Merge branch 'main' into add_op_threshold

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+open_collective: holoviz

.github/workflows/build.yaml

@@ -20,8 +20,8 @@ jobs:
         shell: bash -l {0}
     env:
       CHANS_DEV: "-c pyviz/label/dev -c bokeh"
-      PKG_TEST_PYTHON: "--test-python=py37"
-      PYTHON_VERSION: "3.7"
+      PKG_TEST_PYTHON: "--test-python=py38"
+      PYTHON_VERSION: "3.8"
       CHANS: "-c pyviz"
       MPLBACKEND: "Agg"
       CONDA_UPLOAD_TOKEN: ${{ secrets.CONDA_UPLOAD_TOKEN }}
@@ -35,7 +35,7 @@ jobs:
       - uses: conda-incubator/setup-miniconda@v2
         with:
           miniconda-version: "latest"
-          python-version: 3.8
+          python-version: ${{ matrix.python-version }}
       - name: Fetch unshallow
         run: git fetch --prune --tags --unshallow -f
       - name: Set output
@@ -64,8 +64,8 @@ jobs:
         shell: bash -l {0}
     env:
       CHANS_DEV: "-c pyviz/label/dev -c bokeh"
-      PKG_TEST_PYTHON: "--test-python=py37"
-      PYTHON_VERSION: "3.7"
+      PKG_TEST_PYTHON: "--test-python=py38"
+      PYTHON_VERSION: "3.8"
       CHANS: "-c pyviz"
       MPLBACKEND: "Agg"
       PPU: ${{ secrets.PPU }}

.github/workflows/test.yaml

@@ -16,20 +16,10 @@ concurrency:
 
 jobs:
   pre_commit:
-    name: Run pre-commit hooks
+    name: Run pre-commit
     runs-on: 'ubuntu-latest'
     steps:
-      - uses: actions/checkout@v3
-        with:
-          fetch-depth: "1"
-      - name: set PY
-        run: echo "PY=$(python -VV | sha256sum | cut -d' ' -f1)" >> $GITHUB_ENV
-      - uses: actions/cache@v3
-        with:
-          path: ~/.cache/pre-commit
-          key: pre-commit|${{ env.PY }}|${{ hashFiles('.pre-commit-config.yaml') }}
-      - name: pre-commit
-        uses: pre-commit/action@v3.0.0
+      - uses: holoviz-dev/holoviz_tasks/pre-commit@v0.1a17
   test_suite:
     name: Pytest on ${{ matrix.os }} with Python ${{ matrix.python-version }}
     needs: [pre_commit]
@@ -39,35 +29,27 @@ jobs:
       matrix:
         os: ['ubuntu-latest', 'macos-latest', 'windows-latest']
         # Run on the full set on schedule, workflow_dispatch and push&tags events, otherwise on a subset.
-        python-version: ${{ ( github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' || ( github.event_name == 'push' && github.ref_type == 'tag' ) ) && fromJSON('["3.7", "3.8", "3.9", "3.10", "3.11"]') || fromJSON('["3.7", "3.9", "3.11"]') }}
+        python-version: ${{ ( github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' || ( github.event_name == 'push' && github.ref_type == 'tag' ) ) && fromJSON('["3.8", "3.9", "3.10", "3.11"]') || fromJSON('["3.8", "3.11"]') }}
     timeout-minutes: 90
     defaults:
       run:
         shell: bash -el {0}
     steps:
-      - uses: holoviz-dev/holoviz_tasks/install@v0.1a12
+      - uses: holoviz-dev/holoviz_tasks/install@v0.1a17
         with:
           name: unit_test_suite
           python-version: ${{ matrix.python-version }}
           channel-priority: strict
           channels: pyviz/label/dev,conda-forge,nodefaults
-          envs: "-o examples_tests -o tests"
+          envs: "-o examples_tests -o tests -o examples_conda"
           cache: true
           conda-update: true
-          conda-mamba: mamba
         id: install
-      - name: patch fiona/geostack on Python 3.7 / Macos
-        if: steps.install.outputs.cache-hit != 'true' && contains(matrix.os, 'macos') && matrix.python-version == '3.7'
-        run: |
-          conda activate test-environment
-          mamba install "fiona=1.8" "gdal=3.3"
       - name: doit test_unit
         run: |
           conda activate test-environment
           doit test_unit
       - name: test examples
-        # Should be removed when numba support python 3.11
-        if: matrix.python-version != '3.11'
         run: |
           conda activate test-environment
           bokeh sampledata
@@ -76,50 +58,3 @@ jobs:
         run: |
           conda activate test-environment
           codecov
-  test_suite_36:
-    name: Pytest on ${{ matrix.os }} with Python ${{ matrix.python-version }}
-    needs: [pre_commit]
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      matrix:
-        os: ['macos-latest', 'windows-latest']
-        python-version: ['3.6']
-    timeout-minutes: 90
-    defaults:
-      run:
-        shell: bash -l {0}
-    steps:
-      - uses: holoviz-dev/holoviz_tasks/install@v0.1a12
-        with:
-          name: unit_test_suite_36
-          python-version: ${{ matrix.python-version }}
-          channel-priority: strict
-          channels: pyviz/label/dev,conda-forge,nodefaults
-          envs: "-o tests"
-          cache: true
-          conda-update: true
-          conda-mamba: mamba
-        id: install
-      - name: doit develop_install py
-        if: steps.install.outputs.cache-hit != 'true'
-        run: |
-          conda activate test-environment
-          # - Pin panel on Python 3.6 because one or more dev releases on the 0.13.* series
-          # can be installed on Python 3.6 but are actually not compatible with Python 3.6
-          # Panel 0.13 will support Python >= 3.7 only so the pin here can stay indefinitely.
-          # - Install importlib_resources to fix tqdm that missed adding it as a dependency
-          # for 3.6 (https://github.com/conda-forge/tqdm-feedstock/pull/114)
-          conda install "panel=0.12" "importlib_resources" --no-update-deps
-      - name: doit env_capture
-        run: |
-          conda activate test-environment
-          doit env_capture
-      - name: doit test_unit
-        run: |
-          conda activate test-environment
-          doit test_unit
-      - name: codecov
-        run: |
-          conda activate test-environment
-          codecov

