SSW-Viz

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

Setup

bash

pip install sunpy matplotlib astropy

Single Image Display

python

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:

python

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

python

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

python

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

python

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

python

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

python

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

python

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

python

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