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main.py
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main.py
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import datetime as dt
import glob
import math
import os
from decimal import Decimal
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import toml as toml
from dateutil import rrule
from dateutil.relativedelta import relativedelta
from dotenv import load_dotenv
from jinja2 import Environment, select_autoescape, FileSystemLoader
from plotly.subplots import make_subplots
from pyspark.sql import SparkSession, Window, DataFrame
from pyspark.sql.functions import col, sum, avg, min as ps_min, max as ps_max, to_date, regexp_replace, month, year, \
dayofmonth, dayofweek, countDistinct, when, round as ps_round, lpad, concat, lit, stddev as std
from pyspark.sql.types import DecimalType, StringType, StructType, DateType, IntegerType, StructField
import constants
from services.bank_days import BankDays
from services.esios.esios_indicator import EsiosIndicator
from services.esios.esios_price import EsiosPrice
from services.pvgis.pvgis import Pvgis
from services.rates.rate_20td_info import Rate20TDInfo
from services.rates.rate_fix_info import RateFixInfo
from services.rates.rate_wk_info import RateWKInfo
from utils import df_to_json_file
load_dotenv()
config = toml.load('config.toml')
jinja_env = Environment(loader=FileSystemLoader('templates'), autoescape=select_autoescape(['html']))
now = dt.datetime.now()
today = now.date()
TABLE_CLASSES = ('ui', 'celled', 'table', 'dt')
spark = SparkSession.builder.master('local[*]')\
.appName('energy-calc')\
.config("spark.executor.cores", 4)\
.config('spark.executor.memory', '16G')\
.config('spark.driver.memory', '16G')\
.config('spark.serializer', 'org.apache.spark.serializer.KryoSerializer')\
.config('spark.default.parallelism', '4')\
.getOrCreate()
consumption_schema = StructType([
StructField('date', DateType(), False),
StructField('method', StringType(), False),
StructField('year', IntegerType(), False),
StructField('month', IntegerType(), False),
StructField('month_text', StringType(), False),
StructField('dom', IntegerType(), False),
StructField('dow', IntegerType(), False),
StructField('dow_text', StringType(), False),
StructField('dow_order', IntegerType(), False),
StructField('hour', IntegerType(), False),
StructField('hour_consumption_kwh', DecimalType(precision=10, scale=3), False),
])
def read_consumptions() -> DataFrame:
consumption_files = glob.glob(os.path.join('docs', 'data', 'consumption', 'raw', 'y', 'consumption_raw_*.json'))
sdf = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema=consumption_schema)
if len(consumption_files) > 0:
for consumption_file in consumption_files:
consumption_file_df = pd.read_json(consumption_file, orient='split', convert_dates=False)
consumption_file_sdf = spark.createDataFrame(consumption_file_df) \
.withColumn('date', to_date(col('date'), 'yyyy-MM-dd'))\
.withColumn('year', col('year').cast(IntegerType()))\
.withColumn('month', col('month').cast(IntegerType()))\
.withColumn('dom', col('dom').cast(IntegerType()))\
.withColumn('dow', col('dow').cast(IntegerType()))\
.withColumn('dow_order', col('dow_order').cast(IntegerType()))\
.withColumn('hour', col('hour').cast(IntegerType()))\
.withColumn('hour_consumption_kwh', col('hour_consumption_kwh').cast(DecimalType(precision=10, scale=3)))
# new spark DataFrame is created in order to ensure that it has the same schema as the previous ones
sdf = sdf\
.union(spark.createDataFrame(consumption_file_sdf.toPandas(), schema=consumption_schema)) \
.cache()
print('Existing consumption rows: {rows}'.format(rows=sdf.count()))
return sdf
consumption_sdf = read_consumptions()
new_consumption_files = glob.glob(os.path.join('import', 'consumptions', '*.csv'))
if len(new_consumption_files) == 0:
new_consumption_sdf = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema=consumption_schema)
else:
new_consumption_sdf = spark.read.options(inferSchema=True, header=True, delimiter=';')\
.csv(new_consumption_files)\
.withColumnRenamed('CUPS', 'cups')\
.drop('cups')\
.withColumnRenamed('Hora', 'hour')\
.withColumn('date', to_date(col('Fecha'), "dd/MM/yyyy"))\
.drop('Fecha')\
.withColumn('hour_consumption_kwh', regexp_replace('Consumo_kWh', ',', '.').cast(DecimalType(scale=3)))\
.drop('Consumo_kWh')\
.withColumnRenamed('Metodo_obtencion', 'method')\
.withColumn('year', year('date'))\
.withColumn('month', month('date'))\
.withColumn('month_text',
when(col('month') == 1, 'Enero')
.when(col('month') == 2, 'Febrero')
.when(col('month') == 3, 'Marzo')
.when(col('month') == 4, 'Abril')
.when(col('month') == 5, 'Mayo')
.when(col('month') == 6, 'Junio')
.when(col('month') == 7, 'Julio')
.when(col('month') == 8, 'Agosto')
