Scratch – repr & visualisation testing

Temporary notebook for testing _repr_html_ / __repr__ across all classes and visualisation helpers like coverage_bar() and tree().

Delete when done.

[1]:

import sys
if "google.colab" in sys.modules:
    %pip install -q git+https://github.com/rebase-energy/TimeDataModel.git

[2]:

from datetime import datetime, timezone

import numpy as np

from timedatamodel import (
    AggregationMethod,
    CoverageBar,
    DataType,
    Dimension,
    Frequency,
    GeoLocation,
    HierarchicalTimeSeries,
    HierarchyNode,
    TimeSeriesList,
    TimeSeriesArray,
    TimeSeriesCollection,
    TimeSeriesTable,
    TimeSeriesType,
    get_theme,
    set_theme,
    reset_theme,
)

Theme colors

All repr and visualisation functions read from a shared theme (theme.json). Use get_theme() to inspect the current colors and set_theme() to override them. Changes take effect on the next repr call. Call reset_theme() to restore defaults.

[3]:

# Override colors here — uncomment and edit any line to experiment.
# Changes apply to all reprs rendered after this cell.

set_theme({
    "light": {
        "header_bg": "#f0f0f0",
        "header_text": "#1a1a1a",
        "header_border": "#4a4a4a",
        "meta_bg": "#fafafa",
        "meta_label": "#475569",
        "meta_value": "#1a1a1a",
        "col_header_text": "#555",
        "col_header_border": "#ccc",
        "data_text": "#1a1a1a",
        "index_text": "#1e293b",
        "hover_bg": "#f5f5f5",
        "ellipsis": "#999",
        "coverage_present": "#4CAF50",
        "coverage_absent": "#e0e0e0",
        "coverage_label": "#333",
        "coverage_date": "#666",
    },
    "dark": {
        "header_bg": "#1e293b",
        "header_text": "#e2e8f0",
        "header_border": "#475569",
        "meta_bg": "#0f172a",
        "meta_label": "#94a3b8",
        "meta_value": "#e2e8f0",
        "col_header_text": "#94a3b8",
        "col_header_border": "#334155",
        "data_text": "#e2e8f0",
        "index_text": "#cbd5e1",
        "hover_bg": "#1e293b",
        "ellipsis": "#64748b",
    },
})

# To restore defaults at any point:
# reset_theme()

Helper – generate hourly timestamps

[4]:

def hourly_ts(n=24, start="2024-01-15"):
    base = datetime.fromisoformat(f"{start}T00:00:00+00:00")
    from datetime import timedelta
    return [base + timedelta(hours=i) for i in range(n)]

1. TimeSeriesList

[5]:

ts = TimeSeriesList(
    Frequency.PT1H,
    timezone="UTC",
    timestamps=hourly_ts(24),
    values=[120 + 5 * np.sin(i) for i in range(24)],
    name="power",
    unit="MW",
    data_type=DataType.OBSERVATION,
    description="Hourly power output from wind farm Alpha",
)
ts

[5]:

TimeSeriesList

Name	power
Length	24
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW
Data type	OBSERVATION
Description	Hourly power output from wind farm Alpha

timestamp	power
2024-01-15 00:00	120.0
2024-01-15 01:00	124.207
2024-01-15 02:00	124.546
…	…
2024-01-15 21:00	124.183
2024-01-15 22:00	119.956
2024-01-15 23:00	115.769

[6]:

print(ts)

TimeSeriesList
┌──────────────────────────────────────────────────────────────┐
│  Name:             power                                     │
│  Length:           24                                        │
│  Frequency:        PT1H                                      │
│  Timezone:         UTC (+00:00)                              │
│  Unit:             MW                                        │
│  Data type:        OBSERVATION                               │
│  Description:      Hourly power output from wind farm Alpha  │
├──────────────────────────────────────────────────────────────┤
│  2024-01-15 00:00    120.0                                   │
│  2024-01-15 01:00  124.207                                   │
│  2024-01-15 02:00  124.546                                   │
│  ...                   ...                                   │
│  2024-01-15 21:00  124.183                                   │
│  2024-01-15 22:00  119.956                                   │
│  2024-01-15 23:00  115.769                                   │
└──────────────────────────────────────────────────────────────┘

TimeSeriesList – empty

[7]:

ts_empty = TimeSeriesList(Frequency.PT1H, timestamps=[], values=[], name="empty")
ts_empty

[7]:

TimeSeriesList

Name	empty
Length	0
Frequency	PT1H
Timezone	UTC

timestamp	empty
(empty)

TimeSeriesList – with NaN gaps

[8]:

vals_gap = [float(i) for i in range(24)]
for i in [5, 6, 7, 15, 16]:
    vals_gap[i] = None

ts_gap = TimeSeriesList(
    Frequency.PT1H,
    timestamps=hourly_ts(24),
    values=vals_gap,
    name="gappy_signal",
    unit="kW",
)
ts_gap

