Hinode/SOT-SP Level-0 Map Visualization

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Hinode/SOT-SP Level-0 Map Visualization

Hinode Solar Optical Telescope Spectro-Polarimeter (SOT/SP) Level-0 map data are 2D spatial intensity maps
derived from raw spectropolarimetric scans of the solar photosphere (2006–present).
Each file covers one raster scan and records the total intensity summed over the Fe I 630 nm spectral region.
The dataset page is at DARTS.

Data Files

Files are organized by observation start time under:

https://data.darts.isas.jaxa.jp/pub/hinode/darts/spmap/level0/{YYYY}/{MM}/{DD}/

Each scan map consists of a FITS image and a companion text file:

sprsf{YYYYMMDD}_{HHMMSS}_{SEQNUM}.fits   ← 2D intensity map
sprsf{YYYYMMDD}_{HHMMSS}_{SEQNUM}.txt    ← list of constituent raw SP4D files

The FITS image has dimensions NAXIS1 × NAXIS2 = scan steps × slit pixels
(typically on the order of a few hundred to ~2000 × 512 pixels at ~0.15 arcsec/pixel).

Key FITS header keywords

Keyword Example value Description
DATE_OBS 2017-10-12T00:46:36.936 Observation start time (UTC)
DATE_END 2017-10-12T03:40:15.498 Observation end time (UTC)
XCEN 138.4 FOV center Solar-X (arcsec)
YCEN -722.0 FOV center Solar-Y (arcsec)
FOVX 297.4 Field of view in X (arcsec)
FOVY 81.2 Field of view in Y (arcsec)
CDELT1 0.1476 Step size in X (arcsec/pixel)
CDELT2 0.1585 Pixel scale in Y (arcsec/pixel)
TR_MODE TR3 Transfer mode
SPNINT 6 Number of integrations summed
DATA_LEV 0.1 Processing level

Visualization with Python

Requirements

pip install "sunpy[map]" astropy matplotlib numpy requests

Helper functions

import re
import requests
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.utils.data import download_file

BASE = "https://data.darts.isas.jaxa.jp/pub/hinode/darts/spmap/level0"

def list_sot_sp_files(year, month, day):
    """Return list of (fits_url, txt_url) tuples for a given date."""
    date_url = f"{BASE}/{year:04d}/{month:02d}/{day:02d}/"
    resp = requests.get(date_url, timeout=30, verify=False)
    if resp.status_code != 200:
        return []
    names = re.findall(r'href="(sprsf\w+\.fits)"', resp.text)
    return [(date_url + n, date_url + n.replace(".fits", ".txt")) for n in names]

Example 1: Display a single scan map with matplotlib

pairs = list_sot_sp_files(2017, 10, 12)
print(f"{len(pairs)} scan maps found")

fits_url, _ = pairs[0]
local = download_file(fits_url, cache=True, allow_insecure=True)

with fits.open(local) as hdul:
    image = hdul[0].data.astype(float)
    header = hdul[0].header

print(f"Date:  {header['DATE_OBS']}  →  {header['DATE_END']}")
print(f"Center: ({header['XCEN']:.1f}\", {header['YCEN']:.1f}\")")
print(f"FOV:   {header['FOVX']:.1f}\" × {header['FOVY']:.1f}\"")
print(f"Shape: {image.shape}  ({header['CDELT1']:.4f}\"/px × {header['CDELT2']:.4f}\"/px)")

# Build simple extent (arcsec)
ny, nx = image.shape
dx, dy = header['CDELT1'], header['CDELT2']
xcen, ycen = header['XCEN'], header['YCEN']
extent = [xcen - nx/2*dx, xcen + nx/2*dx,
          ycen - ny/2*dy, ycen + ny/2*dy]

vmin, vmax = np.percentile(image, [1, 99])

fig, ax = plt.subplots(figsize=(10, 4))
im = ax.imshow(image, origin="lower", cmap="gray",
               vmin=vmin, vmax=vmax, extent=extent, aspect="equal")
fig.colorbar(im, ax=ax, label="Intensity (raw counts)")
ax.set_xlabel("Solar-X (arcsec)")
ax.set_ylabel("Solar-Y (arcsec)")
ax.set_title(f"Hinode/SOT-SP  {header['DATE_OBS'][:19]}")
plt.tight_layout()
plt.savefig("hinode_sot_sp_map.png", dpi=150, bbox_inches="tight")
plt.close()

Example 2: Display with sunpy.map (WCS-aware)

sunpy can load SOT-SP map FITS files and apply helioprojective coordinates automatically.

import sunpy.map

fits_url, _ = list_sot_sp_files(2017, 10, 12)[0]
local = download_file(fits_url, cache=True, allow_insecure=True)

smap = sunpy.map.Map(local)
print(f"Date: {smap.date}")
print(f"Scale: {smap.scale[0]:.4f}/pix, {smap.scale[1]:.4f}/pix")

fig = plt.figure(figsize=(10, 4))
ax = fig.add_subplot(projection=smap)
smap.plot(axes=ax, cmap="gray")
smap.draw_limb(axes=ax)
smap.draw_grid(axes=ax)
plt.title(f"Hinode/SOT-SP  {smap.date}")
plt.tight_layout()
plt.savefig("hinode_sot_sp_sunpy.png", dpi=150, bbox_inches="tight")
plt.close()

Example 3: Browse multiple scan maps as a thumbnail grid

pairs = list_sot_sp_files(2017, 10, 12)
step = max(1, len(pairs) // 6)
selected = pairs[::step][:6]

fig, axes = plt.subplots(2, 3, figsize=(14, 6))
for ax, (fits_url, _) in zip(axes.flat, selected):
    local = download_file(fits_url, cache=True, allow_insecure=True)
    with fits.open(local) as hdul:
        image = hdul[0].data.astype(float)
        t = hdul[0].header['DATE_OBS'][11:19]
    vmin, vmax = np.percentile(image, [1, 99])
    ax.imshow(image, origin="lower", cmap="gray", vmin=vmin, vmax=vmax, aspect="auto")
    ax.set_title(t, fontsize=9)
    ax.set_axis_off()

fig.suptitle("Hinode/SOT-SP 2017-10-12 (sample)")
plt.tight_layout()
plt.savefig("hinode_sot_sp_grid.png", dpi=150, bbox_inches="tight")
plt.close()

Notes

  • These are Level-0 map products generated at DARTS from raw SP4D telemetry files. The companion .txt file lists the constituent raw files for each scan.
  • The intensity values are raw uncalibrated counts. For polarimetric science (Stokes I/Q/U/V, vector magnetic fields), use the Level-1 or Level-2 data products.
  • The pixel scale (~0.15 arcsec/pixel) gives much finer spatial resolution than XRT (~8 arcsec/pixel), suited for studying photospheric magnetic structures.
  • The SPNINT keyword records how many on-chip integrations were summed per slit position.
  • SSL certificate verification may fail for data.darts.isas.jaxa.jp; pass verify=False (requests) or allow_insecure=True (astropy) as a workaround.

Acknowledgement

When using this data in publications, please follow the Hinode data use guidelines and cite:

References

Related Datasets