Overview

Juno Orbits and Sunspot Numbers

from IPython.display import clear_output
from juno import DEFAULT_TIMERANGE
import matplotlib.pyplot as plt
from beforerr.matplotlib import unify_axes_fontsize, sync_legend_colors, set_linewidth, hide_legend_lines

timespan = (DEFAULT_TIMERANGE[1] - DEFAULT_TIMERANGE[0]).days
print(f"Time span: {timespan} days")

Time span: 1771 days

import pandas as pd
import polars as pl
import scienceplots as scienceplots
from matplotlib.pyplot import Axes
from space_analysis.plot.basic import savefig
from space_analysis.utils.cdas import Variables

plt.rc("savefig", dpi=300)
plt.rc('figure.subplot', wspace = 0, hspace = 0)

timerange = ["2011-08-01", "2016-07-01"]
fname = "juno-hg-loc_sunspot-number"

datasets = ['JUNO_HELIO1DAY_POSITION', 'EARTH_HELIO1DAY_POSITION', 'STA_HELIO1DAY_POSITION']
names = ['JUNO', 'EARTH', 'STA']

Plotting Juno and other mission location

dfs = []

for dataset, name in zip(datasets, names):
    ds_variables = Variables(
        dataset=dataset,
        timerange=timerange,
    )
    df = ds_variables.to_pandas()
    df = df.rename(columns={c: f"{name}_{c}" for c in df.columns})
    dfs.append(df)
clear_output()

df_concated = pd.concat(dfs, axis=1)

df = pl.DataFrame(df_concated.reset_index()).with_columns(
    JUNO_EARTH_HGI_LON_diff = (pl.col("JUNO_HGI_LON") - pl.col("EARTH_HGI_LON")).abs(),
    JUNO_STA_HGI_LON_diff = (pl.col("JUNO_HGI_LON") - pl.col("STA_HGI_LON")).abs(),
    JUNO_EARTH_HG_LON_diff = (pl.col("JUNO_HG_LON") - pl.col("EARTH_HG_LON")).abs(),
    JUNO_STA_HG_LON_diff = (pl.col("JUNO_HG_LON") - pl.col("STA_HG_LON")).abs(),
).with_columns(
    JUNO_EARTH_HG_LON_diff = pl.when(pl.col("JUNO_EARTH_HG_LON_diff")>180).then(360-pl.col("JUNO_EARTH_HG_LON_diff")).otherwise(pl.col("JUNO_EARTH_HG_LON_diff")),
    JUNO_STA_HG_LON_diff = pl.when(pl.col("JUNO_STA_HG_LON_diff")>180).then(360-pl.col("JUNO_STA_HG_LON_diff")).otherwise(pl.col("JUNO_STA_HG_LON_diff")),
)

def plot_lon_diff(df,ax: Axes):
    ax.plot(df["Epoch"], df["JUNO_EARTH_HG_LON_diff"], label="Earth")
    ax.plot(df["Epoch"], df["JUNO_STA_HG_LON_diff"], label="STA")
    ax.set_ylabel("HG Longitude\nDifference\n[deg]")

Plotting a solar cycle index

https://docs.sunpy.org/en/stable/generated/gallery/plotting/solar_cycle_example.html

import sunpy.timeseries as ts
from astropy.time import Time
from sunpy.net import Fido
from sunpy.net import attrs as a
from sunpy.time import TimeRange

The U.S. Dept. of Commerce, NOAA, Space Weather Prediction Center (SWPC) provides recent solar cycle indices which includes different sunspot numbers, radio flux, and geomagnetic index. They also provide predictions for how the sunspot number and radio flux will evolve. Predicted values are based on the consensus of the Solar Cycle 24 Prediction Panel.

We will first search for and then download the data.

time_range = TimeRange(timerange)
result = Fido.search(a.Time(time_range), a.Instrument('noaa-indices'))
f_noaa_indices = Fido.fetch(result)

noaa = ts.TimeSeries(f_noaa_indices, source='noaaindices').truncate(time_range)

09-Nov-24 23:59:49: /Users/zijin/projects/ids_spatial_evolution_juno/.pixi/envs/default/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function "dtf2d" yielded 1 of "dubious year (Note 6)"
  warn(f'ERFA function "{func_name}" yielded {wmsg}', ErfaWarning)

We then load them into individual ~sunpy.timeseries.TimeSeries objects.

Finally, we plot both noaa and noaa_predict for the sunspot number. In this case we use the S.I.D.C. Brussels International Sunspot Number (RI). The predictions provide both a high and low values, which we plot below as ranges.

def plot_sunspot_number(noaa_indices_ts, ax):
    
    time : Time = noaa_indices_ts.time
    time = time.to_datetime()
    
    ax.scatter(time, noaa_indices_ts.quantity('sunspot RI'))
    ax.plot(time, noaa_indices_ts.quantity('sunspot RI'), label='Monthly')
    ax.plot(
        time, noaa_indices_ts.quantity('sunspot RI smooth'), label='Smoothed'
    )
    
    ax.set_ylim(bottom=0)
    ax.set_ylabel('Sunspot\nNumber\n[#]')

fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(10, 6))

plot_lon_diff(df, axes[0])
plot_sunspot_number(noaa, axes[1])

09-Nov-24 23:59:50: /Users/zijin/projects/ids_spatial_evolution_juno/.pixi/envs/default/lib/python3.12/asyncio/base_events.py:726: ResourceWarning: unclosed event loop <_UnixSelectorEventLoop running=False closed=False debug=False>
  _warn(f"unclosed event loop {self!r}", ResourceWarning, source=self)

09-Nov-24 23:59:50: /Users/zijin/projects/ids_spatial_evolution_juno/.pixi/envs/default/lib/python3.12/asyncio/selector_events.py:879: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=108>
  _warn(f"unclosed transport {self!r}", ResourceWarning, source=self)

Plotting OMNI and STEREO plasma data

import yaml
from space_analysis.ds.tplot.plot import Config, plot, process_panel

file_path = 'omni_stereo.yml'
config = yaml.load(open(file_path), Loader=yaml.FullLoader)
config = Config(**config)
tvars2plot = []

09-Nov-24 23:59:50: /var/folders/lc/6fb8rgv96tsdlvxjrldpx0bm0000gn/T/ipykernel_97398/1864669499.py:5: ResourceWarning: unclosed file <_io.TextIOWrapper name='omni_stereo.yml' mode='r' encoding='UTF-8'>
  config = yaml.load(open(file_path), Loader=yaml.FullLoader)

for p_config in config.panels:
    tvar2plot = process_panel(p_config)
    tvars2plot.append(tvar2plot)

clear_output()

fig, axes = plot(tvars2plot, config)

Overview plot

with plt.style.context(['science', 'nature', 'no-latex']):
    figsize=(3.5, 6)
    fig, axes = plt.subplots(nrows=6, ncols=1, sharex=True, figsize=figsize)

    plot_lon_diff(df, axes[0])
    plot_sunspot_number(noaa, axes[1])
    plot(tvars2plot=tvars2plot, fig=fig, axes=axes[2:], config=config)
    
    for ax in axes:
        if ax.get_legend_handles_labels()[0]:
            legend = ax.legend(handletextpad=0, handlelength=0)
        
    set_linewidth(0.8)
    sync_legend_colors()
    hide_legend_lines()
    unify_axes_fontsize(fontsize='large')
    # savefig(fname)