[8]:

TimeSeriesList

Name	gappy_signal
Length	24
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	kW

timestamp	gappy_signal
2024-01-15 00:00	0.0
2024-01-15 01:00	1.0
2024-01-15 02:00	2.0
…	…
2024-01-15 21:00	21.0
2024-01-15 22:00	22.0
2024-01-15 23:00	23.0

TimeSeriesList – short (no truncation)

[9]:

ts_short = TimeSeriesList(
    Frequency.PT1H,
    timestamps=hourly_ts(5),
    values=[1.0, 2.0, 3.0, 4.0, 5.0],
    name="short",
)
ts_short

[9]:

TimeSeriesList

Name	short
Length	5
Frequency	PT1H
Timezone	UTC (+00:00)

timestamp	short
2024-01-15 00:00	1.0
2024-01-15 01:00	2.0
2024-01-15 02:00	3.0

TimeSeriesList – with location and labels

[10]:

ts_loc = TimeSeriesList(
    Frequency.PT1H,
    timestamps=hourly_ts(24),
    values=[100 + i * 0.5 for i in range(24)],
    name="temperature",
    unit="°C",
    location=GeoLocation(latitude=59.91, longitude=10.75),
    labels={"source": "met.no", "station": "Oslo-Blindern"},
    data_type=DataType.OBSERVATION,
    description="Air temperature at Blindern station",
)
ts_loc

[10]:

TimeSeriesList

Name	temperature
Length	24
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	°C
Data type	OBSERVATION
Location	59.91°N, 10.75°E
Description	Air temperature at Blindern station
Labels	{'source': 'met.no', 'station': 'Oslo-Blindern'}

timestamp	temperature
2024-01-15 00:00	100.0
2024-01-15 01:00	100.5
2024-01-15 02:00	101.0
…	…
2024-01-15 21:00	110.5
2024-01-15 22:00	111.0
2024-01-15 23:00	111.5

2. TimeSeriesTable

[11]:

timestamps = hourly_ts(24)
tbl = TimeSeriesTable(
    Frequency.PT1H,
    timezone="UTC",
    timestamps=timestamps,
    values=np.column_stack([
        [120 + 5 * np.sin(i) for i in range(24)],
        [80 + 3 * np.cos(i) for i in range(24)],
        [200 + 10 * np.sin(i + 1) for i in range(24)],
    ]),
    names=["wind_farm_A", "wind_farm_B", "solar_park_C"],
    units=["MW", "MW", "MW"],
    data_types=[DataType.OBSERVATION, DataType.OBSERVATION, DataType.FORECAST],
)
tbl

[11]:

TimeSeriesTable

Name	unnamed
Columns	wind_farm_A, wind_farm_B, solar_park_C
Length	24 × 3
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW, MW, MW
Data type	OBSERVATION, OBSERVATION, FORECAST

timestamp	wind_farm_A	wind_farm_B	solar_park_C
2024-01-15 00:00	120.0	83.0	208.415
2024-01-15 01:00	124.207	81.6209	209.093
2024-01-15 02:00	124.546	78.7516	201.411
…	…	…	…
2024-01-15 21:00	124.183	78.3568	199.911
2024-01-15 22:00	119.956	77.0001	191.538
2024-01-15 23:00	115.769	78.4015	190.944

[12]:

print(tbl)

TimeSeriesTable
┌────────────────────────────────────────────────────────────┐
│  Name:             unnamed                                 │
│  Columns:          wind_farm_A, wind_farm_B, solar_park_C  │
│  Length:           24 × 3                                  │
│  Frequency:        PT1H                                    │
│  Timezone:         UTC (+00:00)                            │
│  Unit:             MW, MW, MW                              │
│  Data type:        OBSERVATION, OBSERVATION, FORECAST      │
├────────────────────────────────────────────────────────────┤
│                    wind_farm_A  wind_farm_B  solar_park_C  │
│  2024-01-15 00:00        120.0         83.0       208.415  │
│  2024-01-15 01:00      124.207      81.6209       209.093  │
│  2024-01-15 02:00      124.546      78.7516       201.411  │
│  ...                       ...          ...           ...  │
│  2024-01-15 21:00      124.183      78.3568       199.911  │
│  2024-01-15 22:00      119.956      77.0001       191.538  │
│  2024-01-15 23:00      115.769      78.4015       190.944  │
└────────────────────────────────────────────────────────────┘

3. TimeSeriesCollection

[13]:

ts_a = TimeSeriesList(
    Frequency.PT1H, timestamps=hourly_ts(24), values=[float(i) for i in range(24)],
    name="series_A", unit="MW",
)
ts_b = TimeSeriesList(
    Frequency.P1D,
    timestamps=[datetime.fromisoformat(f"2024-01-{d:02d}T00:00:00+00:00") for d in range(1, 8)],
    values=[100.0, 105.0, 98.0, None, 110.0, 115.0, 108.0],
    name="series_B", unit="GWh",
)

coll = TimeSeriesCollection(
    [ts_a, ts_b, tbl],
    name="My Collection",
    description="A mix of different series",
)
coll

