Plot SIS He Mass Spectrogram

In this example, we use solo_sis_loader’s package to plot the He mass spectrogram.

Import Required Modules

from datetime import datetime

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import sunpy.net.attrs as a
from matplotlib import colors

from solo_sis_loader import SIS_histo

t_start = '2022-11-11'
t_end = '2022-11-16'
stime = a.Time(t_start, t_end)
level = a.Level('L2')

sish = SIS_histo()
response = sish.search(stime, level=level)
files = sish.fetch(response)
sish.load(files)

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 Loading file: /home/docs/sunpy/data/solo_L2_epd-sis-a-hehist_20221111_V01.cdf
/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/solo_sis_loader/__init__.py:222: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  data_ = xr.Dataset(

 Loading file: /home/docs/sunpy/data/solo_L2_epd-sis-a-hehist_20221112_V01.cdf
/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/solo_sis_loader/__init__.py:222: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  data_ = xr.Dataset(

 Loading file: /home/docs/sunpy/data/solo_L2_epd-sis-a-hehist_20221113_V01.cdf
/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/solo_sis_loader/__init__.py:222: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  data_ = xr.Dataset(

 Loading file: /home/docs/sunpy/data/solo_L2_epd-sis-a-hehist_20221114_V01.cdf
/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/solo_sis_loader/__init__.py:222: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  data_ = xr.Dataset(

 Loading file: /home/docs/sunpy/data/solo_L2_epd-sis-a-hehist_20221115_V01.cdf
/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/solo_sis_loader/__init__.py:222: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  data_ = xr.Dataset(

 Loading file: /home/docs/sunpy/data/solo_L2_epd-sis-a-hehist_20221116_V01.cdf
/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/solo_sis_loader/__init__.py:222: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  data_ = xr.Dataset(

<xarray.Dataset> Size: 95kB
Dimensions:  (time: 288, mass: 80)
Coordinates:
  * time     (time) datetime64[ns] 2kB 2022-11-11T00:43:16 ... 2022-11-17T00:...
  * mass     (mass) float32 320B 2.025 2.075 2.125 2.175 ... 5.875 5.925 5.975
Data variables:
    flux     (time, mass) float32 92kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Attributes:
    instrument:       <sunpy.net.attrs.Instrument(EPD: Energetic Particle Det...
    detector:         <sunpy.net.attrs.Detector: SIS object at 0x7fbfda587650>
    telescope:        A
    axis_title:       EPD-SIS-A Mass Histogram)
    axis_label_time:  Time (UTC)
    axis_label_mass:  Mass (amu)
    axis_label_flux:  Flux (counts)
    units_time:       UTC
    units_mass:       amu
    units_flux:       counts
    mass_bins_low:    [2.   2.05 2.1  2.15 2.2  2.25 2.3  2.35 2.4  2.45 2.5 ...
    mass_bin_width:   [0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05...

xarray.Dataset

• Dimensions:
  • time: 288
  • mass: 80
• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2022-11-11T00:43:16 ... 2022-11-...

    array(['2022-11-11T00:43:16.000000000', '2022-11-11T01:13:16.000000000',
           '2022-11-11T01:43:16.000000000', ..., '2022-11-16T23:13:17.000000000',
           '2022-11-16T23:43:17.000000000', '2022-11-17T00:13:17.000000000'],
          shape=(288,), dtype='datetime64[ns]')

  • mass
    (mass)
    float32
    2.025 2.075 2.125 ... 5.925 5.975

    array([2.025, 2.075, 2.125, 2.175, 2.225, 2.275, 2.325, 2.375, 2.425, 2.475,
           2.525, 2.575, 2.625, 2.675, 2.725, 2.775, 2.825, 2.875, 2.925, 2.975,
           3.025, 3.075, 3.125, 3.175, 3.225, 3.275, 3.325, 3.375, 3.425, 3.475,
           3.525, 3.575, 3.625, 3.675, 3.725, 3.775, 3.825, 3.875, 3.925, 3.975,
           4.025, 4.075, 4.125, 4.175, 4.225, 4.275, 4.325, 4.375, 4.425, 4.475,
           4.525, 4.575, 4.625, 4.675, 4.725, 4.775, 4.825, 4.875, 4.925, 4.975,
           5.025, 5.075, 5.125, 5.175, 5.225, 5.275, 5.325, 5.375, 5.425, 5.475,
           5.525, 5.575, 5.625, 5.675, 5.725, 5.775, 5.825, 5.875, 5.925, 5.975],
          dtype=float32)

• Data variables: (1)
  • flux
    (time, mass)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(288, 80), dtype=float32)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-11-11 00:43:16', '2022-11-11 01:13:16',
                   '2022-11-11 01:43:16', '2022-11-11 02:13:16',
                   '2022-11-11 02:43:16', '2022-11-11 03:13:16',
                   '2022-11-11 03:43:16', '2022-11-11 04:13:16',
                   '2022-11-11 04:43:16', '2022-11-11 05:13:16',
                   ...
                   '2022-11-16 19:43:17', '2022-11-16 20:13:17',
                   '2022-11-16 20:43:17', '2022-11-16 21:13:17',
                   '2022-11-16 21:43:17', '2022-11-16 22:13:17',
                   '2022-11-16 22:43:17', '2022-11-16 23:13:17',
                   '2022-11-16 23:43:17', '2022-11-17 00:13:17'],
                  dtype='datetime64[ns]', name='time', length=288, freq=None))

