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@robweber
Last active February 8, 2024 16:59
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"""
Takes in a set of files calculates the value sum from time periods designated in a YAML file
Install library requirements first (pandas generally installed via OS, on Linux sudo apt install python3-pandas)
"""
import argparse
import datetime
import os
import os.path
import pandas
import sys
import yaml
from natsort import natsorted
from cerberus import Validator
def check_file(expected_ext, filename):
"""checks if a file exists and is either corrected or uncorrected
based on the expected extension
"""
result = False
if(os.path.exists(filename) and filename.endswith(expected_ext)):
result = True
return result
def read_yaml(file):
"""attempts to read YAML file, exits if file cannot
be read or is not valid YAML
"""
result = {}
try:
with open(file, 'r') as f:
result = yaml.safe_load(f)
except Exception:
print(f"Error parsing YAML file {file}")
sys.exit(2)
return result
def load_yaml_file(file):
"""Load the YAML TOU file and validate it's structure
"""
result = False
yaml_file = read_yaml(file)
# validate the config file
schema = read_yaml('tou_schema.yaml')
v = Validator(schema)
if(not v.validate(yaml_file, schema)):
print(str(v.errors))
sys.exit(2)
# normalize for missing values
result = v.normalized(yaml_file)
return result
def process_rule(rules, df):
"""creates a Dataframe selection conditional from a list of rules
Rules lists include:
start-time = array hours to include
day-of-week = array of days to include [Sun, Mon]
exclude-day-of-week = array of days to exclude [Sun, Mon]
"""
result = None
# must be at least one start time
result = (df['start-time'].str.startswith(f"{rules['start-time'][0]}:"))
for time in rules['start-time'][1:]:
result = result | (df['start-time'].str.startswith(f"{time}:"))
if("day-of-week" in rules):
result = result & df['day-of-week'].isin(rules['day-of-week'])
if("exclude-day-of-week" in rules):
result = result & ~df['day-of-week'].isin(rules['exclude-day-of-week'])
return result
def create_conditional(conditions, df):
"""creates a series of conditionals from a ruleset
these should take the form of an array of dicts, one dict for
each set of conditions. There must be at least one ruleset.
"""
result = process_rule(conditions[0], df)
# if there is more than one condition
for rule in conditions[1:]:
result = result | process_rule(rule, df)
return result
def calculate(times, df_file):
# make sure each file exists
if(os.path.isfile(df_file)):
print(f"Analyzing: {df_file}")
# read in the file
df = pandas.read_csv(df_file)
print(f"Total: {df['value'].sum()}")
print("")
# generate a column for the day of the week
df['day-of-week'] = df['start-date'].apply(lambda x: datetime.datetime.strptime(x, "%m-%d-%Y").strftime("%a"))
# generate the output for each time period given
interval_sum = 0
for period in times:
tou_conditional = create_conditional(period['conditions'], df)
new_df = df[tou_conditional]
print(f"{period['name']}: {df['value'].sum()}")
# save the sume
interval_sum = interval_sum + new_df['value'].sum()
if(abs(df['value'].sum() - interval_sum) > .5):
print(f"TIME PERIODS TOTALS DO NOT MATCH - DIFF {df['value'].sum() - interval_sum}")
else:
raise Exception("File does not exist")
def main():
parser = argparse.ArgumentParser(description="")
parser.add_argument('-d', '--directory', required=True, type=str,
help="The directory containing the csv files to analyze")
parser.add_argument('-t', '--times', required=False, default="times.yaml",
help="A YAML file of TOU rules")
args = parser.parse_args()
print(f"Analyzing files in: {args.directory}")
print(f"Using time period rules from {args.times}")
print("")
# load the tou file
times = load_yaml_file(args.times)
# get a directory listing, filter wanted files
csv_files = natsorted(filter(lambda f: check_file('.csv', os.path.join(args.directory, f)), os.listdir(args.directory)))
# assume each file has a matching reverse file if in net metered mode
for f in csv_files:
calculate(times['time_periods'], os.path.join(args.directory, f))
print("") # space between each set
if __name__ == '__main__':
main()
cerberus
natsort
pyyaml
# schema for the time of use conditional rules - validated at startup
valid_times:
allowed: &valid_times
- "00"
- "01"
- "02"
- "03"
- "04"
- "05"
- "06"
- "07"
- "08"
- "09"
- "10"
- "11"
- "12"
- "13"
- "14"
- "15"
- "16"
- "17"
- "18"
- "19"
- "20"
- "21"
- "22"
- "23"
valid_days:
allowed: &valid_days
- "Sun"
- "Mon"
- "Tue"
- "Wed"
- "Thu"
- "Fri"
- "Sat"
conditions_definition: &conditions_definition
required: False
type: list
minlength: 1
schema:
type: dict
schema:
start-time:
required: True
type: list
minlength: 1
schema:
type: string
allowed: *valid_times
day-of-week:
required: False
type: list
schema:
type: string
allowed: *valid_days
exclude-day-of-week:
required: False
type: list
schema:
type: string
allowed: *valid_days
time_periods:
required: True
type: list
minlength: 1
schema:
type: dict
schema:
name:
required: True
type: string
conditions: *conditions_definition
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 6 columns, instead of 7. in line 1.
