Information on currently available OpSim runs

Run List

Run name Note
baseline2018a Current opsimv4 baseline
kraken_2026 Python 3 baseline2018a replacement (with dome crawl and OL)
colossus_2665 Python 3 baseline2018a replacement (with dome crawl and OL), WFD area increased by 1.5 degrees north an south
pontus_2002 Simulation of a PanSTARRs like survey
colossus_2664 WFD cadence in GP. GP proposal turned off
colossus_2667 Single visits per night survey
pontus_2489 “Many visits” 20s visits with single snap, 40s visits in u band
kraken_2035 9 Deep Drilling Fields (DDFs), 4 already decided + 5 additional
mothra_2045 2 alternating Dec bands switched every other year, WFD off
pontus_2502 2 alternating Dec bands switched every other year, WFD on at 25% level
kraken_2036 Full WFD first and last 2 years, 3 alternating dec bands in between
kraken_2042 Single 30 second snaps in all filters
kraken_2044 Simulation of a PanSTARRs like survey, no pairs
mothra_2049 Simulation of a PanSTARRs like survey, 2 alternating Dec bands switched every other year
nexus_2097 Simulation of a PanSTARRs like survey, Full WFD first and last 2 years, 3 alternating dec bands in between
astro-lsst-01_2039 No North Ecliptic Spur (NES) proposal 
[1]:

import os
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# import lsst.syseng.throughputs as st
import lsst.sims.maf.db as db
import lsst.sims.maf.runComparison as rc

/astro/users/oboberg/userRepos/sims_maf/python/lsst/sims/maf/runComparison/runComparison.py:25: UserWarning:
The generateDiffHtml method requires bokeh to be installed
but it is not needed to use the other methods in this class.
Run: pip install bokeh then restart your jupyter notebook kernel.
  'Run: pip install bokeh then restart your jupyter notebook kernel.')

[2]:

filterlist = ('u', 'g', 'r', 'i', 'z', 'y')
filtercolors = {'u':'b', 'g':'c', 'r':'g',
                'i':'y', 'z':'r', 'y':'m'}

[3]:

runlist = ['baseline2018a', 'kraken_2026', 'colossus_2665',
           'colossus_2664', 'astro-lsst-01_2039','colossus_2667', 'pontus_2489', 'kraken_2042', 'kraken_2035',
           'mothra_2045', 'pontus_2502', 'kraken_2036', 'pontus_2002', 'kraken_2044',
           'mothra_2049', 'nexus_2097']
rundirs = [r + '/all_combine' for r in runlist]
r = rc.RunComparison(baseDir='.', runlist=runlist, rundirs=rundirs)

Total visits

[4]:

m = r.buildMetricDict(metricNameLike='Nvisits', metricMetadataLike='All props', slicerNameLike='Unislicer')
r.addSummaryStats(m)

[5]:

plt.figure(figsize=(10, 8))
rx = np.arange(len(r.runlist))
plt.plot(rx, r.summaryStats['Nvisits All props']/100000)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Total visits (millions)", fontsize='x-large')
base = r.summaryStats['Nvisits All props']['kraken_2026']/100000
plt.axhline(base, color='r', linestyle=':')
plt.grid(True, alpha=0.3)

print(r.summaryStats['Nvisits All props']['kraken_2042']/r.summaryStats['Nvisits All props']['kraken_2026'])

1.07012050584
_images/current_runs_9_1.png

CoaddM5 in WFD

[6]:

for f in filterlist:
    metadata = 'WFD %s band' % f
    m = r.buildMetricDict(metricNameLike='CoaddM5',  metricMetadataLike=metadata)
    r.addSummaryStats(m)

[7]:

plt.figure(figsize=(10, 8))
for f in filterlist:
    metadata = 'WFD %s band' % f
    s = r.summaryStats['Median CoaddM5 %s HealpixSlicer' % metadata]
    plt.plot(rx, s, label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Median WFD Coadded depth", fontsize='x-large')
plt.legend(loc='right', fancybox=True, shadow=True, fontsize='large')
#base = r.summaryStats['Nvisits All props']['kraken_2026']/100000
#plt.axhline(base, color='r', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_12_0.png

fO in WFD

[8]:

# SRD metrics:
m = r.buildMetricDict(metricNameLike='fO', metricMetadataLike='WFD')
r.addSummaryStats(m)

[9]:

fostats = ['fONv MedianNvis', 'fOArea']
base = [825, 18000]
for f, b in zip(fostats, base):
    plt.figure(figsize=(10, 7))
    s = r.summaryStats[f + ' fO WFD HealpixSlicer']
    plt.plot(rx, s, label=f)
    plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
    plt.ylabel("%s" % f, fontsize='x-large')
    #plt.legend(loc='right', fancybox=True, shadow=True, fontsize='large')
    plt.axhline(b, color='r', linestyle=':')
    plt.grid(True, alpha=0.3)
_images/current_runs_15_0.png
_images/current_runs_15_1.png

Proper Motion Error in WFD

[10]:

# SRD metrics:
m = r.buildMetricDict(metricNameLike='Proper Motion Error', metricMetadataLike='WFD')
r.addSummaryStats(m)

