.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/Creating_GS_Files/plot_csv_resolve.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_csv_resolve.py: CSV to NetCDF ------------- This example demonstrates how to convert comma-separated values (CSV) data to the GS NetCDF format. Specifically this example includes: 1. Raw AEM data, from the Resolve system 2. Inverted resistivity models Dataset Reference: Burton, B.L., Minsley, B.J., Bloss, B.R., and Kress, W.H., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2018 - February 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9XBBBUU. .. GENERATED FROM PYTHON SOURCE LINES 15-19 .. code-block:: Python import matplotlib.pyplot as plt from os.path import join import gspy .. GENERATED FROM PYTHON SOURCE LINES 20-22 Convert the resolve csv data to NetCDF ++++++++++++++++++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 24-25 Initialize the Survey .. GENERATED FROM PYTHON SOURCE LINES 25-34 .. code-block:: Python # Path to example files data_path = '..//..//..//..//example_material//example_2' # Survey metadata file metadata = join(data_path, "data//Resolve_survey_md.yml") # Establish the Survey survey = gspy.Survey.from_dict(metadata) .. GENERATED FROM PYTHON SOURCE LINES 35-36 Import raw AEM data from CSV-format. .. GENERATED FROM PYTHON SOURCE LINES 36-46 .. code-block:: Python data_container = survey.gs.add_container('data', **dict(content = "raw and processed data")) # Define input data file and associated metadata file d_data = join(data_path, 'data//Resolve.csv') d_supp = join(data_path, 'data//Resolve_data_md.yml') # Add the raw AEM data as a tabular dataset data_container.gs.add(key='raw_data', data_filename=d_data, metadata_file=d_supp) .. raw:: html 
<xarray.DataTree 'raw_data'>
    Group: /survey/data/raw_data
    │   Dimensions:           (index: 2334, layer_depth: 31, nv: 2, spec_sample: 256,
    │                          frequency: 6)
    │   Coordinates:
    │     * index             (index) int32 9kB 0 1 2 3 4 5 ... 2329 2330 2331 2332 2333
    │     * layer_depth       (layer_depth) float64 248B 2.5 7.5 12.5 ... 147.5 152.5
    │     * nv                (nv) int64 16B 0 1
    │     * spec_sample       (spec_sample) int64 2kB 0 1 2 3 4 ... 251 252 253 254 255
    │     * frequency         (frequency) int64 48B 400 1800 3300 8200 40000 140000
    │       spatial_ref       float64 8B 0.0
    │       x                 (index) float64 19kB 5.351e+05 5.341e+05 ... 5.315e+05
    │       y                 (index) float64 19kB 1.205e+06 1.205e+06 ... 1.205e+06
    │       z                 (index) float64 19kB 42.82 43.96 42.74 ... 42.73 43.3
    │   Data variables: (12/79)
    │       layer_depth_bnds  (layer_depth, nv) float64 496B 0.0 5.0 5.0 ... 150.0 155.0
    │       line              (index) int32 9kB 10010 10010 10010 ... 19020 19020 19020
    │       date              (index) int64 19kB 20180302 20180302 ... 20180226 20180226
    │       utc_time          (index) float64 19kB 1.92e+05 1.92e+05 ... 1.741e+05
    │       flight            (index) int64 19kB 28022 28022 28022 ... 28011 28011 28011
    │       fiducial          (index) float64 19kB 969.8 999.8 ... 2.49e+03 2.52e+03
    │       ...                ...