.when(col('month') == 9, 'Septiembre')
.when(col('month') == 10, 'Octubre')
.when(col('month') == 11, 'Noviembre')
.when(col('month') == 12, 'Diciembre'))\
.withColumn('dom', dayofmonth('date'))\
.withColumn('dow', dayofweek('date'))\
.withColumn('dow_text',
when(col('dow') == 1, 'Domingo')
.when(col('dow') == 2, 'Lunes')
.when(col('dow') == 3, 'Martes')
.when(col('dow') == 4, 'Miércoles')
.when(col('dow') == 5, 'Jueves')
.when(col('dow') == 6, 'Viernes')
.when(col('dow') == 7, 'Sábado'))\
.withColumn('dow_order',
when(col('dow') == 1, 7)
.when(col('dow') == 2, 1)
.when(col('dow') == 3, 2)
.when(col('dow') == 4, 3)
.when(col('dow') == 5, 4)
.when(col('dow') == 6, 5)
.when(col('dow') == 7, 6))
# in days with 25 hours (when there was an hour change from 03:00 to 02:00)
# the extra hour (#4) is discarded from dataset
# in days with 23 hours (when there was an hour change from 02:00 to 03:00)
# hours from #3 (included) are incremented by 1
window_date = Window.partitionBy(col('date'))
new_consumption_sdf = new_consumption_sdf\
.withColumn('hour_changed', when(ps_max('hour').over(window_date) == 25, -1)
.when(ps_max('hour').over(window_date) == 23, 1)
.otherwise(0))\
.withColumn('keep_row', when(col('hour_changed') == 0, 1)
.when((col('hour_changed') == 1), 1)
.when((col('hour_changed') == -1) & (col('hour') != 4), 1)
.otherwise(0))\
.withColumn('hour', when((col('hour_changed') == -1) & (col('hour') > 4), col('hour') - 1)
.when((col('hour_changed') == 1) & (col('hour') > 2), col('hour') + 1)
.otherwise(col('hour')))
new_consumption_sdf = new_consumption_sdf\
.filter(col('keep_row') == 1)\
.drop('hour_changed', 'keep_row')\
.select(
col('date'), col('method'),
col('year'), col('month'), col('month_text'), col('dom'), col('dow'), col('dow_text'), col('dow_order'),
col('hour'), col('hour_consumption_kwh')
).cache()
new_consumption_rows = new_consumption_sdf.count()
print('New consumption rows: {rows}'.format(rows=new_consumption_rows))
if new_consumption_rows > 0:
date_hour_window_spec = Window.partitionBy([col('date'), col('hour')])
consumption_sdf = consumption_sdf \
.withColumn('priority', lit(1))\
.union(new_consumption_sdf.withColumn('priority', lit(2)))\
.withColumn('max_priority', ps_max(col('priority')).over(date_hour_window_spec))\
.filter(col('priority') == col('max_priority'))\
.drop('max_priority', 'priority')\
.cache()
print('Consumption rows after removing duplicates: {rows}'.format(rows=consumption_sdf.count()))
consumption_years = list(consumption_sdf.select(col('year')).distinct().orderBy('year').toPandas()['year'])
for consumption_year in consumption_years:
print('DEBUG: persisting consumptions of year {year}'.format(year=consumption_year))
df_to_json_file(consumption_sdf
.filter(year('date') == consumption_year)
.orderBy(col('date'), col('hour')),
os.path.join('docs', 'data', 'consumption', 'raw', 'y', f'consumption_raw_{consumption_year}.json'), (0,))
consumption_sdf = read_consumptions()
consumption_yearmonths = list(consumption_sdf.select(concat(col('year'), lpad(col('month'), 2, '0')).alias('yearmonth')).distinct().orderBy('yearmonth').toPandas()['yearmonth'])
for consumption_yearmonth in consumption_yearmonths:
print('DEBUG: persisting consumptions of yearmonth {yearmonth}'.format(yearmonth=consumption_yearmonth))
df_to_json_file(consumption_sdf
.withColumn('yearmonth', concat(col('year'), lpad(col('month'), 2, '0')))
.filter(year('yearmonth') == consumption_yearmonth)
.orderBy(col('date'), col('hour'))
.drop('yearmonth'),
os.path.join('docs', 'data', 'consumption', 'raw', 'moy', f'consumption_raw_{consumption_yearmonth}.json'), (0,))
for new_consumption_file in new_consumption_files:
os.replace(new_consumption_file, new_consumption_file.replace(os.path.join('import', 'consumptions', 'consumption_'), os.path.join('import', 'consumptions', 'processed', 'consumption_')))
consumption_sdf.show(24)
# TODO: this line is duplicated
consumption_years = list(consumption_sdf.select(col('year')).distinct().orderBy('year').toPandas()['year'])
consumption_date_min = consumption_sdf.select(ps_min('date').alias('date_min')).first()['date_min']
consumption_date_max = consumption_sdf.select(ps_max('date').alias('date_max')).first()['date_max']
TABLE_CLASSES = ('ui', 'celled', 'table', 'dt')
MONTHS_ES_ORDER = [
'Enero', 'Febrero', 'Marzo',
'Abril', 'Mayo', 'Junio',
'Julio', 'Agosto', 'Septiembre',
'Octubre', 'Noviembre', 'Diciembre'
]
DOW_TEXT_ES_ORDER = ['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sábado', 'Domingo']
jinja_common_context = {
'version': constants.VERSION,
'contextpath': constants.CONTEXT_PATH,
'today': today.strftime('%d/%m/%Y'),
'ts': now.strftime('%Y%m%d%H%M%S'),
'date_min': consumption_date_min.strftime('%d/%m/%Y'),
'date_max': consumption_date_max.strftime('%d/%m/%Y'),
