PyCafe - Vizro - Gas Consumption Analytics

data/
app.py
de.py
requirements.txt
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import pandas as pd
from de import load
import vizro.models as vm
from vizro.models.types import capture

# Sample data_dict where each key has time series data
import vizro as vz
from glob import glob


def ftext(text):
    text = text.replace('data/', '').replace('.csv', '')
    name = '_'.join(text.split('_')[:2])
    end_date = text.split('_')[-1]
    return name, end_date


def load():
    csvs = glob('data/*.csv')
    data_dict = {}
    for csv in csvs:
        name, end_date = ftext(csv)
        key = name + '_' + end_date
        df = pd.read_csv(csv)
        df['name'] = name
        df['Local Time'] = pd.to_datetime(df['Local Time'])
        data_dict[key] = df
    return data_dict

# Load the data (assuming your data loading logic stays the same)
#data_dict = load()
df = pd.read_csv('data/df_combined.csv')
df['Local Time'] = pd.to_datetime(df['Local Time'])
df['Hour'] = df['Local Time'].dt.hour

# Define the function for creating the line plot
@capture("graph")
def create_lineplot(data_frame, **kwargs):
    data_frame = data_frame.loc[
        pd.notnull(data_frame["Gas Consumption (l)"])
    ].sort_values("Local Time")

    fig = go.Figure()
    for name in data_frame['name'].unique():
        df = data_frame.loc[data_frame['name'] == name]
        # Create the line plot
        fig.add_trace(
            go.Scatter(
                    x=df["Local Time"],
                    y=df["Gas Consumption (l)"],
                    mode="lines+markers",
                hovertemplate="Time %{x}><br>Consumption: %{y}<extra></extra>",
                name=name,
                )
        )
    # Customize the layout
    fig.update_layout(
        title=f"Gas Accumulation over Time",
        xaxis_title="Local Time",
        yaxis_title="Gas Consumption (l)",
    )
    return fig


# Define the function for creating the ideal gas plot
@capture("graph")
def create_ideal_gas_plot(data_frame):
    data_frame = data_frame.loc[data_frame['Flow (lph)'] > 0]
    fig = go.Figure()
    for name in data_frame['name'].unique():
        df = data_frame.loc[data_frame['name'] == name]
        fig.add_trace(
            go.Scatter(
                x=df["Pressure (Pa)"],
                y=df["Flow (lph)"],
                mode="markers",
                customdata=df["Temperature (°C)"],
                hovertemplate="Temp %{customdata}><extra></extra>",
                name=name,
                marker=dict(
                    opacity=0.5,
                )
            )
        )
    fig.update_layout(
        title=f"Ideal Gas Diagnosis",
        xaxis_title="Pressure",
        yaxis_title="Flow (lph)",
    )
    return fig


@capture("graph")
def create_hourly_hist(data_frame):
    hourly_df = pd.pivot_table(data_frame, index=['Hour', "name"], values='Flow (lph)', aggfunc="sum").reset_index()
    fig = go.Figure()
    for name in hourly_df['name'].unique():
        df = hourly_df.loc[hourly_df['name'] == name]
        fig.add_trace(
            go.Bar(
                x=df['Hour'], 
                y=df['Flow (lph)'],
            name=name)
        )
    fig.update_layout(
        title=f"Flow by hour of day",
        xaxis_title="Hour",
        yaxis_title="Flow (lph)",
    )
    return fig
    

page = vm.Page(
    title="Graphs",
    id="chart",
    components=[
        vm.Graph(
            figure=create_lineplot(
                data_frame=df,
            ),
            title="Basic",
        ),
        vm.Graph(figure=create_ideal_gas_plot(df)),
        vm.Graph(figure=create_hourly_hist(df))
    ],
    controls=[
        vm.Filter(
            column="name",
            selector=vm.Dropdown(title="Farmer",
                value=['Eipparla_Bharti'],
                                )
        ),
    ],
)
# Define the Vizro components and the page
dashboard = vm.Dashboard(title="Gas Consumption Dashboard", pages=[page])
print('hi')

def get_dash():
    return dashboard
# Run the Vizro dashboard

app = get_dash
vz.Vizro().build(dashboard).run()

if __name__ == "__main__":
    vz.Vizro().build(dashboard).run()
Py.Cafe

vizro-gas-consumption-analytics

Gas Consumption Analytics
