Merge branch 'Nixtla:main' into save_model

.github/workflows/build-docs.yaml

@@ -0,0 +1,52 @@
+name: "build-docs"
+on:
+  push:
+    branches: ["main"]
+  pull_request:
+    branches: ["main"]
+  workflow_dispatch:
+
+defaults:
+  run:
+    shell: bash
+
+jobs:
+  build-docs:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          submodules: 'recursive'
+      - uses: actions/setup-python@v4
+        with:
+          cache: "pip"
+          python-version: '3.10'
+          cache-dependency-path: settings.ini
+      - name: Build docs
+        run: |
+          set -ux
+          python -m pip install --upgrade pip
+          pip install -Uq nbdev
+          pip install -e ".[dev]"
+          mkdir nbs/_extensions
+          cp -r docs-scripts/mintlify/ nbs/_extensions/
+          python docs-scripts/update-quarto.py
+          echo "procs = nbdev_plotly.plotly:PlotlyProc" >> settings.ini
+          nbdev_docs
+      - name: Apply final formats
+        run: bash ./docs-scripts/docs-final-formatting.bash
+      - name: Copy over necessary assets
+        run: |
+          cp nbs/mint.json _docs/mint.json
+          cp docs-scripts/imgs/* _docs/
+      - name: Deploy to Mintlify Docs
+        if: github.event_name == 'push'
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_branch: docs
+          publish_dir: ./_docs
+          # The following lines assign commit authorship to the official GH-Actions bot for deploys to `docs` branch.
+          # You can swap them out with your own user credentials.
+          user_name: github-actions[bot]
+          user_email: 41898282+github-actions[bot]@users.noreply.github.com

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "docs-scripts"]
+	path = docs-scripts
+	url = [email protected]:Nixtla/docs.git
+	branch = scripts

nbs/mint.json

@@ -0,0 +1,122 @@
+{
+  "$schema": "https://mintlify.com/schema.json",
+  "name": "Nixtla",
+  "logo": {
+    "light": "/light.png",
+    "dark": "/dark.png"
+  },
+  "favicon": "/favicon.svg",
+  "colors": {
+    "primary": "#0E0E0E",
+    "light": "#FAFAFA",
+    "dark": "#0E0E0E",
+    "anchors": {
+      "from": "#2AD0CA",
+      "to": "#0E00F8"
+    }
+  },
+  "topbarCtaButton": {
+    "type": "github",
+    "url": "https://github.com/Nixtla/nixtla"
+  },
+  "topAnchor": {
+    "name": "StatsForecast",
+    "icon": "bolt"
+  },
+  "navigation": [
+    {
+      "group": "",
+      "pages": ["index.html"]
+    },
+    {
+      "group": "Getting Started",
+      "pages": [
+        "docs/getting-started/installation.html",
+        "docs/getting-started/getting_started_short.html",
+        "docs/getting-started/getting_started_complete.html"
+      ]
+    },
+    {
+      "group": "Tutorials",
+      "pages": [
+        "docs/tutorials/anomalydetection.html",
+        "docs/tutorials/conformalprediction.html",
+        "docs/tutorials/crossvalidation.html",
+        "docs/tutorials/electricityloadforecasting.html",
+        "docs/tutorials/electricitypeakforecasting.html",
+        "docs/tutorials/garch_tutorial.html",
+        "docs/tutorials/intermittentdata.html",
+        "docs/tutorials/multipleseasonalities.html",
+        "docs/tutorials/statisticalneuralmethods.html",
+        "docs/tutorials/uncertaintyintervals.html"
+      ]
+    },
+    {
+      "group": "How to Guides",
+      "pages": [
+        "docs/how-to-guides/automatic_forecasting.html",
+        "docs/how-to-guides/amazonstatsforecast.html",
+        "docs/how-to-guides/autoarima_vs_prophet.html",
+        "docs/how-to-guides/dask.html",
+        "docs/how-to-guides/ets_ray_m5.html",
+        "docs/how-to-guides/exogenous.html",
+        "docs/how-to-guides/getting_started_complete_polars.html",
+        "docs/how-to-guides/migrating_R",
+        "docs/how-to-guides/numba_cache.html",
+        "docs/how-to-guides/prophet_spark_m5.html",
+        "docs/how-to-guides/ray.html",
+        "docs/how-to-guides/spark.html"
+      ]
+    },
+    {
+      "group": "Model References",
+      "pages": [
+        "docs/models/adida.html",
+        "docs/models/arch.html",
+        "docs/models/arima.html",
+        "docs/models/autoarima.html",
+        "docs/models/autoces.html",
+        "docs/models/autoets.html",
+        "docs/models/autoregressive.html",
+        "docs/models/autotheta.html",
+        "docs/models/crostonclassic.html",
+        "docs/models/crostonoptimized.html",
+        "docs/models/crostonsba.html",
+        "docs/models/dynamicoptimizedtheta.html",
+        "docs/models/dynamicstandardtheta.html",
+        "docs/models/garch.html",
+        "docs/models/holt.html",
+        "docs/models/holtwinters.html",
+        "docs/models/imapa.html",
+        "docs/models/multipleseasonaltrend.html",
+        "docs/models/optimizedtheta.html",
+        "docs/models/seasonalexponentialsmoothing.html",
+        "docs/models/seasonalexponentialsmoothingoptimized.html",
+        "docs/models/simpleexponentialoptimized.html",
+        "docs/models/simpleexponentialsmoothing.html",
+        "docs/models/standardtheta.html",
+        "docs/models/tsb.html"
+      ]
+    },
+    {
+      "group": "API Reference",
+      "pages": [
+        "src/core/core.html",
+        "src/core/distributed.fugue.html",
+        "src/core/models.html",
+        "src/core/models_intro"
+      ]
+    },
+    {
+      "group": "Contributing",
+      "pages": [
+        "docs/contribute/contribute",
+        "docs/contribute/docs",
+        "docs/contribute/issue-labels",
+        "docs/contribute/issues",
+        "docs/contribute/step-by-step",
+        "docs/contribute/techstack"
+      ]
+    }
+  ]
+}

settings.ini

@@ -20,7 +20,7 @@ ray_requirements = fugue[ray]>=0.8.1 protobuf>=3.15.3,<4.0.0
 dask_requirements = fugue[dask]>=0.8.1
 spark_requirements = fugue[spark]>=0.8.1
 plotly_requirements = plotly plotly-resampler
-dev_requirements = nbdev black mypy flake8 ray protobuf>=3.15.3,<4.0.0 matplotlib pmdarima prophet scikit-learn fugue[dask,ray,spark]>=0.8.1 datasetsforecast supersmoother
+dev_requirements = nbdev black mypy flake8 ray protobuf>=3.15.3,<4.0.0 matplotlib pmdarima prophet scikit-learn fugue[dask,ray,spark]>=0.8.1 datasetsforecast supersmoother nbdev_plotly
 nbs_path = nbs
 doc_path = _docs
 recursive = True
@@ -32,4 +32,4 @@ title = %(lib_name)s
 black_formatting = True
 jupyter_hooks = True
 clean_ids = True
-readme_nb = index.ipynb
+readme_nb = index.ipynb