{
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},
"tags": []
},
"source": [
"# Plots"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe",
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"outputs": [],
"source": [
"# Import to be able to import python package from src\n",
"import sys\n",
"sys.path.insert(0, '../src')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f",
"metadata": {
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"end_time": "2023-11-22T14:30:12.438915100Z",
"start_time": "2023-11-22T14:30:12.223262200Z"
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"execution": {
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"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import ontime as on\n",
"from darts.datasets import EnergyDataset"
]
},
{
"cell_type": "markdown",
"id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91",
"metadata": {
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"start_time": "2026-07-28T16:08:56.390547",
"status": "completed"
},
"tags": []
},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "db08372d-8ab2-4290-9196-76eb0c275629",
"metadata": {
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"end_time": "2023-11-22T14:30:12.886891300Z",
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"tags": []
},
"outputs": [],
"source": [
"ts = EnergyDataset().load()"
]
},
{
"cell_type": "markdown",
"id": "789865e9-e840-4267-994a-f10743e46279",
"metadata": {
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"start_time": "2026-07-28T16:08:56.481855",
"status": "completed"
},
"tags": []
},
"source": [
"Complete TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "009bcbe3-b73c-4355-b280-1bcb3d98e113",
"metadata": {
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"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df = ts.to_dataframe()\n",
"df = df.interpolate()\n",
"cols = ['generation biomass', 'generation solar', 'generation nuclear']\n",
"df = df[cols]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7baaa1c5-c460-44bb-a375-f2eff4509cee",
"metadata": {
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"end_time": "2023-11-22T14:30:12.888403100Z",
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"execution": {
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"start_time": "2026-07-28T16:08:56.516043",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts = on.TimeSeries.from_dataframe(df)"
]
},
{
"cell_type": "markdown",
"id": "07386885-6139-43ed-a695-c2fa3cf23904",
"metadata": {
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"status": "completed"
},
"tags": []
},
"source": [
"Prepare data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "07b54c1f-4215-43c0-8c81-bb337b9f50fb",
"metadata": {
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"end_time": "2023-11-22T14:30:12.888403100Z",
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"execution": {
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"status": "completed"
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"tags": []
},
"outputs": [],
"source": [
"ts_uni = ts['generation solar'].slice(pd.Timestamp('2015'), pd.Timestamp('2016'))\n",
"ts_multi = ts.slice(pd.Timestamp('2015'), pd.Timestamp('2016'))"
]
},
{
"cell_type": "markdown",
"id": "a1a7ecfe-894b-42a8-8a94-5163f83b26a4",
"metadata": {
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"start_time": "2026-07-28T16:08:56.552038",
"status": "completed"
},
"tags": []
},
"source": [
"## Primitive Plots"
]
},
{
"cell_type": "markdown",
"id": "a438a78b-3ed5-4501-a316-2414e881dbb3",
"metadata": {
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"exception": false,
"start_time": "2026-07-28T16:08:56.560239",
"status": "completed"
},
"tags": []
},
"source": [
"### Line(s)"
]
},
{
"cell_type": "markdown",
"id": "8c60d9ea-e027-4ba0-8fc6-87bfd8059084",
"metadata": {
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"status": "completed"
},
"tags": []
},
"source": [
"With univariate TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ad694d28-07ff-4563-81a6-84ffe6591b70",
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"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_uni.head(400))\\\n",
" .add(on.marks.line)\\\n",
" .properties(width=600)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "4de943c7-b0fd-4943-8cb1-8ce496599864",
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"tags": []
},
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"with multivariate TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2a4a0582-209b-461a-815b-7d4d4698c0f7",
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"end_time": "2023-11-22T14:30:12.940457Z",
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},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_multi.head(400))\\\n",
