.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/Creating_GS_Files/plot_aseg_tempest.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_examples_Creating_GS_Files_plot_aseg_tempest.py: ASEG to NetCDF -------------- This example demonstrates the workflow for creating a GS file from the `ASEG `_ file format, as well as how to add multiple associated datasets to the Survey (e.g., Tabular and Raster groups). Specifically, this AEM survey contains the following datasets: 1. Raw AEM data, from the Tempest system 2. Inverted resistivity models 3. An interpolated map of total magnetic intensity Dataset Reference: 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. .. GENERATED FROM PYTHON SOURCE LINES 17-21 .. code-block:: Python import matplotlib.pyplot as plt from os.path import join import gspy .. GENERATED FROM PYTHON SOURCE LINES 22-24 Convert the ASEG data to NetCDF +++++++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 26-27 Initialize the Survey .. GENERATED FROM PYTHON SOURCE LINES 27-37 .. 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 survey instance survey = gspy.Survey.from_dict(metadata) .. GENERATED FROM PYTHON SOURCE LINES 38-39 1. Raw Data - .. GENERATED FROM PYTHON SOURCE LINES 39-49 .. code-block:: Python data_container = survey.gs.add_container('data', **dict(content = "raw and processed data")) # Import raw AEM data from ASEG-format. # Define input data file and associated metadata file d_data = join(data_path, 'data//Tempest.dat') d_supp = join(data_path, 'data//Tempest_data_md.yml') # Add the raw AEM data as a tabular dataset rd = data_container.gs.add(key='raw_data', data_filename=d_data, metadata_file=d_supp) .. GENERATED FROM PYTHON SOURCE LINES 50-51 2. Inverted Models .. GENERATED FROM PYTHON SOURCE LINES 51-61 .. code-block:: Python model_container = survey.gs.add_container('models', **dict(content = "inverted models")) # Import inverted AEM models from ASEG-format. # Define input data file and associated metadata file m_data = join(data_path, 'model//Tempest_model.dat') m_supp = join(data_path, 'model//Tempest_model_md.yml') # Read model data and format as Tabular class object model_container.gs.add(key='inverted_models', data_filename=m_data, metadata_file=m_supp) .. raw:: html 
<xarray.DataTree 'inverted_models'>
    Group: /survey/models/inverted_models
        Dimensions:                  (index: 20701, layer_depth: 30, nv: 2,
                                      gate_times: 15)
        Coordinates:
          * index                    (index) int32 83kB 0 1 2 3 ... 20698 20699 20700
          * layer_depth              (layer_depth) float64 240B 1.5 4.65 ... 424.2 467.5
          * nv                       (nv) int64 16B 0 1
          * gate_times               (gate_times) float64 120B 1.085e-05 ... 0.01338
            spatial_ref              float64 8B 0.0
            x                        (index) float64 166kB 3.579e+05 ... 4.906e+05
            y                        (index) float64 166kB 1.211e+06 ... 1.577e+06
            z                        (index) float64 166kB 45.83 46.61 ... 179.4 177.2
        Data variables: (12/49)
            layer_depth_bnds         (layer_depth, nv) float64 480B -4.48 7.48 ... 473.5
            gate_times_bnds          (gate_times, nv) float64 240B -0.0001248 ... 0.0...
            uniqueid                 (index) int32 83kB 0 1 2 3 ... 20698 20699 20700
            survey                   (index) int32 83kB 9999 9999 9999 ... 9999 9999
            date                     (index) int32 83kB 20191128 20191128 ... 20200227
            flight                   (index) int32 83kB 10 10 10 10 10 ... 70 70 70 70
            ...                       ...
            phic                     (index) float64 166kB 0.4491 0.4759 ... 1.61 1.289
            phit                     (index) float64 166kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
            phig                     (index) float64 166kB 0.9652 0.6608 ... 1.457
            phis                     (index) float64 166kB 0.1158 0.1392 ... 0.1705
            lambda                   (index) float64 166kB 0.5968 0.5487 ... 1.771
            iterations               (index) int32 83kB 20 19 25 25 25 ... 30 30 27 29
        Attributes:
            content:  inverted resistivity models
            comment:  This dataset includes inverted resistivity models derived from ...


.. GENERATED FROM PYTHON SOURCE LINES 62-63 3. Magnetic Intensity Map .. GENERATED FROM PYTHON SOURCE LINES 63-76 .. code-block:: Python data_derived = data_container.gs.add_container('derived_maps', **dict(content = "derived maps")) # Import the magnetic data from TIF-format. # Define input metadata file (which contains the TIF filenames linked with desired variable names) r_supp = join(data_path, 'data//Tempest_raster_md.yml') # Read data and format as Raster class object data_derived.gs.add(key='maps', metadata_file = r_supp) # Save NetCDF file d_out = join(data_path, 'data//Tempest.nc') survey.gs.to_netcdf(d_out) .. GENERATED FROM PYTHON SOURCE LINES 77-78 Read back in the NetCDF file .. GENERATED FROM PYTHON SOURCE LINES 78-84 .. code-block:: Python new_survey = gspy.open_datatree(d_out)['survey'] # Once the survey is read in, we can access variables like a standard xarray dataset. print(new_survey['data/derived_maps/maps'].magnetic_tmi) print(new_survey['data/derived_maps/maps']['magnetic_tmi']) .. rst-class:: sphx-glr-script-out .. code-block:: none Size: 6MB [725988 values with dtype=float64] Coordinates: * y (y) float64 10kB 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05 * x (x) float64 5kB 2.928e+05 2.934e+05 ... 6.51e+05 6.516e+05 spatial_ref float64 8B ... Attributes: standard_name: total_magnetic_intensity long_name: Total magnetic intensity, diurnally corrected and filtered units: nT null_value: 1.70141e+38 valid_range: [-17504.6640625 11490.32324219] grid_mapping: spatial_ref Size: 6MB [725988 values with dtype=float64] Coordinates: * y (y) float64 10kB 1.607e+06 1.606e+06 ... 8.808e+05 8.802e+05 * x (x) float64 5kB 2.928e+05 2.934e+05 ... 6.51e+05 6.516e+05 spatial_ref float64 8B ... Attributes: standard_name: total_magnetic_intensity long_name: Total magnetic intensity, diurnally corrected and filtered units: nT null_value: 1.70141e+38 valid_range: [-17504.6640625 11490.32324219] grid_mapping: spatial_ref .. GENERATED FROM PYTHON SOURCE LINES 85-86 Plotting .. GENERATED FROM PYTHON SOURCE LINES 86-95 .. code-block:: Python # Make a scatter plot of a specific tabular variable, using GSPy's plotter plt.figure() new_survey['data']['raw_data'].gs.scatter(x='x', hue='tx_height', cmap='jet') # Make a 2-D map plot of a specific raster variable, using Xarrays's plotter plt.figure() new_survey['data/derived_maps/maps']['magnetic_tmi'].plot(cmap='jet', robust=True) plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_aseg_tempest_001.png :alt: plot aseg tempest :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_aseg_tempest_001.png :class: sphx-glr-multi-img * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_aseg_tempest_002.png :alt: spatial_ref = 0.0 :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_aseg_tempest_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.677 seconds) .. _sphx_glr_download_examples_Creating_GS_Files_plot_aseg_tempest.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_aseg_tempest.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_aseg_tempest.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_aseg_tempest.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_