    │       ip_filtered       (index, frequency) float64 112kB 118.0 302.9 ... 2.008e+03
    │       qd_filtered       (index, frequency) float64 112kB 169.5 423.8 ... 695.7
    │       ip_pgadj          (index, frequency) float64 112kB 118.0 302.9 ... 2.008e+03
    │       qd_pgadj          (index, frequency) float64 112kB 169.5 423.8 ... 695.7
    │       ip_final          (index, frequency) float64 112kB 118.5 302.9 ... 2.008e+03
    │       qd_final          (index, frequency) float64 112kB 169.3 423.8 ... 696.3
    │   Attributes:
    │       content:     raw data
    │       comment:     This dataset includes minimally processed (raw) AEM data
    │       type:        data
    │       method:      electromagnetic
    │       instrument:  resolve
    ├── Group: /survey/data/raw_data/resolve_system
    │       Dimensions:                       (n_transmitter: 6, n_receiver: 6,
    │                                          n_component: 6)
    │       Coordinates:
    │         * n_transmitter                 (n_transmitter) int64 48B 0 1 2 3 4 5
    │         * n_receiver                    (n_receiver) int64 48B 0 1 2 3 4 5
    │         * n_component                   (n_component) int64 48B 0 1 2 3 4 5
    │       Data variables: (12/13)
    │           transmitter_label             (n_transmitter) int64 48B 400 1800 ... 140000
    │           transmitter_orientation       (n_transmitter) <U1 24B 'z' 'z' ... 'z' 'z'
    │           transmitter_actual_frequency  (n_transmitter) int64 48B 381 1829 ... 133400
    │           transmitter_moment            (n_transmitter) int64 48B 359 187 150 72 49 17
    │           receiver_label                (n_receiver) <U1 24B 'z' 'z' 'x' 'z' 'z' 'z'
    │           receiver_orientation          (n_receiver) <U1 24B 'z' 'z' 'x' 'z' 'z' 'z'
    │           ...                            ...
    │           component_transmitters        (n_component) int64 48B 400 1800 ... 140000
    │           component_receivers           (n_component) <U1 24B 'z' 'z' 'x' 'z' 'z' 'z'
    │           component_txrx_dx             (n_component) float64 48B 7.93 7.95 ... 7.97
    │           component_txrx_dy             (n_component) int64 48B 0 0 0 0 0 0
    │           component_txrx_dz             (n_component) int64 48B 0 0 0 0 0 0
    │           component_orientation         (n_component) <U8 192B 'coplanar' ... 'copl...
    │       Attributes:
    │           type:                     system
    │           mode:                     airborne
    │           method:                   electromagnetic
    │           submethod:                frequency domain
    │           instrument:               resolve
    │           name:                     resolve_system
    │           data_normalized:          True
    │           output_data_type:         ppm
    │           reference_frame:          right-handed positive up
    │           output_sample_frequency:  10
    ├── Group: /survey/data/raw_data/magnetic_system
    │       Attributes:
    │           type:              system
    │           mode:              airborne
    │           method:            magnetic
    │           submethod:         total field
    │           instrument:        cesium vapour
    │           name:              magnetic_system
    │           sample_frequency:  10.0
    │           sensitivity:       0.001
    └── Group: /survey/data/raw_data/radiometric_system
            Attributes:
                type:              system
                mode:              airborne
                method:            radiometric
                submethod:         not_defined
                instrument:        not_defined
                name:              radiometric_system
                crystal_type:      NaI
                sample_frequency:  1.0
                downward_volume:   16.8
                upward_volume:     4.2


.. GENERATED FROM PYTHON SOURCE LINES 47-48 Import inverted AEM models from CSV-format. .. GENERATED FROM PYTHON SOURCE LINES 48-58 .. code-block:: Python model_container = survey.gs.add_container('models', **dict(content = "inverted models")) # Define input model file and associated metadata file m_data = join(data_path, 'model//Resolve_model.csv') m_supp = join(data_path, 'model//Resolve_model_md.yml') # Add the inverted AEM models as a tabular dataset model_container.gs.add(key="model", data_filename=m_data, metadata_file=m_supp) .. raw:: html 
<xarray.DataTree 'model'>
    Group: /survey/models/model
        Dimensions:           (index: 9999, layer_depth: 30, nv: 2)
        Coordinates:
          * index             (index) int32 40kB 0 1 2 3 4 ... 9994 9995 9996 9997 9998
          * layer_depth       (layer_depth) float64 240B 0.5 1.55 2.7 ... 119.7 132.5
          * nv                (nv) int64 16B 0 1
            spatial_ref       float64 8B 0.0
            x                 (index) float64 80kB 5.36e+05 5.36e+05 ... 5.297e+05
            y                 (index) float64 80kB 1.205e+06 1.205e+06 ... 1.197e+06
            z                 (index) float64 80kB 41.1 41.1 41.1 ... 41.7 41.7 41.5
        Data variables: (12/18)
            layer_depth_bnds  (layer_depth, nv) float64 480B -1.275 2.275 ... 134.3
            line              (index) int64 80kB 10010 10010 10010 ... 10330 10330 10330
            lat_wgs84_dd      (index) float64 80kB 33.76 33.76 33.76 ... 33.69 33.69
            lon_wgs84_dd      (index) float64 80kB -90.17 -90.17 ... -90.24 -90.24
            x_wgs84_albers    (index) float64 80kB 5.36e+05 5.36e+05 ... 5.297e+05
            y_wgs84_albers    (index) float64 80kB 1.205e+06 1.205e+06 ... 1.197e+06
            ...                ...