conda.recipe/meta.yaml

@@ -28,14 +28,6 @@ requirements:
     - {{ dep }}
     {% endfor %}
 
-test:
-  imports:
-    - hvplot
-  requires:
-    {% for dep in sdata['extras_require']['tests'] %}
-    - {{ dep }}
-    {% endfor %}
-
 about:
   home: {{ sdata['url'] }}
   summary: {{ sdata['description'] }}

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 3.6, 3.7, 3.8, 3.9 and 3.10 on Linux, Windows, or Mac.  The recommended way to install hvPlot is using the [conda](https://conda.io/en/latest/) command provided by [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html):
+hvPlot supports Python 3.8, 3.9, 3.10 and 3.11 on Linux, Windows, or Mac.  The recommended way to install hvPlot is using the [conda](https://conda.io/en/latest/) command provided by [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html):
 
     conda install -c pyviz hvplot
 

examples/reference/pandas/scatter.ipynb

@@ -50,10 +50,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.hvplot.scatter(x='sepal_length', y='sepal_width', by='species', \n",
-    "                  legend='top', height=400, width=400,\n",
+    "df.hvplot.scatter(x='sepal_length', y='sepal_width', s='petal_length', scale=5, by='species', \n",
+    "                  legend='top', height=400, width=600,\n",
     "                  hover_cols=[\"species\", \"sepal_length\", \"sepal_width\", \"petal_width\"])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can add the 's' parameter in scatter to specify the marker plot size and add the 'scale' parameter to specify what the scaling factor should be."
+   ]
   }
  ],
  "metadata": {
@@ -63,5 +70,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/user_guide/Explorer.ipynb

@@ -26,7 +26,7 @@
    "source": [
     "hvPlot API provides a simple and intuitive way to create plots. However when you are exploring data you don't always know in advance the best way to display it, or even what kind of plot would be best to visualize the data. You will very likely embark in an iterative process that implies choosing a kind of plot, setting various options, running some code, and repeat until you're satisfied with the output and the insights you get. The *Explorer* is a *Graphical User Interface* that allows you to easily generate customized plots, which in practice gives you the possibility to **explore** both your data and hvPlot's extensive API.\n",
     "\n",
-    "To create an *Explorer* you pass your data to the high-level `hvplot.explorer` function which returns a [Panel](https://panel.holoviz.org/) layout that can be displayed in a notebook or served in a web application. This object displays on the right-hand side a preview of the plot you are building, and on the left-hand side the various options that you can set to customize the plot.\n",
+    "To create an *Explorer* you pass your data to the high-level `hvplot.explorer` function which returns a [Panel](https://panel.holoviz.org/) layout that can be displayed in a notebook or served in a web application. This object displays on the right-hand side a preview of the plot you are building, and on the left-hand side the various options that you can set to customize the plot. These options can passed to the constructor if you already have pre-defined some, for example `hvplot.explorer(data, title='Penguins', width=200)`.\n",
     "\n",
     "Note that for the explorer to be displayed in a notebook you need to load the hvPlot extension, which happens automatically when you execute `import hvplot.pandas`. If instead of building Bokeh plots you would rather build Matplotlib or Plotly plot, simply execute once `hvplot.extension('matplotlib')` or `hvplot.extension('matplotlib')` before displaying the explorer."
    ]

examples/user_guide/Timeseries_Data.ipynb

@@ -56,6 +56,23 @@
     "sst.hvplot(xformatter=formatter)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Auto range\n",
+    "Automatic auto-ranging on the data in x or y is supported, making it easy to scale the given axes and fit the entire visible curve after a zoom or pan."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst.hvplot(autorange=\"y\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -166,6 +183,23 @@
    "source": [
     "Note that xarray supports grouping and aggregation using a similar syntax. To learn more about timeseries in xarray, see the [xarray timeseries docs](https://xarray.pydata.org/en/stable/time-series.html)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downsample time series\n",
+    "An option when working with large time series is to downsample the data before plotting it. This can be done with `downsample=True`, which applies the `lttb` (Largest Triangle Three Buckets) algorithm to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst.hvplot(label=\"original\") * sst.hvplot(downsample=True, label=\"downsampled\")"
+   ]
   }
  ],
  "metadata": {