CSV to NetCDF conversion

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.

import matplotlib.pyplot as plt
from os.path import join
from gspy import Survey

Convert the CSV data folder to netcdf

# raise Exception("Get the resolve model 0 working. json files need changing etc.")

# Path to example files
data_path = '..//..//supplemental//region//MAP'

metadata = join(data_path, "data//Resolve_survey_md.json")

# Establish the Survey
survey = Survey(metadata)

# Define input CSV-format data file and associated variable mapping file
d_data = join(data_path, 'data//Resolve.csv')
d_supp = join(data_path, 'data//Resolve_data_md.json')

# Read data and format
survey.add_tabular(type='csv', data_filename=d_data, metadata_file=d_supp)

# Define input CSV-format model file and associated variable mapping file
m_data = join(data_path, 'model//Resolve_model.csv')
m_supp = join(data_path, 'model//Resolve_model_md.json')

# Read model data and format
survey.add_tabular(type='csv', data_filename=m_data, metadata_file=m_supp)

# Save NetCDF file
d_out = join(data_path, 'model//Resolve.nc')
survey.write_netcdf(d_out)

Read in the netcdf files

new_survey = Survey().read_netcdf(d_out)

print(type(new_survey))
print(type(new_survey.tabular[0]))

<class 'gspy.src.classes.survey.Survey.Survey'>
<class 'xarray.core.dataset.Dataset'>

Plotting

plt.figure()
new_survey.tabular[0].gs_tabular.scatter('DTM', vmin=30, vmax=50)
plt.xlim([500000, 540000])
plt.ylim([1175000, 1210000])

plt.figure()
new_survey.tabular[1].gs_tabular.scatter('DEM')

# print(new_survey.tabular[0]['qd_final'])
print(new_survey.tabular[1])

plt.show()

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<xarray.Dataset>
Dimensions:           (index: 9999, layer_depth: 30, nv: 2)
Coordinates:
    spatial_ref       float64 ...
  * index             (index) int32 0 1 2 3 4 5 ... 9994 9995 9996 9997 9998
  * layer_depth       (layer_depth) float64 0.5 1.55 2.7 ... 109.2 119.7 132.5
  * nv                (nv) int64 0 1
    x                 (index) float64 5.36e+05 5.36e+05 ... 5.298e+05 5.297e+05
    y                 (index) float64 1.205e+06 1.205e+06 ... 1.197e+06
    z                 (index) float64 ...
Data variables: (12/18)
    layer_depth_bnds  (layer_depth, nv) float64 ...
    LINE              (index) int64 ...
    LAT_WGS84_dd      (index) float64 ...
    LON_WGS84_dd      (index) float64 ...
    X_WGS84_Albers    (index) float64 ...
    Y_WGS84_Albers    (index) float64 ...
    ...                ...
    RESDATA           (index) float64 ...
    RESTOTAL          (index) float64 ...
    RHO_I             (index, layer_depth) float64 ...
    RHO_I_STD         (index, layer_depth) float64 ...
    DOI_CONSERVATIVE  (index) float64 ...
    DOI_STANDARD      (index) float64 ...
Attributes:
    content:  inverted resistivity models
    comment:  This dataset includes inverted resistivity models derived from ...