{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LMI and LI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Module load and IBTRACs read in**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"# close warming info\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"%matplotlib inline\n",
"#\n",
"from netCDF4 import Dataset\n",
"from matplotlib import gridspec\n",
"from scipy.optimize import curve_fit\n",
"from mpl_toolkits.basemap import Basemap\n",
"from datetime import datetime, timedelta\n",
"from termcolor import cprint\n",
"from matplotlib.patches import Polygon\n",
"from matplotlib.colors import BoundaryNorm\n",
"from matplotlib.colorbar import Colorbar\n",
"from sklearn import preprocessing\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import statsmodels.formula.api as sm\n",
"import matplotlib.mlab as mlab\n",
"import matplotlib.colors as mcolors\n",
"import matplotlib, copy, scipy, scipy.stats, statsmodels, random, glob\n",
"import geopy.distance\n",
"import statsmodels.api as sm\n",
"from statsmodels.stats.outliers_influence import summary_table\n",
"from lmfit import Model, Parameters\n",
"import cdsapi\n",
"import atmos\n",
"from pcmin_fullterm import pcmin\n",
"from patsy import dmatrices\n",
"#from pcmin import pcmin\n",
"#from tcpi_allterm import pcmin, pcmin3_kflag\n",
"\n",
"# define the year period\n",
"START_YEAR = 1982\n",
"END_YEAR = 2019\n",
"INDEX_year = np.arange(START_YEAR,END_YEAR+1)\n",
"# basin names\n",
"BASIN_NAMEs = ['NA','EP','WP','NI','SI','SP','NH','SH','GB']\n",
"# Creat CAT_KT that contains the upper and lower winds for different categories\n",
"tmp = np.zeros((2,7))\n",
"tmp[0,0] = -1000.\n",
"tmp[1,0] = 33.\n",
"tmp[0,1] = 34.\n",
"tmp[1,1] = 63.\n",
"tmp[0,2] = 64.\n",
"tmp[1,2] = 82.\n",
"tmp[0,3] = 83.\n",
"tmp[1,3] = 95.\n",
"tmp[0,4] = 96.\n",
"tmp[1,4] = 112.\n",
"tmp[0,5] = 113.\n",
"tmp[1,5] = 136.\n",
"tmp[0,6] = 137.\n",
"tmp[1,6] = 10000.\n",
"CAT_KT = pd.DataFrame(tmp,index=['LOWER','UPPER'],columns=['TD','TS','CAT1','CAT2','CAT3','CAT4','CAT5'])\n",
"#\n",
"ibt = pd.read_csv('./ibtracs.ALL.list.v04r00.csv',skiprows=[1],keep_default_na=False,na_values=[' '])\n",
"#C find the duration from INTENSITY_START to INTENSITY_END, duration unit is hour\n",
"pd.set_option('display.max_rows', None)\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.width', None)\n",
"pd.set_option('display.max_colwidth', -1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Global function define**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: trend and CI**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"from scipy.stats import t\n",
"from scipy.signal import detrend\n",
"\n",
"def get_trend_and_ci_weight(X,Y,sample_weight):\n",
" X = np.asarray(X)\n",
" X_ORIGINAL = X\n",
" Y = np.asarray(Y)\n",
" sample_weight = np.asarray(sample_weight)\n",
" mask = ~np.isnan(X) & ~np.isnan(Y)\n",
" X = X[mask]\n",
" Y = Y[mask]\n",
" sample_weight = sample_weight[mask]\n",
" \n",
" X = sm.add_constant(X)\n",
" res = sm.WLS(Y,X,sample_weight).fit()\n",
" st, data, ss2 = summary_table(res, alpha=0.05)\n",
" fittedvalues = data[:,2]\n",
" predict_mean_se = data[:,3]\n",
" predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T\n",
" p=res.pvalues[1]\n",
" slope = res.params[1]\n",
" intercept = res.params[0]\n",
" fittedvalues = slope*X_ORIGINAL + intercept\n",
" conf_int_slope = res.params[1]-res.conf_int()[1,0]\n",
" if check_autocorrelation_durbin_watson( Y) == False :\n",
" df = len(Y) - 2\n",
" else:\n",
" lagauto = abs(pd.Series(detrend(Y)).autocorr(lag=1))\n",
" df = (len(Y) - 2) * ( (1-lagauto)/(1+lagauto) )\n",
" return X_ORIGINAL, fittedvalues,slope,res.bse[1] * t.ppf(1 - 0.025, df=df),p"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"from scipy.stats import t\n",
"from scipy.signal import detrend\n",
"\n",
"def get_trend_and_ci_weight_CITHRES(X,Y,sample_weight,CITHRES):\n",
" X = np.asarray(X)\n",
" X_ORIGINAL = X\n",
" Y = np.asarray(Y)\n",
" sample_weight = np.asarray(sample_weight)\n",
" mask = ~np.isnan(X) & ~np.isnan(Y)\n",
" X = X[mask]\n",
" Y = Y[mask]\n",
" sample_weight = sample_weight[mask]\n",
" \n",
" X = sm.add_constant(X)\n",
" res = sm.WLS(Y,X,sample_weight).fit()\n",
" st, data, ss2 = summary_table(res, alpha=1-CITHRES)\n",
" fittedvalues = data[:,2]\n",
" predict_mean_se = data[:,3]\n",
" predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T\n",
" p=res.pvalues[1]\n",
" slope = res.params[1]\n",
" intercept = res.params[0]\n",
" fittedvalues = slope*X_ORIGINAL + intercept\n",
" conf_int_slope = res.params[1]-res.conf_int()[1,0]\n",
" if check_autocorrelation_durbin_watson( Y) == False :\n",
" df = len(Y) - 2\n",
" else:\n",
" lagauto = abs(pd.Series(detrend(Y)).autocorr(lag=1))\n",
" df = (len(Y) - 2) * ( (1-lagauto)/(1+lagauto) )\n",
" return X_ORIGINAL, fittedvalues,slope,res.bse[1] * t.ppf(1 - (1-CITHRES)/2, df=df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"from scipy.signal import detrend\n",
"from statsmodels.stats.stattools import durbin_watson\n",
"def check_autocorrelation_durbin_watson(resids):\n",
" xxx = detrend(resids)\n",
" dw_output = durbin_watson(xxx,axis=0)\n",
" if dw_output>=1.427 and dw_output<=4-1.427:\n",
" is_autocorrelated=False\n",
" else:\n",
" is_autocorrelated=True\n",
" return is_autocorrelated\n",
"def check_autocorrelation_plot(var):\n",
" from statsmodels.graphics.tsaplots import plot_acf\n",
" plot_acf(detrend(var))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def poisson_reg(X,Y):\n",
" X = np.asarray(X)\n",
" Y = np.asarray(Y)\n",
" exog, endog = sm.add_constant(X), Y\n",
" res = sm.GLM(endog, exog, family=sm.families.Poisson()).fit()\n",
" ci = (abs(res.params[1] - res.conf_int()[1][0]) + abs(res.params[1] - res.conf_int()[1][1]))/2\n",
" p=res.pvalues[1]\n",
" mean = res.params[1]\n",
" pred = res.predict()\n",
" #print(res.summary())\n",
" \n",
" return pred, mean, ci, p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: moving speed and LMI to LI trajectory length**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def CALC_MOVING_SPEED(tmp_lat,tmp_lon):\n",
" tmp_u = np.nan * np.zeros(shape=tmp_lat.shape)\n",
" tmp_v = np.nan * np.zeros(shape=tmp_lat.shape)\n",
" tmp_uv = np.nan * np.zeros(shape=tmp_lat.shape)\n",
