plotly / plotly/plotly.py

enable plotting vs time on 23 and 25 hour days

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feature P3
Lenguaje dominante
Python
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PR fusionados (30 d)
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Descripción

I'm trying to find a way to plot values over time on 23 and 25 hour days for daylight savings time (DST). Below is my example code that illustrates the problem, and the two resulting plots. The time stamps in x_23 and x_25 are all one hour apart. They reflect the "US/Pacific" time zone. In the spring forward case, I would expect to see twenty-three hours along the x-axis. And, in fall back, twenty-five. But, both times plotly shows 24. In spring forward, there is a visual gap in the spacing of the second and third markers. And, in fall back, there is a visual overlap.

A suggested approach would be to allow the programmer to optionally assign a time zone to the x-axis. In my case, I would assign US/Pacific. Then, plotly knows to adjust for DST if applicable. Might also want to allow assignment of a 'local' time zone. In that case, plotly would get the timezone from the computer on which it's running. If the programmer makes no assignment, plotly's behavior remains as is. Nobody's code gets broken.

A similar alternative would be to allow the programmer to set a flag to true if they want the behavior that is aware of DST. A value of false would keep the current behavior. Again, nobody's code gets broken.

I found two similar issues, but neither was really addressed.

https://github.com/plotly/plotly.js/issues/171
https://github.com/plotly/plotly.js/issues/4358

import plotly.graph_objects as go

# Spring Forward
x_23 = ["2001-04-01T00:00:00-08:00", "2001-04-01T01:00:00-08:00", "2001-04-01T03:00:00-07:00",
        "2001-04-01T04:00:00-07:00", "2001-04-01T05:00:00-07:00", "2001-04-01T06:00:00-07:00",
        "2001-04-01T07:00:00-07:00", "2001-04-01T08:00:00-07:00", "2001-04-01T09:00:00-07:00",
        "2001-04-01T10:00:00-07:00", "2001-04-01T11:00:00-07:00", "2001-04-01T12:00:00-07:00",
        "2001-04-01T13:00:00-07:00", "2001-04-01T14:00:00-07:00", "2001-04-01T15:00:00-07:00",
        "2001-04-01T16:00:00-07:00", "2001-04-01T17:00:00-07:00", "2001-04-01T18:00:00-07:00",
        "2001-04-01T19:00:00-07:00", "2001-04-01T20:00:00-07:00", "2001-04-01T21:00:00-07:00",
        "2001-04-01T22:00:00-07:00", "2001-04-01T23:00:00-07:00", "2001-04-02T00:00:00-07:00"]
y_23 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
fig_23 = go.Figure(data=[go.Scatter(x=x_23, y=y_23, mode="lines+markers")])
fig_23.update_layout(title="Spring Forward")
fig_23.show()

# Fall Back
x_25 = ["2001-10-28T00:00:00-07:00", "2001-10-28T01:00:00-07:00", "2001-10-28T01:00:00-08:00",
        "2001-10-28T02:00:00-08:00", "2001-10-28T03:00:00-08:00", "2001-10-28T04:00:00-08:00",
        "2001-10-28T05:00:00-08:00", "2001-10-28T06:00:00-08:00", "2001-10-28T07:00:00-08:00",
        "2001-10-28T08:00:00-08:00", "2001-10-28T09:00:00-08:00", "2001-10-28T10:00:00-08:00",
        "2001-10-28T11:00:00-08:00", "2001-10-28T12:00:00-08:00", "2001-10-28T13:00:00-08:00",
        "2001-10-28T14:00:00-08:00", "2001-10-28T15:00:00-08:00", "2001-10-28T16:00:00-08:00",
        "2001-10-28T17:00:00-08:00", "2001-10-28T18:00:00-08:00", "2001-10-28T19:00:00-08:00",
        "2001-10-28T20:00:00-08:00", "2001-10-28T21:00:00-08:00", "2001-10-28T22:00:00-08:00",
        "2001-10-28T23:00:00-08:00", "2001-10-29T00:00:00-08:00"]
y_25 = [0, 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]
fig_25 = go.Figure(data=[go.Scatter(x=x_25, y=y_25, mode="lines+markers")])
fig_25.update_layout(title="Fall Back")
fig_25.show()

spring_forward
fall_back

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Línea de trabajo

Empieza reproduciendo los ejemplos de avance y retroceso de hora del issue y compara el espaciado representado con las líneas temporales esperadas de 23 y 25 horas. Revisa los issues relacionados #171 y #4358 antes de decidir el alcance. Se considera terminado cuando se admita el comportamiento de trazado con reconocimiento de DST solicitado por el issue, sin cambiar el comportamiento cuando no se proporciona ninguna zona horaria ni ningún flag.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
python
Área
data-visualization
Tipo de issue
Nueva funcionalidad
Dificultad
5/5
Tiempo estimado
Más de una semana
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
35/100

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