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a2ei_grid_analysis.py
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a2ei_grid_analysis.py
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# cd Documents/git/a2ei_nigeria_grid_analysis/ ; streamlit run a2ei_grid_analysis.py
from sqlalchemy import create_engine
from datetime import datetime
import numpy as np
import os
import pandas as pd
from plotly_calplot import calplot
# import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import streamlit as st
def data_prep(df):
df = df.sort_values(by=['time'])
df['time'] = pd.to_datetime(df['time'])
df.set_index(df['time'], inplace=True)
return df
# GLOBAL VARIABLES
db_select = "aws"
# db_select = "postgres"
db = st.secrets[db_select]
os.environ['lang'] = st.secrets[db_select]['lang']
# conn = create_engine(
# f"{db['lang']}://{db['user']}:{db['password']}@{db['host']}/{db['dbname']}")
conn = create_engine(
f"{db['lang']}://{db['user']}:{db['password']}@{db['host']}/{db['dbname']}")
# conn = create_engine(
# f"{**st.secrets[db_select]['lang']}://{**st.secrets[db_select]['user']}:{**st.secrets[db_select]['password']}@{**st.secrets[db_select]['host']}/{**st.secrets[db_select]['dbname']}")
today = datetime.today()
# PAGE
st.set_page_config(
page_title="A2EI Grid Analysis",
page_icon=":electric_plug:",
initial_sidebar_state="expanded",
layout="wide",
)
# SIDEBAR
st.sidebar.image("images/A2EI_horizontal_white_RGB.png",
use_column_width=True) # logo
st.sidebar.text("")
st.sidebar.text("")
cust = pd.read_sql(f''' SELECT
aam_id,
country,
location,
latitude,
longitude,
cust_type,
bat_size,
inv_size,
mcu_version,
installation_date,
installation_comp
FROM
skgs_customers
''', conn)
aam_id = st.sidebar.selectbox('AAM ID', cust.aam_id)
time_start = st.sidebar.date_input('Start', value=(datetime(2022, 3, 1)))
time_end = st.sidebar.date_input('End', today)
rng = [time_start, time_end]
# IMPORT DATA
df = pd.read_sql(f''' SELECT
time + interval '1 hour' AS time
,input_voltage
,peak_voltage
,output_voltage
FROM
skgsp2_logs
WHERE
aam_id = {aam_id} AND
time between \'{rng[0]}\' and \'{rng[1]}\'
ORDER BY time
''', conn)
data_density = pd.read_sql(f''' SELECT
time + interval '1 hour' AS time
,input_voltage
,peak_voltage
FROM
skgsp2_logs
WHERE
aam_id = {aam_id} AND
time between '2021-01-01' and '2022-12-01'
ORDER BY time
''', conn)
data_density = data_prep(data_density)
# META DATA
st.sidebar.text("")
st.sidebar.text("")
st.sidebar.markdown(
f'<p style="color:#FFFFFF;font-size: 25px;text-align:center;">Meta Data AAM <strong>{aam_id}</strong> <br> </p>', unsafe_allow_html=True)
st.sidebar.markdown(
f'<p style="color:#FFFFFF;font-size: 20px;">{cust.country[0]} <br> Version {cust.mcu_version[0]} <br> Battery {cust.bat_size[0]} Ah <br> Inverter {cust.inv_size[0]} W <br > Installed on {cust.installation_date[0].strftime("%Y-%m-%d")} <br> Installed by {cust.installation_comp[0]} </p>', unsafe_allow_html=True)
grid_sensor = st.sidebar.selectbox(
"Grid measured by:",
('Inverter', 'MCU'))
# DATA PREPARATION
df = data_prep(df)
# df_grid
df_grid = pd.DataFrame(columns=['input_voltage', 'output_voltage', 'input_voltage_0tonan',
'output_voltage_5tonan', 'grid_avl', 'grid_on_time', 'grid_off_time', 'aam_on_time', 'aam_off_time'])
if grid_sensor == 'Inverter':
df_grid['input_voltage'] = df['input_voltage']
else:
df_grid['input_voltage'] = df['peak_voltage']
df_grid['output_voltage'] = df['output_voltage']
df_grid['input_voltage_0tonan'].mask(
df_grid['input_voltage'] > 0, df_grid['input_voltage'], inplace=True) # takes out the 0V data points
df_grid['output_voltage_5tonan'].mask(
df_grid['output_voltage'] > 5, df_grid['output_voltage'], inplace=True) # takes out the 5V data points
# drop nan rows in df_grid
df_grid.dropna(subset=['input_voltage'], inplace=True)