" tmp_tra= np.zeros(shape=tmp_lat.shape)\n",
" for RRR in range(len(tmp_lat)-1):\n",
" coords_1 = (tmp_lat[RRR+1], tmp_lon[RRR])\n",
" coords_2 = (tmp_lat[RRR] , tmp_lon[RRR])\n",
" # poleward speed is positive\n",
" tmp_v[RRR] = np.sign(np.abs(tmp_lat[RRR+1])-np.abs(tmp_lat[RRR]))*geopy.distance.vincenty(coords_1, coords_2).m/3./3600.\n",
" coords_1 = (tmp_lat[RRR ], tmp_lon[RRR ])\n",
" coords_2 = (tmp_lat[RRR+1], tmp_lon[RRR+1])\n",
" ##\n",
" # here RRR+1 means we calculate the velocity and distance moved in the PAST 3 hrs\n",
" ##\n",
" tmp_tra [RRR+1] = geopy.distance.vincenty(coords_1, coords_2).km\n",
" tmp_uv [RRR+1] = tmp_tra [RRR+1]*1e3/3./3600.\n",
" # westward speed is positive\n",
" tmp_u[RRR+1] = -np.sign(tmp_lon[RRR+1]-tmp_lon[RRR])*np.sqrt(tmp_uv[RRR+1]**2-tmp_v[RRR+1]**2)\n",
" return tmp_u,tmp_v,tmp_uv,tmp_tra"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: bootstrap**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def bootstrapping_compare_mean_CI_star(sample1,sample2,CI_THRES):\n",
" sample1 = np.asarray(sample1)\n",
" sample2 = np.asarray(sample2)\n",
" DIFF_MEAN_RESAMPLE = []\n",
" for LLL in range(10000):\n",
" resample1=np.random.choice(sample1,size=sample1.shape,replace=True)\n",
" resample2=np.random.choice(sample2,size=sample2.shape,replace=True)\n",
" DIFF_MEAN_RESAMPLE.extend([np.nanmean(resample2) - np.nanmean(resample1)])\n",
" output_ci1 = scipy.stats.scoreatpercentile(DIFF_MEAN_RESAMPLE,(100.-CI_THRES)/2.)\n",
" output_ci2 = scipy.stats.scoreatpercentile(DIFF_MEAN_RESAMPLE,100.-(100.-CI_THRES)/2.)\n",
" return np.nanmean(DIFF_MEAN_RESAMPLE)-output_ci1,output_ci2-np.nanmean(DIFF_MEAN_RESAMPLE),np.nanmean(DIFF_MEAN_RESAMPLE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: bootstrap2**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def bootstrapping_compare_halfchange(sample1,sample2):\n",
" sample1_0 = np.asarray(sample1[:len(sample1)//2])\n",
" sample1_1 = np.asarray(sample1[len(sample1)//2:])\n",
" DIFF_MEAN_RESAMPLE_1 = []\n",
" for LLL in range(100):\n",
" resample1_0=np.random.choice(sample1_0,size=sample1_0.shape,replace=True)\n",
" resample1_1=np.random.choice(sample1_1,size=sample1_0.shape,replace=True)\n",
" DIFF_MEAN_RESAMPLE_1.extend([np.nanmean(resample1_1) - np.nanmean(resample1_0)])\n",
" sample2_0 = np.asarray(sample2[:len(sample2)//2])\n",
" sample2_1 = np.asarray(sample2[len(sample2)//2:])\n",
" DIFF_MEAN_RESAMPLE_2 = []\n",
" for LLL in range(100):\n",
" resample2_0=np.random.choice(sample2_0,size=sample2_0.shape,replace=True)\n",
" resample2_1=np.random.choice(sample2_1,size=sample2_0.shape,replace=True)\n",
" DIFF_MEAN_RESAMPLE_2.extend([np.nanmean(resample2_1) - np.nanmean(resample2_0)])\n",
" \n",
" DIFF_MEAN_RESAMPLE_1 = np.asarray(DIFF_MEAN_RESAMPLE_1)\n",
" DIFF_MEAN_RESAMPLE_2 = np.asarray(DIFF_MEAN_RESAMPLE_2)\n",
" plt.hist([DIFF_MEAN_RESAMPLE_1,DIFF_MEAN_RESAMPLE_2])\n",
" stats,p = scipy.stats.ttest_ind(DIFF_MEAN_RESAMPLE_1,DIFF_MEAN_RESAMPLE_2)\n",
" return np.nanmean(DIFF_MEAN_RESAMPLE_2),np.nanmean(DIFF_MEAN_RESAMPLE_1),np.nanmean(DIFF_MEAN_RESAMPLE_2)-np.nanmean(DIFF_MEAN_RESAMPLE_1),p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**chi2 poisson distribution fit text**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def chi2_poisson_fit(data):\n",
" \n",
" from scipy.optimize import curve_fit\n",
" from scipy.special import factorial\n",
" from scipy.stats import poisson\n",
"\n",
" # the bins should be of integer width, because poisson is an integer distribution\n",
" bins = np.arange(np.nanmin(data),np.nanmax(data)+2) - 0.5\n",
" entries, bin_edges, patches = plt.hist(data, bins=bins, density=True, label='Data')\n",
"\n",
" # calculate bin centres\n",
" bin_middles = 0.5 * (bin_edges[1:] + bin_edges[:-1])\n",
"\n",
" def fit_function(k, lamb):\n",
" '''poisson function, parameter lamb is the fit parameter'''\n",
" return poisson.pmf(k, lamb)\n",
"\n",
" # fit with curve_fit\n",
" parameters, cov_matrix = curve_fit(fit_function, bin_middles, entries,p0=[5])\n",
" \n",
" # plot poisson-deviation with fitted parameter\n",
" x_plot = np.arange(np.nanmin(data), np.nanmax(data)+1)\n",
"\n",
" #_=plt.plot(\n",
" # x_plot,\n",
" # fit_function(x_plot, *parameters),\n",
" # marker='o', linestyle='',\n",
" # label='Fit result',\n",
" #)\n",
" #_=plt.legend()\n",
"\n",
" Nexp = fit_function(bin_middles, *parameters)\n",
" entries = entries * len(Y)\n",
" Nexp = Nexp * len(Y)\n",
" r = entries - Nexp\n",
" chisq = np.sum(r**2/Nexp)\n",
" df = len(data) - 2\n",
" return print(\"chisq =\",chisq,\"df =\",df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Data pre processing**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The filters are:
\n",
"1. Time from 1982-2019\n",
"1. Only official records at 00 03 06 09 12 15 18 21 UTC\n",
"1. LMI >= 64 kt\n",
"1. At least 24-h consecutive records before and after LMI\n",
"1. within 40 NS\n",
"1. only consider the landfall after the last LMI\n",
"\n",
"**newbt** is the new cleaned variable
\n",
"**newbt_lf** is the new cleaned landfall TCs
\n",
"**newbt_nonlf** is the new cleaned non landfall TCs
\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def index_for_last_max(x):\n",
" x=np.asarray(x)\n",
" return len(x)-np.nanargmax(x[::-1])-1"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def mark_landfall(x):\n",
" DIST2LAND = x.DIST2LAND.values[:]\n",
" WIND = x.USA_WIND.values[:]\n",
" # use 1 to mark landfall time after the last LMI\n",
" LF_MARKER = np.zeros(shape=DIST2LAND.shape)\n",
" for RRR in range(index_for_last_max(WIND),len(WIND)):\n",
" if DIST2LAND[RRR]==0 and DIST2LAND[RRR-1]>0.:\n",
" LF_MARKER[RRR] = 1\n",
" x['LF_MARKER'] = list(LF_MARKER)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def lf_after_lmi(x):\n",
" RRR_LMI= x.iloc[::-1].USA_WIND.idxmax()\n",
" tmp_x=x[x.LF_MARKER==1]\n",
" RRR_LF = tmp_x.USA_WIND.idxmax()\n",
" output_wind = [];\n",
" if RRR_LF-RRR_LMI >= 0 :\n",
" return True\n",
" else:\n",
" return False\n",
" print(\"Strongest LI before the last LMI!!!!:\",x.SID.values[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"newbt = ibt[(ibt.SEASON >= START_YEAR)&(ibt.SEASON <= END_YEAR)]\n",
"newbt = newbt[newbt.BASIN!='SA']\n",
"newbt = newbt[np.abs(newbt.USA_LAT) <= 40]\n",
"newbt = newbt[\n",
