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#5 Work in progress:
- Improve expert_plot method - Merge matplotlib_plot and basemap_plot methods - Create matplotlib_animation method [ci skip]
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‎docs/tutorials/plotting.rst

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Plotting
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--------
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ezGPX currently provides four different ways to plot a :py:class:`~ezgpx.gpx.GPX` object.
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ezGPX currently provides many different ways to plot a :py:class:`~ezgpx.gpx.GPX` object.
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Matplotlib
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^^^^^^^^^^
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This method relies on the well known Matplotlib Python library to create simple yet useable plots of GPX files.
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.. warning:: Requires :py:mod:`~basemap`.
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::
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import ezgpx
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import matplotlib as mpl
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# Parse GPX file
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gpx = ezgpx.GPX("file.gpx")
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# Plot with Matplotlib
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gpx.matplotlib_plot(color="elevation",
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start_stop_colors=("green", "red"),
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way_points_color="blue",
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gpx.matplotlib_plot(figsize=(16,9),
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size=5,
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color="ele",
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cmap=mpl.cm.get_cmap("viridis", 12),
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colorbar=False,
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start_point_color="green",
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stop_point_color="red",
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way_points_color=None,
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background="World_Imagery",
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offset_percentage=0.04,
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dpi=100,
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title=gpx.name(),
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duration=(0, 0),
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distance=(0.5, 0),
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ascent=(1, 0),
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pace=None,
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speed=None,
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file_path="img")
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title_fontsize=20,
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watermark=True,
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file_path="img.png")
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.. image:: ../../img/matplotlib_plot_1.png
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:width: 500
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:alt: Matplotlib plot
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Matplotlib Basemap Toolkit
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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::
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import ezgpx
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# Parse GPX file
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gpx = ezgpx.GPX("file.gpx")
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# Plot with Matplotlib Basemap Toolkit
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gpx.matplotlib_basemap_plot(color="darkorange",
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start_stop_colors=("darkgreen", "darkred"),
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way_points_color="darkblue",
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title=gpx.name(),
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duration=(0,0),
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distance=(0.5,0),
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ascent=None,
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pace=None,
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speed=(1,0),
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file_path="img")
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.. image:: ../../img/matplotlib_basemap_plot_1.png
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:width: 500
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:alt: Matplotlib Basemap Toolkit plot
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gmplot
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^^^^^^
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This method uses the Google map engine to display the content of the GPX file in a HTML page.
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.. warning:: Requires :py:mod:`~gmplot`.
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::
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Folium
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^^^^^^
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The interactive HTML page resulting from this method allows you to visualize the content of a GPX on a nice looking map.
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.. warning:: Requires :py:mod:`~folium`.
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::
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:alt: Folium plot
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PaperMap
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^^^^^^
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^^^^^^^^
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.. warning:: Requires :py:mod:`~papermap`.
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@@ -120,16 +108,112 @@ PaperMap
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# Parse GPX file
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gpx = ezgpx.GPX("file.gpx")
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# Plot with Folium
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# Plot with Papermap
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gpx.papermap_plot(tile_server="OpenStreetMap",
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size = "a4",
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use_landscape = True,
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scale = 25000,
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dpi = 300,
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add_grid = True,
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grid_size = 1000,
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file_path = "map.pdf"):
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file_path = "map.pdf")
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.. image:: ../../img/papermap_plot_1.png
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:width: 500
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:alt: PaperMap plot
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Expert Plot
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^^^^^^^^^^^
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This method creates animations of the activity contained in a GPX file.
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.. warning:: Requires :py:mod:`~basemap`.
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::
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import ezgpx
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# Parse GPX file
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gpx = ezgpx.GPX("file.gpx")
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# Create animation with Matplotlib
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gpx.matplotlib_animation(figsize=(16,9),
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size=4,
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color="red",
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cmap=None,
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colorbar=False,
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start_point_color=None,
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stop_point_color=None,
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way_points_color=None,
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background="World_Imagery",
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offset_percentage=0.04,
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dpi=200,
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interval=10,
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fps=24,
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repeat=False,
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title=gpx.name(),
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title_fontsize=15,
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watermark=True,
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file_path="video_1.mp4")
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Expert Plot
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^^^^^^^^^^^
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This is the most advanced plotting method built into ezGPX. It allows to plot the path with or without a background map, the elevation profile, the pace profile and other relevant data related to the GPX file as tables or graphs.
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.. warning:: Requires :py:mod:`~basemap`.
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::
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import ezgpx
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# Parse GPX file
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gpx = ezgpx.GPX("file.gpx")
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# Expert plot
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gpx.expert_plot(figsize=(16,9),
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subplots=(3,2),
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map_position=(0,0),
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map_size=10,
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map_color="ele",
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map_cmap=matplotlib.cm.get_cmap("viridis", 12),
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map_colorbar=True,
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start_point_color=None,
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stop_point_color=None,
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way_points_color=None,
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background="World_Imagery",
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offset_percentage=0.04,
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xpixels=1000,
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ypixels=None,
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dpi=100,
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elevation_profile_position=(1,0),
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elevation_profile_size=10,
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elevation_profile_color="ele",
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elevation_profile_cmap=matplotlib.cm.get_cmap("viridis", 12),
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elevation_profile_colorbar=False,
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elevation_profile_grid=True,
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elevation_profile_fill_color="lightgray",
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elevation_profile_fill_alpha=0.5,
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pace_graph_position=(2,0),
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pace_graph_size=10,
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pace_graph_color="ele",
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pace_graph_cmap=None,
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pace_graph_colorbar=False,
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pace_graph_grid=True,
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pace_graph_fill_color="lightgray",
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pace_graph_fill_alpha=0.5,
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pace_graph_threshold=15,
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ascent_rate_graph_position=(1,1),
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made_with_ezgpx_position=(0,1),
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shared_color="ele",
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shared_cmap=None,
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shared_colorbar=True,
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data_table_position=(2,1),
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title=test_gpx.name(),
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title_fontsize=20,
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watermark=False,
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file_path="img.png")
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.. image:: ../../img/expert_plot_1.png
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:width: 500
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:alt: PaperMap plot