df_grid['grid_avl'].mask(df['input_voltage'] > 0, 1, inplace=True)
df_grid['grid_avl'].mask(df['input_voltage'] == 0, 0, inplace=True)
# df_grid['grid_avl']=df_grid['grid_avl'].fillna(0)#make all nan to 0 or BL
# writes 5 min in the dataset when grid voltage was higher or lower than 180V
# inplace overwrite the original data frame
df_grid['grid_on_time'].mask(df_grid['grid_avl'] > 0, 5, inplace=True)
# df_grid['grid_on_time'].replace(np.nan,0 ,inplace=True) #make all nan to 0 or BL
df_grid['grid_off_time'].mask(df_grid['grid_avl'] == 0, 5, inplace=True)
df_grid['aam_on_time'].mask(df['input_voltage'] >= 0, 5, inplace=True)
df_grid['aam_off_time'].mask(df['output_voltage'] <= 10, 5, inplace=True)
df_grid['counter_evt'] = df_grid['grid_avl'].diff().ne(0).cumsum()
df_grid['counter_bl'] = df_grid['grid_avl'].diff()
# makes the 1 to 0 transtion back to 0 just to count BL
df_grid['counter_bl'] = df_grid['counter_bl'].replace([1], 0)
# hour_data
# resample the 5min data to 1 hour data
hour_data = pd.DataFrame(
columns=['grid_avl_h', 'grid_avl_h_usb', 'load_w_h', 'pv_w_h'])
hour_data['grid_avl_h'] = df_grid['grid_avl'].resample('H').mean()
# daily_data
daily_data = pd.DataFrame(columns=[])
daily_data['grid_on_time_daily'] = df_grid['grid_on_time'].resample(
'D').sum()/60
# make all nan to 0, just consider the days with >0 grid connection time
daily_data['grid_on_time_daily'].replace(0, np.nan, inplace=True)
daily_data['grid_off_time_daily'] = df_grid['grid_off_time'].resample(
'D').sum()/60
daily_data['aam_on_time_daily'] = df_grid['aam_on_time'].resample('D').sum()/60
daily_data['aam_off_time_daily'] = df_grid['aam_off_time'].resample(
'D').sum()/60
daily_data['bl_daily'] = abs(df_grid['counter_bl'].resample('D').sum())
# monthly_data
monthly_data = pd.DataFrame(columns=[])
monthly_data['bl_monthly'] = abs(df_grid['counter_bl'].resample('M').sum())
# typ_day
# Grouped hourly data to display one typical day
typ_day = pd.DataFrame(columns=[])
typ_day['avg_grid_avl'] = hour_data['grid_avl_h'].groupby(
hour_data.index.hour).mean()
# typ_day_all
# group also by day of year to get all the points for every hour to plot box plot
typ_day_all = pd.DataFrame(
columns=['avg_grid_avl', 'avg_grid_avl_usb', 'avg_load_w', 'avg_pv_w', 'avg_bat_v'])
typ_day_all['avg_grid_avl'] = df_grid['grid_avl'].groupby(
[df_grid.index.dayofyear.rename('day of year'), df_grid.index.hour.rename('hour')]).mean()
# Data frame counting grid events ON/OFF
df_grid_evt = pd.DataFrame(columns=[])
df_grid_evt = df_grid.groupby('counter_evt')['grid_avl'].min().to_frame(
name='grid_avl').join(df_grid.groupby('counter_evt')['grid_avl'].count().rename('length'))
# lenght blackuts in hours #.mask(df_grid_evt['grid_avl']!=0)
df_grid_evt['length_h'] = df_grid_evt['length']*5/60
# drop first and last row, normally this events are not complete
df_grid_evt.drop(df_grid_evt.tail(2).index, inplace=True)
df_grid_evt.drop(df_grid_evt.head(2).index, inplace=True)
# get just on grid off events
df_grid_evt_off = pd.DataFrame(columns=[])
df_grid_evt_off = df_grid_evt[df_grid_evt['grid_avl'] == 0].sort_values(
by='length_h') # , ascending = False)
df_grid_evt_off.reset_index(drop=True, inplace=True)
df_grid_evt_off.index = df_grid_evt_off.index + 1
# SIDEBAR
days_selected = (time_end - time_start).days
st.sidebar.text("days_selected:"+str(days_selected))
days_with_data = len(daily_data[daily_data['aam_on_time_daily'] > 0])
st.sidebar.text("days_with_data:"+str(days_with_data))
# DATA PREPARATION
avg_input_voltage = round((df_grid['input_voltage_0tonan'].mean()), 1)
min_input_voltage = round((df_grid['input_voltage_0tonan'].min()), 1)
max_input_voltage = round((df_grid['input_voltage_0tonan'].max()), 1)
grid_on_time_all = round(
(daily_data['grid_on_time_daily'].sum(skipna=True)), 1)