id,value,start-date,start-time,end-date,end-time
1.280,1.280,1,04-30-2023,01:00,04-30-2023,02:00
1.3030,1.3030,1,04-30-2023,02:00,04-30-2023,03:00
1.1790,1.1790,1,04-30-2023,03:00,04-30-2023,04:00
1.3660,1.3660,1,04-30-2023,04:00,04-30-2023,05:00
1.8570,1.8570,1,04-30-2023,05:00,04-30-2023,06:00
1.2360,1.2360,1,04-30-2023,06:00,04-30-2023,07:00
0.5290,0.5290,1,04-30-2023,07:00,04-30-2023,08:00
0.0760,0.0760,1,04-30-2023,08:00,04-30-2023,09:00
0.0040,0.0040,1,04-30-2023,09:00,04-30-2023,10:00
0.0,0.0,1,04-30-2023,10:00,04-30-2023,11:00
0.0,0.0,1,04-30-2023,11:00,04-30-2023,12:00
0.0,0.0,1,04-30-2023,12:00,04-30-2023,13:00
0.0,0.0,1,04-30-2023,13:00,04-30-2023,14:00
0.0,0.0,1,04-30-2023,14:00,04-30-2023,15:00
0.0,0.0,1,04-30-2023,15:00,04-30-2023,16:00
0.9040,0.9040,1,04-30-2023,16:00,04-30-2023,17:00
0.3910,0.3910,1,04-30-2023,17:00,04-30-2023,18:00
0.6290,0.6290,1,04-30-2023,18:00,04-30-2023,19:00
2.290,2.290,1,04-30-2023,19:00,04-30-2023,20:00
1.6420,1.6420,1,04-30-2023,20:00,04-30-2023,21:00
1.5510,1.5510,1,04-30-2023,21:00,04-30-2023,22:00
1.4660,1.4660,1,04-30-2023,22:00,04-30-2023,23:00
1.2530,1.2530,1,04-30-2023,23:00,05-01-2023,00:00
1.2890,1.2890,1,05-01-2023,00:00,05-01-2023,01:00
1.2430,1.2430,1,05-01-2023,01:00,05-01-2023,02:00
1.1960,1.1960,1,05-01-2023,02:00,05-01-2023,03:00
1.2830,1.2830,1,05-01-2023,03:00,05-01-2023,04:00
1.1470,1.1470,1,05-01-2023,04:00,05-01-2023,05:00
1.4420,1.4420,1,05-01-2023,05:00,05-01-2023,06:00
1.5380,1.5380,1,05-01-2023,06:00,05-01-2023,07:00
0.3780,0.3780,1,05-01-2023,07:00,05-01-2023,08:00
1.6570,1.6570,1,05-01-2023,08:00,05-01-2023,09:00
4.0550,4.0550,1,05-01-2023,09:00,05-01-2023,10:00
1.6780,1.6780,1,05-01-2023,10:00,05-01-2023,11:00
3.8060,3.8060,1,05-01-2023,11:00,05-01-2023,12:00
4.6180,4.6180,1,05-01-2023,12:00,05-01-2023,13:00
1.6840,1.6840,1,05-01-2023,13:00,05-01-2023,14:00
0.0,0.0,1,05-01-2023,14:00,05-01-2023,15:00
0.0,0.0,1,05-01-2023,15:00,05-01-2023,16:00
0.0580,0.0580,1,05-01-2023,16:00,05-01-2023,17:00
0.1390,0.1390,1,05-01-2023,17:00,05-01-2023,18:00
0.8110,0.8110,1,05-01-2023,18:00,05-01-2023,19:00
1.4990,1.4990,1,05-01-2023,19:00,05-01-2023,20:00
1.6160,1.6160,1,05-01-2023,20:00,05-01-2023,21:00
1.4360,1.4360,1,05-01-2023,21:00,05-01-2023,22:00
1.290,1.290,1,05-01-2023,22:00,05-01-2023,23:00
