Merge branch 'main' into optimize_wide_data

.github/workflows/docs.yaml

@@ -105,7 +105,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
       - name: install
-        run: pip install ."[doc, examples, geo]"
+        run: pip install -v --prefer-binary -e ."[doc, examples, geo]"
       - name: install dev nbsite
         run: pip install --pre -U nbsite
       - name: pip list

.github/workflows/test.yaml

@@ -60,10 +60,10 @@ jobs:
         run: |
           MATRIX=$(jq -nsc '{
               "os": ["ubuntu-latest", "macos-latest", "windows-latest"],
-              "python-version": ["3.8", "3.12"],
+              "python-version": ["3.9", "3.12"],
               "exclude": [
                 {
-                  "python-version": "3.8",
+                  "python-version": "3.9",
                   "os": "macos-latest"
                 }
               ]
@@ -74,7 +74,7 @@ jobs:
         run: |
           MATRIX=$(jq -nsc '{
               "os": ["ubuntu-latest", "macos-latest", "windows-latest"],
-              "python-version": ["3.8", "3.12"],
+              "python-version": ["3.9", "3.12"],
               "include": [
                 {
                   "python-version": "3.9",
@@ -91,7 +91,7 @@ jobs:
               ],
               "exclude": [
                 {
-                  "python-version": "3.8",
+                  "python-version": "3.9",
                   "os": "macos-latest"
                 }
               ]
@@ -159,13 +159,8 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: upgrade pip / setuptools
         run: pip install -U pip setuptools
-      - name: install without geo
-        # Because cartopy cannot be installed on Python 3.8 on these platforms
-        if: matrix.python-version == '3.8' && contains(fromJSON('["ubuntu-latest", "windows-latest"]'), matrix.os)
-        run: pip install -ve '.[tests, examples-tests, hvdev, dev-extras]'
       - name: install with geo
-        if: matrix.python-version != '3.8' || !contains(fromJSON('["ubuntu-latest", "windows-latest"]'), matrix.os)
-        run: pip install -ve '.[tests, examples-tests, geo, hvdev, hvdev-geo, dev-extras]'
+        run: pip install -v --prefer-binary -e '.[tests, examples-tests, geo, hvdev, hvdev-geo, dev-extras]'
       - name: pip list
         run: pip list
       - name: bokeh sampledata

doc/developer_guide/index.md

@@ -79,7 +79,7 @@ source .venv/bin/activate
 Install the test dependencies:
 
 ``` bash
-pip install -e '.[tests, examples-tests, geo, hvdev, hvdev-geo, dev-extras]'
+pip install --prefer-binary -e '.[tests, examples-tests, geo, hvdev, hvdev-geo, dev-extras]'
 ```
 
 :::

doc/getting_started/installation.md

@@ -5,7 +5,7 @@
 | Latest release | [![Github release](https://img.shields.io/github/release/holoviz/hvplot.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/hvplot/releases) [![PyPI version](https://img.shields.io/pypi/v/hvplot.svg?colorB=cc77dd)](https://pypi.python.org/pypi/hvplot) [![hvplot version](https://img.shields.io/conda/v/pyviz/hvplot.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/hvplot) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/hvplot.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/hvplot) [![defaults version](https://img.shields.io/conda/v/anaconda/hvplot.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/hvplot) |
 | Python | [![Python support](https://img.shields.io/pypi/pyversions/hvplot.svg)](https://pypi.org/project/hvplot/) |
 
-hvPlot supports Python 3.8 and above on Linux, Windows, or Mac. hvPlot can be installed with [conda](https://conda.io/en/latest/):
+hvPlot supports Python 3.9 and above on Linux, Windows, or Mac. hvPlot can be installed with [conda](https://conda.io/en/latest/):
 