‎examples/plot.py

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@@ -1,32 +1,26 @@
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import ezgpx
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import matplotlib as mpl
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# Parse GPX file
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gpx = ezgpx.GPX("file.gpx")
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# Plot with Matplotlib
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gpx.matplotlib_plot(color="elevation",
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start_stop_colors=("green", "red"),
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way_points_color="blue",
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gpx.matplotlib_plot(figsize=(16,9),
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size=5,
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color="ele",
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cmap=mpl.cm.get_cmap("viridis", 12),
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colorbar=False,
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start_point_color="green",
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stop_point_color="red",
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way_points_color=None,
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background="World_Imagery",
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offset_percentage=0.04,
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dpi=100,
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title=gpx.name(),
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duration=(0, 0),
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distance=(0.5, 0),
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ascent=None,
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pace=(1, 0),
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speed=None,
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title_fontsize=20,
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watermark=True,
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file_path="img_1")
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# Plot with Matplotlib Basemap Toolkit
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gpx.matplotlib_basemap_plot(color="darkorange",
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start_stop_colors=("darkgreen", "darkred"),
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way_points_color="darkblue",
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title=gpx.name(),
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duration=(0,0),
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distance=(0.5,0),
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ascent=None,
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pace=None,
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speed=(1,0),
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file_path="img_2")
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# Plot with gmplot (Google Maps)
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gpx.gmplot_plot(color="yellow",
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start_stop_colors=("green", "red"),
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file_path="map_2.html",
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open=True)
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# Write new GPX file
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gpx.to_gpx("new_file.gpx")
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# Plot with Papermap
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gpx.papermap_plot(tile_server="OpenStreetMap",
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size = "a4",
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use_landscape = True,
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scale = 25000,
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dpi = 300,
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add_grid = True,
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grid_size = 1000,
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file_path = "map_1.pdf")
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# Expert plot
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gpx.expert_plot(figsize=(16,9),
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subplots=(3,2),
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map_position=(0,0),
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map_size=10,
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map_color="ele",
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map_cmap=mpl.cm.get_cmap("viridis", 12),
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map_colorbar=True,
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start_point_color=None,
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stop_point_color=None,
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way_points_color=None,
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background="World_Imagery",
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offset_percentage=0.04,
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xpixels=1000,
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ypixels=None,
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dpi=100,
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elevation_profile_position=(1,0),
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elevation_profile_size=10,
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elevation_profile_color="ele",
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elevation_profile_cmap=mpl.cm.get_cmap("viridis", 12),
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elevation_profile_colorbar=False,
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elevation_profile_grid=True,
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elevation_profile_fill_color="lightgray",
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elevation_profile_fill_alpha=0.5,
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pace_graph_position=(2,0),
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pace_graph_size=10,
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pace_graph_color="ele",
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pace_graph_cmap=None,
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pace_graph_colorbar=False,
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pace_graph_grid=True,
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pace_graph_fill_color="lightgray",
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pace_graph_fill_alpha=0.5,
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pace_graph_threshold=15,
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ascent_rate_graph_position=(1,1),
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made_with_ezgpx_position=(0,1),
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shared_color="ele",
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shared_cmap=None,
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shared_colorbar=True,
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data_table_position=(2,1),
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title=gpx.name(),
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title_fontsize=20,
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watermark=False,
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file_path="img_2.png")
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gpx.matplotlib_animation(figsize=(16,9),
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size=4,
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color="red",
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cmap=None,
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colorbar=False,
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start_point_color=None,
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stop_point_color=None,
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way_points_color=None,
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background="World_Imagery",
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offset_percentage=0.04,
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dpi=200,
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interval=10,
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fps=24,
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repeat=False,
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title=gpx.name(),
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title_fontsize=15,
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watermark=True,
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file_path="video_1.mp4")

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