" .add(on.marks.line)\\\n",
" .properties(width=600)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "2fd66619-22c4-43b5-b827-39b01d16e73b",
"metadata": {
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"exception": false,
"start_time": "2026-07-28T16:08:56.976990",
"status": "completed"
},
"tags": []
},
"source": [
"### Dots"
]
},
{
"cell_type": "markdown",
"id": "e1e97a4e-352d-446f-a70f-66c83e6754e1",
"metadata": {
"papermill": {
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"tags": []
},
"source": [
"With univariate TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f25a59e9-98d3-4519-bc03-feb9f6cd4d2a",
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},
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{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_uni.head(400))\\\n",
" .add(on.marks.dots)\\\n",
" .properties(width=600)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "8b7e5730-9341-404d-93a9-f6811d2775d9",
"metadata": {
"papermill": {
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"status": "completed"
},
"tags": []
},
"source": [
"with multivariate TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e02804dd-0c24-40e4-9acc-8ed817f03a12",
"metadata": {
"execution": {
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"start_time": "2026-07-28T16:08:57.046200",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_multi.head(400))\\\n",
" .add(on.marks.dots)\\\n",
" .properties(width=600)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "f9b6835a-2bbf-48d6-935d-fa6226cf45c1",
"metadata": {
"papermill": {
"duration": 0.005547,
"end_time": "2026-07-28T16:08:57.087994",
"exception": false,
"start_time": "2026-07-28T16:08:57.082447",
"status": "completed"
},
"tags": []
},
"source": [
"### Areas"
]
},
{
"cell_type": "markdown",
"id": "8c8f8fc6-ccfd-4c3f-b513-e14842e7849c",
"metadata": {
"papermill": {
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"exception": false,
"start_time": "2026-07-28T16:08:57.093620",
"status": "completed"
},
"tags": []
},
"source": [
"With a single time series"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7db16759-fbf2-46ef-a0da-4eea94f8ca60",
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},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_uni.head(400))\\\n",
" .add(on.marks.area)\\\n",
" .properties(width=600)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "54c8348a-fafe-49c4-939f-77f1a7a2c9d6",
"metadata": {
"papermill": {
"duration": 0.005506,
"end_time": "2026-07-28T16:08:57.188060",
"exception": false,
"start_time": "2026-07-28T16:08:57.182554",
"status": "completed"
},
"tags": []
},
"source": [
"With a multivariate time series it works with exactly two"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d6b18ef8-7954-4780-8e9a-dd6e8afd6d15",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.199809Z",
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},
"papermill": {
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"end_time": "2026-07-28T16:08:57.202062",
"exception": false,
"start_time": "2026-07-28T16:08:57.193522",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from darts import concatenate"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "70178386-e035-4c7a-91c2-ee206131635d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.214406Z",
"iopub.status.busy": "2026-07-28T16:08:57.214316Z",
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"shell.execute_reply": "2026-07-28T16:08:57.218487Z"
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},
"tags": []
},
"outputs": [],
"source": [
"# First we create the series with two components\n",
"ts_ci = concatenate([\n",
" ts_multi.univariate_component(0), \n",
" ts_multi.univariate_component(1)\n",
"], axis=1)\n",
"ts_ci = on.TimeSeries.from_darts(ts_ci)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "98ca8c64-bcde-4e93-a0ec-df1410242f36",
"metadata": {
"execution": {
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},
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"start_time": "2026-07-28T16:08:57.225417",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Then we plot it\n",
"on.Plot(ts_ci.head(200))\\\n",
" .add(on.marks.area, title='Diff. between solar and biomass generation')\\\n",
" .properties(width=600, height=200)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "f436ba6f-aa4f-47e8-9673-754c9c02e1b4",
"metadata": {
"papermill": {
"duration": 0.005896,
"end_time": "2026-07-28T16:08:57.269227",
"exception": false,
"start_time": "2026-07-28T16:08:57.263331",
"status": "completed"
},
"tags": []
},
"source": [
"### Heatmaps"
]
},
{
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"status": "completed"
},
"tags": []
},
"source": [
"with univariate TimeSeries"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8a70db18-2736-4b52-8a49-a09908048535",