1.3210,1.3210,1,05-01-2023,23:00,05-02-2023,00:00
1.2730,1.2730,1,05-02-2023,00:00,05-02-2023,01:00
1.290,1.290,1,05-02-2023,01:00,05-02-2023,02:00
1.3060,1.3060,1,05-02-2023,02:00,05-02-2023,03:00
1.2780,1.2780,1,05-02-2023,03:00,05-02-2023,04:00
1.1750,1.1750,1,05-02-2023,04:00,05-02-2023,05:00
1.4610,1.4610,1,05-02-2023,05:00,05-02-2023,06:00
1.2640,1.2640,1,05-02-2023,06:00,05-02-2023,07:00
0.1990,0.1990,1,05-02-2023,07:00,05-02-2023,08:00
0.0,0.0,1,05-02-2023,08:00,05-02-2023,09:00
0.0,0.0,1,05-02-2023,09:00,05-02-2023,10:00
0.0,0.0,1,05-02-2023,10:00,05-02-2023,11:00
0.0,0.0,1,05-02-2023,11:00,05-02-2023,12:00
0.0,0.0,1,05-02-2023,12:00,05-02-2023,13:00
0.0,0.0,1,05-02-2023,13:00,05-02-2023,14:00
0.0,0.0,1,05-02-2023,14:00,05-02-2023,15:00
0.0,0.0,1,05-02-2023,15:00,05-02-2023,16:00
0.0030,0.0030,1,05-02-2023,16:00,05-02-2023,17:00
0.0710,0.0710,1,05-02-2023,17:00,05-02-2023,18:00
0.5510,0.5510,1,05-02-2023,18:00,05-02-2023,19:00
1.8220,1.8220,1,05-02-2023,19:00,05-02-2023,20:00
1.4080,1.4080,1,05-02-2023,20:00,05-02-2023,21:00
1.4410,1.4410,1,05-02-2023,21:00,05-02-2023,22:00
1.3670,1.3670,1,05-02-2023,22:00,05-02-2023,23:00
1.270,1.270,1,05-02-2023,23:00,05-03-2023,00:00
1.2430,1.2430,1,05-03-2023,00:00,05-03-2023,01:00
1.3240,1.3240,1,05-03-2023,01:00,05-03-2023,02:00
1.2070,1.2070,1,05-03-2023,02:00,05-03-2023,03:00
1.220,1.220,1,05-03-2023,03:00,05-03-2023,04:00
1.1050,1.1050,1,05-03-2023,04:00,05-03-2023,05:00
1.3810,1.3810,1,05-03-2023,05:00,05-03-2023,06:00
1.4880,1.4880,1,05-03-2023,06:00,05-03-2023,07:00
0.5740,0.5740,1,05-03-2023,07:00,05-03-2023,08:00
0.0,0.0,1,05-03-2023,08:00,05-03-2023,09:00
0.0,0.0,1,05-03-2023,09:00,05-03-2023,10:00
0.0,0.0,1,05-03-2023,10:00,05-03-2023,11:00
0.0,0.0,1,05-03-2023,11:00,05-03-2023,12:00
0.0,0.0,1,05-03-2023,12:00,05-03-2023,13:00
0.0,0.0,1,05-03-2023,13:00,05-03-2023,14:00
0.0,0.0,1,05-03-2023,14:00,05-03-2023,15:00
0.1150,0.1150,1,05-03-2023,15:00,05-03-2023,16:00
0.1570,0.1570,1,05-03-2023,16:00,05-03-2023,17:00
0.0010,0.0010,1,05-03-2023,17:00,05-03-2023,18:00
0.0490,0.0490,1,05-03-2023,18:00,05-03-2023,19:00
0.9160,0.9160,1,05-03-2023,19:00,05-03-2023,20:00
1.8590,1.8590,1,05-03-2023,20:00,05-03-2023,21:00
1.7150,1.7150,1,05-03-2023,21:00,05-03-2023,22:00
1.2620,1.2620,1,05-03-2023,22:00,05-03-2023,23:00
