fix: make sure every plot can be called without ax paramaters

docs/conf.py

@@ -119,7 +119,9 @@
 autoapi_keep_files = False
 
 # -- Options for intersphinx output --------------------------------------------
-intersphinx_mapping = {}
+intersphinx_mapping = {
+    "matplotlib": ("https://matplotlib.org/stable/", None),
+}
 
 # -- options for the autolabel extension ---------------------------------------
 autosectionlabel_prefix_document = True

docs/usage/plot/index.rst

@@ -1,12 +1,99 @@
 Plotting
 ========
 
-We embed some plotting capabilities in the library to help you visualize your data.
+We embed some plotting capabilities in the library to help you visualize your data. For simplicity we decided to map all the plotting function to the :doc:`matplotlib <matplotlib:index>` library as it's the most used static plotting library in the Python ecosystem.
 
 .. toctree::
+   :hidden:
+   :maxdepth: 1
 
    plot-featurecollection
    plot-image
    plot-imagecollection
    map-image
-   map-featurecollection
+   map-featurecollection
+
+.. grid:: 1 2 3 3
+
+   .. grid-item::
+
+      .. card:: :icon:`fa-solid fa-chart-simple` FeatureCollection
+         :link: plot-featurecollection.html
+
+   .. grid-item::
+
+      .. card:: :icon:`fa-solid fa-chart-simple` Image
+         :link: plot-image.html
+
+   .. grid-item::
+
+      .. card:: :icon:`fa-solid fa-chart-simple` ImageCollection
+         :link: plot-imagecollection.html
+
+   .. grid-item::
+
+      .. card:: :icon:`fa-solid fa-image` Image
+         :link: map-image.html
+
+   .. grid-item::
+
+      .. card:: :icon:`fa-solid fa-map` FeatureCollection
+         :link: map-featurecollection.html
+
+
+
+In all these examples we will use the object interface of matplotlib creating the :py:class:`Figure <matplotlib.figure.Figure>` and :py:class:`Axes <matplotlib.axes.Axes>` object before plotting the data. This is the recommended way to use matplotlib as it gives you more control over the plot and the figure.
+
+.. code-block:: python
+
+   # custom image for this specific chart
+   modisSr = (
+      ee.ImageCollection("MODIS/061/MOD09A1")
+      .filter(ee.Filter.date("2018-06-01", "2018-09-01"))
+      .select(["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"])
+      .mean()
+   )
+   histRegion = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22])
+
+   #create a matplotlib figure
+   fig, ax = plt.subplots(figsize=(10, 4))
+
+   # plot the histogram of the reds
+   modisSr.geetools.plot_hist(
+      bands = ["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"],
+      labels = [['Red', 'NIR', 'SWIR']],
+      colors = ["#cf513e", "#1d6b99", "#f0af07"],
+      ax = ax,
+      bins = 100,
+      scale = 500,
+      region = histRegion,
+   )
+
+   # once created the axes can be modified as needed using pure matplotlib functions
+   ax.set_title("Modis SR Reflectance Histogram")
+   ax.set_xlabel("Reflectance (x1e4)")
+
+.. image:: ../../_static/usage/plot/index/histogram.png
+   :alt: Modis SR Reflectance Histogram
+   :align: center
+
+If you are used to the :py:mod:`pyplot <matplotlib.pyplot>` interface of matplotlib you can still use it with the state-base module of matplotlib. Just be aware that the module is a stateful interface and you will have less control over the figure and the plot.
+
+.. code-block:: python
+
+   # get all hydroshed from the the south amercias within the WWF/HydroATLAS dataset.
+   region = ee.Geometry.BBox(-80, -60, -20, 20);
+   fc = ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level04').filterBounds(region)
+
+   # create the plot
+   fc.geetools.plot(property="UP_AREA", cmap="viridis")
+
+   # Customized display
+   plt.colorbar(ax.collections[0], label="Upstream area (km²)")
+   plt.title("HydroATLAS basins of level4")
+   plt.xlabel("Longitude (°)")
+   plt.ylabel("Latitude (°)")
+
+.. image:: ../../_static/usage/plot/index/hydroshed.png
+   :alt: HydroATLAS basins of level4
+   :align: center

docs/usage/plot/map-featurecollection.ipynb

@@ -115,46 +115,13 @@
     "\n",
     "A single property can be ploted on a map using matplotlib. The following example is showing the bassin area in km².\n",
     "\n",
-    "First create a matplotlib figure and axis:\n"
+    "First create a matplotlib figure and axis, then you can add the bassins to the map using the `plot` method. By default it will display the first property of the features. In our case we will opt to display the area of the bassins in km² i.e. the \"UP_AREA\" property. Finally that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.ioff() # remove interactive for the sake of the example\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then you can add the bassins to the map using the `plot` method. By default it will display the first property of the features. In our case we will opt to display the area of the bassins in km² i.e. the \"UP_AREA\" property."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fc.geetools.plot(ax=ax, property=\"UP_AREA\", cmap=\"viridis\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -168,13 +135,19 @@
     }
    ],
    "source": [
+    "# create the plot\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "\n",
+    "# generate the graph\n",
+    "fc.geetools.plot(ax=ax, property=\"UP_AREA\", cmap=\"viridis\")\n",
+    "\n",
     "# you can then customize the figure as you would for any other matplotlib object\n",
     "fig.colorbar(ax.collections[0], label=\"Upstream area (km²)\")\n",
     "ax.set_title(\"HydroATLAS basins of level4\")\n",
     "ax.set_xlabel(\"Longitude (°)\")\n",
     "ax.set_ylabel(\"Latitude (°)\")\n",
     "\n",
-    "display(fig)"
+    "plt.show()"
    ]
   },
   {
@@ -185,45 +158,12 @@
     "\n",
     "Alternatively if you only want to plot the geometries of the featurecollection on a map, you can use the `plot` method with the `boundares` parameter set to `True`.\n",
     "\n",
-    "Similarly to the previous example we start by creating a pyplot figure and axis:"
+    "Similarly to the previous example we start by creating a pyplot figure and axis, then you can start plotting the geometries and finally customize the plot."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.ioff() # remove interactive for the sake of the example\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then you can start plotting the geometries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fc.geetools.plot(ax=ax, boundaries=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and finally customize the plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -238,12 +178,18 @@
     }
    ],
    "source": [
+    "plt.ioff() # remove interactive for the sake of the example\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "\n",
+    "# create the graph\n",
+    "fc.geetools.plot(ax=ax, boundaries=True)\n",
+    "\n",
     "# you can then customize the figure as you would for any other matplotlib object\n",
     "ax.set_title(\"Borders of the HydroATLAS basins of level4\")\n",
     "ax.set_xlabel(\"Longitude (°)\")\n",
     "ax.set_ylabel(\"Latitude (°)\")\n",
     "\n",
-    "display(fig)"
+    "plt.show()"
    ]
   },
   {

geetools/ee_feature_collection.py

@@ -423,7 +423,7 @@ def plot_hist(
 
     def plot(
         self,
-        ax: Axes,
+        ax: Axes | None = None,
         property: str = "",
         crs: str = "EPSG:4326",
         cmap: str = "viridis",
@@ -460,6 +460,9 @@ def plot(
                 fig, ax = plt.subplots()
                 fc.geetools.plot("ADM2_CODE", ax)
         """
+        if ax is None:
+            fig, ax = plt.subplots()
+
         # get the data from the server
         names = self._obj.first().propertyNames()
         names = names.filter(ee.Filter.stringStartsWith("item", "system:").Not())