" newbt.ISO_TIME.str.contains('00:00:00') | \n",
" newbt.ISO_TIME.str.contains('03:00:00') | \n",
" newbt.ISO_TIME.str.contains('06:00:00') | \n",
" newbt.ISO_TIME.str.contains('09:00:00') | \n",
" newbt.ISO_TIME.str.contains('12:00:00') | \n",
" newbt.ISO_TIME.str.contains('15:00:00') | \n",
" newbt.ISO_TIME.str.contains('18:00:00') |\n",
" newbt.ISO_TIME.str.contains('21:00:00')\n",
" ]\n",
"newbt = newbt.groupby('SID').filter(lambda x: np.nanmax(x.USA_WIND) >= CAT_KT.CAT1.LOWER)\n",
"newbt = newbt[~np.isnan(newbt.USA_WIND.values)]\n",
"newbt = newbt.groupby('SID').apply (lambda x: mark_landfall(x))\n",
"# lf\n",
"newbt_lf = newbt .groupby('SID').filter(lambda x: np.any(x.LF_MARKER == 1))\n",
"newbt_lf = newbt_lf.groupby('SID').filter(lambda x: lf_after_lmi(x))\n",
"# non lf\n",
"newbt_nonlf = newbt .groupby('SID').filter(lambda x: np.all(x.LF_MARKER != 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Check annual number**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" NA | \n",
" EP | \n",
" WP | \n",
" NI | \n",
" SI | \n",
" SP | \n",
" NH | \n",
" SH | \n",
" GB | \n",
"
\n",
" \n",
" \n",
" \n",
" 1982 | \n",
" 0 | \n",
" 1 | \n",
" 13 | \n",
" 2 | \n",
" 2 | \n",
" 1 | \n",
" 16 | \n",
" 3 | \n",
" 19 | \n",
"
\n",
" \n",
" 1983 | \n",
" 2 | \n",
" 4 | \n",
" 8 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 14 | \n",
" 2 | \n",
" 16 | \n",
"
\n",
" \n",
" 1984 | \n",
" 2 | \n",
" 4 | \n",
" 4 | \n",
" 2 | \n",
" 3 | \n",
" 1 | \n",
" 12 | \n",
" 4 | \n",
" 16 | \n",
"
\n",
" \n",
" 1985 | \n",
" 5 | \n",
" 1 | \n",
" 11 | \n",
" 0 | \n",
" 2 | \n",
" 2 | \n",
" 17 | \n",
" 4 | \n",
" 21 | \n",
"
\n",
" \n",
" 1986 | \n",
" 1 | \n",
" 3 | \n",
" 7 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 11 | \n",
" 3 | \n",
" 14 | \n",
"
\n",
" \n",
" 1987 | \n",
" 2 | \n",
" 1 | \n",
" 8 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 12 | \n",
" 1 | \n",
" 13 | \n",
"
\n",
" \n",
" 1988 | \n",
" 4 | \n",
" 2 | \n",
" 6 | \n",
" 1 | \n",
" 2 | \n",
" 2 | \n",
" 13 | \n",
" 4 | \n",
" 17 | \n",
"
\n",
" \n",
" 1989 | \n",
" 3 | \n",
" 3 | \n",
" 13 | \n",
" 1 | \n",
" 3 | \n",
" 2 | \n",
" 20 | \n",
" 5 | \n",
" 25 | \n",
"
\n",
" \n",
" 1990 | \n",
" 1 | \n",
" 2 | \n",
" 15 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 19 | \n",
" 2 | \n",
" 21 | \n",
"
\n",
" \n",
" 1991 | \n",
" 0 | \n",
" 0 | \n",
" 9 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 10 | \n",
" 1 | \n",
" 11 | \n",
"
\n",
" \n",
" 1992 | \n",
" 1 | \n",
" 4 | \n",
" 12 | \n",
" 3 | \n",
" 1 | \n",
" 2 | \n",
" 20 | \n",
" 3 | \n",
" 23 | \n",
"
\n",
" \n",
" 1993 | \n",
" 1 | \n",
" 5 | \n",
" 15 | \n",
" 2 | \n",
" 0 | \n",
" 0 | \n",
" 23 | \n",
" 0 | \n",
" 23 | \n",
"
\n",
" \n",
" 1994 | \n",
" 1 | \n",
" 1 | \n",
" 11 | \n",
" 1 | \n",
" 4 | \n",
" 0 | \n",
" 14 | \n",
" 4 | \n",
" 18 | \n",
"
\n",
" \n",
" 1995 | \n",
" 4 | \n",
" 1 | \n",
" 11 | \n",
" 4 | \n",
" 3 | \n",
" 0 | \n",
" 20 | \n",
" 3 | \n",
" 23 | \n",
"
\n",
" \n",
" 1996 | \n",
" 4 | \n",
" 6 | \n",
" 9 | \n",
" 2 | \n",
" 6 | \n",
" 1 | \n",
" 21 | \n",
" 7 | \n",
" 28 | \n",
"
\n",
" \n",
" 1997 | \n",
" 1 | \n",
" 4 | \n",
" 11 | \n",
" 1 | \n",
" 3 | \n",
" 3 | \n",
" 17 | \n",
" 6 | \n",
" 23 | \n",
"
\n",
" \n",
" 1998 | \n",
" 5 | \n",
" 1 | \n",
" 9 | \n",
" 5 | \n",
" 0 | \n",
" 0 | \n",
" 20 | \n",
" 0 | \n",
" 20 | \n",
"
\n",
" \n",
" 1999 | \n",
" 3 | \n",
" 1 | \n",
" 7 | \n",
" 3 | \n",
" 5 | \n",
" 2 | \n",
" 14 | \n",
" 7 | \n",
" 21 | \n",
"
\n",
" \n",
" 2000 | \n",
" 2 | \n",
" 0 | \n",
" 7 | \n",
" 2 | \n",
" 6 | \n",
" 1 | \n",
" 11 | \n",
" 7 | \n",
" 18 | \n",
"
\n",
" \n",
" 2001 | \n",
" 2 | \n",
" 1 | \n",
" 11 | \n",
" 2 | \n",
" 1 | \n",
" 0 | \n",
" 16 | \n",
" 1 | \n",
" 17 | \n",
"
\n",
" \n",
" 2002 | \n",
" 3 | \n",
" 1 | \n",
" 6 | \n",
" 0 | \n",
" 3 | \n",
" 0 | \n",
" 10 | \n",
" 3 | \n",
" 13 | \n",
"
\n",
" \n",
" 2003 | \n",
" 3 | \n",
" 4 | \n",
" 9 | \n",
" 0 | \n",
" 2 | \n",
" 1 | \n",
" 16 | \n",
" 3 | \n",
" 19 | \n",
"
\n",
" \n",
" 2004 | \n",
" 5 | \n",
" 1 | \n",
" 13 | \n",
" 3 | \n",
" 5 | \n",
" 1 | \n",
" 22 | \n",
" 6 | \n",
" 28 | \n",
"
\n",
" \n",
" 2005 | \n",
" 9 | \n",
" 1 | \n",
" 12 | \n",
" 0 | \n",
" 2 | \n",
" 1 | \n",
" 22 | \n",
" 3 | \n",
" 25 | \n",
"
\n",
" \n",
" 2006 | \n",
" 1 | \n",
" 2 | \n",
" 11 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" 16 | \n",
" 4 | \n",
" 20 | \n",
"
\n",
" \n",
" 2007 | \n",
" 4 | \n",
" 1 | \n",
" 11 | \n",
" 3 | \n",
" 7 | \n",
" 0 | \n",
" 19 | \n",
" 7 | \n",
" 26 | \n",
"
\n",
" \n",
" 2008 | \n",
" 5 | \n",
" 1 | \n",
" 9 | \n",
" 1 | \n",
" 5 | \n",
" 0 | \n",
" 16 | \n",
" 5 | \n",
" 21 | \n",
"
\n",
" \n",
" 2009 | \n",
" 1 | \n",
" 2 | \n",
" 9 | \n",
" 1 | \n",
" 3 | \n",
" 0 | \n",
" 13 | \n",
" 3 | \n",
" 16 | \n",
"
\n",
" \n",
" 2010 | \n",
" 4 | \n",
" 0 | \n",
" 7 | \n",
" 3 | \n",
" 3 | \n",
" 2 | \n",
" 14 | \n",
" 5 | \n",
" 19 | \n",
"
\n",
" \n",
" 2011 | \n",
" 3 | \n",
" 1 | \n",
" 7 | \n",
" 1 | \n",
" 2 | \n",
" 1 | \n",
" 12 | \n",
" 3 | \n",
" 15 | \n",
"
\n",
" \n",
" 2012 | \n",
" 4 | \n",
" 2 | \n",
" 11 | \n",
" 0 | \n",
" 4 | \n",
" 0 | \n",
" 17 | \n",
" 4 | \n",
" 21 | \n",
"
\n",
" \n",
" 2013 | \n",
" 2 | \n",
" 2 | \n",
" 10 | \n",
" 4 | \n",
" 2 | \n",
" 1 | \n",
" 18 | \n",
" 3 | \n",
" 21 | \n",
"
\n",
" \n",
" 2014 | \n",
" 0 | \n",
" 3 | \n",
" 8 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 12 | \n",
" 2 | \n",
" 14 | \n",
"
\n",
" \n",
" 2015 | \n",
" 1 | \n",
" 4 | \n",
" 11 | \n",
" 2 | \n",
" 4 | \n",
" 3 | \n",
" 18 | \n",
" 7 | \n",
" 25 | \n",
"
\n",
" \n",
" 2016 | \n",
" 4 | \n",
" 3 | \n",
" 12 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 20 | \n",
" 2 | \n",
" 22 | \n",
"
\n",
" \n",
" 2017 | \n",
" 6 | \n",
" 1 | \n",
" 9 | \n",
" 1 | \n",
" 2 | \n",
" 2 | \n",
" 17 | \n",
" 4 | \n",
" 21 | \n",
"
\n",
" \n",
" 2018 | \n",
" 3 | \n",
" 4 | \n",
" 11 | \n",
" 4 | \n",
" 2 | \n",
" 1 | \n",
" 22 | \n",
" 3 | \n",
" 25 | \n",
"
\n",
" \n",
" 2019 | \n",
" 1 | \n",
" 1 | \n",
" 10 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" 14 | \n",
" 4 | \n",
" 18 | \n",
"
\n",
" \n",
" Total | \n",
" 103 | \n",
" 79 | \n",
" 373 | \n",
" 63 | \n",
" 96 | \n",
" 42 | \n",
" 618 | \n",
" 138 | \n",
