.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_gallery/plot_formatting_coordinates.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_gallery_plot_formatting_coordinates.py: Formatting Coordinates =================================== Before proceeding with all the steps, first import some necessary libraries and packages .. GENERATED FROM PYTHON SOURCE LINES 8-13 .. code-block:: Python import easyclimate as ecl import matplotlib.pyplot as plt import matplotlib.ticker as ticker import cartopy.crs as ccrs .. GENERATED FROM PYTHON SOURCE LINES 14-20 The data we use here is monthly data from Jan 2022 to Feb 2022 and contains 17 vertical levels. .. tip:: You can download following datasets here: :download:`Download uwnd_202201_mon_mean.nc ` .. GENERATED FROM PYTHON SOURCE LINES 20-23 .. code-block:: Python u_data = ecl.tutorial.open_tutorial_dataset("uwnd_202201_mon_mean").sortby("lat").uwnd u_data .. raw:: html 
<xarray.DataArray 'uwnd' (time: 2, level: 17, lat: 73, lon: 144)> Size: 1MB
    [357408 values with dtype=float32]
    Coordinates:
      * time     (time) datetime64[ns] 16B 2022-01-01 2022-02-01
      * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
      * lat      (lat) float32 292B -90.0 -87.5 -85.0 -82.5 ... 82.5 85.0 87.5 90.0
      * level    (level) float32 68B 1e+03 925.0 850.0 700.0 ... 50.0 30.0 20.0 10.0
    Attributes:
        long_name:     Monthly mean u wind
        units:         m/s
        precision:     2
        var_desc:      u-wind
        level_desc:    Pressure Levels
        statistic:     Mean
        parent_stat:   Other
        dataset:       NCEP Reanalysis Derived Products
        actual_range:  [-68.194824 124.399994]


.. GENERATED FROM PYTHON SOURCE LINES 24-27 Formatting of the Latitude and Lontitude Tickes ------------------------------------------------------------------------ `draw_data1` is extracted from time level 0 and 500hPa vertical level. .. GENERATED FROM PYTHON SOURCE LINES 27-30 .. code-block:: Python draw_data1 = u_data.isel(time=0).sel(level=500) draw_data1 .. raw:: html 
<xarray.DataArray 'uwnd' (lat: 73, lon: 144)> Size: 42kB
    [10512 values with dtype=float32]
    Coordinates:
        time     datetime64[ns] 8B 2022-01-01
      * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
      * lat      (lat) float32 292B -90.0 -87.5 -85.0 -82.5 ... 82.5 85.0 87.5 90.0
        level    float32 4B 500.0
    Attributes:
        long_name:     Monthly mean u wind
        units:         m/s
        precision:     2
        var_desc:      u-wind
        level_desc:    Pressure Levels
        statistic:     Mean
        parent_stat:   Other
        dataset:       NCEP Reanalysis Derived Products
        actual_range:  [-68.194824 124.399994]


.. GENERATED FROM PYTHON SOURCE LINES 31-33 Now we call :py:func:`xarray.plot.pcolormesh ` to plot the latitudinal wind field on the 500hPa isobaric surface. Noting that the x-axis and y-axis are not in standard geographic coordinate format, our next step is to format these coordinates. .. GENERATED FROM PYTHON SOURCE LINES 33-39 .. code-block:: Python fig, ax = plt.subplots(1, 1) draw_data1.plot.pcolormesh( ax=ax, ) .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_001.png :alt: time = 2022-01-01, level = 500.0 [millibar] :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 40-43 :py:func:`easyclimate.plot.set_lon_format_axis `, :py:func:`easyclimate.plot.set_lat_format_axis ` can help us quickly format the coordinates on the x-axis and y-axis, respectively, into a geographic coordinate format. .. GENERATED FROM PYTHON SOURCE LINES 43-52 .. code-block:: Python fig, ax = plt.subplots(1, 1) draw_data1.plot.pcolormesh( ax=ax, ) ecl.plot.set_lon_format_axis(ax, axis="x") ecl.plot.set_lat_format_axis(ax, axis="y") .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_002.png :alt: time = 2022-01-01, level = 500.0 [millibar] :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 53-58 It is worth mentioning that the :py:func:`easyclimate.plot.set_lon_format_axis `, :py:func:`easyclimate.plot.set_lat_format_axis ` methods contain a parameter `dmi` which helps us to convert DD (Decimal Degrees) format to DMS (Degrees Minutes Seconds) format. Now let's start by selecting a smaller area .. GENERATED FROM PYTHON SOURCE LINES 58-63 .. code-block:: Python draw_data1_1 = ( u_data.isel(time=0).sel(level=500).sel(lon=slice(100, 110), lat=slice(20, 23)) ) draw_data1_1 .. raw:: html 
<xarray.DataArray 'uwnd' (lat: 2, lon: 5)> Size: 40B
    [10 values with dtype=float32]
    Coordinates:
        time     datetime64[ns] 8B 2022-01-01
      * lon      (lon) float32 20B 100.0 102.5 105.0 107.5 110.0
      * lat      (lat) float32 8B 20.0 22.5
        level    float32 4B 500.0
    Attributes:
        long_name:     Monthly mean u wind
        units:         m/s
        precision:     2
        var_desc:      u-wind
        level_desc:    Pressure Levels
        statistic:     Mean
        parent_stat:   Other
        dataset:       NCEP Reanalysis Derived Products
        actual_range:  [-68.194824 124.399994]