"metadata": {
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"status": "completed"
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"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_uni.head(1000))\\\n",
" .add(on.marks.heatmap)\\\n",
" .properties(width=600, height=50)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "bb43360b-c5ff-4e95-9af8-7ced3fb66d7d",
"metadata": {
"papermill": {
"duration": 0.006375,
"end_time": "2026-07-28T16:08:57.341082",
"exception": false,
"start_time": "2026-07-28T16:08:57.334707",
"status": "completed"
},
"tags": []
},
"source": [
"with multivariate Heatmap"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "95e245b4-d594-45f7-ae7a-04db01fc860d",
"metadata": {
"ExecuteTime": {
"end_time": "2023-11-22T14:30:12.943458900Z",
"start_time": "2023-11-22T14:30:12.778200200Z"
},
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"exception": false,
"start_time": "2026-07-28T16:08:57.347285",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot(ts_multi.head(1000))\\\n",
" .add(on.marks.heatmap)\\\n",
" .properties(width=600, height=150)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "3b07d001-bef6-4b9b-86b6-105ad318adab",
"metadata": {
"papermill": {
"duration": 0.006714,
"end_time": "2026-07-28T16:08:57.405193",
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"start_time": "2026-07-28T16:08:57.398479",
"status": "completed"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "8a52c3c7-3adf-454e-9b6d-b0959adcd8f6",
"metadata": {
"papermill": {
"duration": 0.007285,
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"exception": false,
"start_time": "2026-07-28T16:08:57.412398",
"status": "completed"
},
"tags": []
},
"source": [
"## Combined Plots"
]
},
{
"cell_type": "markdown",
"id": "c29b1243-bc85-4a8d-899a-710cbfc2d77b",
"metadata": {
"papermill": {
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"status": "completed"
},
"tags": []
},
"source": [
"Most of the plots in onTime can be combined as they are based on Altair layered charts. For instance, you can do the following to have a dots on a line."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e18041c8-4c74-4f63-be92-56ad1d971bf7",
"metadata": {
"execution": {
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"start_time": "2026-07-28T16:08:57.439832",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.Plot()\\\n",
" .add(on.marks.dots, ts_multi.univariate_component(1).head(400))\\\n",
" .add(on.marks.line, ts_multi.univariate_component(0).head(400))\\\n",
" .properties(width=600, height=200)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "b7cd4986",
"metadata": {
"papermill": {
"duration": 0.007583,
"end_time": "2026-07-28T16:08:57.507060",
"exception": false,
"start_time": "2026-07-28T16:08:57.499477",
"status": "completed"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "f3bec92b",
"metadata": {
"papermill": {
"duration": 0.007232,
"end_time": "2026-07-28T16:08:57.521807",
"exception": false,
"start_time": "2026-07-28T16:08:57.514575",
"status": "completed"
},
"tags": []
},
"source": [
"## Subplots\n",
"\n",
"A `Plot` is a single panel. To place several panels in one figure, use the\n",
"`on.rows` and `on.cols` factories, which return a `Figure`.\n",
"\n",
"The names describe the *arguments*, not the container: `on.rows(a, b)` means\n",
"\"a and b are rows\", so the panels are stacked vertically. `on.cols(a, b)` places\n",
"them side by side. Both accept `Plot` and `Figure` panels, so they nest freely.\n",
"\n",
"A few rules worth knowing:\n",
"\n",
"- `share_x` / `share_y` share the scale domain across the panels of a group. They\n",
" default to `share_x=True, share_y=False` on `rows` and\n",
" `share_x=False, share_y=False` on `cols`.\n",
"- **Propagation**: a flag passed explicitly propagates down into nested groups;\n",
" a flag left unset lets each nested group use its own default. This is the most\n",
" likely source of surprise.\n",
"- When the x axis is shared in a vertical stack, the inner x axis labels are hidden\n",
" and only drawn on the bottom panel.\n",
"- Panel-level `.properties()` wins over figure-level `.properties()`, which only\n",
" fills the values a panel left unset.\n",
"- `spacing` sets the gap between panels in pixels, and the layout options below\n",
" control how much room the axes get.\n",
"- `sizes` gives the extent of each child along the stacking axis (heights for `rows`,\n",
" widths for `cols`), either as pixels or as fractions summing to 1.0.\n",
"\n",
"> **Known constraint.** In Vega-Lite 5, `selection_interval(bind=\"scales\")` does not\n",
"> reliably propagate across concatenated views, so synchronised pan/zoom across\n",
"> panels is not available. Sharing a scale *domain* works, interactive zoom does not."
