
[0,14,20,0.636,0.641,0.681,0.084],
[0,14,25,0.631,0.628,0.666,0.085],
[0,14,30,0.602,0.589,0.646,0.063],
[0,14,35,0.63,0.619,0.673,0.085],
[0,14,40,0.637,0.63,0.678,0.105],
[0,14,45,0.619,0.606,0.657,0.085],
[0,14,50,0.605,0.596,0.648,0.094],
[0,14,55,0.636,0.632,0.682,0.128],
[0,15,0,0.629,0.631,0.67,0.103],
[0,15,5,0.634,0.637,0.672,0.087],
[0,15,10,0.635,0.64,0.672,0.101],
[0,15,15,0.636,0.636,0.676,0.107],
[0,15,20,0.648,0.657,0.689,0.087],
[0,15,25,0.604,0.602,0.644,0.143],
[0,15,30,0.649,0.654,0.701,0.119],
[0,15,35,0.616,0.619,0.672,0.128],
[0,15,40,0.61,0.609,0.662,0.16],
[0,15,45,0.631,0.626,0.686,0.094],
[0,15,50,0.645,0.645,0.689,0.064],
[0,15,55,0.651,0.644,0.674,0.061],
[0,16,0,0.673,0.682,0.715,0.116],
[0,16,5,0.639,0.641,0.676,0.039],
[0,16,10,0.657,0.658,0.706,0.038],
[0,16,15,0.667,0.672,0.704,0.035],
[0,16,20,0.665,0.685,0.703,0.046],
[0,16,25,0.677,0.688,0.709,0.051],
[0,16,30,0.664,0.676,0.698,0.053],
[0,16,35,0.696,0.706,0.737,0.097],
[0,16,40,0.674,0.687,0.72,0.092],
[0,16,45,0.684,0.701,0.728,0.109],
[0,16,50,0.675,0.689,0.718,0.139],
[0,16,55,0.678,0.703,0.725,0.1],
[0,17,0,0.664,0.678,0.706,0.104],
[0,17,5,0.631,0.649,0.674,0.097],
[0,17,10,0.642,0.674,0.689,0.111],
[0,17,15,0.644,0.654,0.667,0.193],
[0,17,20,0.649,0.663,0.685,0.177],
[0,17,25,0.642,0.655,0.687,0.181],
[0,17,30,0.653,0.665,0.699,0.249],
[0,17,35,0.679,0.698,0.72,0.255],
[0,17,40,0.702,0.719,0.751,0.27],
[0,17,45,0.735,0.751,0.78,0.317],
[0,17,50,0.763,0.779,0.801,0.334],
[0,17,55,0.778,0.791,0.817,0.392],
[0,18,0,0.766,0.79,0.796,0.441],
[0,18,5,0.784,0.795,0.811,0.416],
[0,18,10,0.79,0.799,0.828,0.444],
[0,18,15,0.822,0.824,0.839,0.491],
[0,18,20,0.844,0.854,0.864,0.588],
[0,18,25,0.865,0.877,0.894,0.587],
[0,18,30,0.885,0.902,0.923,0.644],
[0,18,35,0.905,0.913,0.925,0.588],
[0,18,40,0.905,0.908,0.931,0.666],
[0,18,45,0.928,0.928,0.935,0.707],
[0,18,50,0.916,0.917,0.941,0.652],
[0,18,55,0.95,0.952,0.973,0.735],
[0,19,0,0.967,0.978,0.973,0.717],
[0,19,5,0.971,0.978,0.984,0.629],
[0,19,10,0.965,0.965,0.965,0.703],
[0,19,15,0.965,0.965,0.978,0.668],
[0,19,20,0.985,0.994,0.99,0.682],
[0,19,25,0.997,0.987,1.0,0.679],
[0,19,30,0.974,0.97,0.978,0.71],
[0,19,35,0.97,0.981,0.974,0.7],
[0,19,40,0.977,0.989,0.977,0.722],
[0,19,45,0.992,0.997,0.995,0.793],
[0,19,50,1.0,1.0,0.997,0.772],
[0,19,55,0.984,0.996,0.98,0.79],
[0,20,0,0.948,0.952,0.964,0.722],
[0,20,5,0.937,0.931,0.937,0.845],
[0,20,10,0.927,0.928,0.924,0.821],
[0,20,15,0.916,0.919,0.915,0.805],
[0,20,20,0.9,0.91,0.91,0.868],
[0,20,25,0.892,0.899,0.9,0.843],
[0,20,30,0.886,0.892,0.888,0.844],
[0,20,35,0.902,0.909,0.899,0.864],
[0,20,40,0.901,0.908,0.903,0.972],
[0,20,45,0.87,0.874,0.868,0.952],
[0,20,50,0.865,0.873,0.864,0.943],
[0,20,55,0.882,0.894,0.89,0.943],
[0,21,0,0.872,0.886,0.885,1.0],
[0,21,5,0.86,0.869,0.865,0.897],
[0,21,10,0.869,0.871,0.872,0.937],
