Break out jupyter cells, now that it is possible
Jeff Moe
parent
6e73e1d75b
commit
1d33022187
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@ -193,27 +193,90 @@
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},
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"outputs": [],
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"source": [
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" pix_catalog = generate_star_catalog(fname)\n",
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"\n",
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" pix_catalog = generate_star_catalog(fname)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" print(colored(f\"Computing astrometric calibration for {fname}\", \"yellow\"))\n",
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" solved = generate_reference_with_anet(fname, \"\", calfname)\n",
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"\n",
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" solved = generate_reference_with_anet(fname, \"\", calfname)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Generate star catalog\n",
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" pix_catalog = pixel_catalog(f\"{fname}.cat\")\n",
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" \n",
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" pix_catalog = pixel_catalog(f\"{fname}.cat\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Calibrate from reference\n",
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" calibrate_from_reference(fname, calfname, pix_catalog)\n",
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"\n",
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" calibrate_from_reference(fname, calfname, pix_catalog)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Store calibration\n",
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" store_calibration(pix_catalog, f\"{fname}.cal\")\n",
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"\n",
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" store_calibration(pix_catalog, f\"{fname}.cal\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Read Fourframe\n",
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" ff = FourFrame(fname)\n",
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"\n",
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" ff = FourFrame(fname)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Stars available and used\n",
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" nused = np.sum(pix_catalog.flag == 1)\n",
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" nstars = pix_catalog.nstars\n",
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"\n",
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" nstars = pix_catalog.nstars"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Write output\n",
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" screenoutput = \"%s %10.6f %10.6f %4d/%4d %5.1f %5.1f %6.2f +- %6.2f\" % (\n",
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" os.path.basename(ff.fname), ff.crval[0], ff.crval[1], nused, nstars,\n",
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@ -225,32 +288,77 @@
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"\n",
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" else:\n",
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" color = \"red\"\n",
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" print(colored(screenoutput, color))\n",
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"\n",
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" print(colored(screenoutput, color))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Generate predictions\n",
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" predictions = ff.generate_satellite_predictions(cfg)\n",
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"\n",
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" predictions = ff.generate_satellite_predictions(cfg)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Find tracks\n",
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" if is_calibrated(ff):\n",
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" tracks = ff.find_tracks_by_hough3d(cfg)\n",
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"\n",
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" else:\n",
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" tracks = []\n",
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"\n",
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" tracks = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Identify tracks\n",
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" satno = 90000\n",
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" for t in tracks:\n",
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" is_identified = t.identify(predictions, satno, \"22 500A\", None, cfg, abbrevs, tlefiles)\n",
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" if not is_identified:\n",
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" satno += 1\n",
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"\n",
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" satno += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Format observations\n",
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" obs = []\n",
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" for t in tracks:\n",
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" # Add to observation\n",
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" obs.append(Observation(ff, t.tmid, t.x0, t.y0, ff.site_id,\n",
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" t.satno, t.cospar, t.catalogname))\n",
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"\n",
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" t.satno, t.cospar, t.catalogname))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Write observations\n",
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" for o in obs:\n",
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" iod_line = o.to_iod_line()\n",
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@ -269,12 +377,30 @@
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" elif o.catalogname == \"unid\":\n",
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" color = \"magenta\"\n",
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"\n",
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" print(colored(iod_line, color))\n",
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"\n",
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" print(colored(iod_line, color))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # Generate plots\n",
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" # PNG image without lines overlaid\n",
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" ff.diagnostic_plot(predictions, None, None, cfg)\n",
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"\n",
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" ff.diagnostic_plot(predictions, None, None, cfg)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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" # PNG image for each track found\n",
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" for track, o in zip(tracks, obs):\n",
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" ff.diagnostic_plot(predictions, track, o, cfg)"
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