'table_classes': TABLE_CLASSES,
}
bank_days = BankDays(spark)
bank_days_sdf = bank_days.get_bank_days(consumption_years)
bank_days_sdf.show(5)
esios_price = EsiosPrice(jinja_env, jinja_common_context, spark)
price_sdf = esios_price.get_prices(consumption_date_max)
price_sdf.show(24)
price_date_min, price_date_max = esios_price.get_price_date_range()
# -------------------------------
# Yearly consumption
# -------------------------------
print('DEBUG: yearly consumption')
consumption_y = consumption_sdf\
.groupBy(col('year')) \
.agg(
ps_round(sum(col('hour_consumption_kwh')).alias('y_sum_kwh'), 2).alias('y_sum_kwh'),
countDistinct(col('month')).alias('y_months_count')
)\
.orderBy(col('year'))\
.withColumn('year', col('year').cast(StringType()))\
consumption_y_fig = px.bar(consumption_y.toPandas(),
x='year',
y='y_sum_kwh',
orientation='v',
custom_data=['y_months_count'],
labels=dict(year='Año', y_sum_kwh='Consumo (kWh)', y_months_count='Meses'))
consumption_y_fig.update_traces(hovertemplate='Año=%{x}<br>Consumo (kWh)=%{y}<br>%{customdata[0]} meses')
consumption_y_fig.update_layout(xaxis={'title': None})
consumption_y_fig_html = consumption_y_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_y_fig')
df_to_json_file(consumption_y, os.path.join('docs', 'data', 'consumption', 'consumption_y.json'))
# -------------------------------
# Monthly consumption
# -------------------------------
print('DEBUG: monthly consumption')
consumption_moy = consumption_sdf\
.groupBy(col('year'), col('month'))\
.agg(
sum(col('hour_consumption_kwh')).alias('moy_sum_kwh'),
ps_min(col('month_text')).alias('month_text')
)\
consumption_moy_evol_full = consumption_moy\
.orderBy(col('year'), col('month'))\
.withColumn('month_year', concat(lpad(col('month'), 2, '0'), lit('-'), col('year')))\
.drop('month', 'year')\
.select('month_year', 'moy_sum_kwh')
consumption_moy_evol_full_fig = px.line(consumption_moy_evol_full.toPandas(),
x='month_year',
y='moy_sum_kwh',
labels=dict(month_year='Mes/Año', moy_sum_kwh='Consumo (kWh)'))
consumption_moy_evol_full_fig.update_layout(xaxis={'title': None})
consumption_moy_evol_full_fig.update_xaxes(tickangle=90)
consumption_moy_evol_comp = consumption_moy\
.orderBy(col('year'), col('month'))\
.drop('month')\
.withColumnRenamed('month_text', 'month')\
.withColumn('year', col('year').cast(StringType()))\
.select('year', 'month', 'moy_sum_kwh')
consumption_moy_comp_fig = px.bar(consumption_moy_evol_comp.toPandas(),
x='year',
y='moy_sum_kwh',
facet_col='month',
facet_col_wrap=3,
facet_col_spacing=0.02,
facet_row_spacing=0.10,
color='year',
category_orders={'month': constants.MONTHS_ES_ORDER},
labels=dict(year='Año', moy_sum_kwh='Consumo (kWh)', month='Mes'))
consumption_moy_comp_fig.for_each_annotation(lambda a: a.update(text=a.text.replace("Mes=", "")))
for axis in consumption_moy_comp_fig.layout:
if type(consumption_moy_comp_fig.layout[axis]) == go.layout.XAxis:
consumption_moy_comp_fig.layout[axis].title.text = ''
consumption_moy_evol_year_fig = px.line(consumption_moy_evol_comp.toPandas(),
x='month',
y='moy_sum_kwh',
color='year',
category_orders={'month': constants.MONTHS_ES_ORDER},
labels=dict(month='Mes', moy_sum_kwh='Consumo (kWh)', year='Año'))
consumption_moy_evol_year_fig.update_layout(xaxis={'title': None})
consumption_moy_evol_year_fig.update_xaxes(tickangle=90)
consumption_moy_evol_full_fig_html = consumption_moy_evol_full_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_moy_evolution_full_figure')
consumption_moy_evol_year_fig_html = consumption_moy_evol_year_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_moy_evolution_per_year_figure')
consumption_moy_comp_fig_html = consumption_moy_comp_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_moy_comparison_per_month_year_figure', default_height='800px')
df_to_json_file(consumption_moy_evol_comp, os.path.join('docs', 'data', 'consumption', 'consumption_moy_evol_comp.json'))
df_to_json_file(consumption_moy_evol_full, os.path.join('docs', 'data', 'consumption', 'consumption_moy_evol_full.json'))
# -------------------------------
# Day of week consumption
# -------------------------------
print('DEBUG: day of week consumption')
consumption_dom_sum_base = consumption_sdf\
.groupBy(col('year'), col('month'), col('dom'))\
.agg(
sum(col('hour_consumption_kwh')).alias('dom_consumption_kwh'),
ps_min(col('month_text')).alias('month_text'),
ps_min(col('dow_text')).alias('dow_text'),
ps_min(col('dow_order')).alias('dow_order'),
)\
.orderBy(col('year'), col('month'), col('dom'))\
consumption_dow_sum = consumption_dom_sum_base \
.groupBy(col('year'), col('month'), col('dow_text'))\
.agg(
ps_round(sum(col('dom_consumption_kwh')), 3).alias('dow_sum_kwh'),
ps_min(col('month_text')).alias('month_text'),
ps_min(col('dow_order')).alias('dow_order'),
