|
220 | 220 | "\n", |
221 | 221 | "notable_mjds = [np.floor(np.min(visits_table['mjd'])),\n", |
222 | 222 | " 61095.0, 61110.0, 61125.0,\n", |
223 | | - " np.floor(np.max(visits_table['mjd']))]\n", |
| 223 | + " np.ceil(np.max(visits_table['mjd']))]\n", |
224 | 224 | "fig = plt.figure(figsize=(6, 4))\n", |
225 | 225 | "for i, mjd in enumerate(notable_mjds):\n", |
226 | 226 | " plt.axvline(mjd, ls='dotted', color='grey')\n", |
|
241 | 241 | "id": "f4395967-7d9d-4925-85cc-2de4e757dece", |
242 | 242 | "metadata": {}, |
243 | 243 | "source": [ |
244 | | - "> **Figure 1:** The cumulative distribution of visit MJDs (modified Julian dates) for all visits, for all filters combined. Notable dates are marked with vertical dotted lines: the start and end dates included int he file, the dat of first alerts (Feb 24 2026), and a period of engineering time when no alerts were released." |
| 244 | + "> **Figure 1:** The cumulative distribution of visit MJDs (modified Julian dates) for all visits, for all filters combined. Notable dates are marked with vertical dotted lines: the start and end dates included in the file, the date of first alerts (Feb 24 2026), and a period of engineering time when no alerts were released." |
245 | 245 | ] |
246 | 246 | }, |
247 | 247 | { |
|
429 | 429 | "fig = plt.figure(figsize=(6, 4))\n", |
430 | 430 | "for f, filt in enumerate(filter_names):\n", |
431 | 431 | " tx = np.where(visits_table['filter'] == filt)[0]\n", |
432 | | - " plt.hist(visits_table['airmass'][tx], histtype='step', bins=75,\n", |
433 | | - " range=(1.0, 3.0), cumulative=True, log=True,\n", |
| 432 | + " plt.hist(visits_table['airmass'][tx], histtype='step', bins=100,\n", |
| 433 | + " range=(1.0, np.max(visits_table['airmass'])), cumulative=True, log=True,\n", |
434 | 434 | " linestyle=filter_linestyles[filt], color=filter_colors[filt], label=filt)\n", |
435 | 435 | " del tx\n", |
436 | 436 | "plt.legend(loc='best', ncol=2)\n", |
|
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