[13]:

TimeSeriesCollection

name	type	freq	tz	length	begin	end
series_A	TimeSeriesList	PT1H	UTC	24	2024-01-15 00:00	2024-01-15 23:00
series_B	TimeSeriesList	P1D	UTC	7	2024-01-01 00:00	2024-01-07 00:00
wind_farm_A,wind_farm_B,solar_park_C	TimeSeriesTable	PT1H	UTC	24	2024-01-15 00:00	2024-01-15 23:00

[14]:

print(coll)

TimeSeriesCollection
┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  name                                  type             freq  tz   length  begin             end               │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│  series_A                              TimeSeriesList   PT1H  UTC  24      2024-01-15 00:00  2024-01-15 23:00  │
│  series_B                              TimeSeriesList   P1D   UTC  7       2024-01-01 00:00  2024-01-07 00:00  │
│  wind_farm_A,wind_farm_B,solar_park_C  TimeSeriesTable  PT1H  UTC  24      2024-01-15 00:00  2024-01-15 23:00  │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

4. TimeSeriesArray (N-dimensional)

[15]:

time_labels = hourly_ts(12)
scenarios = ["low", "mid", "high"]

arr = TimeSeriesArray(
    Frequency.PT1H,
    name="price_scenarios",
    unit="EUR/MWh",
    data_type=DataType.SCENARIO,
    dimensions=[
        Dimension(name="timestamp", labels=time_labels),
        Dimension(name="scenario", labels=scenarios),
    ],
    values=np.random.default_rng(42).uniform(30, 80, size=(12, 3)),
)
arr

[15]:

TimeSeriesArray

Name	price_scenarios
Dimensions	timestamp: 12, scenario: 3
Shape	(12, 3)
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	EUR/MWh
Data type	SCENARIO

timestamp	low	mid	high
2024-01-15 00:00	68.6978	51.9439	72.9299
2024-01-15 01:00	64.8684	34.7089	78.7811
2024-01-15 02:00	68.057	69.3032	36.4057
…	…	…	…
2024-01-15 09:00	32.1902	37.7145	64.1524
2024-01-15 10:00	67.2381	78.3755	46.2913
2024-01-15 11:00	48.523	53.4778	39.4736

[16]:

print(arr)

TimeSeriesArray
┌────────────────────────────────────────────────┐
│  Name:             price_scenarios             │
│  Dimensions:       timestamp: 12, scenario: 3  │
│  Shape:            (12, 3)                     │
│  Frequency:        PT1H                        │
│  Timezone:         UTC (+00:00)                │
│  Unit:             EUR/MWh                     │
│  Data type:        SCENARIO                    │
└────────────────────────────────────────────────┘

TimeSeriesArray – 1D

[17]:

arr_1d = TimeSeriesArray(
    Frequency.PT1H,
    name="load",
    unit="MW",
    dimensions=[Dimension(name="timestamp", labels=hourly_ts(8))],
    values=np.array([100, 105, 110, 108, 103, 99, 95, 92], dtype=float),
)
arr_1d

[17]:

TimeSeriesArray

Name	load
Dimensions	timestamp: 8
Shape	(8,)
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW

timestamp	load
2024-01-15 00:00:00+00:00	100.0
2024-01-15 01:00:00+00:00	105.0
2024-01-15 02:00:00+00:00	110.0
…	…
2024-01-15 05:00:00+00:00	99.0
2024-01-15 06:00:00+00:00	95.0
2024-01-15 07:00:00+00:00	92.0

5. CoverageBar

[18]:

ts_gap.coverage_bar()

[18]:

[19]:

print(ts_gap.coverage_bar())

gappy_signal  █████░░░███████░░███████
              2024-01-15 00:00  2024-01-15 23:00

[20]:

tbl.coverage_bar()

[20]:

[21]:

print(tbl.coverage_bar())

wind_farm_A   ████████████████████████
wind_farm_B   ████████████████████████
solar_park_C  ████████████████████████
              2024-01-15 00:00  2024-01-15 23:00

6. HierarchicalTimeSeries & tree()

[22]:

timestamps = hourly_ts(24)
rng = np.random.default_rng(0)

def make_ts(name):
    return TimeSeriesList(
        Frequency.PT1H, timestamps=timestamps,
        values=rng.uniform(50, 200, 24).tolist(),
        name=name, unit="MW", data_type=DataType.OBSERVATION,
    )

tree_dict = {
    "total": {
        "Norway": {"Bergen": "bergen", "Oslo": "oslo", "Trondheim": "trondheim"},
        "Sweden": {"Stockholm": "stockholm", "Gothenburg": "gothenburg"},
    }
}
series_map = {
    "bergen": make_ts("Bergen"),
    "oslo": make_ts("Oslo"),
    "trondheim": make_ts("Trondheim"),
    "stockholm": make_ts("Stockholm"),
    "gothenburg": make_ts("Gothenburg"),
}

hts = HierarchicalTimeSeries.from_dict(
    tree_dict, series_map,
    levels=["region", "country", "city"],
    name="Nordic Wind Power",
    description="Hierarchical wind power across Nordic cities",
    aggregation=AggregationMethod.SUM,
)
hts