grid_off_time_all = round((sum(daily_data['grid_off_time_daily'])), 1)
aam_on_time_all = round((sum(daily_data['aam_on_time_daily'])), 1)
aam_off_time_all = round((sum(daily_data['aam_off_time_daily'])), 1)
avg_bl_duration = round((df_grid_evt_off['length_h'].mean()), 1)
max_bl_duration = round((df_grid_evt_off['length_h'].max()), 1)
min_bl_duration = round((df_grid_evt_off['length_h'].min()), 1)
avg_grid_avl_daily = round((daily_data['grid_on_time_daily'].mean()), 1)
max_grid_avl_daily = round((daily_data['grid_on_time_daily'].max()), 1)
min_grid_avl_daily = round((daily_data['grid_on_time_daily'].min()), 1)
avg_bl_nu_daily = round((daily_data['bl_daily'].mean()), 1)
min_bl_nu_daily = round((daily_data['bl_daily'].min()), 1)
max_bl_nu_daily = round((daily_data['bl_daily'].max()), 1)
avg_bl_nu_monthly = round((monthly_data['bl_monthly'].mean()), 1)
min_bl_nu_monthly = round((monthly_data['bl_monthly'].min()), 1)
max_bl_nu_monthly = round((monthly_data['bl_monthly'].max()), 1)
with st.expander('Data Overview all systems'):
st.warning("under construction")
with st.expander('Data Overview for AAM'):
st.header("Number of Data Points Per Day (Grid Values Which are Not Null)")
density_inv = data_density.input_voltage.notnull().groupby(
[data_density.index.floor('d')]).sum().astype(int).to_frame().reset_index(level=0)
density_mcu = data_density.peak_voltage.notnull().groupby(
[data_density.index.floor('d')]).sum().astype(int).to_frame().reset_index(level=0)
st.caption("Inverter")
fig_inv = calplot(
density_inv,
x="time",
y="input_voltage",
name="data_points",
years_title=True
)
st.plotly_chart(fig_inv, use_container_width=True)
st.caption("MCU")
fig_mcu = calplot(
density_mcu,
x="time",
y="peak_voltage",
name="data_points",
years_title=True
)
st.plotly_chart(fig_mcu, use_container_width=True)
# -------------------------------------------
st.header("Average Grid Voltage Per Day, excluding Null and Zero Values")
quality_inv = data_density.loc[(data_density.input_voltage.notnull()) &
(data_density.input_voltage > 0)].input_voltage
quality_inv = quality_inv.groupby([quality_inv.index.floor('d')]).mean().astype(
int).to_frame().reset_index(level=0)
quality_mcu = data_density.loc[(data_density.peak_voltage.notnull()) &
(data_density.peak_voltage > 0)].peak_voltage
quality_mcu = quality_mcu.groupby([quality_mcu.index.floor('d')]).mean().astype(
int).to_frame().reset_index(level=0)
if len(quality_inv) > 0:
st.caption("Inverter")
fig_inv_mean = calplot(
quality_inv,
x="time",
y="input_voltage",
name="mean",
years_title=True
)
st.plotly_chart(fig_inv_mean, use_container_width=True)
else:
st.warning("no data")
if len(quality_mcu) > 0:
st.caption("MCU")
fig_mcu_mean = calplot(
quality_mcu,
x="time",
y="peak_voltage",
name="mean",
years_title=True
)
st.plotly_chart(fig_mcu_mean, use_container_width=True)
else:
st.warning("no data")
# -------------------------------------------
st.header("Grid Measurements Over Selected Timespan")
final = make_subplots(specs=[[{"secondary_y": True}]])
final.add_trace(go.Scatter(
x=df.index, y=df['input_voltage'], name='input_voltage', line_color='rgba(82, 82, 82, .8)'))
final.add_trace(go.Scatter(
x=df.index, y=df['peak_voltage'], name='peak_volatge', line_color='rgb(245, 237, 111)'))
final.add_trace(go.Scatter(
x=df.index, y=df['output_voltage'], name='output_voltage', line_color='rgb(251, 117, 157)'))
final.update_layout(plot_bgcolor='white',
showlegend=True,
font=dict(size=12),
width=500,
height=300,
title={
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'})
final.update_xaxes(
tickformat='%d/%m',
ticks='outside',
gridcolor='lightgrey',
linecolor='lightgrey')
final.update_xaxes(title="Hour of the Day",
row=2, col=1)
final.update_yaxes(title_text='Voltage (V)',
showgrid=True,
secondary_y=False,