)\
.orderBy(col('year'), col('month'), col('dow_order'))\
.drop('month')\
.withColumnRenamed('month_text', 'month')\
.withColumn('year', col('year').cast(StringType()))\
.drop('dow')\
.withColumnRenamed('dow_text', 'dow')
consumption_dow_sum_fig = px.line(consumption_dow_sum.toPandas(),
x='dow',
y='dow_sum_kwh',
color='year',
facet_col='month',
facet_col_wrap=3,
facet_col_spacing=0.02,
facet_row_spacing=0.10,
category_orders={'month': constants.MONTHS_ES_ORDER, 'dow': constants.DOW_TEXT_ES_ORDER},
labels=dict(dow='Día', dow_sum_kwh='Consumo (kWh)', year='Año', month='Mes'))
consumption_dow_sum_fig.for_each_annotation(lambda a: a.update(text=a.text.replace("Mes=", "")))
consumption_dow_sum_fig.update_xaxes(tickangle=90)
for axis in consumption_dow_sum_fig.layout:
if type(consumption_dow_sum_fig.layout[axis]) == go.layout.XAxis:
consumption_dow_sum_fig.layout[axis].title.text = ''
consumption_dow_sum_fig_html = consumption_dow_sum_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_dow_sum_figure', default_height='800px')
consumption_dow_avg = consumption_dom_sum_base\
.groupBy(col('year'), col('month'), col('dow_text')) \
.agg(
ps_round(avg(col('dom_consumption_kwh')), 3).alias('dow_avg_kwh'),
ps_min(col('month_text')).alias('month_text'),
ps_min(col('dow_order')).alias('dow_order'),
)\
.orderBy(col('year'), col('month'), col('dow_order'))\
.drop('month')\
.withColumnRenamed('month_text', 'month')\
.withColumn('year', col('year').cast(StringType()))\
.drop('dow')\
.withColumnRenamed('dow_text', 'dow')
consumption_dow_avg_fig = px.line(consumption_dow_avg.toPandas(),
x='dow',
y='dow_avg_kwh',
color='year',
facet_col='month',
facet_col_wrap=3,
facet_col_spacing=0.02,
facet_row_spacing=0.10,
category_orders={'month': constants.MONTHS_ES_ORDER, 'dow': constants.DOW_TEXT_ES_ORDER},
labels=dict(dow='Día', dow_avg_kwh='Consumo medio (kWh)', year='Año', month='Mes'))
consumption_dow_avg_fig.for_each_annotation(lambda a: a.update(text=a.text.replace("Mes=", "")))
consumption_dow_avg_fig.update_xaxes(tickangle=90)
for axis in consumption_dow_avg_fig.layout:
if type(consumption_dow_avg_fig.layout[axis]) == go.layout.XAxis:
consumption_dow_avg_fig.layout[axis].title.text = ''
consumption_dow_avg_fig_html = consumption_dow_avg_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_dow_avg_figure', default_height='900px')
df_to_json_file(consumption_dow_sum.select('year', 'month', 'dow', 'dow_sum_kwh'), os.path.join('docs', 'data', 'consumption', 'consumption_dow_sum.json'))
df_to_json_file(consumption_dow_avg.select('year', 'month', 'dow', 'dow_avg_kwh'), os.path.join('docs', 'data', 'consumption', 'consumption_dow_avg.json'))
# -------------------------------
# Hour of day consumption
# -------------------------------
print('DEBUG: hour of day consumption')
consumption_hod_avg = consumption_sdf\
.groupBy(col('year'), col('month'), col('hour'))\
.agg(
ps_round(avg(col('hour_consumption_kwh')), 3).alias('hod_avg_kwh'),
ps_min(col('month_text')).alias('month_text')
)\
.orderBy(col('year'), col('month'), col('hour'))\
.drop('month')\
.withColumnRenamed('month_text', 'month')\
.withColumn('year', col('year').cast(StringType()))\
.withColumn('hour', col('hour').cast(StringType()))
consumption_hod_avg_fig = px.line(consumption_hod_avg.toPandas(),
x='hour',
y='hod_avg_kwh',
facet_col='month',
facet_col_wrap=3,
facet_col_spacing=0.02,
facet_row_spacing=0.05,
color='year',
category_orders={'month': constants.MONTHS_ES_ORDER},
labels=dict(hour='Hora', hod_avg_kwh='Consumo medio (kWh)', year='Año', month='Mes'))
consumption_hod_avg_fig.for_each_annotation(lambda a: a.update(text=a.text.replace("Mes=", "")))
consumption_hod_avg_fig.update_xaxes(tickangle=90)
for axis in consumption_hod_avg_fig.layout:
if type(consumption_hod_avg_fig.layout[axis]) == go.layout.XAxis:
consumption_hod_avg_fig.layout[axis].title.text = ''
consumption_hod_avg_fig_html = consumption_hod_avg_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_hod_avg_figure', default_height='900px')
consumption_hod_sum = consumption_sdf\
.groupBy(col('year'), col('month'), col('hour'))\
.agg(
ps_round(sum(col('hour_consumption_kwh')), 3).alias('hod_sum_kwh'),
ps_min(col('month_text')).alias('month_text')
)\
.orderBy(col('year'), col('month'), col('hour'))\
.drop('month')\
.withColumnRenamed('month_text', 'month')\
.withColumn('year', col('year').cast(StringType()))\
.withColumn('hour', col('hour').cast(StringType()))
consumption_hod_sum_fig = px.line(consumption_hod_sum.toPandas(),
x='hour',
y='hod_sum_kwh',
facet_col='month',
facet_col_wrap=3,
facet_col_spacing=0.02,
facet_row_spacing=0.05,
color='year',
category_orders={'month': constants.MONTHS_ES_ORDER},
labels=dict(hour='Hora', hod_sum_kwh='Consumo (kWh)', year='Año', month='Mes'))
