Deploy dev from 4d5fb26

Author: github-actions[bot]

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: b55a12af40e699b56364af9c3b769ff9
+config: 8a063e92db825fff0df4be6264ecaa7f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/004f6d23a04369ca0db9c83de3b6182d/scalebar.ipynb

@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

dev/_downloads/011dc0cae17575719cab06d0e76a5649/patterns.py

@@ -9,15 +9,15 @@
 
 - :meth:`pygmt.Figure.coast`: Land and water masses via ``land`` and ``water``
 - :meth:`pygmt.Figure.histogram`: Histogram bars via ``fill``
-- :meth:`pygmt.Figure.meca`: Focal mechanisms via ``compressionfill`` and
-  ``extensionfill``
+- :meth:`pygmt.Figure.meca`: Focal mechanisms via ``compression_fill`` and
+  ``extension_fill``
 - :meth:`pygmt.Figure.plot`: Symbols and polygons via ``fill``
 - :meth:`pygmt.Figure.rose`: Histogram sectors via ``fill``
 - :meth:`pygmt.Figure.solar`: Day-light terminators via ``fill``
 - :meth:`pygmt.Figure.ternary`: Symbols via ``fill``
 - :meth:`pygmt.Figure.velo`: Uncertainty wedges and velocity error ellipses via
-  ``uncertaintyfill``
-- :meth:`pygmt.Figure.wiggle`: Anomalies via ``fillpositive`` and ``fillnegative``
+  ``uncertainty_fill``
+- :meth:`pygmt.Figure.wiggle`: Anomalies via ``positive_fill`` and ``negative_fill``
 
 GMT provides 90 predefined 1-bit patterns, which are numbered from 1 to 90. In addition,
 custom 1-, 8-, or 24-bit image raster files can also be used as patterns.

dev/_downloads/043329be3aeb3845416fed7987596202/tilemaps.ipynb

@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

dev/_downloads/04a27f4ee668c7dfab99171293a9b062/02_contour_map.py

@@ -43,7 +43,7 @@
 # "oleron" is used; a full list of CPTs can be found at :gmt-docs:`reference/cpts.html`.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.show()
 
 
@@ -64,7 +64,7 @@
 # :meth:`pygmt.Figure.grdimage`.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])
 fig.show()
 
@@ -85,7 +85,7 @@
 # lines can be adjusted (separately) by specifying the desired ``pen``.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.grdcontour(grid=grid, levels=500, annotation=1000)
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])
 fig.show()
@@ -100,7 +100,7 @@
 # the ``shorelines`` parameter draws a border around the islands.
 
 fig = pygmt.Figure()
-fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="oleron")
+fig.grdimage(grid=grid, frame="a", projection="M10c", cmap="SCM/oleron")
 fig.grdcontour(grid=grid, levels=500, annotation=1000)
 fig.coast(shorelines="2p", land="lightgray")
 fig.colorbar(frame=["a1000", "x+lElevation", "y+lm"])