" 756 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" NA EP WP NI SI SP NH SH GB\n",
"1982 0 1 13 2 2 1 16 3 19 \n",
"1983 2 4 8 0 1 1 14 2 16 \n",
"1984 2 4 4 2 3 1 12 4 16 \n",
"1985 5 1 11 0 2 2 17 4 21 \n",
"1986 1 3 7 0 1 2 11 3 14 \n",
"1987 2 1 8 1 0 1 12 1 13 \n",
"1988 4 2 6 1 2 2 13 4 17 \n",
"1989 3 3 13 1 3 2 20 5 25 \n",
"1990 1 2 15 1 1 1 19 2 21 \n",
"1991 0 0 9 1 0 1 10 1 11 \n",
"1992 1 4 12 3 1 2 20 3 23 \n",
"1993 1 5 15 2 0 0 23 0 23 \n",
"1994 1 1 11 1 4 0 14 4 18 \n",
"1995 4 1 11 4 3 0 20 3 23 \n",
"1996 4 6 9 2 6 1 21 7 28 \n",
"1997 1 4 11 1 3 3 17 6 23 \n",
"1998 5 1 9 5 0 0 20 0 20 \n",
"1999 3 1 7 3 5 2 14 7 21 \n",
"2000 2 0 7 2 6 1 11 7 18 \n",
"2001 2 1 11 2 1 0 16 1 17 \n",
"2002 3 1 6 0 3 0 10 3 13 \n",
"2003 3 4 9 0 2 1 16 3 19 \n",
"2004 5 1 13 3 5 1 22 6 28 \n",
"2005 9 1 12 0 2 1 22 3 25 \n",
"2006 1 2 11 2 2 2 16 4 20 \n",
"2007 4 1 11 3 7 0 19 7 26 \n",
"2008 5 1 9 1 5 0 16 5 21 \n",
"2009 1 2 9 1 3 0 13 3 16 \n",
"2010 4 0 7 3 3 2 14 5 19 \n",
"2011 3 1 7 1 2 1 12 3 15 \n",
"2012 4 2 11 0 4 0 17 4 21 \n",
"2013 2 2 10 4 2 1 18 3 21 \n",
"2014 0 3 8 1 1 1 12 2 14 \n",
"2015 1 4 11 2 4 3 18 7 25 \n",
"2016 4 3 12 1 1 1 20 2 22 \n",
"2017 6 1 9 1 2 2 17 4 21 \n",
"2018 3 4 11 4 2 1 22 3 25 \n",
"2019 1 1 10 2 2 2 14 4 18 \n",
"Total 103 79 373 63 96 42 618 138 756"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TABLE = pd.DataFrame(index=list(INDEX_year)+list(['Total']),columns=BASIN_NAMEs)\n",
"for YYY,YEAR in enumerate(INDEX_year):\n",
" for BBB,BASIN in enumerate(BASIN_NAMEs[:-3]):\n",
" TABLE.iloc[YYY,BBB] = newbt_lf[(newbt_lf.SEASON==YEAR)&(newbt_lf.BASIN==BASIN)].groupby('SID').ngroups\n",
"TABLE.iloc[-1,0:6] = np.sum(TABLE.iloc[:-1,0:6])\n",
"TABLE.iloc[:,6] = np.sum(TABLE.iloc[:,:4],axis=1).astype(int)\n",
"TABLE.iloc[:,7] = np.sum(TABLE.iloc[:,4:6],axis=1).astype(int)\n",
"TABLE.iloc[:,8] = np.sum(TABLE.iloc[:,6:8],axis=1).astype(int)\n",
"TABLE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**get ADT_HURSAT ready**"
]
},
{
"cell_type": "raw",
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"source": [
"ROW_BASIN = pd.read_csv('./pnas.1920849117.sd01.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_DAY = pd.read_csv('./pnas.1920849117.sd02.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_HOUR = pd.read_csv('./pnas.1920849117.sd03.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_LAT = pd.read_csv('./pnas.1920849117.sd04.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_LON = pd.read_csv('./pnas.1920849117.sd05.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_MONTH = pd.read_csv('./pnas.1920849117.sd06.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_SID = pd.read_csv('./pnas.1920849117.sd07.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_WIND = pd.read_csv('./pnas.1920849117.sd08.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])\n",
"ROW_YEAR = pd.read_csv('./pnas.1920849117.sd09.csv',skiprows=[1],keep_default_na=True)#,na_values=[' '])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit linear / exponential / reciprocal decay to individual cases LMI->LF"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def from_lmi_to_lf(x):\n",
" RRR_LMI= x.iloc[::-1].USA_WIND.idxmax()\n",
" tmp_x=x[x.LF_MARKER==1]\n",
" RRR_LF = tmp_x.USA_WIND.idxmax()\n",
" output_wind = [];\n",
" \n",
" pick = x[(x.index>=RRR_LMI)&(x.index<=RRR_LF)]\n",
" output_wind = pick.USA_WIND.values[:]\n",
" output_wind = np.asarray(output_wind)\n",
" output_lat = pick.USA_LAT.values[:]\n",
" output_lat = np.asarray(output_lat )\n",
" output_lon = pick.USA_LON.values[:]\n",
" output_lon = np.asarray(output_lon )\n",
" \n",
" return x.USA_WIND.max(),output_wind, x.SEASON.values[0],x[x.index==RRR_LMI].DIST2LAND.values[0],x.SID.values[0],output_lat,output_lon,x[x.index==RRR_LMI].BASIN.values[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"VMAX_PMIN_SERIES = newbt_lf.groupby('SID').apply(lambda x: from_lmi_to_lf(x))\n",
"VMAX_PMIN_SERIES = VMAX_PMIN_SERIES.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def nonlandfall_lmi(x):\n",
" RRR_LMI= x.iloc[::-1].USA_WIND.idxmax()\n",
" return x[x.index==RRR_LMI].USA_WIND.values[0],x[x.index==RRR_LMI].SEASON.values[0],x[x.index==RRR_LMI].BASIN.values[0]\n",
"\n",
"VMAX_NONLF = newbt_nonlf.groupby('SID').apply(lambda x: nonlandfall_lmi(x))\n",
"VMAX_NONLF = VMAX_NONLF.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"speed_inverse = []\n",
"trajectory_inverse = []\n",
"trajectory_inverse_delta = []\n",
"lat_LMI_inverse = []\n",
"lat_LI_inverse = []\n",
"\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" _,_,tmp_uv,tmp_tra = CALC_MOVING_SPEED(VMAX_PMIN_SERIES[CCC][5],VMAX_PMIN_SERIES[CCC][6])\n",
" speed_inverse .extend([np.nanmean(tmp_uv)])\n",
" trajectory_inverse .extend([np.nansum(tmp_tra)])\n",
" trajectory_inverse_delta .append(tmp_tra) \n",
" lat_LMI_inverse .extend([abs(VMAX_PMIN_SERIES[CCC][5][0])])\n",
" lat_LI_inverse .extend([abs(VMAX_PMIN_SERIES[CCC][5][-1])])\n",
" \n",
"speed_inverse = np.asarray(speed_inverse)\n",
"trajectory_inverse = np.asarray(trajectory_inverse)\n",
"lat_LMI_inverse = np.asarray(lat_LMI_inverse)\n",
"lat_LI_inverse = np.asarray(lat_LI_inverse)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"time_inverse_calc = []\n",
"test = []\n",
"\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" # set what kind of translation speed you want: case variable or global mean\n",
" #tmp = trajectory_inverse_delta[CCC]*1e3 / speed_inverse[CCC]\n",
" tmp = trajectory_inverse_delta[CCC]*1e3 / np.nanmean(speed_inverse)\n",
" \n",
" tmptmp = []\n",
" for RRR in range(0,len(trajectory_inverse_delta[CCC])):\n",
" tmptmp.extend([np.nansum(tmp[:RRR+1])])\n",
" \n",
" time_inverse_calc.append(tmptmp)\n",
" test.extend([tmptmp[-1]/3600])\n",
" \n",
"test = np.asarray(test)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def func_linear(x,A,B):\n",
" return A-B*x\n",
"def func_exp(x,A,B):\n",
" return A*np.exp(-B*x)\n",
"def func_inverse(x,A,B):\n",
" return 1./(1./A+B*x)\n",
" #\n",
"def fit_decay(vmax_in,METHOD,year_in,t_in):\n",
" epsilon = 1e-10\n",
" vmax_in = np.asarray(vmax_in) * .51444\n",
" #\n",
" if METHOD == 'INVERSE':\n",
" gmodel = Model(func_inverse)\n",
" pars = Parameters()\n",
" if len(vmax_in) == 1:\n",
