plotly / plotly/plotly.py

enable plotting vs time on 23 and 25 hour days

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まだ誰も着手していません。

feature P3
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説明

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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  1. issue を最後まで読み、次にプロジェクトのコントリビューションガイドを読みます。
  2. 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
  3. リポジトリをフォークし、ブランチを切って変更します。
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調査の方向性

まず、issue にある夏時間への移行と標準時への復帰の例を再現し、プロットされた間隔を、想定される 23 時間および 25 時間のタイムラインと比較します。スコープを決める前に、関連する issue #171 と #4358 を確認します。issue で要求されている DST-aware なプロット動作がサポートされ、タイムゾーンも flag も指定されていない場合の動作が変更されなければ完了です。

索引モデルが issue の本文から書いたものです。

評価

技術スタック
python
領域
data-visualization
issue の種類
機能追加
難易度
5/5
見積もり時間
1週間以上
活発さ
停滞
明瞭さ
おおむね明確
初心者へのやさしさ
35/100

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