dev/_downloads/04ea079531b896097081b26e6faf512b/cartesian_histograms.ipynb

@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "fig = pygmt.Figure()\n\n# Create histogram for data01\nfig.histogram(\n    region=[0, 200, 0, 0],\n    projection=\"X10c\",\n    frame=[\"WSne\", \"xaf10\", \"ya1f1+lCounts\"],\n    data=data01,\n    series=10,\n    # Draw a 1-point thick, dotted outline in \"red3\"\n    pen=\"1p,red3,dotted\",\n    histtype=0,\n    # Draw stair-steps in stead of bars\n    stairs=True,\n)\n\nfig.shift_origin(xshift=\"w+2c\")\n\n# Create histogram for data02\nfig.histogram(\n    region=[0, 200, 0, 0],\n    projection=\"X10c\",\n    frame=[\"WSne\", \"xaf10\", \"ya1f1+lCounts\"],\n    data=data02,\n    series=10,\n    # Draw a 1.5-points thick, dashed outline in \"orange\"\n    pen=\"1.5p,orange,dashed\",\n    histtype=0,\n    stairs=True,\n)\n\nfig.show()"
+        "fig = pygmt.Figure()\n\n# Create histogram for data01\nfig.histogram(\n    region=[0, 200, 0, 0],\n    projection=\"X10c\",\n    frame=[\"WSne\", \"xaf10\", \"ya1f1+lCounts\"],\n    data=data01,\n    series=10,\n    # Draw a 1-point thick, dotted outline in \"red3\"\n    pen=\"1p,red3,dotted\",\n    histtype=0,\n    # Draw stair-steps in stead of bars\n    stairs=True,\n)\n\nfig.shift_origin(xshift=\"w+2c\")\n\n# Create histogram for data02\nfig.histogram(\n    region=[0, 200, 0, 0],\n    projection=\"X10c\",\n    frame=[\"WSne\", \"xaf10\", \"ya1f1+lCounts\"],\n    data=data02,\n    series=10,\n    # Draw a 1.5-point thick, dashed outline in \"orange\"\n    pen=\"1.5p,orange,dashed\",\n    histtype=0,\n    stairs=True,\n)\n\nfig.show()"
       ]
     },
     {
@@ -155,7 +155,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Grouped bars\n\nBy setting the ``barwidth`` parameter in respect to the values passed to the\n``series`` parameter histograms with grouped bars can be created.\n\nLimitations of histograms with grouped bars are:\n\n- Careful setting width and position of the bars in respect to the bin width\n- Difficult to see the variations of the single data sets\n\n"
+        "## Grouped bars\n\nBy setting the ``bar_width`` parameter in respect to the values passed to the\n``series`` parameter histograms with grouped bars can be created.\n\nLimitations of histograms with grouped bars are:\n\n- Careful setting width and position of the bars in respect to the bin width\n- Difficult to see the variations of the single data sets\n\n"
       ]
     },
     {
@@ -166,7 +166,7 @@
       },
       "outputs": [],
       "source": [
-        "# Width used for binning the data\nbinwidth = 10\n\nfig = pygmt.Figure()\n\n# Create histogram for data01\nfig.histogram(\n    region=[0, 200, 0, 0],\n    projection=\"X10c\",\n    frame=[\"WSne\", \"xaf10g10\", \"ya1f1+lCounts\"],\n    data=data01,\n    series=binwidth,\n    fill=\"red3\",\n    pen=\"1p,darkgray,solid\",\n    histtype=0,\n    # Calculate the bar width in respect to the bin width, here for two data sets half\n    # of the bin width\n    # Offset (\"+o\") the bars to align each bar with the left limit of the corresponding\n    # bin\n    barwidth=f\"{binwidth / 2}+o-{binwidth / 4}\",\n    label=\"data01\",\n)\n\n# Create histogram for data02\nfig.histogram(\n    data=data02,\n    series=binwidth,\n    fill=\"orange\",\n    pen=\"1p,darkgray,solid\",\n    histtype=0,\n    barwidth=f\"{binwidth / 2}+o{binwidth / 4}\",\n    label=\"data02\",\n)\n\n# Add legend\nfig.legend()\n\nfig.show()"
+        "# Width used for binning the data\nbinwidth = 10\n\nfig = pygmt.Figure()\n\n# Create histogram for data01\nfig.histogram(\n    region=[0, 200, 0, 0],\n    projection=\"X10c\",\n    frame=[\"WSne\", \"xaf10g10\", \"ya1f1+lCounts\"],\n    data=data01,\n    series=binwidth,\n    fill=\"red3\",\n    pen=\"1p,darkgray,solid\",\n    histtype=0,\n    # Calculate the bar width in respect to the bin width, here for two data sets half\n    # of the bin width\n    bar_width=binwidth / 2,\n    # Offset the bars to align each bar with the left limit of the corresponding bin\n    bar_offset=-binwidth / 4,\n    label=\"data01\",\n)\n\n# Create histogram for data02\nfig.histogram(\n    data=data02,\n    series=binwidth,\n    fill=\"orange\",\n    pen=\"1p,darkgray,solid\",\n    histtype=0,\n    bar_width=binwidth / 2,\n    bar_offset=binwidth / 4,\n    label=\"data02\",\n)\n\n# Add legend\nfig.legend()\n\nfig.show()"
       ]
     }
   ],
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