  • mass
    PandasIndex

    PandasIndex(Index([2.0250000953674316,  2.075000047683716,              2.125,
           2.1750001907348633, 2.2250001430511475, 2.2750000953674316,
            2.325000047683716,              2.375, 2.4250001907348633,
           2.4750001430511475, 2.5250000953674316,  2.575000047683716,
                        2.625, 2.6750001907348633, 2.7250001430511475,
           2.7750000953674316,  2.825000047683716,              2.875,
           2.9250001907348633, 2.9750001430511475, 3.0250000953674316,
            3.075000047683716,              3.125, 3.1750001907348633,
           3.2250001430511475, 3.2750000953674316,  3.325000047683716,
                        3.375, 3.4250001907348633, 3.4750001430511475,
           3.5250000953674316,  3.575000047683716,              3.625,
           3.6750001907348633, 3.7250001430511475, 3.7750000953674316,
            3.825000047683716,              3.875, 3.9250001907348633,
           3.9750001430511475,  4.025000095367432,  4.075000286102295,
                        4.125,  4.175000190734863,  4.224999904632568,
            4.275000095367432,  4.325000286102295,              4.375,
            4.425000190734863,  4.474999904632568,  4.525000095367432,
            4.575000286102295,              4.625,  4.675000190734863,
            4.724999904632568,  4.775000095367432,  4.825000286102295,
                        4.875,  4.925000190734863,  4.974999904632568,
            5.025000095367432,  5.075000286102295,              5.125,
            5.175000190734863,  5.224999904632568,  5.275000095367432,
            5.325000286102295,              5.375,  5.425000190734863,
            5.474999904632568,  5.525000095367432,  5.575000286102295,
                        5.625,  5.675000190734863,  5.724999904632568,
            5.775000095367432,  5.825000286102295,              5.875,
            5.925000190734863,  5.974999904632568],
          dtype='float32', name='mass'))

• Attributes: (12)

  instrument :

  <sunpy.net.attrs.Instrument(EPD: Energetic Particle Detector) object at 0x7fbfb774c550>

  detector :

  <sunpy.net.attrs.Detector: SIS object at 0x7fbfda587650>

  telescope :

  A

  axis_title :

  EPD-SIS-A Mass Histogram)

  axis_label_time :

  Time (UTC)

  axis_label_mass :

  Mass (amu)

  axis_label_flux :

  Flux (counts)

  units_time :

  UTC

  units_mass :

  amu

  units_flux :

  counts

  mass_bins_low :

  [2. 2.05 2.1 2.15 2.2 2.25 2.3 2.35 2.4 2.45 2.5 2.55 2.6 2.65 2.7 2.75 2.8 2.85 2.9 2.95 3. 3.05 3.1 3.15 3.2 3.25 3.3 3.35 3.4 3.45 3.5 3.55 3.6 3.65 3.7 3.75 3.8 3.85 3.9 3.95 4. 4.05 4.1 4.15 4.2 4.25 4.3 4.35 4.4 4.45 4.5 4.55 4.6 4.65 4.7 4.75 4.8 4.85 4.9 4.95 5. 5.05 5.1 5.15 5.2 5.25 5.3 5.35 5.4 5.45 5.5 5.55 5.6 5.65 5.7 5.75 5.8 5.85 5.9 5.95]

  mass_bin_width :

  [0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05]

fig, axis = plt.subplots(figsize=(6, 5), dpi=200)

sish.dataset.flux.plot.pcolormesh(x='time',
                                  cmap='turbo',
                                  add_colorbar=False,
                                  norm=colors.LogNorm(vmin=1, vmax=100),
                                  ax=axis)

axis.axhline(y=3.016029, linestyle='--', color='tab:red')
axis.axhline(y=4.002603, linestyle='--', color='tab:blue')

axis.set_xlim([datetime(2022, 11, 11), datetime(2022, 11, 16)])
axis.set_ylim([2, 5])

axis.set_yticklabels(axis.get_yticks(), rotation=90, va='center')

axis.set_ylabel('Mass (AMU)')
axis.set_xlabel('Time (UTC)')

axis.yaxis.minorticks_on()

axis.xaxis.set_major_locator(mdates.AutoDateLocator())
axis.xaxis.set_minor_locator(mdates.HourLocator())

plt.setp(axis.get_xticklabels(), rotation=0, ha='center')

axis.set_title('Helium mass spectrogram (SolO SIS-A, 0.5-2 MeV/nuc)')

fig.tight_layout()

plt.show()

/home/docs/checkouts/readthedocs.org/user_builds/solo-sis-loader/checkouts/latest/docs/source/examples/Basic_Plots/solo_sis_plot_histogram.py:48: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axis.set_yticklabels(axis.get_yticks(), rotation=90, va='center')