     conda install hvplot
 

doc/reference/pandas/paths.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import hvplot.pandas  # noqa\n",
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Paths are useful if you are plotting lines on a geographic map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({\"city\": [\"NY\", \"Delhi\"], \"lon\": [-75, 77.23], \"lat\": [43, 28.61]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the line in blue between New York and Delhi is not straight on a flat PlateCarree map, this is because the Geodetic coordinate system is a truly spherical coordinate system, where a line between two points is defined as the shortest path between those points on the globe rather than 2d Cartesian space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "common_kwargs = dict(\n",
+    "    x=\"lon\",\n",
+    "    y=\"lat\",\n",
+    "    geo=True,\n",
+    "    project=True,\n",
+    "    projection=ccrs.GOOGLE_MERCATOR,\n",
+    "    global_extent=True\n",
+    ")\n",
+    "shortest_path = df.hvplot.paths(color=\"blue\", crs=ccrs.Geodetic(), tiles=True, **common_kwargs)\n",
+    "straight_path = df.hvplot.paths(color=\"grey\", line_dash=\"dashed\", **common_kwargs)\n",
+    "labels = df.hvplot.labels(text_color=\"black\", text=\"city\", **common_kwargs)\n",
+    "shortest_path * straight_path * labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example adapted from https://scitools.org.uk/cartopy/docs/latest/matplotlib/intro.html."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

doc/user_guide/Customization.ipynb

@@ -154,6 +154,29 @@
     "        Aggregator to use when applying rasterize or datashade operation\n",
     "        (valid options include 'mean', 'count', 'min', 'max' and more, and\n",
     "        datashader reduction objects)\n",
+    "    downsample (default=False):\n",
+    "        Controls the application of downsampling to the plotted data,\n",
+    "        which is particularly useful for large timeseries datasets to\n",
+    "        reduce the amount of data sent to browser and improve\n",
+    "        visualization performance. Requires HoloViews >= 1.16. Additional\n",
+    "        dependencies: Installing the `tsdownsample` library is required\n",
+    "        for using any downsampling methods other than the default 'lttb'.\n",
+    "        Acceptable values:\n",
+    "        - False: No downsampling is applied.\n",
+    "        - True: Applies downsampling using HoloViews' default algorithm\n",
+    "            (LTTB - Largest Triangle Three Buckets).\n",
+    "        - 'lttb': Explicitly applies the Largest Triangle Three Buckets\n",
+    "          algorithm.\n",
+    "        - 'minmax': Applies the MinMax algorithm, selecting the minimum\n",
+    "          and maximum values in each bin. Requires `tsdownsample`.\n",
+    "        - 'm4': Applies the M4 algorithm, selecting the minimum, maximum,\n",
+    "          first, and last values in each bin. Requires `tsdownsample`.\n",
+    "        - 'minmax-lttb': Combines MinMax and LTTB algorithms for\n",
+    "          downsampling, first applying MinMax to reduce to a preliminary\n",
+    "          set of points, then LTTB for further reduction. Requires\n",
+    "          `tsdownsample`.\n",
+    "        Other string values corresponding to supported algorithms in\n",
+    "        HoloViews may also be used.\n",
     "    dynamic (default=True):\n",
     "        Whether to return a dynamic plot which sends updates on widget and\n",
     "        zoom/pan events or whether all the data should be embedded\n",
@@ -168,6 +191,10 @@
     "    rasterize (default=False):\n",
     "        Whether to apply rasterization using the datashader library\n",
     "        returning an aggregated Image\n",
+    "    resample_when (default=None):\n",
+    "        Applies a resampling operation (datashade, rasterize or downsample) if\n",
+    "        the number of individual data points present in the current zoom range\n",
+    "        is above this threshold. The raw plot is displayed otherwise.\n",
     "    x_sampling/y_sampling (default=None):\n",
     "        Specifies the smallest allowed sampling interval along the x/y axis."
    ]

doc/user_guide/Large_Timeseries.ipynb

@@ -256,6 +256,29 @@
     "           min_height=300, autorange='y', title=\"Datashader Rasterize\", colorbar=False, line_width=2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5a98e727",
+   "metadata": {},
+   "source": [
+    "### Rasterize Conditionally\n",
+    "\n",
+    "Alternatively, it's possible to activate `rasterize` *conditionally* with `resample_when`.\n",
+    "\n",
+    "When the number of individual data points present in the current zoom range is below the provided threshold, the raw plot is displayed; otherwise the `rasterize`, `datashade`, or `downsample` operation is applied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12910d8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df0.hvplot(x=\"time\", y=\"value\", rasterize=True, resample_when=1000, cnorm='eq_hist', padding=(0, 0.1),\n",
+    "           min_height=300, autorange='y', title=\"Datashader Rasterize\", colorbar=False, line_width=2)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "naughty-adventure",