dev/_downloads/0511f0ea20672dfb94a45be5bdc7a003/linestrings.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# GeoPandas: Plotting lines with LineString or MultiLineString geometry\n\nThe :meth:`pygmt.Figure.plot` method allows us to plot geographical data such as lines\nwith LineString or MultiLineString geometry types stored in a\n:class:`geopandas.GeoDataFrame` object or any object that implements the\n[__geo_interface__](https://gist.github.com/sgillies/2217756)_ property.\n\nUse :func:`geopandas.read_file` to load data from any supported OGR format such as a\nshapefile (.shp), GeoJSON (.geojson), geopackage (.gpkg), etc. Then, pass the\n:class:`geopandas.GeoDataFrame` object as an argument to the ``data`` parameter of\n:meth:`pygmt.Figure.plot`, and style the lines using the ``pen`` parameter.\n"
+        "\n# GeoPandas: Plotting lines with LineString or MultiLineString geometry\n\nThe :meth:`pygmt.Figure.plot` method allows us to plot geographical data such as lines\nwith LineString or MultiLineString geometry types stored in a\n:class:`geopandas.GeoDataFrame` object. Use :func:`geopandas.read_file` to load data\nfrom any supported OGR format such as a shapefile (.shp), GeoJSON (.geojson), geopackage\n(.gpkg), etc. Then, pass the :class:`geopandas.GeoDataFrame` object as an argument to\nthe ``data`` parameter of :meth:`pygmt.Figure.plot`, and style the lines using the\n``pen`` parameter.\n"
       ]
     },
     {
@@ -15,18 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import geodatasets\nimport geopandas as gpd\nimport pygmt\n\n# Read a sample dataset provided by the geodatasets package.\n# The dataset contains large rivers in Europe, stored as LineString/MultiLineString\n# geometry types.\ngdf = gpd.read_file(geodatasets.get_path(\"eea large_rivers\"))\n\n# Convert object to EPSG 4326 coordinate system\ngdf = gdf.to_crs(\"EPSG:4326\")\ngdf.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "fig = pygmt.Figure()\n\nfig.coast(\n    projection=\"M10c\",\n    region=[-10, 30, 35, 57],\n    resolution=\"l\",\n    land=\"gray95\",\n    shorelines=\"1/0.1p,gray50\",\n    borders=\"1/0.1,gray30\",\n    frame=True,\n)\n\n# Add rivers to map\nfig.plot(data=gdf, pen=\"1p,steelblue\")\n\nfig.show()"
+        "import geopandas as gpd\nimport pygmt\n\n# Read a sample dataset provided by Natural Earth. The dataset contains rivers stored\n# as LineString/MultiLineString geometry types. Here will focus on Asia.\nprovider = \"https://naciscdn.org/naturalearth\"\nrivers = gpd.read_file(f\"{provider}/50m/physical/ne_50m_rivers_lake_centerlines.zip\")\nrivers_asia = rivers.cx[57:125, 7:47].copy()\n\nfig = pygmt.Figure()\nfig.basemap(region=[57, 125, 7, 47], projection=\"M10c\", frame=True)\nfig.coast(land=\"gray95\", shorelines=\"1/0.3p,gray50\")\n\n# Add rivers to map\nfig.plot(data=rivers_asia, pen=\"1p,steelblue\")\n\nfig.show()"
       ]
     }
   ],
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

dev/_downloads/06138b8ef0f348c8ea4ec7ea6cd766b0/azim_general_perspective.ipynb

@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

dev/_downloads/0727c24f87fbaf3990ceff4f49599878/text_formatting.ipynb

@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

dev/_downloads/07c438f7ea67f18fb2ae785888e59cda/cartesian_logarithmic.ipynb

@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,