Venue SN church - Thermopro loggers#
Venue SN was able to deploy a bunch of Thermopro TP357s at once. The diary is approximate - we have a specific diary for Christmas week and then just a fairly typical week, but only recorded over an atypical period. The New Year’s midnight heating is for people in the space, just not in the diary.
You can get a line to disappear by clicking on it in the legend, sweep out part of the plot to zoom in, and so on.
Show code cell source
# Using plotly.express
plot_height = 800
plot_width = 1000
venue_data_dir = "./venue-SN/"
venue_temp_data_dir = "thermopro/"
weather_data_dir = "./weather_data/"
temp_filenames = ['1.csv', '2.csv' ,'3.csv', '4.csv' ,'5.csv', '9.csv']
location_labels= ["1 - chancel","2 - priests' vestry","3 - mid-pews right", "4 - mid-pews left","5 - church back left", "9 - pulpit"]
weather_location_labels = ["2 - priests' vestry","3 - mid-pews right"]
#main_location_label = "1" # used to plot against weather data.
use_diary_filepath = venue_data_dir + "use_diary-church.csv"
heating_timings_filepath = venue_data_dir + "heating_timings.csv"
weather_filepath = weather_data_dir + "gcht2024_weather_data.csv"
# import ipywidgets as widgets
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import os as os
from datetime import datetime as dt
list_of_temp_traces = []
list_of_rh_traces = []
list_of_traces_for_weather_comparison = []
dfHeatTimes = pd.DataFrame()
dfUseDiary = pd.DataFrame()
if os.path.isfile(use_diary_filepath):
dfUseDiary = pd.read_csv(use_diary_filepath)
dfUseDiary = dfUseDiary.fillna('')
dfUseDiary['annotation_position'] = dfUseDiary['annotation_position'].replace(to_replace="", value="top left")
dfUseDiary['start'] = pd.to_datetime(dfUseDiary['startUse'])
dfUseDiary['end'] = pd.to_datetime(dfUseDiary['endUse'])
#print(dfUseDiary)
if os.path.isfile(heating_timings_filepath):
dfHeatTimes = pd.read_csv(heating_timings_filepath)
dfHeatTimes = dfHeatTimes.fillna('')
dfHeatTimes['start'] = pd.to_datetime(dfHeatTimes['startHeat'])
dfHeatTimes['end'] = pd.to_datetime(dfHeatTimes['endHeat'])
# find some useful limits for rendering the weather data
end = dt.strptime("1970-01-01 00:00", '%Y-%m-%d %H:%M')
start = dt.strptime("2030-01-01 00:00", '%Y-%m-%d %H:%M')
for temp_filename,location_label in zip(temp_filenames,location_labels):
df = pd.read_csv(os.path.join(venue_data_dir, venue_temp_data_dir, temp_filename),encoding='utf-8-sig', usecols=[0,1,2], header=2, names=[ 'time','temperature','rh'])
df["timestamp"] = pd.to_datetime(df['time'], dayfirst=True)
df = df.fillna('')
# check whether this changes required range for weather data
start_time_in_file = df['timestamp'].min()
end_time_in_file = df['timestamp'].max()
if start_time_in_file < start:
start = start_time_in_file
if end_time_in_file > end:
end = end_time_in_file
trace = go.Scatter(customdata=df,
y=df['temperature'],
x = df['timestamp'],
mode='lines',
hoverinfo='all',
name=location_label,
)
list_of_temp_traces.append(trace)
# take one of the less wild traces and add it to plot it against the weather.
if (location_label in weather_location_labels):
list_of_traces_for_weather_comparison.append(trace)
trace = go.Scatter(customdata=df,
y=df['rh'],
x = df['timestamp'],
mode='lines',
hoverinfo='all',
name=location_label,
)
list_of_rh_traces.append(trace)
# create a trace for the weather data
df = pd.read_csv(weather_filepath,encoding='latin-1',usecols=[0,1], header=1, names=[ 'time','temperature'])
df["timestamp"] = pd.to_datetime(df['time'])
df = df.fillna('')
# removing unuseful weather data
df = df[(df['timestamp'] > start) & (df['timestamp'] < end)]
trace = go.Scatter(customdata=df,
y=df['temperature'],
x = df['timestamp'],
mode='lines',
hoverinfo='all',
name="outside",
)
list_of_traces_for_weather_comparison.append(trace)
temp_g = go.FigureWidget(data=list_of_temp_traces,
layout = go.Layout(
#yaxis=dict(range=[-6,25])
))
rh_g = go.FigureWidget(data=list_of_rh_traces,
layout = go.Layout(
yaxis=dict(range=[0,100])
))
weather_g = go.FigureWidget(data=list_of_traces_for_weather_comparison,
layout = go.Layout(
yaxis=dict(range=[-6,25])
))
temp_fig = go.Figure(temp_g)
temp_fig.update_layout(showlegend=True,
autosize = True,
title= "Temperature (deg C))",
width=plot_width,
height=plot_height,
)