" pars.add('A' , value=vmax_in[0] , min=0. , max=vmax_in[0]*2 , vary=False)\n",
" else:\n",
" # change this line if want to vary LMI in the fit\n",
" pars.add('A' , value=vmax_in[0] , min=0. , max=vmax_in[0]*2 , vary=False)\n",
" pars.add('B' , value=1e-7 , min=2e-8 , max=6e-7 , vary=True)\n",
" #\n",
" result = gmodel.fit(vmax_in, pars, x=t_in )\n",
" vmax_pred = result.best_fit\n",
" #\n",
" A = result.params.valuesdict()['A']\n",
" B = result.params.valuesdict()['B']\n",
" if (result.params['B'].stderr == None):\n",
" B_sd = np.nan\n",
" else:\n",
" B_sd = result.params['B'].stderr \n",
" slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(vmax_in, vmax_pred)\n",
" rmse = np.sqrt(np.mean((vmax_pred-vmax_in)**2))\n",
" bias = np.mean(vmax_pred-vmax_in)\n",
" rmse_lf = abs(vmax_pred[-1]-vmax_in[-1])\n",
" bias_lf = (vmax_pred[-1]-vmax_in[-1])\n",
" interp_vmax_in = np.interp(np.linspace((np.arange(len(vmax_in))*6.*3600)[0],(np.arange(len(vmax_in))*6.*3600)[-1],11),(np.arange(len(vmax_in))*6.*3600),vmax_in )\n",
" interp_vmax_pred = np.interp(np.linspace(t_in[0],t_in[-1],11),t_in,vmax_pred)\n",
" return r_value**2, p_value, rmse, bias, A, B, rmse_lf, bias_lf, year_in,interp_vmax_in,interp_vmax_pred,vmax_in,vmax_pred, B_sd"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"r_value_linear = []; p_value_linear = []; rmse_linear = []; bias_linear = []; A_linear = []; B_linear = []; rmself_linear = []; biaslf_linear = []; year_linear = []; dur_linear = []; hl_linear = [];interp_vmax_in_linear =[];interp_vmax_pred_linear =[];vmax_in_linear =[];vmax_pred_linear =[];\n",
"r_value_exp = []; p_value_exp = []; rmse_exp = []; bias_exp = []; A_exp = []; B_exp = []; rmself_exp = []; biaslf_exp = []; year_exp = []; dur_exp = []; hl_exp = [];interp_vmax_in_exp =[];interp_vmax_pred_exp =[];vmax_in_exp =[];vmax_pred_exp =[];\n",
"r_value_inverse= []; p_value_inverse= []; rmse_inverse= []; bias_inverse= []; A_inverse= []; B_inverse= []; rmself_inverse= []; biaslf_inverse= []; year_inverse= []; dur_inverse= []; hl_inverse= [];interp_vmax_in_inverse=[];interp_vmax_pred_inverse=[];vmax_in_inverse=[];vmax_pred_inverse=[];B_sd_inverse=[];\n",
"r_value_inverse_dis= []; p_value_inverse_dis= []; rmse_inverse_dis= []; bias_inverse_dis= []; A_inverse_dis= []; B_inverse_dis= []; rmself_inverse_dis= []; biaslf_inverse_dis= []; year_inverse_dis= []; dur_inverse_dis= []; hl_inverse_dis= [];interp_vmax_in_inverse_dis=[];interp_vmax_pred_inverse_dis=[];vmax_in_inverse_dis=[];vmax_pred_inverse_dis=[];B_sd_inverse_dis=[];\n",
"\n",
"#for CAT in range(len(tmp_vmax)): #range(len(tmp_vmax)): [2,3,4]: [0,1]: \n",
"if 1==1:\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" \n",
" r_value, p_value, rmse, bias, A, B, rmse_lf, bias_lf, year_lf, interp_vmax_in,interp_vmax_pred,vmax_in,vmax_pred, B_sd = fit_decay(VMAX_PMIN_SERIES[CCC][1],'INVERSE',VMAX_PMIN_SERIES[CCC][2],np.arange(len(VMAX_PMIN_SERIES[CCC][1])) * 3 * 3600)\n",
" #\n",
" r_value_inverse.extend([r_value])\n",
" p_value_inverse.extend([p_value]) \n",
" rmse_inverse .extend([rmse ])\n",
" bias_inverse .extend([bias ])\n",
" A_inverse .extend([A ])\n",
" B_inverse .extend([B ])\n",
" B_sd_inverse .extend([B_sd ])\n",
" rmself_inverse .extend([rmse_lf])\n",
" biaslf_inverse .extend([bias_lf])\n",
" year_inverse .extend([year_lf])\n",
" dur_inverse .extend([3*(len(VMAX_PMIN_SERIES[CCC][1])-1)])\n",
" hl_inverse .extend([3*np.nanargmin(abs(VMAX_PMIN_SERIES[CCC][1]-VMAX_PMIN_SERIES[CCC][1]/2))])\n",
" interp_vmax_in_inverse .append(interp_vmax_in)\n",
" interp_vmax_pred_inverse.append(interp_vmax_pred)\n",
" vmax_in_inverse .append(vmax_in)\n",
" vmax_pred_inverse.append(vmax_pred)\n",
" #\n",
" r_value, p_value, rmse, bias, A, B, rmse_lf, bias_lf, year_lf, interp_vmax_in,interp_vmax_pred,vmax_in,vmax_pred, B_sd = fit_decay(VMAX_PMIN_SERIES[CCC][1],'INVERSE',VMAX_PMIN_SERIES[CCC][2],time_inverse_calc[CCC])\n",
" #\n",
" r_value_inverse_dis.extend([r_value])\n",
" p_value_inverse_dis.extend([p_value]) \n",
" rmse_inverse_dis .extend([rmse ])\n",
" bias_inverse_dis .extend([bias ])\n",
" A_inverse_dis .extend([A ])\n",
" B_inverse_dis .extend([B ])\n",
" B_sd_inverse_dis .extend([B_sd ])\n",
" rmself_inverse_dis .extend([rmse_lf])\n",
" biaslf_inverse_dis .extend([bias_lf])\n",
" year_inverse_dis .extend([year_lf])\n",
" dur_inverse_dis .extend([3*(len(VMAX_PMIN_SERIES[CCC][1])-1)])\n",
" hl_inverse_dis .extend([3*np.nanargmin(abs(VMAX_PMIN_SERIES[CCC][1]-VMAX_PMIN_SERIES[CCC][1]/2))])\n",
" interp_vmax_in_inverse_dis .append(interp_vmax_in)\n",
" interp_vmax_pred_inverse_dis.append(interp_vmax_pred)\n",
" vmax_in_inverse_dis .append(vmax_in)\n",
" vmax_pred_inverse_dis.append(vmax_pred)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"B_inverse = np.asarray(B_inverse)\n",
"dur_inverse = np.asarray(dur_inverse)\n",
"B_inverse[dur_inverse==0] = np.nan\n",
"speed_inverse[dur_inverse==0] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**$y=V_o-Bt \\quad$ $y=V_oe^{-Bt} \\quad$ $y=\\frac{1}{\\frac{1}{V_o}+Bt}$**"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def fit_test(AREA):\n",
" tmp_r = []\n",
" tmp_rmse = []\n",
" tmp_bias = []\n",
" tmp_maelf = []\n",
" tmp_biaslf = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA) & (dur_inverse[CCC]>3): # dur_inverse[CCC]==3 means there is only one intensity reading during decay, ie having last LMI at landfall\n",
" tmp_r .extend([r_value_inverse_dis [CCC] ])\n",
" tmp_rmse .extend([rmse_inverse_dis [CCC] ])\n",
" tmp_bias .extend([bias_inverse_dis [CCC] ])\n",
" tmp_maelf .extend([rmself_inverse_dis [CCC] ])\n",
" tmp_biaslf .extend([biaslf_inverse_dis [CCC] ])\n",
" return np.round(np.nanmean(tmp_r),2),np.round(np.nanmean(tmp_rmse)),np.round(np.nanmean(tmp_bias)),np.round(np.nanmean(tmp_maelf)),np.round(np.nanmean(tmp_biaslf))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" NH | \n",
" SH | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" r2 | \n",
" 0.87 | \n",
" 0.87 | \n",
" 0.88 | \n",
" 0.89 | \n",
" 0.85 | \n",
" 0.77 | \n",
" 0.91 | \n",
" 0.87 | \n",
" 0.88 | \n",
"
\n",
" \n",
" rmse (m s$^{-1}$) | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" 3 | \n",
" 3 | \n",
" 2 | \n",
" 2 | \n",
" 3 | \n",
"
\n",
" \n",
" bias (m s$^{-1}$) | \n",
" -0 | \n",
" -0 | \n",
" 0 | \n",
" -0 | \n",
" -0 | \n",
" -0 | \n",