[0,21,15,0.834,0.844,0.842,0.98],
[0,21,20,0.839,0.852,0.845,0.961],
[0,21,25,0.874,0.876,0.873,0.876],
[0,21,30,0.833,0.839,0.835,0.908],
[0,21,35,0.849,0.853,0.85,0.937],
[0,21,40,0.813,0.814,0.818,0.933],
[0,21,45,0.821,0.827,0.836,0.914],
[0,21,50,0.816,0.82,0.827,0.914],
[0,21,55,0.819,0.817,0.822,0.953],
[0,22,0,0.796,0.807,0.805,0.977],
[0,22,5,0.785,0.795,0.794,0.904],
[0,22,10,0.78,0.783,0.781,0.856],
[0,22,15,0.78,0.783,0.774,0.949],
[0,22,20,0.766,0.77,0.762,0.934],
[0,22,25,0.734,0.751,0.73,0.898],
[0,22,30,0.749,0.763,0.753,0.936],
[0,22,35,0.734,0.748,0.734,0.905],
[0,22,40,0.726,0.734,0.724,0.935],
[0,22,45,0.72,0.734,0.72,0.901],
[0,22,50,0.697,0.713,0.694,0.915],
[0,22,55,0.697,0.711,0.699,0.933],
[0,23,0,0.677,0.694,0.675,0.947],
[0,23,5,0.685,0.695,0.679,0.906],
[0,23,10,0.648,0.651,0.636,0.918],
[0,23,15,0.655,0.674,0.647,0.862],
[0,23,20,0.638,0.65,0.628,0.935],
[0,23,25,0.612,0.63,0.613,0.967],
[0,23,30,0.592,0.616,0.595,0.967],
[0,23,35,0.58,0.598,0.576,0.995],
[0,23,40,0.562,0.582,0.557,0.935],
[0,23,45,0.547,0.564,0.539,0.948],
[0,23,50,0.528,0.544,0.523,0.899],
[0,23,55,0.515,0.53,0.519,0.877],
[1,0,0,0.445,0.443,0.45,0.856],
[1,0,5,0.432,0.428,0.44,0.863],
[1,0,10,0.41,0.409,0.426,0.836],
[1,0,15,0.395,0.392,0.407,0.831],
[1,0,20,0.388,0.381,0.393,0.824],
[1,0,25,0.38,0.381,0.382,0.853],
[1,0,30,0.37,0.371,0.373,0.84],
[1,0,35,0.361,0.35,0.356,0.801],
[1,0,40,0.351,0.339,0.347,0.777],
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[6,13,55,0.088,0.076,0.08,0.248],
[6,14,0,0.082,0.066,0.078,0.335],
[6,14,5,0.08,0.065,0.077,0.298],
[6,14,10,0.087,0.079,0.086,0.313],
[6,14,15,0.078,0.068,0.078,0.259],
[6,14,20,0.078,0.062,0.08,0.265],
[6,14,25,0.076,0.057,0.085,0.258],
[6,14,30,0.073,0.052,0.084,0.274],
[6,14,35,0.081,0.055,0.09,0.316],
[6,14,40,0.096,0.076,0.1,0.293],
[6,14,45,0.092,0.074,0.101,0.294],
[6,14,50,0.091,0.081,0.091,0.24],
[6,14,55,0.098,0.081,0.096,0.269],
[6,15,0,0.105,0.085,0.108,0.269],
[6,15,5,0.12,0.102,0.125,0.293],
[6,15,10,0.111,0.096,0.117,0.266],
[6,15,15,0.12,0.096,0.12,0.273],
[6,15,20,0.125,0.103,0.121,0.286],
[6,15,25,0.139,0.115,0.138,0.206],
[6,15,30,0.145,0.124,0.151,0.266],
[6,15,35,0.156,0.127,0.151,0.29],
[6,15,40,0.158,0.142,0.168,0.262],
[6,15,45,0.16,0.146,0.174,0.273],
[6,15,50,0.184,0.174,0.188,0.247],
[6,15,55,0.182,0.172,0.197,0.323],
[6,16,0,0.207,0.193,0.22,0.277],
[6,16,5,0.224,0.215,0.246,0.307],
[6,16,10,0.232,0.233,0.249,0.327],
[6,16,15,0.234,0.236,0.246,0.284],
[6,16,20,0.241,0.239,0.246,0.332],
[6,16,25,0.257,0.257,0.26,0.326],
[6,16,30,0.283,0.288,0.301,0.288],
[6,16,35,0.29,0.294,0.302,0.308],
[6,16,40,0.303,0.302,0.31,0.313],
[6,16,45,0.324,0.325,0.339,0.368],
[6,16,50,0.338,0.341,0.354,0.384],
[6,16,55,0.355,0.363,0.376,0.352],
[6,17,0,0.41,0.413,0.407,0.429],
[6,17,5,0.432,0.438,0.447,0.413],
[6,17,10,0.446,0.469,0.466,0.451],