consumption_hod_sum_fig.for_each_annotation(lambda a: a.update(text=a.text.replace("Mes=", "")))
consumption_hod_sum_fig.update_xaxes(tickangle=90)
for axis in consumption_hod_sum_fig.layout:
if type(consumption_hod_sum_fig.layout[axis]) == go.layout.XAxis:
consumption_hod_sum_fig.layout[axis].title.text = ''
consumption_hod_sum_fig_html = consumption_hod_sum_fig.to_html(include_plotlyjs=False, full_html=False, div_id='consumption_dow_sum_figure', default_height='900px')
df_to_json_file(consumption_hod_avg.select('year', 'month', 'hour', 'hod_avg_kwh'), os.path.join('docs', 'data', 'consumption', 'consumption_hod_avg.json'))
df_to_json_file(consumption_hod_sum.select('year', 'month', 'hour', 'hod_sum_kwh'), os.path.join('docs', 'data', 'consumption', 'consumption_hod_sum.json'))
# -------------------------------
# Rate periods monthly
# -------------------------------
print('DEBUG: rate periods monthly consumption')
rate_period_from = consumption_date_max - relativedelta(years=1)
rate_period_from = max(price_date_min, consumption_date_min, rate_period_from)
# 2.0TD
rate_20td_info = Rate20TDInfo()
year_month_window_spec = Window.partitionBy([col('year'), col('month')])
rate_20td_m_periods_data = consumption_sdf\
.filter(col('date') > rate_period_from)\
.join(bank_days_sdf
.select(col('year'), col('month'), col('dom'), lit(True).alias('is_bank_day')),
on=['year', 'month', 'dom'], how='left')\
.fillna(False, subset=['is_bank_day'])\
.withColumn('period', rate_20td_info.get_period('hour', 'dow', 'is_bank_day'))\
.groupBy(col('year'), col('month'), col('period'))\
.agg(
sum(col('hour_consumption_kwh')).alias('period_kwh')
)\
.orderBy(col('year').desc(), col('month').desc(), col('period'))\
.withColumn('month_kwh', sum(col('period_kwh')).over(year_month_window_spec))\
.withColumn('period_pct', ps_round(col('period_kwh') / col('month_kwh') * 100, 2))\
.drop('month_kwh')\
.orderBy(col('year'), col('month'), col('period'))\
.withColumn('month_year', concat(lpad(col('month'), 2, '0'), lit('-'), col('year')))\
rate_20td_m_periods = rate_20td_m_periods_data\
.drop('month', 'year') \
.select('month_year', 'period', 'period_kwh', 'period_pct')
rate_20td_m_periods_fig = px.line(rate_20td_m_periods.toPandas(),
x='month_year',
y='period_kwh',
color='period',
color_discrete_sequence=rate_20td_info.get_color_sequence(),
hover_data=['period_pct'],
labels=dict(month_year='Mes/Año',
period_kwh='Consumo (kWh)',
period='Periodo',
period_pct='% Periodo'))
rate_20td_m_periods_fig.update_layout(xaxis={'title': None})
rate_20td_m_periods_fig.update_xaxes(tickangle=90)
rate_20td_m_periods_fig_html = rate_20td_m_periods_fig.to_html(include_plotlyjs=False, full_html=False, div_id='rate_20td_m_periods_figure')
rate_20td_m_periods_pct_fig = px.line(rate_20td_m_periods.toPandas(),
x='month_year',
y='period_pct',
color='period',
color_discrete_sequence=rate_20td_info.get_color_sequence(),
hover_data=['period_kwh'],
labels=dict(month_year='Mes/Año',
period_kwh='Consumo (kWh)',
period='Periodo',
period_pct='% Periodo'))
rate_20td_m_periods_pct_fig.update_layout(xaxis={'title': None})
rate_20td_m_periods_pct_fig.update_xaxes(tickangle=90)
rate_20td_m_periods_pct_fig_html = rate_20td_m_periods_pct_fig.to_html(include_plotlyjs=False, full_html=False, div_id='rate_20td_m_periods_pct_figure')
# Weekend
rate_wk_info = RateWKInfo()
rate_wk_m_periods_data = consumption_sdf \
.filter(col('date') > rate_period_from) \
.join(bank_days_sdf
.select(col('year'), col('month'), col('dom'), lit(True).alias('is_bank_day')),
on=['year', 'month', 'dom'], how='left')\
.fillna(False, subset=['is_bank_day'])\
.withColumn('period', rate_wk_info.get_period('hour', 'dow', 'is_bank_day'))\
.groupBy(col('year'), col('month'), col('period'))\
.agg(
sum(col('hour_consumption_kwh')).alias('period_kwh')
)\
.orderBy(col('year').desc(), col('month').desc(), col('period'))\
.withColumn('month_kwh', sum(col('period_kwh')).over(year_month_window_spec))\
.withColumn('period_pct', ps_round(col('period_kwh') / col('month_kwh') * 100, 2))\
.drop('month_kwh')\
.orderBy(col('year'), col('month'), col('period'))\
.withColumn('month_year', concat(lpad(col('month'), 2, '0'), lit('-'), col('year')))\
rate_wk_m_periods = rate_wk_m_periods_data\
.drop('month', 'year')\
.select('month_year', 'period', 'period_kwh', 'period_pct')
rate_wk_m_periods_fig = px.line(rate_wk_m_periods.toPandas(),
x='month_year',
y='period_kwh',
color='period',
color_discrete_sequence=rate_wk_info.get_color_sequence(),
hover_data=['period_pct'],
labels=dict(month_year='Mes/Año',
period_kwh='Consumo (kWh)',
period='Periodo',
period_pct='% Periodo'))
rate_wk_m_periods_fig.update_layout(xaxis={'title': None})
rate_wk_m_periods_fig.update_xaxes(tickangle=90)