]
},
{
"cell_type": "markdown",
"id": "73df96b8",
"metadata": {
"papermill": {
"duration": 0.007546,
"end_time": "2026-07-28T16:08:57.536584",
"exception": false,
"start_time": "2026-07-28T16:08:57.529038",
"status": "completed"
},
"tags": []
},
"source": [
"Let's prepare a few short series to plot."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c3e5fbbe",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.552624Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"solar = ts_multi['generation solar'].head(400)\n",
"nuclear = ts_multi['generation nuclear'].head(400)\n",
"biomass = ts_multi['generation biomass'].head(400)\n",
"\n",
"def line(series, title=None):\n",
" plot = on.Plot(series).add(on.marks.line)\n",
" return plot.properties(title=title) if title is not None else plot"
]
},
{
"cell_type": "markdown",
"id": "4610e4a2",
"metadata": {
"papermill": {
"duration": 0.007578,
"end_time": "2026-07-28T16:08:57.573247",
"exception": false,
"start_time": "2026-07-28T16:08:57.565669",
"status": "completed"
},
"tags": []
},
"source": [
"### Stacked signals with a shared time axis\n",
"\n",
"The x axis is shared, so its labels are only drawn on the bottom panel."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "0a2b6a9d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.589037Z",
"iopub.status.busy": "2026-07-28T16:08:57.588886Z",
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"shell.execute_reply": "2026-07-28T16:08:57.654177Z"
},
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"duration": 0.074934,
"end_time": "2026-07-28T16:08:57.655534",
"exception": false,
"start_time": "2026-07-28T16:08:57.580600",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.VConcatChart(...)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.rows(line(solar), line(nuclear), line(biomass))\\\n",
" .properties(width=800, height=140)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "db439728",
"metadata": {
"papermill": {
"duration": 0.007892,
"end_time": "2026-07-28T16:08:57.672754",
"exception": false,
"start_time": "2026-07-28T16:08:57.664862",
"status": "completed"
},
"tags": []
},
"source": [
"### Unequal heights in pixels\n",
"\n",
"A tall main panel with a thin heatmap strip below it."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "fc32c9fb",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.690115Z",
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"shell.execute_reply": "2026-07-28T16:08:57.745041Z"
},
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"duration": 0.06571,
"end_time": "2026-07-28T16:08:57.746420",
"exception": false,
"start_time": "2026-07-28T16:08:57.680710",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.VConcatChart(...)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main = on.Plot(solar).add(on.marks.line)\n",
"strip = on.Plot(solar).add(on.marks.heatmap)\n",
"\n",
"on.rows(main, strip, sizes=[240, 40])\\\n",
" .properties(width=800)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "79020ddb",
"metadata": {
"papermill": {
"duration": 0.007847,
"end_time": "2026-07-28T16:08:57.763017",
"exception": false,
"start_time": "2026-07-28T16:08:57.755170",
"status": "completed"
},
"tags": []
},
"source": [
"### Fractional sizes\n",
"\n",
"Fractions must sum to 1.0 and are taken along the stacking axis, here the total\n",
"height of the figure."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "adbe6be6",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.780059Z",
"iopub.status.busy": "2026-07-28T16:08:57.779925Z",
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"end_time": "2026-07-28T16:08:57.825292",
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"start_time": "2026-07-28T16:08:57.771380",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.VConcatChart(...)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecast = on.Plot(nuclear).add(on.marks.line)\n",
"residuals = on.Plot(nuclear.diff()).add(on.marks.line)\n",
"\n",
"on.rows(forecast, residuals, sizes=[0.72, 0.28])\\\n",
" .properties(width=700, height=340)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "dd73f112",
"metadata": {
"papermill": {
"duration": 0.008897,
"end_time": "2026-07-28T16:08:57.843078",
"exception": false,
"start_time": "2026-07-28T16:08:57.834181",
"status": "completed"
},
"tags": []
},
"source": [
"### Side by side comparison\n",
"\n",
"A comparison inverts both defaults: the panels share the y scale so the magnitudes\n",
"are comparable, but keep their own x scale because they cover different periods."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "6eacf032",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.861057Z",
"iopub.status.busy": "2026-07-28T16:08:57.860863Z",
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"shell.execute_reply": "2026-07-28T16:08:57.901285Z"
},
"papermill": {
"duration": 0.051232,
"end_time": "2026-07-28T16:08:57.902495",
"exception": false,
