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from dateutil import parser
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import datetime
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import pandas as pd
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import numpy as np
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dataset_name = "1h_5ms"
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start = parser.isoparse("2024-10-22T00:00:00Z")
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end = parser.isoparse("2024-10-22T01:00:00Z")
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resolution = datetime.timedelta(milliseconds=5)
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dt = end - start
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n = int((dt.total_seconds() * 1e6 + dt.microseconds) / resolution.microseconds)
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np.random.seed(2)
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cols = ["value"]
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# Generate a fake signal
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signal = np.random.normal(0, 0.3, size=n).cumsum() + 50
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# Generate many noisy samples from the signal
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noise = lambda var, bias, n: np.random.normal(bias, var, n)
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data = {c: signal + noise(1, 10*(np.random.random() - 0.5), n) for c in cols}
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# Pick a few samples from the first line and really blow them out
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locs = np.random.choice(n, 10)
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data["value"][locs] *= 2
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data["Time"] = [(start + resolution * i).isoformat() for i in range(n)]
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df_gen = pd.DataFrame(data)
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df_gen

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dataset = nm.upload_pandas(df_gen, dataset_name, "Time", timestamp_type="iso_8601")
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dataset

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channel = dataset.get_channel("value")
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df_decimated = channel.get_decimated(start, end, buckets=2000)
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df_decimated

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import plotly.graph_objects as go
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if "value" in df_decimated.columns:
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    # range had less than 1000 points
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    fig = go.Figure([
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        go.Scatter(
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            x=df_decimated.index,
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            y=df_decimated["value"],
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            mode="lines",
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            # line=dict(width=0),
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            showlegend=False,
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            # line_shape="vh"
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        ),
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    ])
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else:
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    fig = go.Figure([
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        go.Scatter(
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            x=df_decimated.index,
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            y=df_decimated["max"],
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            mode="lines",
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            line=dict(width=0),
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            showlegend=False,
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            line_shape="vh"
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        ),
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        go.Scatter(
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            x=df_decimated.index,
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            y=df_decimated["min"],
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            mode="lines",
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            fill="tonexty",
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            fillcolor="blue",
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            line=dict(width=0),
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            showlegend=False,
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            line_shape="vh"
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        )
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    ])
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fig.update_layout(
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    xaxis_title="Time",
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    yaxis_title="Value"
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)
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fig.show()

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def on_range_change(layout, range_x):
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    start = parser.isoparse(range_x[0]+"Z")
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    end = parser.isoparse(range_x[1]+"Z")
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    df = channel.get_decimated(start, end, buckets=2000)
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    # Create a new figure with the new data
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    fig = make_fig(df)
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    fig.update_layout(xaxis={"range": range_x})
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    # Update the existing figure widget with the new figure
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    fig_widget.layout = fig.layout
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    fig_widget.layout.on_change(on_range_change, "xaxis.range")
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    fig_widget.data = []
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    fig_widget.add_traces(fig.data)
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def make_fig(df):
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    if "value" in df.columns:
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        # range had less than 1000 points
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        fig = go.Figure([
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            go.Scatter(
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                x=df.index,
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                y=df["value"],
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                mode="lines",
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                showlegend=False,
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            ),
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        ])
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    else:
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        fig = go.Figure([
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            go.Scatter(
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                x=df.index,
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                y=df["max"],
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                mode="lines",
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                line=dict(width=0),
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                showlegend=False,
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                line_shape="vh"
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            ),
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            go.Scatter(
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                x=df.index,
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                y=df["min"],
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                mode="lines",
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                fill="tonexty",
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                fillcolor="blue",
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                line=dict(width=0),
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                showlegend=False,
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                line_shape="vh"
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            )
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        ])
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    fig.update_layout(
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        xaxis_title="Time",
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        yaxis_title="Value",
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    )
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    return fig
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fig_range = make_fig(channel.get_decimated(start, end, buckets=2000))
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fig_widget = go.FigureWidget(fig_range)
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fig_widget.layout.on_change(on_range_change, "xaxis.range")
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fig_widget