rate_wk_m_periods_fig_html = rate_wk_m_periods_fig.to_html(include_plotlyjs=False, full_html=False, div_id='rate_wk_m_periods_figure')
rate_wk_m_periods_pct_fig = px.line(rate_wk_m_periods.toPandas(),
x='month_year',
y='period_pct',
color='period',
color_discrete_sequence=rate_wk_info.get_color_sequence(),
hover_data=['period_kwh'],
labels=dict(month_year='Mes/Año',
period_kwh='Consumo (kWh)',
period='Periodo',
period_pct='% Periodo'))
rate_wk_m_periods_pct_fig.update_layout(xaxis={'title': None})
rate_wk_m_periods_pct_fig.update_xaxes(tickangle=90)
rate_wk_m_periods_pct_fig_html = rate_wk_m_periods_pct_fig.to_html(include_plotlyjs=False, full_html=False, div_id='rate_wk_m_periods_pct_figure')
# Fix
rate_fix_info = RateFixInfo()
rate_fix_m_periods_data = consumption_sdf \
.filter(col('date') > rate_period_from) \
.join(bank_days_sdf
.select(col('year'), col('month'), col('dom'), lit(True).alias('is_bank_day')),
on=['year', 'month', 'dom'], how='left')\
.fillna(False, subset=['is_bank_day'])\
.withColumn('period', rate_fix_info.get_period('hour', 'dow', 'is_bank_day'))\
.groupBy(col('year'), col('month'), col('period'))\
.agg(
sum(col('hour_consumption_kwh')).alias('period_kwh')
)\
.orderBy(col('year').desc(), col('month').desc(), col('period'))\
.orderBy(col('year'), col('month'), col('period'))\
.withColumn('month_year', concat(lpad(col('month'), 2, '0'), lit('-'), col('year')))\
rate_fix_m_periods = rate_fix_m_periods_data\
.drop('month', 'year')\
.select('month_year', 'period', 'period_kwh')
rate_fix_m_periods_fig = px.line(rate_fix_m_periods.toPandas(),
x='month_year',
y='period_kwh',
color='period',
color_discrete_sequence=rate_fix_info.get_color_sequence(),
labels=dict(month_year='Mes/Año',
period_kwh='Consumo (kWh)',
period='Periodo'))
rate_fix_m_periods_fig.update_layout(xaxis={'title': None})
rate_fix_m_periods_fig.update_xaxes(tickangle=90)
rate_fix_m_periods_fig_html = rate_fix_m_periods_fig.to_html(include_plotlyjs=False, full_html=False, div_id='rate_fix_m_periods_figure')
df_to_json_file(rate_20td_m_periods, os.path.join('docs', 'data', 'consumption', 'rate_20td_m_periods.json'))
df_to_json_file(rate_wk_m_periods, os.path.join('docs', 'data', 'consumption', 'rate_wk_m_periods.json'))
df_to_json_file(rate_fix_m_periods, os.path.join('docs', 'data', 'consumption', 'rate_fix_m_periods.json'))
# -------------------------------
# Day of month consumption
# -------------------------------
print('DEBUG: day of month consumption')
# -------------------------------
# Costs
# -------------------------------
print('DEBUG: costs')
print(f"DEBUG: consumption rows: {consumption_sdf.filter(col('date') > rate_period_from).count()}")
consumption_with_price_sdf = consumption_sdf\
.filter(col('date') > rate_period_from)\
.join(price_sdf.select(col('year'), col('month'), col('dom'), col('hour'),
col('hour_pricebuy_ekwh'), col('hour_pricesell_ekwh'),
col('month_avg_pricebuy_ekwh'), col('month_avg_pricesell_ekwh')),
on=['year', 'month', 'dom', 'hour'], how='left')\
.cache()
print(f'DEBUG: consumption rows (including price): {consumption_with_price_sdf.count()}')
rate_20td_pvpc_cost = consumption_with_price_sdf\
.withColumn('hour_cost', ps_round(col('hour_consumption_kwh') * col('hour_pricebuy_ekwh'), 5))\
.groupBy(col('year'), col('month'))\
.agg(sum(col('hour_cost')).alias('month_cost'))\
.orderBy(col('year'), col('month'))\
.select('month_cost')
rate_20td_pvpc_cost_data = {
'title': 'PVPC',
'values': [float(n) for n in list(rate_20td_pvpc_cost.toPandas()['month_cost'])]
}
month_year_list = [dt_i.strftime('%m-%Y') for dt_i in rrule.rrule(rrule.MONTHLY, dtstart=rate_period_from, until=consumption_date_max)]
# -------------------------------
# self-supply
# -------------------------------
print('DEBUG: self-supply')
consumption_with_price_and_bankdays_sdf = consumption_with_price_sdf\
.join(bank_days_sdf
.select(col('year'), col('month'), col('dom'), lit(True).alias('is_bank_day')),
on=['year', 'month', 'dom'], how='left')\
.fillna(False, subset=['is_bank_day'])
pvgis = Pvgis(config['pvgis'], spark, consumption_with_price_and_bankdays_sdf, consumption_date_min, consumption_date_max, [rate_20td_info, rate_wk_info, rate_fix_info])
has_calibrations = pvgis.has_calibrations()
jinja_common_context['has_calibrations'] = has_calibrations
if has_calibrations:
# angle calibration
print('DEBUG: angle calibration')
angle_calibration_m_fig_html = pvgis.get_monthly_calibration_figure_html('angle')
angle_calibration_y_fig_html = pvgis.get_yearly_calibration_figure_html('angle')
# aspect calibration
print('DEBUG: aspect calibration')
aspect_calibration_m_fig_html = pvgis.get_monthly_calibration_figure_html('aspect')
aspect_calibration_y_fig_html = pvgis.get_yearly_calibration_figure_html('aspect')
# angle+aspect calibration
print('DEBUG: angle+aspect calibration')