"start_time": "2026-07-28T16:08:57.851263",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts_2015 = ts['generation solar'].slice(pd.Timestamp('2015-06-01'), pd.Timestamp('2015-06-08'))\n",
"ts_2016 = ts['generation solar'].slice(pd.Timestamp('2016-06-01'), pd.Timestamp('2016-06-08'))\n",
"\n",
"on.cols(line(ts_2015, title='2015'), line(ts_2016, title='2016'), share_y=True, share_x=False)\\\n",
" .properties(width=350, height=200)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "985a8d19",
"metadata": {
"papermill": {
"duration": 0.010427,
"end_time": "2026-07-28T16:08:57.921804",
"exception": false,
"start_time": "2026-07-28T16:08:57.911377",
"status": "completed"
},
"tags": []
},
"source": [
"### Small multiples\n",
"\n",
"Groups nest, so a row of columns gives a grid of small multiples."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "65c23282",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:57.939125Z",
"iopub.status.busy": "2026-07-28T16:08:57.938999Z",
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"shell.execute_reply": "2026-07-28T16:08:58.001279Z"
},
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"duration": 0.072593,
"end_time": "2026-07-28T16:08:58.002734",
"exception": false,
"start_time": "2026-07-28T16:08:57.930141",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.VConcatChart(...)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"panels = [line(ts_multi[c].head(400), title=c) for c in ts_multi.components]\n",
"\n",
"on.rows(on.cols(*panels[:2]), on.cols(*panels[2:]), share_y=True)\\\n",
" .properties(width=350, height=140)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "827b0852",
"metadata": {
"papermill": {
"duration": 0.009456,
"end_time": "2026-07-28T16:08:58.023823",
"exception": false,
"start_time": "2026-07-28T16:08:58.014367",
"status": "completed"
},
"tags": []
},
"source": [
"### Nesting\n",
"\n",
"Groups nest freely. Here the inner `rows` inherits nothing from the outer `cols`,\n",
"so it uses its own default `share_x=True`, while the outer `cols` keeps\n",
"`share_x=False`."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "f9acbd96",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:58.046178Z",
"iopub.status.busy": "2026-07-28T16:08:58.046029Z",
"iopub.status.idle": "2026-07-28T16:08:58.110599Z",
"shell.execute_reply": "2026-07-28T16:08:58.110090Z"
},
"papermill": {
"duration": 0.078401,
"end_time": "2026-07-28T16:08:58.111470",
"exception": false,
"start_time": "2026-07-28T16:08:58.033069",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"profile = on.Plot(biomass).add(on.marks.line)\n",
"\n",
"on.cols(on.rows(main, strip, sizes=[240, 50]), profile, sizes=[620, 180])\\\n",
" .properties(height=290)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "c9e17aff",
"metadata": {
"papermill": {
"duration": 0.011709,
"end_time": "2026-07-28T16:08:58.134022",
"exception": false,
"start_time": "2026-07-28T16:08:58.122313",
"status": "completed"
},
"tags": []
},
"source": [
"### Adjusting the layout\n",
"\n",
"Four options of `Figure.properties()` control how the panels are placed.\n",
"\n",
"- `spacing` is the gap between panels in pixels (default `4`). The gap is measured\n",
" between the *full* bounds of the panels, axes and titles included, so panels never\n",
" overrun each other.\n",
"- `bounds` selects how a panel is measured: `\"full\"` (default) accounts for the axes\n",
" and titles, `\"flush\"` only accounts for the plotting areas. `\"flush\"` packs the\n",
" panels tightly but lets axis labels and titles overlap their neighbours, so it is\n",
" only useful when the inner axes are hidden.\n",
"- `hide_shared_axes` (default `True`) drops the redundant inner axes of a shared\n",
" scale: the x axis is kept on the bottom row only, and the y axis on the first\n",
" column only. Set it to `False` to draw every axis.\n",
"- `axis_extent` (default `40`) is the minimum width reserved for the y axis of every\n",
" panel, which keeps the y axis titles aligned across panels. Use `0` to let each\n",
" panel size its own axis.\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "e7e10194",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.VConcatChart(...)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on.rows(line(solar), line(nuclear), line(biomass))\\\n",
" .properties(width=800, height=140, spacing=24, axis_extent=60)\\\n",
" .show()"
]
},
{
"cell_type": "markdown",
"id": "69fab142",
"metadata": {
"papermill": {
"duration": 0.012044,
"end_time": "2026-07-28T16:08:58.236107",
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"start_time": "2026-07-28T16:08:58.224063",
"status": "completed"
},
"tags": []
},
"source": [
"The layout of a figure is available as a symbolic tree, which is handy to check\n",
"what was built without touching the data. It reads `/` for a vertical stack, `|`\n",
"for a horizontal one, and annotates the panels with their size when they have\n",
"one."
