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import os
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from lsst.sims.photUtils import BandpassDict
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import pandas as pd
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import obscond
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%matplotlib inline
import matplotlib.pyplot as plt
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import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('talk')
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TotbpDict, hwbpDict = BandpassDict.loadBandpassesFromFiles()
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fig, ax = plt.subplots()
ax.plot(hwbpDict['g'].wavelen, hwbpDict['g'].sb, 'k')
ax.plot(hwbpDict['g'].wavelen, TotbpDict['g'].sb, 'r')
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In [15]:
pointings = pd.read_csv(os.path.join(obscond.example_data_dir, 'example_pointings.csv'), index_col='obsHistID')
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skycalc = obscond.SkyCalculations(photparams="LSST", hwBandpassDict=hwbpDict)
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pointings[['fieldRA', 'fieldDec', 'expMJD', 'airmass', 'FWHMeff', 'filter']].head()
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plt.plot(skycalc.adb.bandpassForAirmass('g', 1.00).sb)
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skycalc.fiveSigmaDepth('g', 1.086662, 0.925184, -0.4789, 61044.077855, use_provided_airmass=False)
Out[19]:
Try it out when a value has not been provided but use_provided_airmass is True
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skycalc.fiveSigmaDepth('g', 1.086662, 0.925184, -0.4789, 61044.077855, use_provided_airmass=True)
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skycalc.fiveSigmaDepth('g', 1.086662, 0.925184, -0.4789, 61044.077855, provided_airmass=1.008652,
use_provided_airmass=True)
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In [22]:
skycalc.skymag('r', 0.925, -0.4789, 59580.14)
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In [23]:
skycalc.calculatePointings(pointings)
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x = skycalc.calculatePointings(pointings).join(pointings, rsuffix='opsim')
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x['airmass_diff'] = x.airmass - x.airmassopsim
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x.airmass_diff.hist()
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