1.2040,1.2040,1,05-03-2023,23:00,05-04-2023,00:00
1.1950,1.1950,1,05-04-2023,00:00,05-04-2023,01:00
1.2460,1.2460,1,05-04-2023,01:00,05-04-2023,02:00
1.1830,1.1830,1,05-04-2023,02:00,05-04-2023,03:00
1.0990,1.0990,1,05-04-2023,03:00,05-04-2023,04:00
1.2510,1.2510,1,05-04-2023,04:00,05-04-2023,05:00
1.2360,1.2360,1,05-04-2023,05:00,05-04-2023,06:00
1.0530,1.0530,1,05-04-2023,06:00,05-04-2023,07:00
0.9730,0.9730,1,05-04-2023,07:00,05-04-2023,08:00
0.0030,0.0030,1,05-04-2023,08:00,05-04-2023,09:00
0.0010,0.0010,1,05-04-2023,09:00,05-04-2023,10:00
0.0,0.0,1,05-04-2023,10:00,05-04-2023,11:00
0.0,0.0,1,05-04-2023,11:00,05-04-2023,12:00
0.0,0.0,1,05-04-2023,12:00,05-04-2023,13:00
0.0,0.0,1,05-04-2023,13:00,05-04-2023,14:00
0.0,0.0,1,05-04-2023,14:00,05-04-2023,15:00
0.0,0.0,1,05-04-2023,15:00,05-04-2023,16:00
0.0,0.0,1,05-04-2023,16:00,05-04-2023,17:00
0.0,0.0,1,05-04-2023,17:00,05-04-2023,18:00
0.0660,0.0660,1,05-04-2023,18:00,05-04-2023,19:00
0.9730,0.9730,1,05-04-2023,19:00,05-04-2023,20:00
1.2530,1.2530,1,05-04-2023,20:00,05-04-2023,21:00
1.7110,1.7110,1,05-04-2023,21:00,05-04-2023,22:00
1.3990,1.3990,1,05-04-2023,22:00,05-04-2023,23:00
1.3580,1.3580,1,05-04-2023,23:00,05-05-2023,00:00
1.2780,1.2780,1,05-05-2023,00:00,05-05-2023,01:00
1.2770,1.2770,1,05-05-2023,01:00,05-05-2023,02:00
1.1980,1.1980,1,05-05-2023,02:00,05-05-2023,03:00
1.3560,1.3560,1,05-05-2023,03:00,05-05-2023,04:00
1.2780,1.2780,1,05-05-2023,04:00,05-05-2023,05:00
1.3920,1.3920,1,05-05-2023,05:00,05-05-2023,06:00
1.4290,1.4290,1,05-05-2023,06:00,05-05-2023,07:00
0.9280,0.9280,1,05-05-2023,07:00,05-05-2023,08:00
0.7970,0.7970,1,05-05-2023,08:00,05-05-2023,09:00
2.1350,2.1350,1,05-05-2023,09:00,05-05-2023,10:00
4.4150,4.4150,1,05-05-2023,10:00,05-05-2023,11:00
1.0830,1.0830,1,05-05-2023,11:00,05-05-2023,12:00
3.410,3.410,1,05-05-2023,12:00,05-05-2023,13:00
1.8070,1.8070,1,05-05-2023,13:00,05-05-2023,14:00
1.3880,1.3880,1,05-05-2023,14:00,05-05-2023,15:00
1.7010,1.7010,1,05-05-2023,15:00,05-05-2023,16:00
4.7670,4.7670,1,05-05-2023,16:00,05-05-2023,17:00
1.4080,1.4080,1,05-05-2023,17:00,05-05-2023,18:00
1.0350,1.0350,1,05-05-2023,18:00,05-05-2023,19:00
2.6490,2.6490,1,05-05-2023,19:00,05-05-2023,20:00
2.260,2.260,1,05-05-2023,20:00,05-05-2023,21:00