angle_aspect_calibration_y_fig_html = pvgis.get_yearly_calibration_figure_html('angle+aspect')
with open('config.toml', 'w', encoding='utf-8') as config_file:
toml.dump(config, config_file)
# -------------------------------
# production estimation
# -------------------------------
print('DEBUG: production estimation')
production_estimation_configurations = pvgis.get_production_estimation_configurations()
# TODO: delete this
# best_production_estimation_m_sdf = pvgis.get_best_production_estimation()
#
# best_production_estimation_m_df = best_production_estimation_m_sdf.toPandas()
#
# x = [
# list(best_production_estimation_m_df['month']),
# list(best_production_estimation_m_df['name']),
# ]
#
# production_estimation_m_fig = go.Figure()
# production_estimation_m_fig.add_trace(go.Bar(x=x, y=list(best_production_estimation_m_df['month_selfsupply_kwh']), name='Autoconsumo'))
# production_estimation_m_fig.add_trace(go.Bar(x=x, y=list(best_production_estimation_m_df['month_finalconsumption_kwh']), name='Consumo'))
# production_estimation_m_fig.add_trace(go.Bar(x=x, y=list(best_production_estimation_m_df['month_exceedingsold_simplified_kwh']), name='Exceso Vendido'))
# production_estimation_m_fig.add_trace(go.Bar(x=x, y=list(best_production_estimation_m_df['month_exceedingwasted_simplified_kwh']), name='Exceso Regalado'))
#
# production_estimation_m_fig.update_xaxes(tickangle=90)
# production_estimation_m_fig.update_layout(barmode='relative', legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1))
#
# production_estimation_m_fig_html = production_estimation_m_fig.to_html(include_plotlyjs=False, full_html=False, div_id='production_estimation_m_figure', default_height='800px')
# ----------------------------------------------------------------------------------------------------------------------
# -------------------------------
# Configuration
# -------------------------------
print('DEBUG: configuration')
# -------------------------------
# Data
# -------------------------------
# Esios Indicators
print('DEBUG: esios indicators')
EsiosIndicator(jinja_env, jinja_common_context).refresh_esios_indicators()
# Esios price
print('DEBUG: esios price')
price_period_from = price_date_max - relativedelta(years=1)
price_period_from = max(price_date_min, price_period_from)
esios_price_years = list(price_sdf.select(col('year')).distinct().orderBy('year').toPandas()['year'])
price_pvpc_evol = price_sdf\
.filter(col('date') >= price_period_from)\
.orderBy(col('date'))\
.cache()
# Esios Buy Price
print('DEBUG: esios buy price')
price_rate_figure_ids = {}
price_dates_sdf = price_pvpc_evol\
.select(col('date'))\
.distinct()\
.orderBy(col('date'))\
.cache()
price_buy_pvpc_evol_fig = px.line(price_pvpc_evol.toPandas(),
x='datetime',
y='hour_pricebuy_ekwh',
labels=dict(date='Fecha',
hour_pricebuy_ekwh='Precio (€/kWh)'))
price_buy_pvpc_evol_fig.update_layout(xaxis={'title': None})
price_buy_pvpc_evol_fig.update_xaxes(
tickangle=90,
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=3, label="3m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=9, label="9m", step="month", stepmode="backward"),
dict(label="12m", step="all")
])
))
price_buy_pvpc_evol_fig_html = price_buy_pvpc_evol_fig.to_html(include_plotlyjs=False, full_html=False, div_id='price_pvpc_evol_figure')
def missing_value(x):
return not x or math.isnan(x)
rate_info_list = [rate_20td_info, rate_wk_info, rate_fix_info]
html_template_data = {}
price_evol_with_bank_days_sdf = price_pvpc_evol\
.join(bank_days_sdf
.select(col('year'), col('month'), col('dom'), lit(True).alias('is_bank_day')),
on=['year', 'month', 'dom'], how='left')\
.fillna(False, subset=['is_bank_day'])\
.cache()
for rate_info in rate_info_list:
print('DEBUG: {rate_type}'.format(rate_type=rate_info.get_rate_type()))
price_pvpc_rate_evol = price_evol_with_bank_days_sdf\
.withColumn('period', rate_info.get_period('hour', 'dow', 'is_bank_day'))\
.groupBy(col('date'), col('period'))\
.agg(
ps_round(avg(col('hour_pricebuy_ekwh')), 3).alias('avg_period_kwh'),
ps_round(std(col('hour_pricebuy_ekwh')), 3).alias('std_period_kwh')
)\
.withColumn('avg_period_kwh', col('avg_period_kwh').cast(DecimalType(10, 3)))\
.withColumn('std_period_kwh', col('std_period_kwh').cast(DecimalType(10, 3)))\
.withColumn('upp_period_kwh', col('avg_period_kwh') + col('std_period_kwh'))\
.withColumn('low_period_kwh', col('avg_period_kwh') - col('std_period_kwh'))\
.orderBy('date', 'period')\
.cache()
rate_period_list = rate_info.get_periods()
price_pvpc_rate_evol_fig = make_subplots(rows=len(rate_period_list), cols=1,
vertical_spacing=0.2,
row_titles=rate_info.get_periods())
for (rate_i, rate_period) in enumerate(rate_period_list):
row = rate_i + 1
price_pvpc_rate_period_evol = price_dates_sdf\