showline=True,
linewidth=1,
linecolor='lightgrey',
showticklabels=True,
gridcolor='lightgrey',
ticks='outside',
range=[0, 300])
st.plotly_chart(final, use_container_width=True)
with st.expander('Grid Analysis'):
st.markdown("<h3 style='text-align: center'>Input and Output Voltage (AC)</h3>",
unsafe_allow_html=True)
col1, col2 = st.columns([2, 1])
with col1:
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_grid.index,
y=df_grid['input_voltage_0tonan'], name="Grid Input Voltage",
line_shape='linear', line_color='lightslategrey', line_width=1.5))
fig.add_trace(go.Scatter(x=df_grid.index,
y=df_grid['output_voltage_5tonan'], name="AAM Output Voltage",
line_shape='linear', line_color='rgb(223,116,149)', line_width=1.5))
annotation_min = "Min: "+str(min_input_voltage) + " V"
annotation_max = "Max: "+str(max_input_voltage) + " V"
fig.update_layout( # title='Grid Data',
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=True,
linewidth=1.5,
ticks='outside',
title="Time",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
tickformat='%d/%m %H:%M', # %H:%M',
tickfont=dict(family="Fugue", size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title=" Grid Voltage in V",
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue", size=12, color='Black'),
range=[100, 250],
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=300, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=False,
legend=dict(title="",
orientation="h",
yanchor="top",
y=.95,
xanchor="center",
traceorder='normal',
x=0.5,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("grid_V_time_plot.pdf")
#################
with col2:
fig = go.Figure()
fig.add_trace(go.Box(y=df_grid['input_voltage_0tonan'], name="input_voltage",
marker_color='lightslategrey'))
fig.add_trace(go.Box(y=df_grid['output_voltage'], name="output_voltage",
marker_color='rgb(223,116,149)'))
fig.update_layout( # title='Grid Data',
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=False,
linewidth=1.5,
ticks='outside',
title="",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
tickformat='%d/%m %H:%M', # %H:%M',
tickfont=dict(family="Fugue", size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title=" Grid Voltage in V",
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue", size=12, color='Black'),
range=[100, 250],
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=300, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=True,
legend=dict(title="",
orientation="v",
yanchor="bottom",
y=0.05,
xanchor="right",
traceorder='normal',
x=0.95,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("grid_V_box_plot.pdf")
#########################################
st.markdown("<h3 style='text-align: center'>Daily hours with grid connection</h3>",
unsafe_allow_html=True)
fig = go.Figure([go.Bar(y=daily_data['grid_on_time_daily'], x=daily_data.index,
name='hours of grid per day',
marker_color='lightslategrey')],)
annotation_avg = "Avg: "+str(avg_grid_avl_daily) + "h"
fig.add_hline(y=avg_grid_avl_daily,
annotation_text=annotation_avg,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
annotation_min = "Min: "+str(min_grid_avl_daily) + "h "
fig.add_hline(y=min_grid_avl_daily,
annotation_text=annotation_min,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
annotation_max = "Max: "+str(max_grid_avl_daily) + "h "
fig.add_hline(y=max_grid_avl_daily,
annotation_text=annotation_max,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
fig.update_layout(
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=True,
linewidth=1.5,
ticks='outside',
title="Day/Month",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
tickformat='%d/%m/%y', # %H:%M',
tickfont=dict(family="Fugue", size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title=" hours of grid per day",
