Reading and plotting XRS data

Purpose:

Python example of netcdf and plot commands for use with GOES-R EXIS files

__author__ = "jmachol"

import netCDF4 as nc
import numpy as np
import cftime
import matplotlib.pyplot as plt
from datetime import datetime
import os
import requests

num_vars = 2
make_plot = 1

• Relevant data files can be downloaded here. To run this code, it may be necessary to update the filename version string (“v2-1-0”) to match available files.

• Other data links and information about EXIS data can be found here

dir0 = "./"
file0 = "sci_xrsf-l2-avg1m_g16_d20200601_v2-2-0.nc"

# Download `file0` if it does not exist locally
if not os.path.exists(dir0 + file0):
    with open(dir0 + file0, "wb") as f:
        url_path = "https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/goes16/l2/data/xrsf-l2-avg1m_science/2020/06/"
        r = requests.get(url_path + file0)
        f.write(r.content)

Open netcdf file for reading data

ff = nc.Dataset(dir0 + file0)

Time conversion

datetime0 = cftime.num2pydate(ff.variables["time"][:], ff["time"].units)
print("Filename:  ", file0)
print("start time in file [{}]: {}".format(ff["time"].units, ff.variables["time"][0]))
print("start and end times:", datetime0[0], datetime0[-1])

Filename:   sci_xrsf-l2-avg1m_g16_d20200601_v2-2-0.nc
start time in file [seconds since 2000-01-01 12:00:00]: 644241600.0
start and end times: 2020-06-01 00:00:00 2020-06-01 23:59:00

Print some variable names and values

for ii in np.arange(num_vars):
    var = list(ff.variables.keys())[ii]
    val = ff.variables[var][:]
    print("{}[0]: {:8g}".format(var, val[0]))

xrsa_flux[0]: 2.20548e-09
xrsa_flux_observed[0]: 6.10827e-09

Print a global attribute

platform = getattr(ff, "platform")
print("satellite: ", platform)

satellite:  g16

Print all variable names

print("\nAll variable names")
print(list(ff.variables.keys()), "\n")

All variable names
['xrsa_flux', 'xrsa_flux_observed', 'xrsa_flux_electrons', 'xrsb_flux', 'xrsb_flux_observed', 'xrsb_flux_electrons', 'xrsa_flag', 'xrsb_flag', 'xrsa_num', 'xrsb_num', 'time', 'xrsa_flag_excluded', 'xrsb_flag_excluded', 'au_factor', 'corrected_current_xrsb2', 'roll_angle', 'xrsa1_flux', 'xrsa1_flux_observed', 'xrsa1_flux_electrons', 'xrsa2_flux', 'xrsa2_flux_observed', 'xrsa2_flux_electrons', 'xrsb1_flux', 'xrsb1_flux_observed', 'xrsb1_flux_electrons', 'xrsb2_flux', 'xrsb2_flux_observed', 'xrsb2_flux_electrons', 'xrs_primary_chan', 'xrsa1_flag', 'xrsa2_flag', 'xrsb1_flag', 'xrsb2_flag', 'xrsa1_num', 'xrsa2_num', 'xrsb1_num', 'xrsb2_num', 'xrsa1_flag_excluded', 'xrsa2_flag_excluded', 'xrsb1_flag_excluded', 'xrsb2_flag_excluded', 'yaw_flip_flag']

Plot 1 day of XRS data

var_name = ["xrsa_flux", "xrsb_flux"]
if make_plot:
    chan_color = ["mediumorchid", "green", "darkviolet", "indigo", "b",
                  "darkcyan", "greenyellow", "yellow", "gold", "orange",
                  "orangered", "darkred"][0:num_vars]
    plt.figure(0, figsize=[10, 7])
    for ii in range(num_vars):
        plt.plot(
            datetime0[:],
            ff.variables[var_name[ii]][:],
            linewidth=1,
            color=chan_color[ii],
            label="{} {}".format(platform, var_name[ii]),
        )
    plt.yscale("log")
    plt.legend(loc="upper right", prop={"size": 12})
    plt.xlabel("Time [UT]")
    plt.ylabel("X-Ray Flux [{}]".format(ff[var_name[0]].units))
    plt.show()
print("Done.\n")

Done.