2.050,2.050,1,05-05-2023,21:00,05-05-2023,22:00
2.1240,2.1240,1,05-05-2023,22:00,05-05-2023,23:00
2.0090,2.0090,1,05-05-2023,23:00,05-06-2023,00:00
1.9210,1.9210,1,05-06-2023,00:00,05-06-2023,01:00
1.8370,1.8370,1,05-06-2023,01:00,05-06-2023,02:00
1.9740,1.9740,1,05-06-2023,02:00,05-06-2023,03:00
1.8950,1.8950,1,05-06-2023,03:00,05-06-2023,04:00
1.960,1.960,1,05-06-2023,04:00,05-06-2023,05:00
1.9410,1.9410,1,05-06-2023,05:00,05-06-2023,06:00
1.8580,1.8580,1,05-06-2023,06:00,05-06-2023,07:00
1.2960,1.2960,1,05-06-2023,07:00,05-06-2023,08:00
1.3290,1.3290,1,05-06-2023,08:00,05-06-2023,09:00
0.8810,0.8810,1,05-06-2023,09:00,05-06-2023,10:00
0.8470,0.8470,1,05-06-2023,10:00,05-06-2023,11:00
0.0320,0.0320,1,05-06-2023,11:00,05-06-2023,12:00
0.0740,0.0740,1,05-06-2023,12:00,05-06-2023,13:00
0.3790,0.3790,1,05-06-2023,13:00,05-06-2023,14:00
1.3910,1.3910,1,05-06-2023,14:00,05-06-2023,15:00
1.1950,1.1950,1,05-06-2023,15:00,05-06-2023,16:00
1.5910,1.5910,1,05-06-2023,16:00,05-06-2023,17:00
1.6320,1.6320,1,05-06-2023,17:00,05-06-2023,18:00
1.4780,1.4780,1,05-06-2023,18:00,05-06-2023,19:00
0.9360,0.9360,1,05-06-2023,19:00,05-06-2023,20:00
1.4860,1.4860,1,05-06-2023,20:00,05-06-2023,21:00
9.1940,9.1940,1,05-06-2023,21:00,05-06-2023,22:00
9.2170,9.2170,1,05-06-2023,22:00,05-06-2023,23:00
9.0240,9.0240,1,05-06-2023,23:00,05-07-2023,00:00
9.150,9.150,1,05-07-2023,00:00,05-07-2023,01:00
9.1110,9.1110,1,05-07-2023,01:00,05-07-2023,02:00
3.3030,3.3030,1,05-07-2023,02:00,05-07-2023,03:00
1.1720,1.1720,1,05-07-2023,03:00,05-07-2023,04:00
1.1610,1.1610,1,05-07-2023,04:00,05-07-2023,05:00
1.1880,1.1880,1,05-07-2023,05:00,05-07-2023,06:00
0.9330,0.9330,1,05-07-2023,06:00,05-07-2023,07:00
0.2260,0.2260,1,05-07-2023,07:00,05-07-2023,08:00
0.0,0.0,1,05-07-2023,08:00,05-07-2023,09:00
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1.2170,1.2170,1,05-27-2023,04:00,05-27-2023,05:00
1.3180,1.3180,1,05-27-2023,05:00,05-27-2023,06:00
0.9990,0.9990,1,05-27-2023,06:00,05-27-2023,07:00
0.1330,0.1330,1,05-27-2023,07:00,05-27-2023,08:00
0.0,0.0,1,05-27-2023,08:00,05-27-2023,09:00
0.0,0.0,1,05-27-2023,09:00,05-27-2023,10:00
0.0,0.0,1,05-27-2023,10:00,05-27-2023,11:00
0.0,0.0,1,05-27-2023,11:00,05-27-2023,12:00
0.0,0.0,1,05-27-2023,12:00,05-27-2023,13:00
0.0,0.0,1,05-27-2023,13:00,05-27-2023,14:00