doc/user_guide/NetworkX.ipynb

@@ -239,7 +239,7 @@
     "print(\"source vertex {target:length, }\")\n",
     "for v in G.nodes():\n",
     "    spl = dict(nx.single_source_shortest_path_length(G, v))\n",
-    "    print('{} {} '.format(v, spl))\n",
+    "    print(f'{v} {spl} ')\n",
     "    for p in spl:\n",
     "        pathlengths.append(spl[p])\n",
     "\n",

envs/py3.10-tests.yaml

@@ -14,7 +14,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.10
-  - bokeh>=1.0.0
+  - bokeh>=3.1
   - cartopy
   - colorcet>=2
   - dask
@@ -26,7 +26,7 @@ dependencies:
   - geodatasets>=2023.12.0
   - geopandas
   - geoviews-core>=1.9.0
-  - holoviews>=1.11.0
+  - holoviews>=1.19.0
   - ibis-duckdb
   - intake-parquet>=0.2.3
   - intake-xarray>=0.5.0
@@ -38,10 +38,10 @@ dependencies:
   - networkx>=2.6.3
   - notebook>=5.4
   - numba>=0.51.0
-  - numpy>=1.15
+  - numpy>=1.21
   - packaging
-  - pandas
-  - panel>=0.11.0
+  - pandas>=1.3
+  - panel>=1.0
   - param<3.0,>=1.12.0
   - parameterized
   - pillow>=8.2.0

envs/py3.11-docs.yaml

@@ -14,7 +14,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.11
-  - bokeh>=1.0.0
+  - bokeh>=3.1
   - cartopy
   - colorcet>=2
   - dask>=2021.3.0
@@ -25,7 +25,7 @@ dependencies:
   - geodatasets>=2023.12.0
   - geopandas
   - geoviews-core>=1.9.0
-  - holoviews>=1.11.0
+  - holoviews>=1.19.0
   - ibis-duckdb
   - intake-parquet>=0.2.3
   - intake-xarray>=0.5.0
@@ -37,10 +37,10 @@ dependencies:
   - networkx>=2.6.3
   - notebook>=5.4
   - numba>=0.51.0
-  - numpy>=1.15
+  - numpy>=1.21
   - packaging
-  - pandas
-  - panel>=0.11.0
+  - pandas>=1.3
+  - panel>=1.0
   - param<3.0,>=1.12.0
   - pillow>=8.2.0
   - plotly

envs/py3.11-tests.yaml

@@ -14,7 +14,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.11
-  - bokeh>=1.0.0
+  - bokeh>=3.1
   - cartopy
   - colorcet>=2
   - dask
@@ -26,7 +26,7 @@ dependencies:
   - geodatasets>=2023.12.0
   - geopandas
   - geoviews-core>=1.9.0
-  - holoviews>=1.11.0
+  - holoviews>=1.19.0
   - ibis-duckdb
   - intake-parquet>=0.2.3
   - intake-xarray>=0.5.0
@@ -38,10 +38,10 @@ dependencies:
   - networkx>=2.6.3
   - notebook>=5.4
   - numba>=0.51.0
-  - numpy>=1.15
+  - numpy>=1.21
   - packaging
-  - pandas
-  - panel>=0.11.0
+  - pandas>=1.3
+  - panel>=1.0
   - param<3.0,>=1.12.0
   - parameterized
   - pillow>=8.2.0

envs/py3.12-tests.yaml

@@ -14,7 +14,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.12
-  - bokeh>=1.0.0
+  - bokeh>=3.1
   - cartopy
   - colorcet>=2
   - dask
@@ -26,7 +26,7 @@ dependencies:
   - geodatasets>=2023.12.0
   - geopandas
   - geoviews-core>=1.9.0
-  - holoviews>=1.11.0
+  - holoviews>=1.19.0
   - ibis-duckdb
   - intake-parquet>=0.2.3
   - intake-xarray>=0.5.0
@@ -38,10 +38,10 @@ dependencies:
   - networkx>=2.6.3
   - notebook>=5.4
   - numba>=0.51.0
-  - numpy>=1.15
+  - numpy>=1.21
   - packaging
-  - pandas
-  - panel>=0.11.0
+  - pandas>=1.3
+  - panel>=1.0
   - param<3.0,>=1.12.0
   - parameterized
   - pillow>=8.2.0