.. GENERATED FROM PYTHON SOURCE LINES 64-65 Note the difference in geo-labeling on the x-axis. .. GENERATED FROM PYTHON SOURCE LINES 65-79 .. code-block:: Python fig, ax = plt.subplots(1, 2, figsize=(15, 5)) for axi in ax.flat: draw_data1_1.plot(ax=axi, cmap="Reds") axi.xaxis.set_major_locator(ticker.LinearLocator(5)) ecl.plot.set_lon_format_axis(ax[0], axis="x") ecl.plot.set_lat_format_axis(ax[0], axis="y") ax[0].set_title("dms = False") ecl.plot.set_lon_format_axis(ax[1], axis="x", dms=True) ecl.plot.set_lat_format_axis(ax[1], axis="y", dms=True) ax[1].set_title("dms = True") .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_003.png :alt: dms = False, dms = True :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 1.0, 'dms = True') .. GENERATED FROM PYTHON SOURCE LINES 80-83 Barometric Profile Label Formatting ------------------------------------------------------------------------ Here we choose the longitudinally averaged latitudinal direction at time level 0 to plot the profile .. GENERATED FROM PYTHON SOURCE LINES 83-85 .. code-block:: Python draw_data2 = u_data.isel(time=0).mean(dim="lon") .. GENERATED FROM PYTHON SOURCE LINES 86-87 Notice that the x-axis and y-axis labels are unformatted, so we'll take care of that next. .. GENERATED FROM PYTHON SOURCE LINES 87-91 .. code-block:: Python fig, ax = plt.subplots(1, 1) draw_data2.plot.contourf(ax=ax, levels=21, yincrease=False) .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_004.png :alt: time = 2022-01-01 :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 92-94 :py:func:`easyclimate.plot.set_p_format_axis ` can help us format barometric vertical labels and similarly :py:func:`easyclimate.plot.set_lat_format_axis ` can help us format latitude labels. .. GENERATED FROM PYTHON SOURCE LINES 94-101 .. code-block:: Python fig, ax = plt.subplots(1, 1) draw_data2.plot.contourf(ax=ax, levels=21, yincrease=False) ecl.plot.set_lat_format_axis(ax, axis="x") ecl.plot.set_p_format_axis(ax, axis="y") .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_005.png :alt: time = 2022-01-01 :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 102-106 Polar Stereo of the Circle Boundary ------------------------------------------------------------------------ For the sake of illustration, we use here the sea ice concentration (SIC) data from the Barents-Kara Seas (30°−90°E, 65°−85°N). The results of the data under the 10th time level are described below. .. GENERATED FROM PYTHON SOURCE LINES 106-109 .. code-block:: Python sic_data = ecl.tutorial.open_tutorial_dataset("mini_HadISST_ice").sic.isel(time=10) sic_data.plot.contourf(cmap="Blues", levels=11) .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_006.png :alt: time = 1981-11-30 :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 110-112 :py:func:`easyclimate.plot.draw_Circlemap_PolarStereo ` helps us to easily establish the boundary of the circle under the projection of the polar stereo. .. GENERATED FROM PYTHON SOURCE LINES 112-128 .. code-block:: Python fig, ax = plt.subplots(subplot_kw={"projection": ccrs.NorthPolarStereo()}) ax.coastlines(edgecolor="black", linewidths=0.5) ax.stock_img() ecl.plot.draw_Circlemap_PolarStereo( ax=ax, lon_step=30, lat_step=10, lat_range=[50, 90], draw_labels=True, gridlines_kwargs={"color": "grey", "alpha": 0.5, "linestyle": "--"}, ) sic_data.plot.contourf(cmap="Blues", levels=11, transform=ccrs.PlateCarree()) .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_007.png :alt: time = 1981-11-30 :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_007.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 129-130 Adjusting `north_pad` and `south_pad` appropriately can help us compensate for not completing the circle boundaries. .. GENERATED FROM PYTHON SOURCE LINES 130-147 .. code-block:: Python fig, ax = plt.subplots(subplot_kw={"projection": ccrs.NorthPolarStereo()}) ax.coastlines(edgecolor="black", linewidths=0.5) ax.stock_img() ecl.plot.draw_Circlemap_PolarStereo( ax=ax, lon_step=30, lat_step=10, lat_range=[50, 90], draw_labels=True, set_map_boundary_kwargs={"north_pad": 0.3, "south_pad": 0.4}, gridlines_kwargs={"color": "grey", "alpha": 0.5, "linestyle": "--"}, ) sic_data.plot(cmap="Blues", levels=11, transform=ccrs.PlateCarree()) .. image-sg:: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_008.png :alt: time = 1981-11-30 :srcset: /auto_gallery/images/sphx_glr_plot_formatting_coordinates_008.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.211 seconds) .. _sphx_glr_download_auto_gallery_plot_formatting_coordinates.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_formatting_coordinates.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_formatting_coordinates.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_formatting_coordinates.zip `