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "933cb415",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:58.259315Z",
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"start_time": "2026-07-28T16:08:58.246389",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"solar:0.5 / nuclear:0.5"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"figure = on.rows(line(solar, title='solar'), line(nuclear, title='nuclear'), sizes=[0.5, 0.5])\n",
"figure.layout"
]
},
{
"cell_type": "markdown",
"id": "8c1f4d05-d5b2-4d86-8cf0-6165175354eb",
"metadata": {
"papermill": {
"duration": 0.010134,
"end_time": "2026-07-28T16:08:58.293081",
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"start_time": "2026-07-28T16:08:58.282947",
"status": "completed"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "e87e999c-4ffa-4f3a-92a8-db894982a2ae",
"metadata": {
"papermill": {
"duration": 0.009864,
"end_time": "2026-07-28T16:08:58.312763",
"exception": false,
"start_time": "2026-07-28T16:08:58.302899",
"status": "completed"
},
"tags": []
},
"source": [
"## Thematic Plots"
]
},
{
"cell_type": "markdown",
"id": "b65f9e0b-381a-4085-b919-3074b9b18b0a",
"metadata": {
"papermill": {
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"start_time": "2026-07-28T16:08:58.322604",
"status": "completed"
},
"tags": []
},
"source": [
"### Forecasts"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "79328857-d576-43d4-a54a-29196312448b",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts_train, ts_test = ts_uni.split_before(0.9)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "53bdff9e-f198-4a24-9968-95c3efaf5ebb",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:58.380362Z",
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"start_time": "2026-07-28T16:08:58.369271",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from ontime.context import common"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c592bccc-95bf-4b2d-9952-f1b4075288da",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = common.GenericPredictor()\n",
"model.fit(ts_train)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "7a92c059-8854-4d26-a51e-5c44cb7fa1e1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.583739Z",
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"exception": false,
"start_time": "2026-07-28T16:08:59.572703",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts_pred = model.predict(24 * 3)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "609375fd-e994-4336-b1e8-07ea11b38d49",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.623057Z",
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"exception": false,
"start_time": "2026-07-28T16:08:59.611776",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts_train = ts_train.rename({'generation solar':'Training set'})\n",
"ts_test = ts_test.rename({'generation solar':'Test set'})\n",
"ts_pred = ts_pred.rename({'generation solar':'Forecast'})"
]
},
{
"cell_type": "markdown",
"id": "2f6c72b8-2124-452f-9577-f4d4ece10803",
"metadata": {
"papermill": {
"duration": 0.010098,
"end_time": "2026-07-28T16:08:59.649263",
"exception": false,
"start_time": "2026-07-28T16:08:59.639165",
"status": "completed"
},
"tags": []
},
"source": [
"Plot a prediction"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "99165fdc-d0a5-49d5-a882-adb8b8534e54",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.670327Z",
"iopub.status.busy": "2026-07-28T16:08:59.670187Z",
"iopub.status.idle": "2026-07-28T16:08:59.714827Z",
"shell.execute_reply": "2026-07-28T16:08:59.714238Z"
},
"papermill": {
"duration": 0.056078,
"end_time": "2026-07-28T16:08:59.715460",
"exception": false,
"start_time": "2026-07-28T16:08:59.659382",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(\n",
"on.Plot()\n",
" .add(on.marks.line, ts_test.head(24 * 3), type='dashed')\n",
" .add(on.marks.line, ts_train.tail(24 * 4))\n",
" .add(on.marks.line, ts_pred)\n",
" .properties(width=600, height=200)\n",
" .show()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8b2d1199-7a74-4586-a834-67fc367229e4",
"metadata": {
"papermill": {
"duration": 0.010409,
"end_time": "2026-07-28T16:08:59.736592",
"exception": false,
"start_time": "2026-07-28T16:08:59.726183",