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue", size=12, color='Black'),
# range=[140,250],
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=600, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=False,
legend=dict(title="",
orientation="h",
yanchor="bottom",
y=1.05,
xanchor="center",
traceorder='normal',
x=0.5,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("bl_freq_d.pdf")
#########################################
st.info('avg_input_voltage: '+str(avg_input_voltage))
st.info('min_input_voltage: '+str(min_input_voltage))
st.info('max_input_voltage: '+str(max_input_voltage))
##########################################
st.markdown("<h3 style='text-align: center'>Grid and AAM Availability</h3>",
unsafe_allow_html=True)
col1, col2 = st.columns([1, 1])
with col1:
labels = ['Grid On', 'Grid Off']
values = [grid_on_time_all, grid_off_time_all]
colors = ['GoldenRod', 'lightslategrey']
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.8)])
fig.update_traces(hoverinfo='label+percent', textinfo='label+percent', textfont_size=12,
marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.update_layout(annotations=[dict(
text="Grid Availability", x=0.5, y=0.5, font_size=12, showarrow=False)])
fig.update_layout(font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=300, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=False)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("grid_availability.pdf")
with col2:
labels = ['AAM Available', 'AAM Not Available']
values = [aam_on_time_all, aam_off_time_all]
colors = ['rgb(223,116,149)', 'DarkSlateGrey']
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.8)])
fig.update_traces(hoverinfo='label+percent', textinfo='label+percent', textfont_size=12,
marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.update_layout(annotations=[dict(
text="AAM Availability <br> All Day", x=0.5, y=0.5, font_size=12, showarrow=False)])
fig.update_layout(font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=300, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
#margin=dict(l=75, r=75, b=80, t=80, pad=2),
showlegend=False,
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("AAM_availability.pdf")
################################
st.markdown("<h3 style='text-align: center'>Hourly Grid Availability Profile</h3>",
unsafe_allow_html=True)
###########################################
fig = go.Figure()
fig.add_trace(go.Scatter(x=typ_day.index,
y=typ_day['avg_grid_avl'], name="grid availability mean",
line_shape='linear', line_color='lightslategrey', line_width=4,))
fig.add_trace(go.Box(y=typ_day_all['avg_grid_avl'], x=typ_day_all.index.get_level_values(1)+1, marker_color='darkgrey',
jitter=0.3,
# pointpos=-1.8,
# boxpoints='all',
))
fig.update_layout( # title='Hourly grid availability profile USB',
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=True,
linewidth=1.5,
ticks='outside',
title="hour",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
# tickformat='%d/%m %H:%M', #%H:%M',
tickfont=dict(family="Fugue",
size=12, color='Black'),
range=[0.5, 24.5],
dtick=1,
),
yaxis=dict(
title=" Probability of grid availability in %",
# showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue",
size=12, color='Black'),
# range=[140,250],
dtick=0.1,
ticks='outside',
tickformat='0.0%',
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=600, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=False,
legend=dict(title="",
orientation="h",
yanchor="bottom",
y=1.05,
xanchor="center",
traceorder='normal',
x=0.5,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("hour_profile.pdf")
################################
st.markdown("<h3 style='text-align: center'>Power Outage Duration</h3>",
unsafe_allow_html=True)
col1, col2, col3 = st.columns([1, 1, 1])
col1.subheader('Duration sorted by length')