0.0,0.0,1,05-27-2023,14:00,05-27-2023,15:00
0.0,0.0,1,05-27-2023,15:00,05-27-2023,16:00
0.0190,0.0190,1,05-27-2023,16:00,05-27-2023,17:00
0.080,0.080,1,05-27-2023,17:00,05-27-2023,18:00
0.0990,0.0990,1,05-27-2023,18:00,05-27-2023,19:00
0.7160,0.7160,1,05-27-2023,19:00,05-27-2023,20:00
1.4430,1.4430,1,05-27-2023,20:00,05-27-2023,21:00
1.9180,1.9180,1,05-27-2023,21:00,05-27-2023,22:00
2.1120,2.1120,1,05-27-2023,22:00,05-27-2023,23:00
1.3140,1.3140,1,05-27-2023,23:00,05-28-2023,00:00
1.270,1.270,1,05-28-2023,00:00,05-28-2023,01:00
1.3680,1.3680,1,05-28-2023,01:00,05-28-2023,02:00
1.3330,1.3330,1,05-28-2023,02:00,05-28-2023,03:00
1.2980,1.2980,1,05-28-2023,03:00,05-28-2023,04:00
1.310,1.310,1,05-28-2023,04:00,05-28-2023,05:00
1.2710,1.2710,1,05-28-2023,05:00,05-28-2023,06:00
0.5960,0.5960,1,05-28-2023,06:00,05-28-2023,07:00
0.0380,0.0380,1,05-28-2023,07:00,05-28-2023,08:00
0.0,0.0,1,05-28-2023,08:00,05-28-2023,09:00
0.5850,0.5850,1,05-28-2023,09:00,05-28-2023,10:00
0.0,0.0,1,05-28-2023,10:00,05-28-2023,11:00
0.0,0.0,1,05-28-2023,11:00,05-28-2023,12:00
0.0,0.0,1,05-28-2023,12:00,05-28-2023,13:00
0.0,0.0,1,05-28-2023,13:00,05-28-2023,14:00
0.0,0.0,1,05-28-2023,14:00,05-28-2023,15:00
0.0,0.0,1,05-28-2023,15:00,05-28-2023,16:00
0.0,0.0,1,05-28-2023,16:00,05-28-2023,17:00
0.6930,0.6930,1,05-28-2023,17:00,05-28-2023,18:00
1.610,1.610,1,05-28-2023,18:00,05-28-2023,19:00
0.7760,0.7760,1,05-28-2023,19:00,05-28-2023,20:00
1.6090,1.6090,1,05-28-2023,20:00,05-28-2023,21:00
1.4090,1.4090,1,05-28-2023,21:00,05-28-2023,22:00
1.4540,1.4540,1,05-28-2023,22:00,05-28-2023,23:00
1.4180,1.4180,1,05-28-2023,23:00,05-29-2023,00:00
1.3840,1.3840,1,05-29-2023,00:00,05-29-2023,01:00
1.3930,1.3930,1,05-29-2023,01:00,05-29-2023,02:00
1.3670,1.3670,1,05-29-2023,02:00,05-29-2023,03:00
1.3750,1.3750,1,05-29-2023,03:00,05-29-2023,04:00
1.5880,1.5880,1,05-29-2023,04:00,05-29-2023,05:00
1.3340,1.3340,1,05-29-2023,05:00,05-29-2023,06:00
0.6640,0.6640,1,05-29-2023,06:00,05-29-2023,07:00
0.0090,0.0090,1,05-29-2023,07:00,05-29-2023,08:00
0.0,0.0,1,05-29-2023,08:00,05-29-2023,09:00
0.0020,0.0020,1,05-29-2023,09:00,05-29-2023,10:00
0.0130,0.0130,1,05-29-2023,10:00,05-29-2023,11:00
0.0010,0.0010,1,05-29-2023,11:00,05-29-2023,12:00
0.0,0.0,1,05-29-2023,12:00,05-29-2023,13:00
0.0,0.0,1,05-29-2023,13:00,05-29-2023,14:00