[22]:

HierarchicalTimeSeries

Name	Nordic Wind Power
Levels	region, country, city
Nodes	16 (5 leaves)
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW
Aggregation	sum

name	level	length	begin	end
Bergen	country	24	2024-01-15 00:00	2024-01-15 23:00
Oslo	country	24	2024-01-15 00:00	2024-01-15 23:00
Trondheim	country	24	2024-01-15 00:00	2024-01-15 23:00
Stockholm	country	24	2024-01-15 00:00	2024-01-15 23:00
Gothenburg	country	24	2024-01-15 00:00	2024-01-15 23:00

[23]:

print(hts)

HierarchicalTimeSeries
┌───────────────────────────────────────────────────────────────────┐
│  Name:             Nordic Wind Power                              │
│  Levels:           region, country, city                          │
│  Nodes:            16 (5 leaves)                                  │
│  Frequency:        PT1H                                           │
│  Timezone:         UTC (+00:00)                                   │
│  Unit:             MW                                             │
│  Aggregation:      sum                                            │
├───────────────────────────────────────────────────────────────────┤
│  name        level    length  begin             end               │
├───────────────────────────────────────────────────────────────────┤
│  Bergen      country  24      2024-01-15 00:00  2024-01-15 23:00  │
│  Oslo        country  24      2024-01-15 00:00  2024-01-15 23:00  │
│  Trondheim   country  24      2024-01-15 00:00  2024-01-15 23:00  │
│  Stockholm   country  24      2024-01-15 00:00  2024-01-15 23:00  │
│  Gothenburg  country  24      2024-01-15 00:00  2024-01-15 23:00  │
└───────────────────────────────────────────────────────────────────┘

HierarchyTree – HTML

[24]:

hts.tree()

[24]:

total (region)

Norway (region)

Bergen (region)

Bergen [24 pts]

Oslo (region)

Oslo [24 pts]

Trondheim (region)

Trondheim [24 pts]

Sweden (region)

Stockholm (region)

Stockholm [24 pts]

Gothenburg (region)

Gothenburg [24 pts]

[25]:

print(hts.tree())

└── total (region)
    └── total (region)
        ├── Norway (region)
        │   └── Norway (region)
        │       ├── Bergen (region)
        │       │   └── Bergen [24 pts]
        │       ├── Oslo (region)
        │       │   └── Oslo [24 pts]
        │       └── Trondheim (region)
        │           └── Trondheim [24 pts]
        └── Sweden (region)
            └── Sweden (region)
                ├── Stockholm (region)
                │   └── Stockholm [24 pts]
                └── Gothenburg (region)
                    └── Gothenburg [24 pts]

7. Wide TimeSeriesTable (many columns)

Test whether the repr truncates columns when there are too many to fit.

[26]:

n_cols = 20
timestamps = hourly_ts(48)
rng = np.random.default_rng(7)

tbl_wide = TimeSeriesTable(
    Frequency.PT1H,
    timezone="UTC",
    timestamps=timestamps,
    values=rng.uniform(50, 300, size=(48, n_cols)),
    names=[f"turbine_{i:02d}" for i in range(n_cols)],
    units=["MW"] * n_cols,
)
tbl_wide

[26]:

TimeSeriesTable

Name	unnamed
Columns	turbine_00, turbine_01, turbine_02, turbine_03, turbine_04, turbine_05, turbine_06, turbine_07, turbine_08, turbine_09, turbine_10, turbine_11, turbine_12, turbine_13, turbine_14, turbine_15, turbine_16, turbine_17, turbine_18, turbine_19
Length	48 × 20
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW

timestamp	turbine_00	turbine_01	turbine_02	turbine_03	turbine_04	turbine_05	turbine_06	turbine_07	turbine_08	turbine_09	turbine_10	turbine_11	turbine_12	turbine_13	turbine_14	turbine_15	turbine_16	turbine_17	turbine_18	turbine_19
2024-01-15 00:00	206.274	274.303	243.921	106.302	125.042	268.388	51.3163	255.307	249.267	166.984	125.758	119.606	113.717	161.269	176.137	188.374	298.875	248.165	205.545	297.24
2024-01-15 01:00	103.827	90.053	203.135	60.9855	58.9201	178.722	166.552	279.292	207.307	178.529	174.218	111.879	52.9485	98.1005	223.008	100.152	142.384	50.9336	257.512	88.6153
2024-01-15 02:00	116.9	270.083	177.448	261.788	209.929	235.443	72.8739	185.286	176.943	267.835	140.316	199.546	64.8129	146.908	130.759	87.5499	254.085	144.862	294.687	197.498
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
2024-01-16 21:00	115.077	195.91	288.058	254.283	210.182	179.705	53.783	272.679	66.7135	82.2703	223.268	253.036	246.638	163.101	94.2639	58.8256	262.845	294.424	111.034	266.519
2024-01-16 22:00	150.633	127.836	176.289	52.042	160.679	99.8887	228.114	175.643	267.213	274.162	129.19	120.096	284.783	175.999	74.1289	61.4713	297.606	194.791	63.6232	177.814
2024-01-16 23:00	176.026	158.847	287.82	287.193	81.2419	87.9302	151.872	258.507	205.938	232.458	194.015	102.514	254.441	53.5052	224.621	98.5849	167.773	293.783	184.759	64.2372

[27]:

print(tbl_wide)

TimeSeriesTable
┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  Name:             unnamed                                                                                                                                                                                                                                         │
│  Columns:          turbine_00, turbine_01, turbine_02, turbine_03, turbine_04, turbine_05, turbine_06, turbine_07, turbine_08, turbine_09, turbine_10, turbine_11, turbine_12, turbine_13, turbine_14, turbine_15, turbine_16, turbine_17, turbine_18, turbine_19  │
│  Length:           48 × 20                                                                                                                                                                                                                                         │
│  Frequency:        PT1H                                                                                                                                                                                                                                            │
│  Timezone:         UTC (+00:00)                                                                                                                                                                                                                                    │
│  Unit:             MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW, MW                                                                                                                                                                  │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│                    turbine_00  turbine_01  turbine_02  turbine_03  turbine_04  turbine_05  turbine_06  turbine_07  turbine_08  turbine_09  turbine_10  turbine_11  turbine_12  turbine_13  turbine_14  turbine_15  turbine_16  turbine_17  turbine_18  turbine_19  │
│  2024-01-15 00:00     206.274     274.303     243.921     106.302     125.042     268.388     51.3163     255.307     249.267     166.984     125.758     119.606     113.717     161.269     176.137     188.374     298.875     248.165     205.545      297.24  │
│  2024-01-15 01:00     103.827      90.053     203.135     60.9855     58.9201     178.722     166.552     279.292     207.307     178.529     174.218     111.879     52.9485     98.1005     223.008     100.152     142.384     50.9336     257.512     88.6153  │
│  2024-01-15 02:00       116.9     270.083     177.448     261.788     209.929     235.443     72.8739     185.286     176.943     267.835     140.316     199.546     64.8129     146.908     130.759     87.5499     254.085     144.862     294.687     197.498  │
│  ...                      ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...  │
│  2024-01-16 21:00     115.077      195.91     288.058     254.283     210.182     179.705      53.783     272.679     66.7135     82.2703     223.268     253.036     246.638     163.101     94.2639     58.8256     262.845     294.424     111.034     266.519  │
│  2024-01-16 22:00     150.633     127.836     176.289      52.042     160.679     99.8887     228.114     175.643     267.213     274.162      129.19     120.096     284.783     175.999     74.1289     61.4713     297.606     194.791     63.6232     177.814  │
│  2024-01-16 23:00     176.026     158.847      287.82     287.193     81.2419     87.9302     151.872     258.507     205.938     232.458     194.015     102.514     254.441     53.5052     224.621     98.5849     167.773     293.783     184.759     64.2372  │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Same data as pandas DataFrame (for comparison)

[28]:

tbl_wide.df

[28]:

