` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_Creating_GS_Files_plot_tifs.py: GeoTIFFs to NetCDF ------------------ In this example, we demonstrates the workflow for creating a GS file from the GeoTIFF (.tif/.tiff) file format. This includes adding individual TIF files as single 2-D variables, as well as how to create a 3-D variable by stacking multiple TIF files along a specified dimension. Additionally, this example shows how to handle Raster data that have differing x-y grids. Specifically, this example creates the following Raster datasets: 1. Raster Dataset #1 1a. 2-D magnetic grid, original x-y discretization (600 m cell size) 2. Raster Dataset #2 2a. 2-D magnetic grid, aligned to match the x-y dimensions of the resistivity layers (1000 m cell size) 2b. 3-D resistivity grid Dataset References: Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., Hoogenboom, B.E., and Burton, B.L., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9E44CTQ. James, S.R., and Minsley, B.J., 2021, Combined results and derivative products of hydrogeologic structure and properties from airborne electromagnetic surveys in the Mississippi Alluvial Plain: U.S. Geological Survey data release, https://doi.org/10.5066/P9382RCI. .. GENERATED FROM PYTHON SOURCE LINES 24-30 .. code-block:: Python import matplotlib.pyplot as plt from os.path import join import gspy from gspy import Survey from pprint import pprint .. GENERATED FROM PYTHON SOURCE LINES 31-33 Convert data from GeoTIFF to NetCDF +++++++++++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 35-36 Initialize the Survey .. GENERATED FROM PYTHON SOURCE LINES 36-51 .. code-block:: Python # Path to example files data_path = "..//..//..//..//example_material//example_2" # Survey metadata file metadata = join(data_path, "data//Tempest_survey_md.yml") # Establish the Survey survey = Survey.from_dict(metadata) container = survey.gs.add_container('derived_products', **dict(content = "raw and processed data", comment = "This is a test")) .. GENERATED FROM PYTHON SOURCE LINES 52-55 Create the First Raster Dataset Import 2-D magnetic data, discretized on 600 m x 600 m grid Define input metadata file (which contains the TIF filename linked with desired variable name) .. GENERATED FROM PYTHON SOURCE LINES 55-60 .. code-block:: Python d_supp1 = join(data_path, 'data//Tempest_raster_md.yml') # Read data and format as Raster class object container.gs.add(key="map", metadata_file=d_supp1) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 61-62 Create the Second Raster Dataset .. GENERATED FROM PYTHON SOURCE LINES 62-70 .. code-block:: Python # Import both 3-D resistivity and 2-D magnetic data, aligned onto a common 1000 m x 1000 m grid # Define input metadata file (which contains the TIF filenames linked with desired variable names) d_supp2 = join(data_path, 'data//Tempest_rasters_md.yml') # Read data and format as Raster class object container.gs.add(key="maps", metadata_file=d_supp2) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 71-72 Save to NetCDF file .. GENERATED FROM PYTHON SOURCE LINES 72-75 .. code-block:: Python d_out = join(data_path, 'data//tifs.nc') survey.gs.to_netcdf(d_out) .. GENERATED FROM PYTHON SOURCE LINES 76-77 Reading back in the GS NetCDF file .. GENERATED FROM PYTHON SOURCE LINES 77-79 .. code-block:: Python new_survey = gspy.open_datatree(d_out)['survey'] .. GENERATED FROM PYTHON SOURCE LINES 80-81 Plotting .. GENERATED FROM PYTHON SOURCE LINES 81-94 .. code-block:: Python # Make a map-view plot of a specific data variable, using Xarray's plotter # In this case, we slice the 3-D resistivity variable along the depth dimension new_survey['derived_products']["maps"]['resistivity'].plot(col='z', vmax=3, cmap='jet', robust=True) # Make a map-view plot comparing the different x-y discretization of the two magnetic variables, using Xarray's plotter plt.figure() ax=plt.gca() new_survey['derived_products']["maps"]['magnetic_tmi'].plot(ax=ax, cmap='jet', robust=True) new_survey['derived_products']["map"]['magnetic_tmi'].plot(ax=ax, cmap='Greys', cbar_kwargs={'label': ''}, robust=True) plt.ylim([1.20556e6, 1.21476e6]) plt.xlim([3.5201e5, 3.6396e5]) plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_tifs_001.png :alt: z = 0.0, z = 5.0, z = 10.0, z = 15.0, z = 20.0 :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_tifs_001.png :class: sphx-glr-multi-img * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_tifs_002.png :alt: spatial_ref = 0.0 :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_tifs_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.573 seconds) .. _sphx_glr_download_examples_Creating_GS_Files_plot_tifs.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_tifs.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_tifs.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_tifs.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_