col2.subheader('Histogram ')
col3.subheader('Distribution')
with col1:
fig = go.Figure([go.Bar(y=df_grid_evt_off['length_h'], x=df_grid_evt_off.index,
name='Blackouts per day',
marker_color='lightslategrey')],)
annotation_avg = "Avg: "+str(avg_bl_duration) + " hours"
annotation_max = "Max: "+str(max_bl_duration) + " hours"
annotation_min = "Min: "+str(min_bl_duration) + " hours"
fig.add_hline(y=avg_bl_duration,
annotation_text=annotation_avg,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
fig.add_hline(y=max_bl_duration,
annotation_text=annotation_max,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
fig.add_hline(y=min_bl_duration,
annotation_text=annotation_min,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
fig.update_layout( # title='Grid Data',
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=True,
linewidth=1.5,
ticks='outside',
title="Blackout events",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
# tickformat='%d/%m %H:%M', #%H:%M',
tickfont=dict(family="Fugue",
size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title='Lenght of Blackout in h',
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue", size=12, color='Black'),
# range=[140,250],
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=600, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=False,
legend=dict(title="",
orientation="h",
yanchor="bottom",
y=1.05,
xanchor="center",
traceorder='normal',
x=0.5,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("bl_duration_plot.pdf")
#################################### HISTOGRAM ##############################################
with col3:
fig = go.Figure()
fig.add_trace(go.Histogram(x=df_grid_evt_off['length_h'], histnorm='percent',
marker_color='rgb(223,116,149)', cumulative_enabled=False, name='Histogram'))
fig.add_trace(go.Histogram(x=df_grid_evt_off['length_h'], histnorm='percent',
marker_color='lightslategrey', cumulative_enabled=True, name='Cumulative Histogram'))
#name='Blackouts per day',
# marker_color='DarkSeaGreen')],)
# fig.add_vline(x=avg_bl_bat_h,
# annotation_text= annotation_load,
# line_color='dimgrey',
# annotation_position="bottom left",
# line_width=1,
# line_dash='dash',)
fig.update_traces(xbins=dict( # bins used for histogram
# start=0.0,
# end=10.0,
size=1),
autobinx=False)
fig.update_layout( # title_text='Histogram', # title of plot
xaxis_title_text='Lenght of Blackout in h', # xaxis label
yaxis_title_text='Percentage of Blackouts in %', # yaxis label
bargap=0.1, # gap between bars of adjacent location coordinates
# bargroupgap=0.1 # gap between bars of the same location coordinates
autosize=False,
width=300, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
legend=dict(title="",
orientation="h",
yanchor="top",
y=1,
xanchor="left",
traceorder='normal',
x=0,
font=dict(
family="Fugue",
size=12,
color='black'
),
),
plot_bgcolor='white',
font=dict(
family="Fugue",
size=12,
color='black'
),
xaxis=dict(
range=[0, 10],
showline=True,
showgrid=False,
showticklabels=True,
linewidth=1.5,
ticks='outside',
# title="Day/Month",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
# tickformat='%',
tick0=1,
dtick=1,
),
yaxis=dict(
range=[0, 110],
#title=" Number of Power Outages per day",
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
# tickformat='%',
)
# color='rgb(82, 82, 82)',),)
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("Histogramm_945.pdf")
with col2:
fig = go.Figure()
fig.add_trace(go.Box(y=df_grid_evt_off['length_h'], name="length_h",
marker_color='lightslategrey'))
fig.update_layout( # title='Grid Data',
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=False,
linewidth=1.5,
ticks='outside',
title="",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
tickformat='%d/%m %H:%M', # %H:%M',