.join(price_pvpc_rate_evol.filter(col('period') == rate_period), on='date', how='left')\
.fillna(rate_period, subset=['period'])\
.toPandas()
price_pvpc_rate_period_x = list(price_pvpc_rate_period_evol['date'])
price_pvpc_rate_period_y = list(price_pvpc_rate_period_evol['avg_period_kwh'])
price_pvpc_rate_period_y_upper = list(price_pvpc_rate_period_evol['upp_period_kwh'])
price_pvpc_rate_period_y_lower = list(price_pvpc_rate_period_evol['low_period_kwh'])
price_pvpc_rate_evol_fig.add_trace(go.Scatter(
name=rate_period,
x=price_pvpc_rate_period_x,
y=price_pvpc_rate_period_y,
mode='lines',
line=dict(color=rate_info.get_period_color_rgba(rate_period)),
showlegend=True,
connectgaps=False,
customdata=np.dstack((price_pvpc_rate_period_y_lower, price_pvpc_rate_period_y_upper))[0],
hovertemplate='Fecha=%{x}<br>Precio (\u20ac/kWh)=[%{customdata[0]} > %{y} < %{customdata[1]}]'
), row=row, col=1)
filling_x = price_pvpc_rate_period_x + price_pvpc_rate_period_x[::-1]
filling_y = price_pvpc_rate_period_y_upper + price_pvpc_rate_period_y_lower[::-1]
gaps = []
for (prev_pos, curr) in enumerate(filling_y[1:]):
prev = filling_y[prev_pos]
pos = prev_pos + 1
if not missing_value(prev) and missing_value(curr):
gaps.append(dict(pos=pos, date=filling_x[prev_pos]))
elif missing_value(prev) and not missing_value(curr):
gaps.append(dict(pos=pos, date=filling_x[pos]))
for (gap_i, gap) in enumerate(gaps):
filling_x.insert(gap['pos'] + gap_i, gap['date'])
filling_y.insert(gap['pos'] + gap_i, Decimal('0.0'))
filling_y = [y if y and not math.isnan(y) else Decimal('0.0') for y in filling_y]
price_pvpc_rate_evol_fig.add_trace(go.Scatter(
name=f'{rate_period} (±SD)',
x=filling_x,
y=filling_y,
fill='toself',
fillcolor=rate_info.get_period_color_rgba(rate_period, 0.2),
line=dict(width=0),
showlegend=True,
hoverinfo='skip',
connectgaps=True
), row=row, col=1)
if row > 1:
price_pvpc_rate_evol_fig.update_yaxes(matches='y', row=row, col=1)
price_pvpc_rate_evol_fig.update_xaxes(matches='x', row=row, col=1)
price_pvpc_rate_evol_fig.update_layout(**{f'yaxis{row}': {'title': 'Precio medio (€/kWh)'}})
price_pvpc_rate_evol_fig.update_layout(xaxis={'title': None}, yaxis={'title': 'Precio medio (€/kWh)'}, legend=dict(title='Periodo'), hovermode='x')
price_pvpc_rate_evol_fig.update_xaxes(
tickangle=90,
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=3, label="3m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=9, label="9m", step="month", stepmode="backward"),
dict(label="12m", step="all")
])
)
)
figure_id = f'price_pvpc_{rate_info.get_rate_type()}_evol_figure'
price_rate_figure_ids[rate_info.get_rate_type()] = figure_id
price_pvpc_rate_evol_fig_html = price_pvpc_rate_evol_fig.to_html(include_plotlyjs=False,
full_html=False,
div_id=figure_id,
default_height=f'{len(rate_period_list) * 400}px')
html_template_data[f'price_pvpc_{rate_info.get_rate_type()}_evol_fig'] = price_pvpc_rate_evol_fig_html
html_template_data[f'rate_{rate_info.get_rate_type()}_info_series'] = rate_wk_info.get_series()
html_template_data[f'rate_{rate_info.get_rate_type()}_info_periods'] = rate_wk_info.get_periods()
price_pvpc_rate_evol.unpersist()
price_evol_with_bank_days_sdf.unpersist()
price_dates_sdf.unpersist()
# Esios sell price
print('DEBUG: esios sell price')
price_sell_pvpc_evol_fig = px.line(price_pvpc_evol.toPandas(),
x='datetime',
y='hour_pricesell_ekwh',
labels=dict(date='Fecha',
hour_pricesell_ekwh='Precio (€/kWh)'))
price_sell_pvpc_evol_fig.update_layout(xaxis={'title': None})
price_sell_pvpc_evol_fig.update_xaxes(
tickangle=90,
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=3, label="3m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=9, label="9m", step="month", stepmode="backward"),
dict(label="12m", step="all")
])
))
price_sell_pvpc_evol_fig_html = price_sell_pvpc_evol_fig.to_html(include_plotlyjs=False, full_html=False, div_id='price_pvpc_evol_figure')
price_pvpc_evol_m_sdf = price_pvpc_evol\
.groupBy(col('year'), col('month'))\
.agg(
ps_round(avg(col('hour_pricesell_ekwh')).alias('avg_price_sell_kwh'), 3).alias('avg_price_sell_kwh'),
ps_round(std(col('hour_pricesell_ekwh')).alias('std_price_sell_kwh'), 3).alias('std_price_sell_kwh'),
)\
.withColumn('avg_price_sell_kwh', col('avg_price_sell_kwh').cast(DecimalType(10, 3)))\
.withColumn('std_price_sell_kwh', col('std_price_sell_kwh').cast(DecimalType(10, 3)))\
.withColumn('upp_price_sell_kwh', col('avg_price_sell_kwh') + col('std_price_sell_kwh'))\
.withColumn('low_price_sell_kwh', col('avg_price_sell_kwh') - col('std_price_sell_kwh'))\
.orderBy(col('year'), col('month'))\
.withColumn('month_year', concat(lpad(col('month'), 2, '0'), lit('-'), col('year')))\
.drop('month', 'year')\
price_pvpc_evol_m_df = price_pvpc_evol_m_sdf.toPandas()
price_sell_pvpc_evol_m_x = list(price_pvpc_evol_m_df['month_year'])