	turbine_00	turbine_01	turbine_02	turbine_03	turbine_04	turbine_05	turbine_06	turbine_07	turbine_08	turbine_09	turbine_10	turbine_11	turbine_12	turbine_13	turbine_14	turbine_15	turbine_16	turbine_17	turbine_18	turbine_19
timestamp
2024-01-15 00:00:00+00:00	206.273867	274.303450	243.921423	106.301797	125.041571	268.388361	51.316326	255.307105	249.267357	166.983738	125.758107	119.606403	113.717397	161.269076	176.137065	188.374338	298.875071	248.165480	205.544807	297.240037
2024-01-15 01:00:00+00:00	103.827175	90.053008	203.134901	60.985502	58.920070	178.722205	166.551506	279.291943	207.306564	178.529412	174.218359	111.878731	52.948506	98.100536	223.008030	100.151681	142.384078	50.933561	257.511932	88.615270
2024-01-15 02:00:00+00:00	116.899826	270.083038	177.447702	261.787562	209.929292	235.442737	72.873901	185.285955	176.943059	267.834844	140.316015	199.546017	64.812911	146.907950	130.759087	87.549932	254.084526	144.861543	294.686971	197.497923
2024-01-15 03:00:00+00:00	201.264063	209.499145	219.112561	87.697005	160.078367	109.890990	150.624575	74.176023	291.957013	103.751009	217.941291	125.105020	268.519257	215.553685	82.903954	261.268580	286.237043	275.979197	192.429787	86.364988
2024-01-15 04:00:00+00:00	98.115874	281.976421	188.081622	95.138125	271.014224	210.392926	192.423569	144.071959	152.738821	109.872303	59.514322	269.054702	166.932554	186.908800	130.540827	237.831230	56.299218	143.046318	57.587574	80.723026
2024-01-15 05:00:00+00:00	291.787059	214.440183	157.055062	180.935027	268.202302	136.052667	197.572746	220.921093	138.853444	179.774622	241.311846	277.294828	87.765570	283.354848	51.294716	238.244376	252.631708	84.191013	154.725913	253.814069
2024-01-15 06:00:00+00:00	53.567797	207.115487	248.255915	178.250896	231.462355	106.605871	99.630287	140.781738	94.851507	136.515360	287.031015	193.333179	135.017017	117.881155	288.009873	161.119552	295.098688	178.880667	180.291532	274.135131
2024-01-15 07:00:00+00:00	235.691855	195.163216	156.662376	269.546965	152.911537	280.689894	67.178838	157.499215	179.878705	287.734549	112.749812	251.509787	219.117800	229.271476	207.405546	292.890177	133.170365	149.568890	100.727938	62.676014
2024-01-15 08:00:00+00:00	103.227049	278.866099	260.042204	78.101435	200.944756	169.799124	198.671218	214.818752	126.664873	290.337711	166.460009	207.025214	208.806546	95.972349	65.466355	152.879206	241.007522	253.805447	232.497312	78.301238
2024-01-15 09:00:00+00:00	278.338715	250.509144	269.422842	180.826038	278.908859	61.663059	57.572208	55.053893	113.192169	112.142443	96.875834	191.763955	59.746460	197.596967	91.502788	219.468427	55.268839	127.642549	284.585322	184.599095
2024-01-15 10:00:00+00:00	252.896851	214.506520	202.687696	97.813170	193.598687	59.921605	250.416101	290.017729	263.502268	62.677411	134.665021	129.500799	78.179248	206.652955	249.364544	128.430368	265.702309	249.281728	82.284485	241.714789
2024-01-15 11:00:00+00:00	270.655180	99.320642	193.410295	209.687492	202.333564	74.061419	215.297865	207.988681	255.971372	250.878175	131.792052	230.511833	266.818347	273.236940	90.378082	56.675588	212.701861	103.669068	190.927431	286.201132
2024-01-15 12:00:00+00:00	144.829906	113.193638	164.127512	214.310978	75.274742	145.146191	83.430297	215.611555	257.638133	144.213445	142.930988	184.880415	103.764445	111.852398	132.463071	164.356425	70.382865	238.183034	194.763644	124.923468
2024-01-15 13:00:00+00:00	69.386643	240.795238	82.769747	83.301661	82.671061	70.315578	276.598091	117.311075	126.602645	258.198578	204.980865	96.785847	158.703353	270.980616	143.843504	227.720388	74.201927	231.831194	244.118381	256.441695
2024-01-15 14:00:00+00:00	218.550248	142.683199	66.053000	179.694059	239.364977	97.709551	116.559307	184.030011	237.082891	274.146683	81.435379	96.067553	249.884243	211.130406	230.243198	299.192791	284.794990	260.756267	244.278952	148.754804
2024-01-15 15:00:00+00:00	210.307278	96.111411	239.873616	239.422666	230.323803	161.199756	144.545082	154.942733	58.334809	261.080029	185.592469	146.879660	187.007192	230.410055	145.365217	257.660168	279.865837	146.858074	84.454039	240.093324
2024-01-15 16:00:00+00:00	298.237215	86.997037	228.168919	256.330850	280.142986	80.845354	72.952478	296.967895	79.189121	94.201890	193.738233	161.568259	237.598048	97.639312	278.606940	104.298712	242.277748	66.900881	168.350647	58.139582
2024-01-15 17:00:00+00:00	128.452677	128.057669	229.937110	163.754371	64.193591	298.840415	272.174827	279.080984	111.643883	148.527564	106.794875	81.226596	58.255981	175.834112	80.783414	94.076093	265.118920	171.060692	95.925880	217.466140
2024-01-15 18:00:00+00:00	116.466219	181.734300	120.738217	179.040297	207.133398	184.052020	148.901027	247.703450	268.359362	94.842816	84.077181	78.297910	294.899183	285.397826	107.666796	292.476893	101.954417	176.619004	174.346261	278.738976
2024-01-15 19:00:00+00:00	60.132215	128.837704	199.993675	66.599555	109.137644	166.265864	270.214257	240.239880	257.245117	240.267443	226.930194	262.423336	220.369173	233.920765	125.411126	91.910181	239.131245	91.459324	279.863963	199.160709
2024-01-15 20:00:00+00:00	132.358528	284.160789	88.782568	178.616584	72.888518	291.356927	193.844015	250.916649	120.480605	250.449157	225.710491	210.920367	287.640704	158.372835	153.772108	223.030282	258.763827	133.769005	217.414264	102.259352
2024-01-15 21:00:00+00:00	187.924993	242.292925	66.328738	231.960516	53.858119	289.587507	167.167450	152.267714	230.111163	180.824717	232.712214	71.189474	190.697968	189.527173	283.028473	59.901395	163.204911	207.768228	187.686738	68.540274
2024-01-15 22:00:00+00:00	198.307098	105.549221	98.887295	269.672721	99.460527	163.572688	237.571721	226.828847	188.364751	251.761437	166.454044	205.096358	254.729160	219.522909	210.457092	151.532181	189.580865	149.020666	236.073064	145.107539
2024-01-15 23:00:00+00:00	166.655289	238.864272	175.913950	134.508143	256.704735	145.240649	261.182275	246.310101	161.822206	228.222735	58.597425	147.449601	265.083422	194.884839	189.456457	216.184721	219.417908	195.899563	155.157328	95.870922
