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Copy pathcompute_waterMassProbabilities.py
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compute_waterMassProbabilities.py
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from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import matplotlib.pyplot as plt
import scipy.interpolate as interpolate
import pandas as pd
import numpy as np
import numpy.ma as ma
import matplotlib
from matplotlib import gridspec
def compute_monthly_conditional_prob_weighted(dfwm, wm1='DSW', wm2='CDW', save=False, savename='untitled.png',
show=True, width=90/25.4, height=90/25.4):
matplotlib.rcParams.update({'font.size': 8})
prob_DSW = np.zeros(12)
prob_noDSW = np.zeros(12)
prob_CDW = np.zeros(12)
prob_noCDW = np.zeros(12)
prob_DSW_cap_CDW = np.zeros(12)
prob_DSW_cap_noCDW = np.zeros(12)
prob_CDW_cap_noDSW = np.zeros(12)
prob_DSW_cond_CDW = np.zeros(12)
prob_DSW_cond_noCDW = np.zeros(12)
prob_CDW_cond_DSW = np.zeros(12)
prob_CDW_cond_noDSW = np.zeros(12)
wm1gt0 = dfwm[wm1] > 0
wm2gt0 = dfwm[wm2] > 0
DSW_cap_CDW = wm1gt0 & wm2gt0
DSW_cap_noCDW = wm1gt0 & ~wm2gt0
CDW_cap_noDSW = ~wm1gt0 & wm2gt0
noNull = ~dfwm["DSW"].isnull()
for i in range(12):
monthMask = (dfwm.month.isin([i+1]) & noNull)
prob_DSW[i] = np.nansum(dfwm.loc[monthMask, wm1]) / np.abs(np.nansum(dfwm.loc[monthMask, 'zlowest']))
prob_CDW[i] = np.nansum(dfwm.loc[monthMask, wm2]) / np.abs(np.nansum(dfwm.loc[monthMask, 'zlowest']))
prob_noCDW[i] = 1 - prob_CDW[i]
prob_noDSW[i] = 1 - prob_DSW[i]
prob_DSW_cap_CDW[i] = (np.nansum(dfwm.loc[monthMask & DSW_cap_CDW, wm1]) + np.nansum(dfwm.loc[monthMask & DSW_cap_CDW, wm2]) ) / np.abs(np.nansum(dfwm.loc[monthMask, 'zlowest']))
prob_DSW_cap_noCDW[i] = np.nansum(dfwm.loc[monthMask & DSW_cap_noCDW, wm1]) / np.abs(np.nansum(dfwm.loc[monthMask, 'zlowest']))
prob_CDW_cap_noDSW[i] = np.nansum(dfwm.loc[monthMask & CDW_cap_noDSW, wm2]) / np.abs(np.nansum(dfwm.loc[monthMask, 'zlowest']))
prob_DSW_cond_CDW = prob_DSW_cap_CDW / prob_CDW
prob_DSW_cond_noCDW = prob_DSW_cap_noCDW / prob_noCDW
prob_CDW_cond_DSW = prob_DSW_cap_CDW / prob_DSW
prob_CDW_cond_noDSW = prob_CDW_cap_noDSW / prob_noDSW
timeaxis = np.arange(1,13,1)
wd = 0.2
wm1 = wm1
wm2 = wm2
fig = plt.figure(1, figsize=(width, height))
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 0.2])
ax = plt.subplot(gs[0,0])
ax.bar(timeaxis-1.5*wd, prob_DSW_cond_CDW, wd, label='P('+wm1+' | '+wm2+')')
ax.bar(timeaxis-wd*0.5, prob_DSW_cond_noCDW, wd, label='P('+wm1+' | no '+wm2+')')
ax.bar(timeaxis+wd*0.5, prob_CDW_cond_DSW, wd, label='P('+wm2+' | '+wm1+')')
plt.bar(timeaxis+wd*1.5, prob_CDW_cond_noDSW, wd, label='P('+wm2+' | no '+wm1+')')
ax.set_xticks(timeaxis)
timeaxis_ticklabel = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']
ax.set_xticklabels(timeaxis_ticklabel)