# example syntax for two plots on same x-axis - I'd like to show the boiler temperature in
# parallel - but havne't had time to sort the syntax.
#temp_fig = make_subplots(rows=2, cols=1, shared_xaxes=True)
# for i, col in enumerate(cols, start=1):
# fig.add_trace(go.Scatter(x=df[col].index, y=df[col].values), row=i, col=1)
rh_fig = go.Figure(rh_g)
rh_fig.update_layout(showlegend=True,
autosize = True,
title= "Relative Humidity (%RH)",
width=plot_width,
height=plot_height,
)
weather_fig = go.Figure(weather_g)
weather_fig.update_layout(showlegend=True,
autosize = True,
title= "Indoor vs outdoor temperature from weather station (deg C))",
width=plot_width,
height=plot_height,
)
temp_fig.update_layout(
hovermode='x unified',
hoverlabel=dict(
bgcolor="white",
# font_size=16,
font_family="Rockwell"
)
)
#Add range slider
temp_fig.update_layout(
xaxis=dict(
rangeselector=dict(
buttons=list([
dict(
label="All",
step="all"
),
dict(count=1,
label="Hour",
step="hour",
stepmode="todate"),
dict(count=1,
label="Day",
step="day",
stepmode="backward"),
dict(count=7,
label="Week",
step="day",
stepmode="backward"),
dict(count=1,
label="Year",
step="year",
stepmode="backward")
])
),
rangeslider=dict(
visible=True,
),
type="date"
)
)
for ind in dfUseDiary.index:
temp_fig.add_vrect(x0=dfUseDiary['start'][ind], x1=dfUseDiary['end'][ind],
annotation_text=dfUseDiary['label'][ind],
annotation_position=dfUseDiary['annotation_position'][ind],
annotation=dict(font_size=14,
font_family="Times New Roman"),
fillcolor="green",
opacity=0.25,
line_width=0)
for ind in dfHeatTimes.index:
temp_fig.add_vrect(x0=dfHeatTimes['start'][ind], x1=dfHeatTimes['end'][ind])
temp_fig.show()
rh_fig.update_layout(
hovermode='x unified',
hoverlabel=dict(
bgcolor="white",
# font_size=16,
font_family="Rockwell"
)
)
#Add range slider
rh_fig.update_layout(
xaxis=dict(
rangeselector=dict(
buttons=list([
dict(
label="All",
step="all"
),
dict(count=1,
label="Hour",
step="hour",
stepmode="todate"),
dict(count=1,
label="Day",
step="day",
stepmode="backward"),
dict(count=7,
label="Week",
step="day",
stepmode="backward"),
dict(count=1,
label="Year",
step="year",
stepmode="backward")
])
),
rangeslider=dict(
visible=True,
),
type="date"
)
)
#fig.add_hline(y=16, annotation_text='16C - usual minimum for children', annotation_font_color="blue", line_color='red', layer='above', line_dash='dash')
# fig.update_yaxes(range = [-5, dfCollatedDataSet['temperature'].max()+5])
rh_fig.show()
weather_fig.update_layout(
hovermode='x unified',
hoverlabel=dict(
bgcolor="white",
# font_size=16,
font_family="Rockwell"
)
)
#Add range slider
weather_fig.update_layout(
xaxis=dict(
rangeselector=dict(
buttons=list([
dict(
label="All",
step="all"
),
dict(count=1,
label="Hour",
step="hour",
stepmode="todate"),
dict(count=1,
label="Day",
step="day",
stepmode="backward"),
dict(count=7,
label="Week",
step="day",
stepmode="backward"),
dict(count=1,
label="Year",
step="year",
stepmode="backward")
])
),
rangeslider=dict(
visible=True,
),
type="date"
)
)
for ind in dfHeatTimes.index:
weather_fig.add_vrect(x0=dfHeatTimes['start'][ind], x1=dfHeatTimes['end'][ind])
#fig.add_hline(y=16, annotation_text='16C - usual minimum for children', annotation_font_color="blue", line_color='red', layer='above', line_dash='dash')
# fig.update_yaxes(range = [-5, dfCollatedDataSet['temperature'].max()+5])
weather_fig.show()