2024-01-16 00:00:00+00:00	123.160509	123.230544	157.686231	299.764689	138.137985	161.844820	142.906891	195.356935	286.974645	272.268223	144.284686	116.725286	271.099651	173.458335	221.385287	68.322856	266.639424	128.241291	173.631739	99.505270
2024-01-16 01:00:00+00:00	155.060656	257.012229	256.894812	168.418003	241.977675	164.516716	143.339085	186.472244	100.335192	129.140381	226.632193	242.628495	64.177034	233.206535	254.357484	161.298393	65.613478	220.728314	111.540915	210.839332
2024-01-16 02:00:00+00:00	144.155627	209.290880	114.533720	169.134256	181.168685	207.753296	100.791960	290.512769	122.284519	126.505033	114.078096	66.869065	155.730728	242.959078	227.391734	98.893113	84.232976	130.356985	242.063788	172.019534
2024-01-16 03:00:00+00:00	280.796046	214.095941	205.226695	241.922782	237.564562	135.959531	114.252100	159.405347	172.311795	85.430693	160.333049	209.955814	83.928626	84.509472	93.430353	120.832851	290.427319	56.447823	95.934780	91.261960
2024-01-16 04:00:00+00:00	168.587006	53.768195	164.543305	90.730612	117.410056	248.110151	96.016805	204.282208	151.578067	154.398892	254.211354	90.228739	280.952556	273.372714	217.080491	62.361830	215.827132	200.086921	243.051477	103.457896
2024-01-16 05:00:00+00:00	288.339003	271.549287	78.589285	128.800465	58.111047	257.736803	278.773687	297.452313	202.981254	216.496395	219.601383	192.537109	288.105728	201.965307	133.931510	178.219310	54.665719	231.031576	223.480269	155.262785
2024-01-16 06:00:00+00:00	262.870273	238.881524	134.171150	69.374492	83.055756	96.068057	125.979507	161.878931	281.999145	119.604474	274.646416	220.115545	263.373229	150.813349	50.819185	246.741841	111.368963	85.620051	249.872900	221.582162
2024-01-16 07:00:00+00:00	103.103800	116.895454	284.804958	117.134494	225.601470	82.388312	253.758967	79.397503	179.010282	58.804407	187.519636	261.655403	71.099628	118.564145	241.173266	202.024580	128.199227	207.225970	163.677580	265.589066
2024-01-16 08:00:00+00:00	123.234976	239.432651	208.918539	235.107165	144.565403	231.230255	224.606524	146.872223	56.613058	290.728330	228.400099	101.184662	232.701803	119.670841	167.321348	264.972040	162.018026	122.001643	126.372778	69.254317
2024-01-16 09:00:00+00:00	104.876395	117.776557	93.242278	65.766429	166.447291	103.599033	234.692628	280.631941	118.246175	193.062073	176.681416	68.777135	81.218596	271.194864	168.538009	251.152478	66.645186	198.703284	266.327989	266.766524
2024-01-16 10:00:00+00:00	51.430607	180.430026	142.251187	240.010507	68.304749	116.231641	253.160696	167.389056	163.287580	292.139662	129.972666	60.430515	148.050209	88.343390	70.472964	191.965089	288.393904	194.851894	211.778637	130.907215
2024-01-16 11:00:00+00:00	192.780714	238.061128	197.321214	250.694594	187.617670	99.536014	195.681564	172.733962	91.483883	204.791463	254.240136	83.771923	186.236465	233.421783	150.754399	117.511560	143.070103	185.424630	221.665093	189.793805
2024-01-16 12:00:00+00:00	76.756259	254.471598	280.964929	75.323211	112.379866	93.420297	262.288791	277.569681	61.068439	133.104853	98.123653	167.383177	280.397607	67.543310	92.819778	171.757320	181.008674	236.375321	159.421510	85.284188
2024-01-16 13:00:00+00:00	138.402968	295.523280	237.680537	79.086093	252.052295	127.731616	245.349121	53.180362	267.817539	90.577581	163.937649	215.438900	176.967392	91.349442	84.779628	121.133598	63.097939	104.136192	82.526254	166.199254
2024-01-16 14:00:00+00:00	139.127482	196.708062	120.219716	58.477762	132.777540	238.795176	113.959540	151.137108	238.743595	151.670321	271.781033	279.517126	127.632410	233.990913	113.279246	84.880843	159.470260	200.618107	283.897723	86.475380
2024-01-16 15:00:00+00:00	228.090669	168.866126	82.927324	293.164512	223.942237	82.214669	276.438855	75.773540	73.164809	132.887749	284.489790	58.256120	279.551810	202.902895	66.762910	192.147931	164.697681	190.996137	234.300885	219.654774
2024-01-16 16:00:00+00:00	299.306887	89.167266	253.213855	152.773247	245.826880	272.944793	297.999547	116.510461	163.234568	285.694818	286.434595	143.218461	285.052364	151.472789	143.691525	239.154849	167.802957	162.049614	146.529748	158.200983
2024-01-16 17:00:00+00:00	84.954412	65.259287	238.498286	191.124987	217.972662	250.583863	107.526541	108.443232	119.477158	125.981248	240.172729	143.545397	130.797216	209.075392	96.642625	76.605038	224.283310	239.111333	124.091151	209.010294
2024-01-16 18:00:00+00:00	58.519142	151.079745	194.489591	113.354632	137.053725	202.387391	197.474461	88.547684	226.645472	70.007319	292.431169	100.590490	113.387036	100.075137	297.874740	121.648730	201.323069	297.658538	287.516964	60.587457
2024-01-16 19:00:00+00:00	180.296017	200.326169	225.598765	133.977329	131.977153	51.071004	58.809175	187.514292	68.971775	100.620303	133.317979	67.617815	57.714960	281.730112	158.802157	101.221115	227.112950	52.361335	111.254312	290.187410
2024-01-16 20:00:00+00:00	274.422543	208.981313	73.300693	129.512711	56.983138	104.797751	56.358531	81.540920	208.303635	75.110711	88.940769	275.701996	133.863106	133.249231	71.634868	248.982630	219.094310	186.783200	204.478210	75.095173
2024-01-16 21:00:00+00:00	115.076517	195.910379	288.057985	254.282986	210.181590	179.704645	53.783044	272.679067	66.713524	82.270263	223.267935	253.035995	246.637666	163.101030	94.263944	58.825554	262.844903	294.424366	111.033876	266.519082
2024-01-16 22:00:00+00:00	150.632729	127.835940	176.288584	52.042045	160.678617	99.888742	228.114298	175.642845	267.213157	274.162048	129.189732	120.095951	284.782596	175.999390	74.128882	61.471283	297.606381	194.790907	63.623243	177.813862
2024-01-16 23:00:00+00:00	176.026126	158.846960	287.820100	287.193008	81.241920	87.930176	151.872151	258.506618	205.938149	232.457532	194.014596	102.514234	254.441357	53.505246	224.621064	98.584904	167.772870	293.783301	184.758964	64.237205