" -0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" mae (m s$^{-1}$) LF | \n",
" 3 | \n",
" 3 | \n",
" 3 | \n",
" 3 | \n",
" 4 | \n",
" 4 | \n",
" 4 | \n",
" 3 | \n",
" 4 | \n",
"
\n",
" \n",
" bias (m s$^{-1}$) LF | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
" -0 | \n",
" 2 | \n",
" 1 | \n",
" 3 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global NH SH WP EP NA NI SI SP\n",
"r2 0.87 0.87 0.88 0.89 0.85 0.77 0.91 0.87 0.88\n",
"rmse (m s$^{-1}$) 2 2 2 2 3 3 2 2 3 \n",
"bias (m s$^{-1}$) -0 -0 0 -0 -0 -0 -0 0 0 \n",
"mae (m s$^{-1}$) LF 3 3 3 3 4 4 4 3 4 \n",
"bias (m s$^{-1}$) LF 2 2 2 2 2 -0 2 1 3 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TABLE = pd.DataFrame(index=['r2','rmse (m s$^{-1}$)','bias (m s$^{-1}$)','mae (m s$^{-1}$) LF','bias (m s$^{-1}$) LF'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" \n",
" output1,output2,output3,output4,output5 = fit_test(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
"\n",
"TABLE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get var: VMAX_PMIN_SERIES"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def from_lmi_to_lf(x):\n",
" RRR_LMI= x.iloc[::-1].USA_WIND.idxmax()\n",
" tmp_x=x[x.LF_MARKER==1]\n",
" RRR_LF = tmp_x.USA_WIND.idxmax()\n",
" output_wind = [];\n",
" \n",
" pick = x[(x.index>=RRR_LMI)&(x.index<=RRR_LF)]\n",
" output_wind = pick.USA_WIND.values[:]\n",
" output_wind = np.asarray(output_wind)\n",
" output_lat = pick.USA_LAT.values[:]\n",
" output_lat = np.asarray(output_lat )\n",
" output_lon = pick.USA_LON.values[:]\n",
" output_lon = np.asarray(output_lon )\n",
" \n",
" return x.USA_WIND.max(),output_wind, x.SEASON.values[0],x[x.index==RRR_LMI].DIST2LAND.values[0],x.SID.values[0],output_lat,output_lon,x[x.index==RRR_LMI].BASIN.values[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"VMAX_PMIN_SERIES = newbt_lf.groupby('SID').apply(lambda x: from_lmi_to_lf(x))\n",
"VMAX_PMIN_SERIES = VMAX_PMIN_SERIES.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Annual trend of LMI and LI (FIGURE 1)"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.18419597367047671\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,2); fig.set_size_inches(7,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=33)] = np.nan\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))])\n",
"tmp_LI = np.asarray(Y)\n",
"_=ax[0].plot(X,Y,'-',color='r',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"print(p)\n",
"_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='r',label='LI ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' m s$^{-1}$ decade$^{-1}$)')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C1', alpha=.2, edgecolor='none', interpolate=True)\n",
"_=ax[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0].legend(fontsize=fontsize,loc=2)\n",
"_=ax[0].set_xticks(range(1980,2021,5))\n",
"_=ax[0].set_ylim([25,70])\n",
"_=ax[0].set_xlim([1980,2020])\n",
"_=ax[0].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[0].set_ylabel('Observed intensity (m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax[0].text(1973,70,'(a)',fontsize=fontsize+2,fontweight='bold')\n",
"\n",
"#----------------------------------------------------------------------------------------------------------\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1].plot(X,Y,'-',color='C0',lw=1,zorder=6)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1].plot(x_trend,y_trend,'-',lw=3,color='C0',label='Major LMI ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' count decade$^{-1}$)',zorder=7)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1].plot(X,Y,'-',color='C1',lw=1,zorder=6)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1].plot(x_trend,y_trend,'-',lw=3,color='C1',label='Major LI ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' count decade$^{-1}$)',zorder=7)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=135)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1].plot(X,Y,'-',color='gray',lw=1,zorder=6)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1].plot(x_trend,y_trend,'-',lw=3,color='gray',label='CAT-5 LMI ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' count decade$^{-1}$)',zorder=7)\n",
"\n",
"_=ax[1].legend(fontsize=fontsize)\n",
"_=ax[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1].legend(fontsize=fontsize,loc=2)\n",
"_=ax[1].set_xticks(range(1980,2021,5))\n",
"_=ax[1].set_ylim([0,24])\n",
"_=ax[1].set_yticks(np.arange(0,25,3))\n",
"_=ax[1].set_xlim([1980,2020])\n",
"_=ax[1].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[1].set_ylabel('Global TC counts',fontsize=fontsize)\n",
"_=ax[1].text(1973,24,'(b)',fontsize=fontsize+2,fontweight='bold')\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_1.png',dpi=600,bbox_inches='tight')\n",
"#plt.savefig('/home/sw1013/www/FIGURES/Figure_1.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"β=0.021±0.016 p=0.0129\n",
"18.82048136119845 158.7162687700718\n",
"β=0.014±0.009\n",
"β=0.014±0.441\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,3); fig.set_size_inches(7,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.4); fontsize = 8\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96)]) #96\n",
" sample_weight.extend([1])\n",
"tmp_LI = np.asarray(Y)\n",
"\n",
"_=ax.flatten()[0].plot(X,Y,'-',color='k',lw=1,zorder=6)\n",
"#x_trend, y_trend,slope,ci = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"#_=ax.plot(x_trend,y_trend,'-',lw=3,color='k',label=str(round(slope*10,1))+'±'+str(round(ci*10,1))+' count decade$^{-1}$',zorder=7)\n",
"\n",
"pred, mean, ci, p = poisson_reg(X,Y)\n",
"STR = ''\n",
"if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
"_=ax.flatten()[0].plot(X,pred,'-',color='k',lw=3,label='$T_D$='+str(round(np.log(2)/mean).astype(int))+' year'+STR,zorder=7)\n",
"print('β='+str(round(mean,3))+'±'+str(round(ci,3)),'p='+str(round(p,4)))\n",
"print(np.log(2)/(mean+ci),np.log(2)/(mean-ci))\n",
"_=ax.flatten()[0].legend(fontsize=fontsize)\n",
"_=ax.flatten()[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[0].legend(fontsize=fontsize,loc=2)\n",
"_=ax.flatten()[0].set_xticks(range(1980,2021,10))\n",
"_=ax.flatten()[0].xaxis.set_minor_locator(plt.MultipleLocator(5))\n",
"_=ax.flatten()[0].set_ylim([0,8])\n",
"_=ax.flatten()[0].set_yticks(np.arange(0,9,1))\n",