handles, labels = ax.get_legend_handles_labels()
axlegend = plt.subplot(gs[1,0])
axlegend.legend(handles, labels, ncol=2, loc=9)
axlegend.set_yticklabels([])
axlegend.set_yticks([])
axlegend.set_xticklabels([])
axlegend.set_xticks([])
axlegend.spines["top"].set_visible(False)
axlegend.spines["right"].set_visible(False)
axlegend.spines["left"].set_visible(False)
axlegend.spines["bottom"].set_visible(False)
ax.grid()
if save:
plt.savefig(savename, dpi=300, bbox_inches='tight')
if show:
plt.show()
print(' P('+wm1+' | '+wm2+')=',prob_DSW_cond_CDW.mean(),"\n", 'P('+wm1+' | no '+wm2+')=', prob_DSW_cond_noCDW.mean(),
"\n", 'P('+wm2+' | '+wm1+')=', prob_CDW_cond_DSW.mean(), "\n",
'P('+wm2+' | no '+wm1+')=',prob_CDW_cond_noDSW.mean())
def compute_prob_unweighted(dfwm, wm1='DSW', wm2='CDW', save=False, savename='untitled.png',
show=True, width=90/25.4, height=90/25.4):
wm1gt0 = dfwm[wm1] > 0
dfwm.loc[wm1gt0, wm1+"_flag"] = 1
dfwm.loc[~wm1gt0, wm1+"_flag"] = 0
wm2gt0 = dfwm[wm2] > 0
dfwm.loc[wm2gt0, wm2+"_flag"] = 1
dfwm.loc[~wm2gt0, wm2+"_flag"] = 0
matplotlib.rcParams.update({'font.size': 8})
prob_DSW = np.zeros(12)
prob_noDSW = np.zeros(12)
prob_CDW = np.zeros(12)
prob_noCDW = np.zeros(12)
prob_DSW_cap_CDW = np.zeros(12)
prob_DSW_cap_noCDW = np.zeros(12)
prob_CDW_cap_noDSW = np.zeros(12)
prob_DSW_cond_CDW = np.zeros(12)
prob_DSW_cond_noCDW = np.zeros(12)
prob_CDW_cond_DSW = np.zeros(12)
prob_CDW_cond_noDSW = np.zeros(12)
DSW_cap_CDW = wm1gt0 & wm2gt0
DSW_cap_noCDW = wm1gt0 & ~wm2gt0
CDW_cap_noDSW = ~wm1gt0 & wm2gt0
noNull = ~dfwm["DSW"].isnull()
for i in range(12):
monthMask = (dfwm.month.isin([i+1]) & noNull)
totalLength = float(len(dfwm.loc[monthMask]))
prob_DSW[i] = np.nansum(dfwm.loc[monthMask, wm1+"_flag"]) / totalLength
prob_CDW[i] = np.nansum(dfwm.loc[monthMask, wm2+"_flag"]) / totalLength
prob_noCDW[i] = 1 - prob_CDW[i]
prob_noDSW[i] = 1 - prob_DSW[i]
prob_DSW_cap_CDW[i] = (np.nansum(dfwm.loc[monthMask & DSW_cap_CDW, wm1+"_flag"]) ) / totalLength
prob_DSW_cap_noCDW[i] = np.nansum(dfwm.loc[monthMask & DSW_cap_noCDW, wm1+"_flag"]) / totalLength
prob_CDW_cap_noDSW[i] = np.nansum(dfwm.loc[monthMask & CDW_cap_noDSW, wm2+"_flag"]) / totalLength
prob_DSW_cond_CDW = prob_DSW_cap_CDW / prob_CDW
prob_DSW_cond_noCDW = prob_DSW_cap_noCDW / prob_noCDW
prob_CDW_cond_DSW = prob_DSW_cap_CDW / prob_DSW
prob_CDW_cond_noDSW = prob_CDW_cap_noDSW / prob_noDSW
timeaxis = np.arange(1,13,1)
wd = 0.2
wm1 = wm1
wm2 = wm2
fig = plt.figure(1, figsize=(width, height))
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 0.2])
ax = plt.subplot(gs[0,0])
ax.bar(timeaxis-1.5*wd, prob_DSW_cond_CDW, wd, label='P('+wm1+' | '+wm2+')')
ax.bar(timeaxis-wd*0.5, prob_DSW_cond_noCDW, wd, label='P('+wm1+' | no '+wm2+')')
ax.bar(timeaxis+wd*0.5, prob_CDW_cond_DSW, wd, label='P('+wm2+' | '+wm1+')')
plt.bar(timeaxis+wd*1.5, prob_CDW_cond_noDSW, wd, label='P('+wm2+' | no '+wm1+')')
ax.set_xticks(timeaxis)
timeaxis_ticklabel = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']
ax.set_xticklabels(timeaxis_ticklabel)
handles, labels = ax.get_legend_handles_labels()
axlegend = plt.subplot(gs[1,0])
axlegend.legend(handles, labels, ncol=2, loc=9)
axlegend.set_yticklabels([])
axlegend.set_yticks([])
axlegend.set_xticklabels([])
axlegend.set_xticks([])
axlegend.spines["top"].set_visible(False)
axlegend.spines["right"].set_visible(False)
axlegend.spines["left"].set_visible(False)
axlegend.spines["bottom"].set_visible(False)
ax.grid()
if save:
plt.savefig(savename, dpi=300, bbox_inches='tight')
if show:
plt.show()
print(' P('+wm1+' | '+wm2+')=',prob_DSW_cond_CDW.mean(),"\n", 'P('+wm1+' | no '+wm2+')=', prob_DSW_cond_noCDW.mean(),
"\n", 'P('+wm2+' | '+wm1+')=', prob_CDW_cond_DSW.mean(), "\n",
'P('+wm2+' | no '+wm1+')=',prob_CDW_cond_noDSW.mean())
def compute_prob_unweighted_bootstrapped(dfwm, save=False, savename='untitled.png', reps=10000,
show=True, width=90/25.4, height=90/25.4):
matplotlib.rcParams.update({'font.size': 8}) # setting fontsize for plot elements
wm1='DSW'
wm2='CDW'
prob_DSW_cond_CDW = np.zeros(2)
prob_DSW_cond_CDW_yerr = np.zeros((2,2))
prob_DSW_cond_CDW_CI = np.zeros((2,2))
prob_DSW_cond_noCDW = np.zeros(2)
prob_DSW_cond_noCDW_yerr = np.zeros((2,2))
prob_DSW_cond_noCDW_CI = np.zeros((2,2))
prob_CDW_cond_DSW = np.zeros(2)
prob_CDW_cond_DSW_yerr = np.zeros((2,2))
prob_CDW_cond_DSW_CI = np.zeros((2,2))
prob_CDW_cond_noDSW = np.zeros(2)
prob_CDW_cond_noDSW_yerr = np.zeros((2,2))
prob_CDW_cond_noDSW_CI = np.zeros((2,2))
season = [[12,1,2,3,4,5], [6,7,8,9,10,11]]
for i in range(2):
selMask = ( (dfwm.month.isin(season[i]) ) & (dfwm["DSW"].notnull()) )
totalLength = float(len(dfwm.loc[selMask]))
seldDSW = dfwm[selMask].DSW.values
seldCDW = dfwm[selMask].CDW.values
prob_DSW_cap_NoCDW = len(seldDSW[seldCDW < 20].nonzero()[0]) / totalLength # P(DSW intersection noCDW)
prob_noCDW = len(seldCDW[seldCDW < 20]) / totalLength
prob_DSW_cond_noCDW[i] = prob_DSW_cap_NoCDW / prob_noCDW
randidxr = np.random.choice(len(seldDSW[seldCDW < 20]), (len(seldDSW[seldCDW < 20]), reps), replace=True)
MC_DSW_cap_NoCDW = seldDSW[seldCDW < 20][randidxr]
MC_prob_DSW_cap_noCDW = np.count_nonzero(MC_DSW_cap_NoCDW, axis=0) / totalLength
randidxr = np.random.choice(len(seldCDW), (len(seldCDW), reps), replace=True)
MC_CDW = seldCDW[randidxr]
MC_prob_noCDW = ma.count(ma.masked_array(MC_CDW, mask=(MC_CDW > 20) ), axis=0) / totalLength
MC_prob_DSW_cond_noCDW = MC_prob_DSW_cap_noCDW / MC_prob_noCDW
MC_prob_DSW_cond_noCDW[MC_prob_DSW_cond_noCDW > 1] = 1 # random sampling can sometimes return count exceeding total count, which will give a probability exceeding value of 1.
MC_prob_DSW_cond_noCDW[MC_prob_DSW_cond_noCDW < 0] = 0 # random sampling can sometimes return count exceeding total count, which will give a probability exceeding value of 1.
prob_DSW_cond_noCDW_yerr[i] = abs(prob_DSW_cond_noCDW[i] - np.percentile(MC_prob_DSW_cond_noCDW, [2.5, 97.5]))
prob_DSW_cond_noCDW_CI[i] = np.percentile(MC_prob_DSW_cond_noCDW, [2.5, 97.5])
prob_CDW_cap_NoDSW = len(seldCDW[seldDSW < 20].nonzero()[0]) / float(totalLength)
prob_noDSW = len(seldDSW[seldDSW < 20]) / float(totalLength)
prob_CDW_cond_noDSW[i] = prob_CDW_cap_NoDSW / prob_noDSW
randidxr = np.random.choice(len(seldCDW[seldDSW < 20]), (len(seldCDW[seldDSW < 20]), reps), replace=True)
MC_CDW_cap_NoDSW = seldCDW[seldDSW < 20][randidxr]
MC_prob_CDW_cap_noDSW = np.count_nonzero(MC_CDW_cap_NoDSW, axis=0) / totalLength
randidxr = np.random.choice(len(seldDSW), (len(seldDSW), reps), replace=True)
MC_DSW = seldDSW[randidxr]
MC_prob_noDSW = ma.count(ma.masked_array(MC_DSW, mask=(MC_DSW > 20) ), axis=0) / totalLength
MC_prob_CDW_cond_noDSW = MC_prob_CDW_cap_noDSW / MC_prob_noDSW
MC_prob_CDW_cond_noDSW[MC_prob_CDW_cond_noDSW > 1] = 1
MC_prob_CDW_cond_noDSW[MC_prob_CDW_cond_noDSW < 0] = 0
prob_CDW_cond_noDSW_yerr[i] = abs(prob_CDW_cond_noDSW[i] - np.percentile(MC_prob_CDW_cond_noDSW, [2.5, 97.5]))
prob_CDW_cond_noDSW_CI[i] = np.percentile(MC_prob_CDW_cond_noDSW, [2.5, 97.5])
prob_DSW_cap_CDW = len(seldDSW[seldCDW > 20].nonzero()[0]) / totalLength
prob_CDW = len(seldCDW.nonzero()[0]) / totalLength
prob_DSW_cond_CDW[i] = prob_DSW_cap_CDW / prob_CDW
randidxr = np.random.choice(len(seldDSW[seldCDW > 20]), (len(seldDSW[seldCDW > 20]), reps), replace=True)
MC_DSW_cap_CDW = seldDSW[seldCDW > 20][randidxr]
MC_prob_DSW_cap_CDW = np.count_nonzero(MC_DSW_cap_CDW, axis=0) / totalLength
MC_prob_CDW = ma.count(ma.masked_array(MC_CDW, mask=(MC_CDW < 20) ), axis=0) / totalLength
MC_prob_DSW_cond_CDW = MC_prob_DSW_cap_CDW / MC_prob_CDW
MC_prob_DSW_cond_CDW[MC_prob_DSW_cond_CDW > 1] = 1
MC_prob_DSW_cond_CDW[MC_prob_DSW_cond_CDW < 0] = 0
prob_DSW_cond_CDW_yerr[i] = abs(prob_DSW_cond_CDW[i] - np.percentile(MC_prob_DSW_cond_CDW, [2.5, 97.5]))
prob_DSW_cond_CDW_CI[i] = np.percentile(MC_prob_DSW_cond_CDW, [2.5, 97.5])
prob_CDW_cap_DSW = len(seldCDW[seldDSW > 20].nonzero()[0]) / totalLength
prob_DSW = len(seldDSW[seldDSW > 20]) / totalLength
prob_CDW_cond_DSW[i] = prob_CDW_cap_DSW / prob_DSW
randidxr = np.random.choice(len(seldCDW[seldDSW > 20]), (len(seldCDW[seldDSW > 20]), reps), replace=True)
MC_CDW_cap_DSW = seldCDW[seldDSW > 20][randidxr]
MC_prob_CDW_cap_DSW = np.count_nonzero(MC_CDW_cap_DSW, axis=0) / totalLength
MC_prob_DSW = ma.count(ma.masked_array(MC_DSW, mask=(MC_DSW < 20) ), axis=0) / totalLength
MC_prob_CDW_cond_DSW = MC_prob_CDW_cap_DSW / MC_prob_DSW
MC_prob_CDW_cond_DSW[MC_prob_CDW_cond_DSW > 1] = 1
MC_prob_CDW_cond_DSW[MC_prob_CDW_cond_DSW < 0] = 0
prob_CDW_cond_DSW_yerr[i] = abs(prob_CDW_cond_DSW[i] - np.percentile(MC_prob_CDW_cond_DSW, [2.5, 97.5]))
prob_CDW_cond_DSW_CI[i] = np.percentile(MC_prob_CDW_cond_DSW, [2.5, 97.5])
timeaxis = np.array([1,2])
fig = plt.figure(1, figsize=(width, height))
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 0.2])
ax = plt.subplot(gs[0,0])
wth = 0.1
ax.errorbar(timeaxis-1.5*wth, prob_DSW_cond_CDW, yerr=prob_DSW_cond_CDW_yerr.T, label='P('+wm1+' | '+wm2+')', color="aqua", fmt="s", capsize=3)
ax.errorbar(timeaxis-wth*0.5, prob_DSW_cond_noCDW, yerr=prob_DSW_cond_noCDW_yerr.T, label='P('+wm1+' | no '+wm2+')', color="b", fmt="o", capsize=3)
ax.errorbar(timeaxis+wth*0.5, prob_CDW_cond_DSW, yerr=prob_CDW_cond_DSW_yerr.T, label='P('+wm2+' | '+wm1+')', color="darkorange", fmt="v", capsize=3)
ax.errorbar(timeaxis+wth*1.5, prob_CDW_cond_noDSW, yerr=prob_CDW_cond_noDSW_yerr.T, label='P('+wm2+' | no '+wm1+')', color="r", fmt="^", capsize=3)
ax.set_xticks(timeaxis)
timeaxis_ticklabel = ['summer', 'winter']
ax.set_xticklabels(timeaxis_ticklabel)
handles, labels = ax.get_legend_handles_labels()
axlegend = plt.subplot(gs[1,0])
axlegend.legend(handles, labels, ncol=2, loc=9)
axlegend.set_yticklabels([])
axlegend.set_yticks([])
axlegend.set_xticklabels([])
axlegend.set_xticks([])
axlegend.spines["top"].set_visible(False)
axlegend.spines["right"].set_visible(False)
axlegend.spines["left"].set_visible(False)
axlegend.spines["bottom"].set_visible(False)
ax.set_xlim(0.3, 2.75)
ax.set_ylim(0,1)
ax.set_yticks(np.arange(0,1.1,0.1))
ax.grid()
if save:
plt.savefig(savename, dpi=300, bbox_inches='tight')
if show:
plt.show()
print(' P('+wm1+' | '+wm2+')=',prob_DSW_cond_CDW,prob_DSW_cond_CDW_CI,
"\n", 'P('+wm1+' | no '+wm2+')=', prob_DSW_cond_noCDW, prob_DSW_cond_noCDW_CI,
"\n", 'P('+wm2+' | '+wm1+')=', prob_CDW_cond_DSW, prob_CDW_cond_DSW_CI,
"\n", 'P('+wm2+' | no '+wm1+')=',prob_CDW_cond_noDSW, prob_CDW_cond_noDSW_CI)
return prob_DSW_cond_noCDW, prob_DSW_cond_noCDW_CI, prob_DSW_cond_noCDW_yerr
def compute_prob_weighted_bootstrapped(dfwm, save=False, savename='untitled.png', reps=10000,
show=True, width=90/25.4, height=90/25.4):
matplotlib.rcParams.update({'font.size': 8}) # setting fontsize for plot elements
wm1='DSW'
wm2='CDW'
prob_DSW_cond_CDW = np.zeros(2)
prob_DSW_cond_CDW_yerr = np.zeros((2,2))
prob_DSW_cond_CDW_CI = np.zeros((2,2))
prob_DSW_cond_noCDW = np.zeros(2)
prob_DSW_cond_noCDW_yerr = np.zeros((2,2))
prob_DSW_cond_noCDW_CI = np.zeros((2,2))
prob_CDW_cond_DSW = np.zeros(2)
prob_CDW_cond_DSW_yerr = np.zeros((2,2))
prob_CDW_cond_DSW_CI = np.zeros((2,2))
prob_CDW_cond_noDSW = np.zeros(2)
prob_CDW_cond_noDSW_yerr = np.zeros((2,2))
prob_CDW_cond_noDSW_CI = np.zeros((2,2))
season = [[12,1,2,3,4,5], [6,7,8,9,10,11]]
for i in range(2):
selMask = ( (dfwm.month.isin(season[i]) ) & (dfwm["DSW"].notnull()) )
totalVolume = np.nansum(dfwm.loc[selMask, "zlowest"])
totalLength = float(len(dfwm.loc[selMask]))
seldDSW = dfwm[selMask].DSW.values
seldCDW = dfwm[selMask].CDW.values
prob_DSW_cap_NoCDW = np.nansum(seldDSW[(seldCDW < 20)]) / totalVolume # P(DSW intersection noCDW)
prob_CDW = np.nansum(seldCDW) / totalVolume
prob_noCDW = 1 - prob_CDW
prob_DSW_cond_noCDW[i] = prob_DSW_cap_NoCDW / prob_noCDW
randidxr = np.random.choice(len(seldDSW[seldCDW < 20]), (len(seldDSW[seldCDW < 20]), reps), replace=True)
MC_DSW_cap_NoCDW = seldDSW[seldCDW < 20][randidxr]
MC_prob_DSW_cap_noCDW = np.nansum(MC_DSW_cap_NoCDW, axis=0) / totalVolume
randidxr = np.random.choice(len(seldCDW), (len(seldCDW), reps), replace=True)
MC_CDW = seldCDW[randidxr]
MC_prob_CDW = np.nansum(MC_CDW, axis=0) / totalVolume
MC_prob_noCDW = 1 - MC_prob_CDW
MC_prob_DSW_cond_noCDW = MC_prob_DSW_cap_noCDW / MC_prob_noCDW
MC_prob_DSW_cond_noCDW[MC_prob_DSW_cond_noCDW > 1] = 1 # random sampling can sometimes return count exceeding total count, which will give a probability exceeding value of 1.
MC_prob_DSW_cond_noCDW[MC_prob_DSW_cond_noCDW < 0] = 0 # random sampling can sometimes return count exceeding total count, which will give a probability exceeding value of 1.
prob_DSW_cond_noCDW_yerr[i] = abs(prob_DSW_cond_noCDW[i] - np.percentile(MC_prob_DSW_cond_noCDW, [2.5, 97.5]))
prob_DSW_cond_noCDW_CI[i] = np.percentile(MC_prob_DSW_cond_noCDW, [2.5, 97.5])
prob_CDW_cap_NoDSW = np.nansum(seldCDW[seldDSW < 20]) / totalVolume
prob_DSW = np.nansum(seldDSW[seldDSW > 20]) / totalVolume
prob_noDSW = 1 - prob_DSW
prob_CDW_cond_noDSW[i] = prob_CDW_cap_NoDSW / prob_noDSW
randidxr = np.random.choice(len(seldCDW[seldDSW < 20]), (len(seldCDW[seldDSW < 20]), reps), replace=True)
MC_CDW_cap_NoDSW = seldCDW[seldDSW < 20][randidxr]
MC_prob_CDW_cap_noDSW = np.nansum(MC_CDW_cap_NoDSW, axis=0) / totalVolume
randidxr = np.random.choice(len(seldDSW), (len(seldDSW), reps), replace=True)
MC_DSW = seldDSW[randidxr]
MC_prob_DSW = np.nansum(MC_DSW, axis=0) / totalVolume
MC_prob_noDSW = 1 - MC_prob_DSW
MC_prob_CDW_cond_noDSW = MC_prob_CDW_cap_noDSW / MC_prob_noDSW
MC_prob_CDW_cond_noDSW[MC_prob_CDW_cond_noDSW > 1] = 1
MC_prob_CDW_cond_noDSW[MC_prob_CDW_cond_noDSW < 0] = 0
prob_CDW_cond_noDSW_yerr[i] = abs(prob_CDW_cond_noDSW[i] - np.percentile(MC_prob_CDW_cond_noDSW, [2.5, 97.5]))
prob_CDW_cond_noDSW_CI[i] = np.percentile(MC_prob_CDW_cond_noDSW, [2.5, 97.5])
prob_DSW_cap_CDW = np.nansum(seldDSW[seldCDW > 20]) / totalVolume
prob_DSW_cond_CDW[i] = prob_DSW_cap_CDW / prob_CDW
randidxr = np.random.choice(len(seldDSW[seldCDW > 20]), (len(seldDSW[seldCDW > 20]), reps), replace=True)
MC_DSW_cap_CDW = seldDSW[seldCDW > 0][randidxr]
MC_prob_DSW_cap_CDW = np.nansum(MC_DSW_cap_CDW, axis=0) / totalVolume
MC_prob_DSW_cond_CDW = MC_prob_DSW_cap_CDW / MC_prob_CDW
MC_prob_DSW_cond_CDW[MC_prob_DSW_cond_CDW > 1] = 1
MC_prob_DSW_cond_CDW[MC_prob_DSW_cond_CDW < 0] = 0
prob_DSW_cond_CDW_yerr[i] = abs(prob_DSW_cond_CDW[i] - np.percentile(MC_prob_DSW_cond_CDW, [2.5, 97.5]))
prob_DSW_cond_CDW_CI[i] = np.percentile(MC_prob_DSW_cond_CDW, [2.5, 97.5])
prob_CDW_cap_DSW = np.nansum(seldCDW[seldDSW > 20]) / totalVolume
prob_CDW_cond_DSW[i] = prob_CDW_cap_DSW / prob_DSW
randidxr = np.random.choice(len(seldCDW[seldDSW > 20]), (len(seldCDW[seldDSW > 20]), reps), replace=True)
MC_CDW_cap_DSW = seldCDW[seldDSW > 20][randidxr]
MC_prob_CDW_cap_DSW = np.nansum(MC_CDW_cap_DSW, axis=0) / totalVolume
MC_prob_CDW_cond_DSW = MC_prob_CDW_cap_DSW / MC_prob_DSW
MC_prob_CDW_cond_DSW[MC_prob_CDW_cond_DSW > 1] = 1
MC_prob_CDW_cond_DSW[MC_prob_CDW_cond_DSW < 0] = 0
prob_CDW_cond_DSW_yerr[i] = abs(prob_CDW_cond_DSW[i] - np.percentile(MC_prob_CDW_cond_DSW, [2.5, 97.5]))
prob_CDW_cond_DSW_CI[i] = np.percentile(MC_prob_CDW_cond_DSW, [2.5, 97.5])
timeaxis = np.array([1,2])
fig = plt.figure(1, figsize=(width, height))
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 0.2])
ax = plt.subplot(gs[0,0])
wth = 0.1
print(prob_DSW_cond_CDW, prob_DSW_cond_CDW_yerr)
ax.errorbar(timeaxis-1.5*wth, prob_DSW_cond_CDW, yerr=prob_DSW_cond_CDW_yerr.T, label='P('+wm1+' | '+wm2+')', color="aqua", fmt="s", capsize=3)
ax.errorbar(timeaxis-wth*0.5, prob_DSW_cond_noCDW, yerr=prob_DSW_cond_noCDW_yerr.T, label='P('+wm1+' | no '+wm2+')', color="b", fmt="o", capsize=3)
ax.errorbar(timeaxis+wth*0.5, prob_CDW_cond_DSW, yerr=prob_CDW_cond_DSW_yerr.T, label='P('+wm2+' | '+wm1+')', color="darkorange", fmt="v", capsize=3)
ax.errorbar(timeaxis+wth*1.5, prob_CDW_cond_noDSW, yerr=prob_CDW_cond_noDSW_yerr.T, label='P('+wm2+' | no '+wm1+')', color="r", fmt="^", capsize=3)
ax.set_xticks(timeaxis)
timeaxis_ticklabel = ['summer', 'winter']
ax.set_xticklabels(timeaxis_ticklabel)
handles, labels = ax.get_legend_handles_labels()
axlegend = plt.subplot(gs[1,0])
axlegend.legend(handles, labels, ncol=2, loc=9)
axlegend.set_yticklabels([])
axlegend.set_yticks([])
axlegend.set_xticklabels([])
axlegend.set_xticks([])
axlegend.spines["top"].set_visible(False)
axlegend.spines["right"].set_visible(False)
axlegend.spines["left"].set_visible(False)
axlegend.spines["bottom"].set_visible(False)
ax.set_xlim(0.3, 2.75)
ax.set_ylim(0,1)
ax.set_yticks(np.arange(0,1.1,0.1))
ax.grid()
if save:
plt.savefig(savename, dpi=300, bbox_inches='tight')
if show:
plt.show()
print(' P('+wm1+' | '+wm2+')=',prob_DSW_cond_CDW,prob_DSW_cond_CDW_CI,
"\n", 'P('+wm1+' | no '+wm2+')=', prob_DSW_cond_noCDW, prob_DSW_cond_noCDW_CI,
"\n", 'P('+wm2+' | '+wm1+')=', prob_CDW_cond_DSW, prob_CDW_cond_DSW_CI,
"\n", 'P('+wm2+' | no '+wm1+')=',prob_CDW_cond_noDSW, prob_CDW_cond_noDSW_CI)
return prob_DSW_cond_noCDW, prob_DSW_cond_noCDW_CI, prob_DSW_cond_noCDW_yerr