8. Side-by-side: light vs dark

Quick way to preview both colour schemes without changing your OS setting — wrap the HTML repr in a <div> that forces a colour scheme.

[29]:

from IPython.display import HTML

light = f'<div style="color-scheme: light; padding: 10px;">{ts._repr_html_()}</div>'
dark = f'<div style="color-scheme: dark; background: #0f172a; padding: 10px; border-radius: 8px;">{ts._repr_html_()}</div>'

HTML(f"<h4>Light mode</h4>{light}<br><h4>Dark mode</h4>{dark}")

[29]:

Light mode

TimeSeriesList

Name	power
Length	24
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW
Data type	OBSERVATION
Description	Hourly power output from wind farm Alpha

timestamp	power
2024-01-15 00:00	120.0
2024-01-15 01:00	124.207
2024-01-15 02:00	124.546
…	…
2024-01-15 21:00	124.183
2024-01-15 22:00	119.956
2024-01-15 23:00	115.769

Dark mode

TimeSeriesList

Name	power
Length	24
Frequency	PT1H
Timezone	UTC (+00:00)
Unit	MW
Data type	OBSERVATION
Description	Hourly power output from wind farm Alpha

timestamp	power
2024-01-15 00:00	120.0
2024-01-15 01:00	124.207
2024-01-15 02:00	124.546
…	…
2024-01-15 21:00	124.183
2024-01-15 22:00	119.956
2024-01-15 23:00	115.769

[ ]:

[ ]: