Skip to content

plots

measurement.dash_apps.plots ¤

Functions¤

create_empty_plot() ¤

Creates empty plot

Returns:

Type Description
scatter

px.Scatter: Plot
Source code in measurement\dash_apps\plots.py 
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
def create_empty_plot() -> px.scatter:
    """Creates empty plot

    Returns:
        px.Scatter: Plot
    """
    fig = px.scatter(title="No data to plot")
    fig.update_layout(
        autosize=True,
        margin=dict(
            l=50,
            r=20,
            b=0,
            t=50,
        ),
        title_font=dict(
            size=14,
        ),
    )
    return fig

create_report_plot(data, variable_name, station_code, agg='') ¤

Creates plot for Report app

Parameters:

Name Type Description Default
data DataFrame

Data

 required
variable_name str

Variable name

 required
station_code str

Station code

 required
agg str

Aggregation level. Defaults to "".

 ''

Returns:

Type Description
Figure

go.Figure: Plot
Source code in measurement\dash_apps\plots.py 
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
153
154
155
156
157
158
159
160
161
162
163
def create_report_plot(
    data: pd.DataFrame,
    variable_name: str,
    station_code: str,
    agg: str = "",
) -> go.Figure:
    """Creates plot for Report app

    Args:
        data (pd.DataFrame): Data
        variable_name (str): Variable name
        station_code (str): Station code
        agg (str, optional): Aggregation level. Defaults to "".

    Returns:
        go.Figure: Plot
    """

    fig = px.scatter(
        data,
        x="time",
        y=["value", "minimum", "maximum"],
        title=f"{station_code} - {variable_name}" + agg,
        labels={
            "time": "Date",
        },
    )

    fig.for_each_trace(
        lambda trace: trace.update(name=trace.name.title()),
    )
    fig.update_traces(marker=dict(size=3))
    fig.update_layout(
        legend=dict(
            title=dict(text="", font=dict(size=12)),
            x=1,
            y=1,
            xanchor="auto",
            yanchor="auto",
        ),
        autosize=True,
        margin=dict(
            l=50,
            r=20,
            b=0,
            t=50,
        ),
        yaxis_title=f"{variable_name}",
        title_font=dict(
            size=14,
        ),
    )

    return fig

create_validation_plot(data, variable_name, field) ¤

Creates plot for Validation app

Parameters:

Name Type Description Default
data DataFrame

Data

 required
variable_name str

Variable name

 required
field str

'value', 'minimum' or 'maximum'

 required

Returns:

Type Description
Figure

go.Figure: Plot
Source code in measurement\dash_apps\plots.py 
 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
def create_validation_plot(
    data: pd.DataFrame, variable_name: str, field: str
) -> go.Figure:
    """Creates plot for Validation app

    Args:
        data (pd.DataFrame): Data
        variable_name (str): Variable name
        field (str): 'value', 'minimum' or 'maximum'

    Returns:
        go.Figure: Plot
    """

    def status(row):
        if not row["is_validated"]:
            return "Not validated"
        if row["is_active"]:
            return "Active"
        return "Inactive"

    color_map = {
        "Active": "#00CC96",
        "Inactive": "#636EFA",
        "Not validated": "black",
    }

    fig = px.scatter(
        data,
        x="time",
        y=field,
        color=data.apply(status, axis=1),
        color_discrete_map=color_map,
        labels={"time": "Date", field: f"{variable_name} ({field.capitalize()})"},
    )

    fig.update_traces(marker=dict(size=3))
    fig.update_layout(
        legend=dict(
            title=dict(text="Status", font=dict(size=12)),
            x=1,
            y=1,
            xanchor="auto",
            yanchor="auto",
        ),
        autosize=True,
        margin=dict(
            l=50,
            r=20,
            b=0,
            t=50,
        ),
    )

    return fig

get_aggregation_level(timeseries, aggregate=False) ¤

Calculates the aggregation level based on the timeseries separation.

Parameters:

Name Type Description Default
timeseries Series

Time data to be aggregated.

 required
aggregate bool

Flag indicating if there should be aggregation

 False
Return

String indicating the aggregation level as " - LEVEL UNITS aggregation" or an empty string if no aggregation is required.

Source code in measurement\dash_apps\plots.py 
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
def get_aggregation_level(timeseries: pd.Series, aggregate: bool = False) -> str:
    """Calculates the aggregation level based on the timeseries separation.

    Args:
        timeseries: Time data to be aggregated.
        aggregate: Flag indicating if there should be aggregation

    Return:
        String indicating the aggregation level as " - LEVEL UNITS aggregation" or an
        empty string if no aggregation is required.
    """
    if not aggregate:
        return ""

    aggregation = timeseries.diff().dt.seconds.median() / 60
    unit = "minutes"
    if aggregation > 60:
        aggregation = aggregation / 60
        unit = "hours"
        if aggregation > 24:
            aggregation = aggregation / 24
            unit = "days"
    return f" - {aggregation:.1f} {unit} aggregation"