"_=ax.flatten()[0].set_xlim([1980,2020])\n",
"_=ax.flatten()[0].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.flatten()[0].set_ylabel('Counts of global landfalling TCs\\n(LI≥50 m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax.flatten()[0].text(1967,8,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"\n",
"# scipy.stats.kstest(preprocessing.scale(y_trend-Y),'norm')\n",
"# check_autocorrelation_durbin_watson(y_trend-Y)\n",
"# scipy.stats.kstest(preprocessing.scale(pred-Y),'norm')\n",
"# check_autocorrelation_durbin_watson(pred-Y)\n",
"\n",
"#----------------------------------------------------------------------------------------------------------\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"YYYYYY = Y\n",
"_=ax.flatten()[1].plot(X,Y,'-',color='k',lw=1,zorder=6)\n",
"#x_trend, y_trend,slope,ci = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"#_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='k',label=str(round(slope*10,1))+'±'+str(round(ci*10,1))+' count decade$^{-1}$',zorder=7)\n",
"\n",
"pred, mean, ci, p = poisson_reg(X,Y)\n",
"STR = ''\n",
"if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
"_=ax.flatten()[1].plot(X,pred,'-',color='k',lw=3,label='$T_D$='+str(round(np.log(2)/mean).astype(int))+' year'+STR,zorder=7)\n",
"print('β='+str(round(mean,3))+'±'+str(round(ci,3)))\n",
"\n",
"_=ax.flatten()[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[1].legend(fontsize=fontsize,loc=2)\n",
"_=ax.flatten()[1].set_xticks(range(1980,2021,10))\n",
"_=ax.flatten()[1].xaxis.set_minor_locator(plt.MultipleLocator(5))\n",
"_=ax.flatten()[1].set_ylim([3,21])\n",
"_=ax.flatten()[1].set_yticks(np.arange(3,22,2))\n",
"_=ax.flatten()[1].set_xlim([1980,2020])\n",
"_=ax.flatten()[1].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.flatten()[1].set_ylabel('Counts of global landfalling TCs\\n(LMI≥50 m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax.flatten()[1].legend(fontsize=fontsize)\n",
"_=ax.flatten()[1].text(1967,21,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"\n",
"# scipy.stats.kstest(preprocessing.scale(y_trend-Y),'norm')\n",
"# check_autocorrelation_durbin_watson(y_trend-Y)\n",
"# scipy.stats.kstest(preprocessing.scale(pred-Y),'norm')\n",
"# check_autocorrelation_durbin_watson(pred-Y)\n",
"#----------------------------------------------------------------------------------------------\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp1 = []\n",
" tmp2 = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96) == 0:\n",
" Y.extend([np.nan])\n",
" else:\n",
" Y.extend([sum(np.asarray(tmp2)>=96) / sum(np.asarray(tmp1)>=96) * 100])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"YYYYYY = Y\n",
"_=ax.flatten()[2].plot(X,Y,'-',color='k',lw=1,zorder=6)\n",
"#x_trend, y_trend,slope,ci = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"#_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='k',label=str(round(slope*10,1))+'±'+str(round(ci*10,1))+' count decade$^{-1}$',zorder=7)\n",
"\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"STR = ''\n",
"if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
"else:\n",
" STR = ' ($p$='+str(round(p,2))+')'\n",
"_=ax.flatten()[2].plot(x_trend,y_trend,'-',lw=3,color='k',label=str(round(slope*10,1))+'% decade$^{-1}$\\n'+STR)\n",
"print('β='+str(round(mean,3))+'±'+str(round(ci,3)))\n",
"\n",
"_=ax.flatten()[2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[2].legend(fontsize=fontsize,loc=2)\n",
"_=ax.flatten()[2].set_xticks(range(1980,2021,10))\n",
"_=ax.flatten()[2].xaxis.set_minor_locator(plt.MultipleLocator(5))\n",
"_=ax.flatten()[2].set_ylim([0,80])\n",
"_=ax.flatten()[2].set_yticks(np.arange(0,81,10))\n",
"_=ax.flatten()[2].set_xlim([1980,2020])\n",
"_=ax.flatten()[2].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.flatten()[2].set_ylabel('Count ratio of landfalling TCs (%)\\nwith LI≥50 m s$^{-1}$ to LMI≥50 m s$^{-1}$',fontsize=fontsize)\n",
"_=ax.flatten()[2].legend(loc=1,fontsize=fontsize)\n",
"_=ax.flatten()[2].text(1967,80,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"\n",
"_=plt.savefig('/home/sw1013/www/FIGURES/Figure_1.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"67.57224413331328\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,2); fig.set_size_inches(7,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"# #----------------------------------------------------------------------------------------------\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" tmptmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96:\n",
" Y.extend([tmp])\n",
"Y = np.asarray(Y)\n",
"hist,bin_edges=np.histogram(Y,bins=np.arange(0,101,10),density=False)\n",
"_=ax.flatten()[1].bar(bin_edges[:-1]+10/2,hist,color='b',width=9)\n",
"_=ax.flatten()[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[1].set_xticks(range(0,100,10))\n",
"_=ax.flatten()[1].set_ylim([0,80])\n",
"#_=ax.flatten()[1].set_yticks(np.arange(115,76,5))\n",
"#_=ax.flatten()[1].set_yticklabels(np.arange(5,76,5),color='k')\n",
"_=ax.flatten()[1].set_xlim([0,100])\n",
"_=ax.flatten()[1].set_xticks(np.arange(0,101,10))\n",
"_=ax.flatten()[1].set_xlabel('Ratio (%) of LI to LMI (LMI≥50 m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax.flatten()[1].set_ylabel('TC count',fontsize=fontsize,color='k')\n",
"_=ax.flatten()[1].text(-18,80,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"print(np.nanmean(Y))\n",
"\n",
"_=plt.savefig('/home/sw1013/www/FIGURES/Figure_3.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"67.08253153554458\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(2,2); fig.set_size_inches(7,7); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"# #----------------------------------------------------------------------------------------------\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" tmptmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96:\n",
" Y1.extend([tmp])\n",
" tmp = dis_inverse[CCC]\n",
" if VMAX_PMIN_SERIES[CCC][1][0]>=96:\n",
" Y2.extend([tmp])\n",
"Y1,Y2 = np.asarray(Y1),np.asarray(Y2)\n",
"hist,bin_edges=np.histogram(Y1,bins=np.arange(0,101,10),density=False)\n",
"_=ax.flatten()[2].bar(bin_edges[:-1]+10/2,hist,color='gray',width=9)\n",
"_=ax.flatten()[2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[2].set_xticks(range(0,100,10))\n",