            resdata           (index) float64 80kB 0.422 0.886 0.813 ... 0.396 0.355
            restotal          (index) float64 80kB 0.293 0.293 0.293 ... 0.293 0.293
            RHO_I             (index, layer_depth) float64 2MB 9.74 14.9 ... 15.9 16.6
            RHO_I_STD         (index, layer_depth) float64 2MB 3.14 2.46 ... 10.8 99.0
            doi_conservative  (index) float64 80kB 27.8 65.0 65.0 ... 70.1 70.4 70.6
            doi_standard      (index) float64 80kB 66.2 78.2 78.2 ... 87.6 87.9 88.0
        Attributes:
            content:  inverted resistivity models
            comment:  This dataset includes inverted resistivity models derived from ...


.. GENERATED FROM PYTHON SOURCE LINES 59-60 Save to NetCDF file .. GENERATED FROM PYTHON SOURCE LINES 60-63 .. code-block:: Python d_out = join(data_path, 'model//Resolve.nc') survey.gs.to_netcdf(d_out) .. GENERATED FROM PYTHON SOURCE LINES 64-65 Reading back in the GS NetCDF file .. GENERATED FROM PYTHON SOURCE LINES 65-68 .. code-block:: Python new_survey = gspy.open_datatree(d_out)['survey'] # Check the Survey information .. GENERATED FROM PYTHON SOURCE LINES 69-70 Plotting .. GENERATED FROM PYTHON SOURCE LINES 70-92 .. code-block:: Python # Make a scatter plot of a specific data variable, using GSPy's plotter plt.figure() new_survey['data/raw_data'].gs.scatter(hue='dtm', vmin=30, vmax=50) # Subsetting by line number, and plotting by distance along that line tmp = new_survey['data/raw_data'].gs.subset('line', 10010) # tmp = new_survey['data'].where(new_survey['data'].dataset['line']==10010) plt.figure() # plt.subplot(121) # tmp.gs_tabular.plot(hue='DTM') # plt.subplot(122) # tmp.gs_tabular.scatter(x='x', y='DTM') tmp.gs.scatter(y='dtm') #IF YOU SPECIFY HUE ITS A 2D COLOUR Plot #OTHERWISE ITS JUST A PLOT (LINE POINTS ETC) # Make a scatter plot of a specific model variable, using GSPy's plotter plt.figure() new_survey['models/model'].gs.scatter(hue='doi_standard') plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_csv_resolve_001.png :alt: plot csv resolve :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_csv_resolve_001.png :class: sphx-glr-multi-img * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_csv_resolve_002.png :alt: plot csv resolve :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_csv_resolve_002.png :class: sphx-glr-multi-img * .. image-sg:: /examples/Creating_GS_Files/images/sphx_glr_plot_csv_resolve_003.png :alt: plot csv resolve :srcset: /examples/Creating_GS_Files/images/sphx_glr_plot_csv_resolve_003.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.764 seconds) .. _sphx_glr_download_examples_Creating_GS_Files_plot_csv_resolve.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_csv_resolve.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_csv_resolve.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_csv_resolve.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_