"status": "completed"
},
"tags": []
},
"source": [
"### Anomalies"
]
},
{
"cell_type": "markdown",
"id": "9424f948-96fe-4a51-987a-9c323c1af142",
"metadata": {
"papermill": {
"duration": 0.010363,
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"exception": false,
"start_time": "2026-07-28T16:08:59.746587",
"status": "completed"
},
"tags": []
},
"source": [
"Create the mock data"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "0bea5b4f-e11b-4836-a039-d61117a4684d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.777735Z",
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},
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"duration": 0.013106,
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"start_time": "2026-07-28T16:08:59.767197",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"td_point = on.detectors.quantile(high_quantile=0.99)\n",
"td_collective = on.detectors.threshold(low_threshold=-30)\n",
"td_contextual = on.detectors.quantile(high_quantile=0.98)"
]
},
{
"cell_type": "markdown",
"id": "06a7f815-c162-4243-97d9-c8e7e84a6b02",
"metadata": {
"papermill": {
"duration": 0.010921,
"end_time": "2026-07-28T16:08:59.801097",
"exception": false,
"start_time": "2026-07-28T16:08:59.790176",
"status": "completed"
},
"tags": []
},
"source": [
"Add anomalies"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e5c7dba4-1ff8-47c9-907b-4bde47ca09dc",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.821815Z",
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"exception": false,
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import random\n",
"\n",
"def add_point_anomalies(ts, n, value):\n",
" df = ts.to_dataframe()\n",
" random_indices = np.random.choice(df.index, size=n, replace=False)\n",
" df.loc[random_indices] = value\n",
" return on.TimeSeries.from_dataframe(df)\n",
"\n",
"def add_collective_anomalies(ts, n, min_duration=10, max_duration=20):\n",
" df = ts.to_dataframe()\n",
" for i in range(n+1):\n",
" block_duration = random.randint(min_duration, max_duration)\n",
" start_index = np.random.choice(df.index[:-block_duration])\n",
" end_index = start_index + pd.Timedelta(days=block_duration - 1)\n",
" df.loc[start_index:end_index] = -40\n",
" return on.TimeSeries.from_dataframe(df)"
]
},
{
"cell_type": "markdown",
"id": "610cfd2a-c6eb-45bb-a51d-f384dc5a29e9",
"metadata": {
"papermill": {
"duration": 0.009964,
"end_time": "2026-07-28T16:08:59.844714",
"exception": false,
"start_time": "2026-07-28T16:08:59.834750",
"status": "completed"
},
"tags": []
},
"source": [
"Select univariate component"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "9e89c5ca-3456-446c-8b5e-507574f43df5",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.865081Z",
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"shell.execute_reply": "2026-07-28T16:08:59.867575Z"
},
"papermill": {
"duration": 0.014123,
"end_time": "2026-07-28T16:08:59.868606",
"exception": false,
"start_time": "2026-07-28T16:08:59.854483",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts = ts.univariate_component(0)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "03364cbd-61aa-4660-9789-ab7f4662ab79",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.888668Z",
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"shell.execute_reply": "2026-07-28T16:08:59.896863Z"
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"start_time": "2026-07-28T16:08:59.878312",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts = add_point_anomalies(ts, 10, 30)\n",
"ts = add_collective_anomalies(ts, 4)"
]
},
{
"cell_type": "markdown",
"id": "370bdde9-662f-43e7-ab93-cd79b8e2f624",
"metadata": {
"papermill": {
"duration": 0.009902,
"end_time": "2026-07-28T16:08:59.917819",
"exception": false,
"start_time": "2026-07-28T16:08:59.907917",
"status": "completed"
},
"tags": []
},
"source": [
"Create binary time series"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "4f810223-cfd2-429b-8d7d-6182c26d7246",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.937955Z",
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"exception": false,
"start_time": "2026-07-28T16:08:59.927319",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"td_point.fit(ts)\n",
"td_contextual.fit(ts)\n",
"\n",
