.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "source/auto_examples/discovery/plot_neon.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_source_auto_examples_discovery_plot_neon.py: NEON Data --------- This example shows how to download data from NEON and ARM 2m surface meteorology stations on the North Slope and plot them .. GENERATED FROM PYTHON SOURCE LINES 10-64 .. image-sg:: /source/auto_examples/discovery/images/sphx_glr_plot_neon_001.png :alt: NEON tempSingleMean on 20221001 :srcset: /source/auto_examples/discovery/images/sphx_glr_plot_neon_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [DOWNLOADING] nsametC1.b1.20221005.000000.cdf [DOWNLOADING] nsametC1.b1.20221004.000000.cdf [DOWNLOADING] nsametC1.b1.20221002.000000.cdf [DOWNLOADING] nsametC1.b1.20221003.000000.cdf [DOWNLOADING] nsametC1.b1.20221007.000000.cdf [DOWNLOADING] nsametC1.b1.20221006.000000.cdf [DOWNLOADING] nsametC1.b1.20221001.000000.cdf If you use these data to prepare a publication, please cite: Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation (MET). Atmospheric Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1786358 [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.variables.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.sensor_positions.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.010.030.SAAT_30min.2022-10.basic.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.EML.20221001-20221101.20240127T000425Z.xml [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.basic.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.020.001.SAAT_1min.2022-10.basic.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.030.001.SAAT_1min.2022-10.basic.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.readme.20240127T000425Z.txt [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.030.030.SAAT_30min.2022-10.basic.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.020.030.SAAT_30min.2022-10.basic.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.variables.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.020.030.SAAT_30min.2022-10.expanded.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.020.001.SAAT_1min.2022-10.expanded.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.EML.20221001-20221101.20240127T000425Z.xml [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.sensor_positions.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.010.030.SAAT_30min.2022-10.expanded.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.expanded.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.readme.20240127T000425Z.txt [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.030.001.SAAT_1min.2022-10.expanded.20230220T172200Z.csv [DOWNLOADING] NEON.D18.BARR.DP1.00002.001.000.030.030.SAAT_30min.2022-10.expanded.20230220T172200Z.csv | .. code-block:: Python import os import glob import matplotlib.pyplot as plt import act # Place your username and token here username = os.getenv('ARM_USERNAME') token = os.getenv('ARM_PASSWORD') if token is not None and len(token) > 0: # Download ARM data if a username/token are set files = act.discovery.download_arm_data( username, token, 'nsametC1.b1', '2022-10-01', '2022-10-07' ) ds = act.io.arm.read_arm_netcdf(files) # Download NEON Data # NEON sites can be found through the NEON website # https://www.neonscience.org/field-sites/explore-field-sites site_code = 'BARR' product_code = 'DP1.00002.001' result = act.discovery.neon.download_neon_data(site_code, product_code, '2022-10') # A number of files are downloaded and further explained in the readme file that's downloaded. # These are the files we will need for reading 1 minute NEON data file = glob.glob( os.path.join( '.', 'BARR_DP1.00002.001', 'NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.expanded.*.csv', ) ) variable_file = glob.glob( os.path.join('.', 'BARR_DP1.00002.001', 'NEON.D18.BARR.DP1.00002.001.variables.*.csv') ) position_file = glob.glob( os.path.join( '.', 'BARR_DP1.00002.001', 'NEON.D18.BARR.DP1.00002.001.sensor_positions.*.csv', ) ) # Read in the data using the ACT reader, passing with it the variable and position files # for added information in the dataset ds2 = act.io.read_neon_csv(file, variable_files=variable_file, position_files=position_file) # Plot up the two datasets display = act.plotting.TimeSeriesDisplay({'ARM': ds, 'NEON': ds2}) display.plot('temp_mean', 'ARM', marker=None, label='ARM') display.plot('tempSingleMean', 'NEON', marker=None, label='NEON') display.day_night_background('ARM') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 15.720 seconds) .. _sphx_glr_download_source_auto_examples_discovery_plot_neon.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_neon.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_neon.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_neon.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_