ssw-plugin:ssw-viz

SSW-Viz

Visualization patterns for solar observation data, preprocessing results, and ML model outputs. Uses SunPy Map, matplotlib, and astropy visualization tools.

Setup

pip install sunpy matplotlib astropy

Single Image Display

import matplotlib.pyplot as plt
from sunpy.map import Map
from astropy.visualization import ImageNormalize, AsinhStretch

smap = Map('preprocessed.fits')

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(projection=smap)
smap.plot(axes=ax, norm=ImageNormalize(vmin=0, vmax=5000, stretch=AsinhStretch(0.01)))
smap.draw_limb(axes=ax, color='white', linewidth=0.5)
smap.draw_grid(axes=ax, color='white', linewidth=0.5, alpha=0.3)
ax.set_title(f'SDO/AIA {smap.wavelength} - {smap.date.iso[:19]}')
plt.colorbar(ax.images[0], ax=ax, fraction=0.046, pad=0.04, label='DN/s')
plt.savefig('solar_image.png', dpi=300, bbox_inches='tight')

Wavelength-Specific Colormaps

SunPy auto-selects correct colormaps, or specify manually:

smap.plot(cmap='sdoaia171')   # 171A - corona (gold)
smap.plot(cmap='sdoaia193')   # 193A - corona (bronze)
smap.plot(cmap='sdoaia304')   # 304A - chromosphere (red)
smap.plot(cmap='sdoaia211')   # 211A - active regions (purple)
smap.plot(cmap='sdoaia094')   # 94A - flare plasma (green)
smap.plot(cmap='sdoaia131')   # 131A - flare/transition (teal)
smap.plot(cmap='sdoaia335')   # 335A - active regions (blue)

Normalization Options

from astropy.visualization import (
    ImageNormalize, AsinhStretch, LogStretch, SqrtStretch, HistEqStretch
)

# Best for solar EUV (reveals faint coronal features)
norm = ImageNormalize(vmin=0, vmax=5000, stretch=AsinhStretch(0.01))

# High dynamic range
norm = ImageNormalize(vmin=1, vmax=10000, stretch=LogStretch())

# Maximize local contrast
norm = ImageNormalize(stretch=HistEqStretch(smap.data))

Multi-Wavelength Panel

wavelengths = [171, 193, 211, 304]
files = [f'prep_{wl}A.fits' for wl in wavelengths]
maps = [Map(f) for f in files]

fig, axes = plt.subplots(2, 2, figsize=(14, 14),
                          subplot_kw={'projection': maps[0]})

for ax, smap in zip(axes.flatten(), maps):
    smap.plot(axes=ax, norm=ImageNormalize(vmin=0, vmax=5000,
              stretch=AsinhStretch(0.01)))
    smap.draw_limb(axes=ax, color='white', linewidth=0.5)
    ax.set_title(f'{smap.wavelength}')

plt.suptitle(f'Multi-Wavelength - {maps[0].date.iso[:10]}', fontsize=16)
plt.tight_layout()
plt.savefig('multi_wavelength.png', dpi=300, bbox_inches='tight')

Before/After Preprocessing

fig = plt.figure(figsize=(16, 8))
raw, prep = Map('raw.fits'), Map('prep.fits')

ax1 = fig.add_subplot(121, projection=raw)
raw.plot(axes=ax1)
ax1.set_title('Raw Level 1')

ax2 = fig.add_subplot(122, projection=prep)
prep.plot(axes=ax2, norm=ImageNormalize(vmin=0, vmax=5000, stretch=AsinhStretch(0.01)))
ax2.set_title('ML-Preprocessed')

plt.savefig('before_after.png', dpi=300, bbox_inches='tight')

ML Model Output Comparison

import numpy as np

fig, axes = plt.subplots(1, 3, figsize=(20, 7))

input_data = Map('input_171A.fits').data
target_data = Map('target_193A.fits').data
prediction = np.load('model_output.npy')  # or from model inference

norm = ImageNormalize(vmin=0, stretch=AsinhStretch(0.01))

axes[0].imshow(input_data, origin='lower', cmap='sdoaia171', norm=norm)
axes[0].set_title('Input (171A)')

axes[1].imshow(prediction, origin='lower', cmap='sdoaia193', norm=norm)
axes[1].set_title('Prediction (193A)')

axes[2].imshow(target_data, origin='lower', cmap='sdoaia193', norm=norm)
axes[2].set_title('Ground Truth (193A)')

for ax in axes:
    ax.axis('off')

plt.tight_layout()
plt.savefig('ml_comparison.png', dpi=300, bbox_inches='tight')

Difference / Error Map

diff = prediction - target_data

fig, ax = plt.subplots(figsize=(8, 8))
im = ax.imshow(diff, origin='lower', cmap='RdBu_r', vmin=-500, vmax=500)
ax.set_title('Prediction Error (Pred - Truth)')
ax.axis('off')
plt.colorbar(im, ax=ax, fraction=0.046, label='DN/s difference')
plt.savefig('error_map.png', dpi=300, bbox_inches='tight')

Time-Lapse Animation

import matplotlib.animation as animation
from pathlib import Path

fits_files = sorted(Path('./prep_data/').glob('*.fits'))
maps = [Map(str(f)) for f in fits_files]

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(projection=maps[0])
norm = ImageNormalize(vmin=0, vmax=5000, stretch=AsinhStretch(0.01))
maps[0].plot(axes=ax, norm=norm)

def update(frame):
    ax.clear()
    maps[frame].plot(axes=ax, norm=norm)
    ax.set_title(f'{maps[frame].date.iso[:19]}')

ani = animation.FuncAnimation(fig, update, frames=len(maps), interval=200)
ani.save('timelapse.mp4', writer='ffmpeg', dpi=150)

Pixel Distribution

import numpy as np

data = Map('prep.fits').data.flatten()
data = data[data > 0]

fig, axes = plt.subplots(1, 2, figsize=(14, 5))
axes[0].hist(data, bins=200, color='steelblue', edgecolor='none')
axes[0].set_xlabel('DN/s'); axes[0].set_title('Linear')

axes[1].hist(np.log10(data), bins=200, color='coral', edgecolor='none')
axes[1].set_xlabel('log10(DN/s)'); axes[1].set_title('Log Scale')

plt.tight_layout()
plt.savefig('distribution.png', dpi=300, bbox_inches='tight')

Related Skills

• ssw-download: Download solar observation data
• ssw-prep: Preprocess raw data for ML
• ssw-ml: Train and evaluate ML models