[6,17,15,0.486,0.495,0.504,0.46],
[6,17,20,0.49,0.499,0.518,0.523],
[6,17,25,0.522,0.544,0.55,0.538],
[6,17,30,0.559,0.563,0.592,0.519],
[6,17,35,0.601,0.608,0.623,0.512],
[6,17,40,0.661,0.661,0.684,0.595],
[6,17,45,0.698,0.704,0.722,0.591],
[6,17,50,0.73,0.736,0.755,0.607],
[6,17,55,0.766,0.773,0.784,0.705],
[6,18,0,0.82,0.831,0.837,0.731],
[6,18,5,0.861,0.864,0.876,0.727],
[6,18,10,0.907,0.916,0.918,0.755],
[6,18,15,0.919,0.942,0.941,0.742],
[6,18,20,0.954,0.962,0.965,0.737],
[6,18,25,0.986,0.994,0.993,0.775],
[6,18,30,0.984,0.983,0.995,0.859],
[6,18,35,1.0,0.999,1.0,0.878],
[6,18,40,0.992,1.0,0.987,0.749],
[6,18,45,0.985,0.98,0.98,0.783],
[6,18,50,0.977,0.966,0.961,0.682],
[6,18,55,0.975,0.971,0.972,0.598],
[6,19,0,0.972,0.971,0.968,0.65],
[6,19,5,0.973,0.97,0.967,0.638],
[6,19,10,0.974,0.97,0.959,0.619],
[6,19,15,0.96,0.957,0.962,0.605],
[6,19,20,0.962,0.953,0.96,0.57],
[6,19,25,0.942,0.937,0.949,0.606],
[6,19,30,0.953,0.946,0.949,0.578],
[6,19,35,0.949,0.93,0.949,0.537],
[6,19,40,0.951,0.94,0.949,0.541],
[6,19,45,0.938,0.928,0.937,0.631],
[6,19,50,0.939,0.937,0.94,0.586],
[6,19,55,0.933,0.927,0.938,0.586],
[6,20,0,0.929,0.92,0.932,0.578],
[6,20,5,0.917,0.913,0.905,0.628],
[6,20,10,0.918,0.907,0.905,0.636],
[6,20,15,0.903,0.886,0.896,0.649],
[6,20,20,0.913,0.893,0.905,0.665],
[6,20,25,0.911,0.902,0.907,0.704],
[6,20,30,0.886,0.877,0.879,0.742],
[6,20,35,0.899,0.895,0.887,0.737],
[6,20,40,0.879,0.877,0.868,0.765],
[6,20,45,0.874,0.872,0.872,0.784],
[6,20,50,0.875,0.861,0.864,0.816],
[6,20,55,0.857,0.861,0.859,0.784],
[6,21,0,0.837,0.835,0.835,0.739],
[6,21,5,0.838,0.838,0.845,0.769],
[6,21,10,0.825,0.815,0.83,0.813],
[6,21,15,0.827,0.815,0.829,0.819],
[6,21,20,0.819,0.804,0.82,0.829],
[6,21,25,0.801,0.791,0.807,0.829],
[6,21,30,0.806,0.806,0.819,0.852],
[6,21,35,0.816,0.81,0.822,0.868],
[6,21,40,0.825,0.821,0.832,0.922],
[6,21,45,0.818,0.816,0.818,0.94],
[6,21,50,0.827,0.818,0.829,0.94],
[6,21,55,0.816,0.812,0.825,0.936],
[6,22,0,0.831,0.827,0.822,0.905],
[6,22,5,0.833,0.826,0.825,0.896],
[6,22,10,0.835,0.828,0.831,0.881],
[6,22,15,0.803,0.795,0.814,0.942],
[6,22,20,0.817,0.816,0.825,0.899],
[6,22,25,0.821,0.822,0.82,0.943],
[6,22,30,0.807,0.813,0.8,0.969],
[6,22,35,0.802,0.803,0.803,0.958],
[6,22,40,0.8,0.805,0.807,1.0],
[6,22,45,0.777,0.782,0.779,0.941],
[6,22,50,0.767,0.778,0.776,0.959],
[6,22,55,0.768,0.773,0.767,0.898],
[6,23,0,0.752,0.767,0.768,0.939],
[6,23,5,0.75,0.759,0.758,0.962],
[6,23,10,0.751,0.754,0.743,0.921],
[6,23,15,0.743,0.735,0.743,0.965],
[6,23,20,0.722,0.719,0.73,0.936],
[6,23,25,0.705,0.711,0.717,0.923],
[6,23,30,0.697,0.702,0.699,0.985],
[6,23,35,0.681,0.687,0.686,0.961],
[6,23,40,0.676,0.68,0.667,0.97],
[6,23,45,0.657,0.663,0.651,0.912],
[6,23,50,0.64,0.648,0.64,0.979],
[6,23,55,0.631,0.635,0.627,0.896]],columns =['WeekDay','Hour','Min','IA','IB','IV','IN'])
return default