"ts_ano_point = td_point.detect(ts)\n",
"ts_ano_collective = td_collective.detect(ts)\n",
"ts_ano_contextual = td_contextual.detect(ts)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "c9616b57",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:08:59.978266Z",
"iopub.status.busy": "2026-07-28T16:08:59.978124Z",
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"shell.execute_reply": "2026-07-28T16:08:59.985623Z"
},
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"duration": 0.023139,
"end_time": "2026-07-28T16:08:59.987149",
"exception": false,
"start_time": "2026-07-28T16:08:59.964010",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ts_ano_point = ts_ano_point.rename({'generation biomass': 'Ponctual anomalies'})\n",
"ts_ano_collective = ts_ano_collective.rename({'generation biomass': 'Collective anomalies'})\n",
"ts_ano_contextual = ts_ano_contextual.rename({'generation biomass': 'Contextual anomalies'})"
]
},
{
"cell_type": "markdown",
"id": "873160d1-4b22-41c5-b374-67b9f9124754",
"metadata": {
"papermill": {
"duration": 0.030589,
"end_time": "2026-07-28T16:09:00.038391",
"exception": false,
"start_time": "2026-07-28T16:09:00.007802",
"status": "completed"
},
"tags": []
},
"source": [
"Plot the time series with marked anomalies"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "2e944b87-b85d-4c5c-b963-857f79e6b2a4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:09:00.062711Z",
"iopub.status.busy": "2026-07-28T16:09:00.062565Z",
"iopub.status.idle": "2026-07-28T16:09:00.064916Z",
"shell.execute_reply": "2026-07-28T16:09:00.064458Z"
},
"papermill": {
"duration": 0.014522,
"end_time": "2026-07-28T16:09:00.065416",
"exception": false,
"start_time": "2026-07-28T16:09:00.050894",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Define windows for plotting\n",
"start = 24 * 7 * 54\n",
"duration = 24 * 7 * 15\n",
"end = start + duration"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "d4e93bfd-5e3e-40d4-9c45-577eff06579a",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-28T16:09:00.091408Z",
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},
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"duration": 0.097672,
"end_time": "2026-07-28T16:09:00.176920",
"exception": false,
"start_time": "2026-07-28T16:09:00.079248",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Actually plot\n",
"(\n",
"on.Plot(ts[start:end])\n",
" .add(on.marks.mark, data=ts_ano_contextual[start:end], type='highlight')\n",
" .add(on.marks.mark, data=ts_ano_collective[start:end], type='background')\n",
" .add(on.marks.line)\n",
" .add(on.marks.mark, data=ts_ano_point[start:end], type='dot')\n",
" .properties(width=800, height=200)\n",
" .show()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ba882e58-d1ba-4bd9-9b8b-11c5257986b2",
"metadata": {
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"### Confidence Intervals"
]
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"# Generate two time series\n",
"ts1 = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))\n",
"ts2 = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))"
]
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"# First we create the series with two components\n",
"ts1_abs = ts1.map(np.abs)\n",
"ts2_abs = ts2.map(np.abs)\n",
"ts_ci = concatenate([ts1_abs, ts2_abs], axis=1)\n",
"ts_ci = on.TimeSeries.from_darts(ts_ci)\n",
"ts_ci = ts_ci.rename({'random_walk': 'CI Upper bound', 'random_walk_1': 'CI Lower bound'})\n",
"\n",
"# Then the hypothetical measurement\n",
"ts_mid = (ts1_abs + ts2_abs) / 2\n",
"ts_mid = ts_mid.rename({'random_walk': 'Measurement'})"
]
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"text/html": [
"\n",
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""
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"alt.LayerChart(...)"
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"source": [
"# Then we plot it\n",
"(\n",
"on.Plot() # main line\n",
" .add(on.marks.area, ts_ci.head(200), title='Confidence interval')\n",
" .add(on.marks.line, ts_mid.head(200))\n",
" .properties(width=600, height=200)\n",
" .show()\n",
")"
]
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"source": [
"---"
]
}
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