tickfont=dict(family="Fugue", size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title=" Length of blackout in h",
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue", size=12, color='Black'),
# range=[0,300],
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=300, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=True,
legend=dict(title="",
orientation="v",
yanchor="bottom",
y=0.05,
xanchor="right",
traceorder='normal',
x=0.95,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("grid_V_box_plot.pdf")
##################################################################################
##################################################################################
st.markdown("<h3 style='text-align: center'>Power Outage Frequency</h3>",
unsafe_allow_html=True)
col1, col2, col3 = st.columns([1, 1, 1])
col1.subheader('Daily')
col2.subheader('Monthly')
col3.subheader('Distribution')
with col1:
fig = go.Figure([go.Bar(y=daily_data['bl_daily'], x=daily_data.index,
name='Blackouts per day',
marker_color='lightslategrey')],)
annotation_avg = "Avg: "+str(avg_bl_nu_daily) + " "
fig.add_hline(y=avg_bl_nu_daily,
annotation_text=annotation_avg,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
annotation_min = "Min: "+str(min_bl_nu_daily) + " "
fig.add_hline(y=min_bl_nu_daily,
annotation_text=annotation_min,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
annotation_max = "Max: "+str(max_bl_nu_daily) + " "
fig.add_hline(y=max_bl_nu_daily,
annotation_text=annotation_max,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
fig.update_layout(
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=True,
linewidth=1.5,
ticks='outside',
title="Day/Month",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
tickformat='%d/%m', # %H:%M',
tickfont=dict(family="Fugue", size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title=" Number of Power Outages per day",
showgrid=True,
zeroline=True,
showline=True,
linewidth=1.5,
ticks='outside',
linecolor='lightgrey',
mirror=True,
showticklabels=True,
gridcolor='lightgrey',
tickfont=dict(family="Fugue", size=12, color='Black'),
# range=[140,250],
),
font=dict(family="Fugue", size=12, color='black'),
autosize=False,
width=600, height=300,
margin=dict(l=15, r=15, b=15, t=15, pad=2),
showlegend=False,
legend=dict(title="",
orientation="h",
yanchor="bottom",
y=1.05,
xanchor="center",
traceorder='normal',
x=0.5,
),
)
st.plotly_chart(fig, use_container_width=True)
# fig.write_image("bl_freq_d.pdf")
###############################
with col2:
fig = go.Figure([go.Bar(y=monthly_data['bl_monthly'], x=monthly_data.index,
name='Power Outages per month',
xperiod="M1",
xperiodalignment="middle",
marker_color='lightslategrey')],)
#annotation_avg= "Max: "+str(max_bl_nu_m) +" "
# fig.add_hline(y=max_bl_nu_m,
# annotation_text= annotation_avg,
# line_color='dimgrey',
# annotation_position="bottom left",
# line_width=1,
# line_dash='dash')
#annotation_min= "Min: "+str(min_bl_nu_m) +" "
# fig.add_hline(y=min_bl_nu,
# annotation_text= annotation_min,
# line_color='dimgrey',
# annotation_position="bottom left",
# line_width=1,
# line_dash='dash')
annotation_max = "AVG: "+str(avg_bl_nu_monthly) + " "
fig.add_hline(y=avg_bl_nu_monthly,
annotation_text=annotation_max,
line_color='dimgrey',
annotation_position="bottom left",
line_width=1,
line_dash='dash')
fig.update_layout(
plot_bgcolor='white',
xaxis=dict(
showline=True,
showgrid=True,
showticklabels=True,
linewidth=1.5,
ticks='outside',
title="Month",
gridcolor='lightgrey',
linecolor='lightgrey',
mirror=True,
dtick="M1",
ticklabelmode="period",
tickformat="%b",
# tickformat='%m', #%H:%M',
tickfont=dict(family="Fugue",
size=12, color='Black')
# range=[0,110],
),
yaxis=dict(
title=" Number of Power Outages per month",
showgrid=True,
zeroline=True,
showline=True,