Venue 83 - Lascar loggers

Venue 83 was able to deploy a bunch of Lascars at once - commercial USB-stick loggers that record temperature, and relative humidity if you pay more. We can’t always support this, but it can be useful for understanding balance and circulation problems and thinking about which rooms are really struggling with heat loss.

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# Using plotly.express

# import ipywidgets as widgets
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go   
#from IPython.display import display

# now the homebuilt monitor

rooms = ["balcony_left","balcony_right","balcony_middle", "platform_left_pillar","platform_right_pillar", "under_piano"]


list_of_temp_traces = []
list_of_rh_traces = []

for x in rooms:
    if (x == "under_piano"):
        df = pd.read_csv("./venue-83/" + x + ".csv",encoding='latin-1',usecols=[0,2,3],header=1,names=['timestamp', 'temperature','rh'])
        df["timestamp"] = pd.to_datetime(df['timestamp'])
        df = df.fillna('')
    else:
        df = pd.read_csv("./venue-83/" + x + ".csv",encoding='latin-1',usecols=[1,2,3], header=1, names=['time', 'temperature','rh'])
        df["timestamp"] = pd.to_datetime(df['time'])
        df = df.fillna('')
    
    #range_min = df['timestamp'].min()
    #range_max = df['timestamp'].max()
    #print(range_min, " to ", range_max) # this just uses the range of the last file, not ideal
    
    
    trace = go.Scatter(customdata=df, 
                        y=df['temperature'], 
                        x = df['timestamp'], 
                        mode='lines', 
                        hoverinfo='all', 
                        name=x,
                        )
    list_of_temp_traces.append(trace)
    
    trace = go.Scatter(customdata=df, 
                        y=df['rh'], 
                        x = df['timestamp'], 
                        mode='lines', 
                        hoverinfo='all', 
                        name=x,
                        )
    list_of_rh_traces.append(trace)

temp_g = go.FigureWidget(data=list_of_temp_traces,
                    layout = go.Layout(
                        yaxis=dict(range=[10,21])
                    ))

rh_g = go.FigureWidget(data=list_of_rh_traces,
                    layout = go.Layout(
                        yaxis=dict(range=[0,100])
                    ))



# 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.
#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)

temp_fig = go.Figure(temp_g)
temp_fig.update_layout(showlegend=True, 
              autosize = True, 
              title= "Temperature (deg C))",
              width=1000, 
              height=500,
)

rh_fig = go.Figure(rh_g)
rh_fig.update_layout(showlegend=True, 
              autosize = True, 
              title= "Relative Humidity (%RH)",
              width=1000, 
              height=500,
)


temp_fig.update_layout(
    hovermode='x unified',
   # range=[range_min, range_max],
    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"
    )
)


#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])
temp_fig.show()

rh_fig.update_layout(
    hovermode='x unified',
   # range=[range_min, range_max],
    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()