0.0,0.0,1,05-29-2023,14:00,05-29-2023,15:00
0.0,0.0,1,05-29-2023,15:00,05-29-2023,16:00
0.0,0.0,1,05-29-2023,16:00,05-29-2023,17:00
2.5250,2.5250,1,05-29-2023,17:00,05-29-2023,18:00
4.4390,4.4390,1,05-29-2023,18:00,05-29-2023,19:00
4.5030,4.5030,1,05-29-2023,19:00,05-29-2023,20:00
4.1220,4.1220,1,05-29-2023,20:00,05-29-2023,21:00
9.180,9.180,1,05-29-2023,21:00,05-29-2023,22:00
9.2750,9.2750,1,05-29-2023,22:00,05-29-2023,23:00
9.2950,9.2950,1,05-29-2023,23:00,05-30-2023,00:00
9.0750,9.0750,1,05-30-2023,00:00,05-30-2023,01:00
3.9360,3.9360,1,05-30-2023,01:00,05-30-2023,02:00
1.1280,1.1280,1,05-30-2023,02:00,05-30-2023,03:00
1.1750,1.1750,1,05-30-2023,03:00,05-30-2023,04:00
1.1630,1.1630,1,05-30-2023,04:00,05-30-2023,05:00
1.2020,1.2020,1,05-30-2023,05:00,05-30-2023,06:00
1.1070,1.1070,1,05-30-2023,06:00,05-30-2023,07:00
0.6610,0.6610,1,05-30-2023,07:00,05-30-2023,08:00
0.0050,0.0050,1,05-30-2023,08:00,05-30-2023,09:00
0.0,0.0,1,05-30-2023,09:00,05-30-2023,10:00
0.0,0.0,1,05-30-2023,10:00,05-30-2023,11:00
0.0,0.0,1,05-30-2023,11:00,05-30-2023,12:00
0.0,0.0,1,05-30-2023,12:00,05-30-2023,13:00
0.0,0.0,1,05-30-2023,13:00,05-30-2023,14:00
0.0,0.0,1,05-30-2023,14:00,05-30-2023,15:00
0.0,0.0,1,05-30-2023,15:00,05-30-2023,16:00
1.5880,1.5880,1,05-30-2023,16:00,05-30-2023,17:00
4.2330,4.2330,1,05-30-2023,17:00,05-30-2023,18:00
4.1590,4.1590,1,05-30-2023,18:00,05-30-2023,19:00
4.0150,4.0150,1,05-30-2023,19:00,05-30-2023,20:00
4.0140,4.0140,1,05-30-2023,20:00,05-30-2023,21:00
11.420,11.420,1,05-30-2023,21:00,05-30-2023,22:00
10.340,10.340,1,05-30-2023,22:00,05-30-2023,23:00
9.6810,9.6810,1,05-30-2023,23:00,05-31-2023,00:00
8.210,8.210,1,05-31-2023,00:00,05-31-2023,01:00
time_periods:
- name: "Time Period 1"
conditions:
# condition 1 - 4pm-8pm M-F only
- start-time:
- "16"
- "17"
- "18"
- "19"
exclude-day-of-week:
- "Sat"
- "Sun"
- name: "Time Period 2"
conditions:
# condition 1 - all hours not included in the first
- start-time:
- "00"
- "01"
- "02"
- "03"
- "04"
- "05"
- "06"
- "07"
- "08"
- "09"
- "10"
- "11"
- "12"
- "13"
- "14"
- "15"
- "20"
- "21"
- "22"
- "23"
# condition 2 - include the weekend (reverse from 1)
- start-time:
- "16"
- "17"
- "18"
- "19"
day-of-week:
- "Sat"
- "Sun"
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