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%matplotlib inline
from matplotlib import pyplot as plt
import pandas as pd
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import sys
sys.path.insert(0, '../')
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from paleopy import proxy
from paleopy import analogs
from paleopy import ensemble
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djsons = '../jsons/'
pjsons = '../jsons/proxies'
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p = proxy(sitename='Rarotonga', \
lon = -159.82, \
lat = -21.23, \
djsons = djsons, \
pjsons = pjsons, \
pfname = 'Rarotonga.json', \
dataset = 'ersst', \
variable ='sst', \
measurement ='delta O18', \
dating_convention = 'absolute', \
calendar = 'gregorian',\
chronology = 'historic', \
season = 'DJF', \
value = 0.6, \
calc_anoms = True, \
detrend = True)
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p.find_analogs()
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p.proxy_repr(pprint=True)
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from paleopy import WR
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w = WR(p, classification='New Zealand')
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f = w.plot_bar(sig=1)
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f.savefig('/Users/nicolasf/Desktop/proxy.png')
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w = WR(p, classification='SW Pacific')
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f = w.plot_bar(sig=1)
this is consistent with the known relationships between the SW Pacific regimes and the large-scale SST anomalies:
i.e. the SW#4 is strongly positively correlated to a La Niña pattern in the ERSST / SST (see e.g. the proxy.ipynb notebook), and here we show that warm anomalies in Rarotonga are related to increased probability of the SW Pacific regime #4. On the other hand, SW#1 and SW#3 see their probability reduced, consistent with their positive correlation with El Niño patterns.
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w.df_probs
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ens = ensemble(djsons=djsons, pjsons=pjsons, season='DJF')
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classification = 'SW Pacific'
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w = WR(ens, classification=classification)
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w.parent.description
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w.climatology
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w.probs_anomalies(kind='many')
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w.df_anoms
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f = w.plot_heatmap()
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f = w.plot_bar()
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w.df_anoms.to_csv('/Users/nicolasf/Desktop/table.csv')
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w.df_probs_MC
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