[11]:

pm = ['Median Proper Motion Error @ 24.0', 'Median Proper Motion Error @ 20.5']
plt.figure(figsize=(10, 7))
for f in pm:
    s = r.summaryStats[f + ' WFD HealpixSlicer']
    plt.plot(rx, s, label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Proper Motion Error", fontsize='x-large')
plt.legend(loc='upper left', fancybox=True, shadow=True, fontsize='large')
plt.axhline(1, color='k', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_18_0.png

Parallax Error in WFD

[12]:

# SRD metrics:
m = r.buildMetricDict(metricNameLike='Parallax Error', metricMetadataLike='WFD')
r.addSummaryStats(m)

[13]:

pm = ['Median Parallax Error @ 24.0', 'Median Parallax Error @ 22.4']
plt.figure(figsize=(10, 7))
for f in pm:
    s = r.summaryStats[f + ' WFD HealpixSlicer']
    plt.plot(rx, s, label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Parallax Error", fontsize='x-large')
plt.legend(loc='upper left', fancybox=True, shadow=True, fontsize='large')
plt.axhline(3, color='k', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_21_0.png

Rapid Revisit in WFD

[14]:

# SRD metrics:
m = r.buildMetricDict(metricNameLike='RapidRevisit', metricMetadataLike='WFD')
r.addSummaryStats(m)

[15]:

plt.figure(figsize=(10, 7))
s = r.summaryStats['Area (sq deg) RapidRevisits WFD HealpixSlicer']
plt.plot(rx, s/1000, label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Rapid Revisit Area (1000's sq deg)", fontsize='x-large')
#plt.legend(loc='upper left', fancybox=True, shadow=True, fontsize='large')
#plt.axhline(2000, color='k', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_24_0.png

Fraction spent in WFD

[16]:

# Fraction of time spent on WFD?
m = r.buildMetricDict(metricNameLike='Nvisits', metricMetadataLike='WFD', slicerNameLike='UniSlicer')
r.addSummaryStats(m)

[17]:

plt.figure(figsize=(10, 7))
s = r.summaryStats['Fraction of total Nvisits WFD']
plt.plot(rx, s, label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Fraction of visits in WFD", fontsize='x-large')
#plt.legend(loc='upper left', fancybox=True, shadow=True, fontsize='large')
#plt.axhline(2000, color='k', linestyle=':')
plt.grid(True, alpha=0.3)
plt.ylim(0.8, 1.0)

[17]:

(0.8, 1.0)
_images/current_runs_27_1.png

Median Inter-Night Gap in WFD (per filter)

[18]:

# rolling cadence internight gaps
m = r.buildMetricDict(metricNameLike='Median Inter-Night Gap', metricMetadataLike='WFD')
r.addSummaryStats(m)

[19]:

plt.figure(figsize=(10, 8))
for f in filterlist:
    metadata = 'WFD %s band' % f
    s = r.summaryStats['Median Median Inter-Night Gap %s HealpixSlicer' % metadata]
    plt.plot(rx, s, color=filtercolors[f], label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Median Inter-night Gap", fontsize='x-large')
plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize='large')
#base = r.summaryStats['Nvisits All props']['kraken_2026']/100000
#plt.axhline(base, color='r', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_30_0.png

Median Inter-Night Gap in WFD (all filters)

[20]:

plt.figure(figsize=(10, 8))
metadata = 'WFD all bands'
s = r.summaryStats['Median Median Inter-Night Gap %s HealpixSlicer' % metadata]
plt.plot(rx, s, marker='.', color=filtercolors[f], label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Median Inter-night Gap WFD (All bands)", fontsize='x-large')
#plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize='large')
#base = r.summaryStats['Nvisits All props']['kraken_2026']/100000
#plt.axhline(base, color='r', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_32_0.png

Median Season Length in WFD

[21]:

# season length
m = r.buildMetricDict(metricNameLike='Season Length', metricMetadataLike='WFD')
r.addSummaryStats(m)

[22]:

plt.figure(figsize=(10, 8))
metadata = 'WFD all bands'
s = r.summaryStats['Median Median Season Length %s HealpixSlicer' % metadata]
plt.plot(rx, s, marker='.', color=filtercolors[f], label=f)
plt.xticks(rx, r.runlist, rotation=75, fontsize='x-large')
plt.ylabel("Median Season Length WFD", fontsize='x-large')
#plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize='large')
#base = r.summaryStats['Nvisits All props']['kraken_2026']/100000
#plt.axhline(base, color='r', linestyle=':')
plt.grid(True, alpha=0.3)
_images/current_runs_35_0.png

HA histograms

[23]:

import lsst.sims.maf.metricBundles as mb
ha = {}
for f in filterlist:
    ha[f] = mb.createEmptyMetricBundle()
    # kraken_2026_HA_Histogram_g_band_ONED
    ha[f].read(os.path.join('kraken_2026', 'all_combine', 'kraken_2026_HA_Histogram_%s_band_ONED.npz' % f))

[24]:

import lsst.sims.maf.plots as plots

[25]:

ph = plots.PlotHandler()

[26]:

ph.setMetricBundles(ha)

[27]:

ph.plot(plotFunc=plots.OneDBinnedData(), plotDicts={'yMin': 0, 'yMax':32000, 'figsize': (10, 7)})

[27]:

1
_images/current_runs_41_1.png 
[ ]: