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