"_=ax.flatten()[2].set_ylim([0,80])\n",
"#_=ax.flatten()[2].set_yticks(np.arange(115,76,5))\n",
"#_=ax.flatten()[2].set_yticklabels(np.arange(5,76,5),color='k')\n",
"_=ax.flatten()[2].set_xlim([100,0])\n",
"_=ax.flatten()[2].set_xticks(np.arange(0,101,10))\n",
"_=ax.flatten()[2].set_xlabel('Ratio (%) of individual LI to LMI (LMI≥50 m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax.flatten()[2].set_ylabel('TC count',fontsize=fontsize,color='k')\n",
"_=ax.flatten()[2].text(122.5,80,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"print(np.nanmean(Y))\n",
"\n",
"#---------------------------------------------------------------------------------------------------------\n",
"\n",
"Y1,Y2 = [],[]\n",
"for YEAR in X:\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96:\n",
" Y1.extend([tmp*.5144444])\n",
" tmp = VMAX_PMIN_SERIES[CCC][1][ 0]\n",
" if VMAX_PMIN_SERIES[CCC][1][0]>=96:\n",
" Y2.extend([tmp*.5144444])\n",
"Y1,Y2 = np.asarray(Y1),np.asarray(Y2)\n",
"_=ax.flatten()[3].boxplot([Y2,Y1],showfliers=False,whis=[5,95])\n",
"_=ax.flatten()[3].set_xticks(np.arange(1,3))\n",
"_=ax.flatten()[3].set_xticklabels(['LMI\\n(LMI≥50 m s$^{-1}$)','LI\\n(LMI≥50 m s$^{-1}$)'],fontsize=fontsize)\n",
"_=ax.flatten()[3].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[3].set_ylabel('Intensity (m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax.flatten()[3].set_ylim([10,90])\n",
"_=ax.flatten()[3].text(.05,90,'(D)',fontsize=fontsize+2,fontweight='bold')\n",
"\n",
"_=plt.savefig('/home/sw1013/www/FIGURES/Figure_3.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"65.3344388"
]
},
"execution_count": 242,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"16.10210972"
]
},
"execution_count": 242,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanpercentile(Y1,95)\n",
"np.nanpercentile(Y1,5)\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAALBCAYAAABIuif5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXxTVfo/8E+6U+lOoUJbSoVKEbtAWQWBkVHWn4zgMoMswkxhVGZkEXVcWQQZGcZBximKKKBfFBFFQBRwAWWnUBVblkqBhgJdgKSFtrTN+f1xSWjaJE3S5OYm+bxfr7za5Obee5K0T5577nPOVQkhBIiIiIiIyCIfVzeAiIiIiMgdMHEmIiIiIrICE2ciIiIiIiswcSYiIiIisgITZyIiIiIiKzBxJiIiIiKyAhNnIiIiIiIrMHEmIiIiIrICE2ciIiIiIiswcSYiIiIisgITZyIiIiIiKzBxJnJTly9fxuTJkxEXF+fqphAReTXGY+/BxJlcLiEhAUePHjX5eOvWrVFTU2N47Ntvv4VKpcKsWbMsrmuve++9FykpKUhLS0P//v2Rk5Nj1TJXiIiIwLvvvovbb7/dpe0gIs+hpHickJCAzp07Iy0tDWlpafj4448Ny06ePIm+ffsiKSkJPXv2RG5ursP2aw/GY+/h5+oGEFkSHx+PL774AqNHjwYArFy5EhkZGVat++WXX6J79+5o06aN1ftbt24dwsPDAQCff/45Jk2ahMOHDze5zJkuXryIsWPHGj3WrVs3/POf/3T6vomI9OSOxwCwfv16dO3atdHjU6ZMQWZmJiZOnIj169dj8uTJ2Lt3r03btgfjMTFxJkWbNGkSVq5cidGjR0Oj0WDfvn344x//iMrKyibXPXjwIP72t78hPDwcw4YNw9ChQ9GrVy/4+Jg/0aJPjAFAo9EYPdfSsoZUKhUWLFiAzz77DKWlpXj77bfxzTff4KuvvsL169exbt063HHHHUbrVFZWYuLEifjll1/g7++PNm3aYNu2bWjTpg127NjR5OslInImueOxOcXFxTh8+DC2bdsGABg9ejSefPJJnD59GgkJCY2ez3hMjsRSDVK0u+++G6dOncK5c+ewdu1aPPjgg/D19bVq3Zdffhn5+fn44IMPEBERgZdeeglxcXEYO3YsNBqN2fXGjx+PuLg4vPDCC1i1apXVyxoKDQ3FgQMHsGjRItx///3o168fjhw5ggkTJuDVV19t9PyvvvoKly9fRm5uLn766Sd89NFHTb7GqVOn4tixY5g6dSp+++23Jp9PRGQvV8TjsWPH4s4778Sf//xnlJSUAAAKCwvRtm1b+PlJfX8qlQrx8fE4e/as2e0wHpOjMHEmxRs3bhxWrVqFlStXYtKkSTavX11dbbgBaDLQr169GoWFhZg/fz6efvppq5c19PDDDwOQTuP5+Phg+PDhAIDu3bvj1KlTjZ6fmpqKY8eO4fHHH8fHH38Mf3//Jl9bVlYW1Go1srKycNtttzX5fCKi5pAzHu/atQs//fQTDh8+jKioKEyYMMGwTKVSGT1XCGFxv4zH5ChMnEnxJk6ciKVLlyIoKAidOnWyer1//OMfaN++Pf7617+ipqYGS5YsgVqtxurVqxEWFtbk+hMmTMB3332HsrIym5bpBQUFAZC+GAIDAw2P+/r6ora2ttHzExMTkZubiyFDhmD37t3o2rUrLl++bM1LJSKShZzxOD4+HgDg7++Pp556Cj/88AMAIC4uDmq12hBHhRAoLCw0PN8UxmNyFIcmzmVlZQgNDUVoaKjFUy+mvPTSS1CpVHj++ecd2STyAG3btsXChQuxaNEim9a75557cOTIEezZswcvvvgiMjIyGvVS1KfValFUVGS4/9lnnyEqKgqRkZEWlzmKWq2GSqXC//t//w+LFy82fBkQ2aM58Xj8+PFQqVR45513nNQ6cldyxeOrV6/iypUrhvtr165Feno6AKB169ZIT0/HBx98AAD49NNPkZCQYLK+2V6Mx2SOQwcHvv766ygvL8ff//53oyPIiooKxMTE4OrVqwCAV155BS+//LLRuk8++SQWLVqEpUuXYvr06WjVqpUjm0YKN3jwYEO9GgDs27fPaPljjz1m8zZfeeUV5OXlmVx25MiRRvNtajQajB49GpWVlfDx8UF0dDQ2b94MlUplcZmj/PLLL3j22WchhIBOp8O4ceOQkpLisO2Td2kYj99//32j/yOVSoWQkBAkJydjxowZeOihhwzLZs6ciTVr1mDevHmYOHGiVaepyXMoIR5fvHgRo0ePRl1dHYQQSExMxOrVqw3Lly9fjokTJ2LBggUIDQ1tcsyJrRiPyRyVaKowyEo1NTVo27YtSktLcfDgQaMpat577z2jWqgOHTrgt99+a5R0jBgxAlu2bMGSJUswffp0RzSLiMjrmIrH9RPn5ORktGzZEnl5eaioqICPjw/27t2Lnj17GrbRtWtX/Prrr9iwYQP+8Ic/uOqlEBEpisNKNbZv347S0lLceuutjeZ1fP/99wHA8HhBQQF27drVaBsjR44EAKtGrxIRkWmW4jEAvPXWWzhw4AC2bt0KANDpdIb6UT3GYyKixhyWOOuDbo8ePYweLygoMCybM2eOIYjrk+n69L0dhw8fNpR1EBGRbczF4/qEEMjPzzfcbzgLgD4eN0yoiYi8mcMS55MnTwJAo+L8999/H0IItG7dGvfeey/GjRsHQLoaUMPkuH379gCA2tpanD592lFNIyLyKubisd6gQYPg4+NjKN0YN24cRo0aZfQcfTw+f/48OzKIiG5wWOKsH7UdEhJieEwIgTVr1gAA/vjHP8LPz8/ws6KiAuvXrzfaRmhoaKPtERGRbUzF4/qSk5PRq1cvtG3bFgCwZs0aZGVlGT2H8ZiIqDGHJc76IFtRUWF47Pvvv0dBQQEA4J133kF4eDg6deoEnU4HoHG5hlarbbS9+u69916kpKQgLS0N/fv3R05ODgCpd6Vv375ISkpCz549kZub66iXRUTkdkzF4/reeust7Nu3D4WFhejTpw8AYN68eUbPYTwmImrMYYmzfiL0M2fOGB6rnxhfu3YNGo0GGo3GkDjv3LnTqCRDv66vr6/JU4zr1q3Dzz//jJycHMycOdMwU8eUKVOQmZmJEydOYPbs2Zg8ebKjXhYRkdsxFY/N0U+spL+Sm55+3TZt2qBly5aN1mM8JiJv5LDEuV+/fgCA7OxsAFJPx6effgoAmDFjBoQQhtvly5fh7+8PIYTR3IsHDhwAAKSnp5sM1OHh4YbfNRoNfHx8UFxcjMOHD+PRRx8FAIwePRoFBQWskSYir9UwHjf0+OOPo3fv3oiLizPM0duwxlkfj/v3729yG4zHROSNHHYBlHvvvRdRUVEoLCxETk4OcnJyDANKxowZY/Tc8PBwDBo0CNu2bcOqVasMVw3cvHkzAOCRRx4xu5/x48fju+++AwB89dVXKCwsRNu2bQ2TtatUKsTHx+Ps2bMme62rq6uNelZ0Oh0uXbqEqKgoh17Mgoi8hxAC5eXlaNu2LXx8HHpBVrs0jMdpaWlGy/UXovD398dtt92G+++/H/Pnzzd6DuMxEbkjp8dj4UCzZ88WAMSMGTNsXre4uFgEBgaKW265RZSUlDT5/Pfff18MHTpUHDp0SHTp0sVoWUZGhti5c6fJ9V5++WUBgDfeeOPN4bfCwkKbY5+zNCce//TTTwKAiIuLE9XV1U0+n/GYN954U9rNWfHYYVcOBICysjJ06NABKpUKZ8+eNbrsdlNeeuklzJs3D8899xwWLFhg1TotWrTA6dOnkZSUhLKyMvj5+UEIgVtvvRX79u2zqodDo9EgPj4ehYWFJgfAEBE1RavVIi4uDleuXLEp7jlTc+Lx+PHjsWbNGixfvhyZmZlWrcN4TERK4Ox47LBSDQCIiooyGolti7lz52Lu3Llml2u1WlRUVBimT/rss88QFRWF1q1bIz09HR988AEmTpyITz/9FAkJCWbnLw0MDERgYGCjx0NDQxmoiahZlFRe0Jx4vHr1aqxevdrscsZjIlI6Z8VjhybOzqTRaDB69GhUVlbCx8cH0dHR2Lx5M1QqFZYvX46JEydiwYIFCA0NNRpwSEREjsV4TETeyqGlGu5Iq9UiLCwMGo2mUQ+HEAK1tbWoq6tzUeuIqDn8/f3h6+vr9P1YiiNkPcZjIs8mR0x2djx2mx5nuV2/fh3nz5/HtWvXXN0UIrKTSqVCbGysyektyX0wHhN5Bk+IyUycTdDpdCgoKICvry/atm2LgIAARdUuElHThBAoKSmBWq1Gp06dZOl5JsdjPCbyDJ4Sk5k4m3D9+nXodDrExcUhODjY1c0hIjtFR0fj9OnTqKmpcdsg7e0Yj4k8hyfEZNfP1K9gSriQARHZjz2TnoPxmMj9eUJMZiQiIiIiIrICE2ciIiIiIiswcSYiIiIisgITZyIFuHz5MiZPnoy4uDhXN4WIyKsxHpMlTJzdTEJCAo4ePWry8datW6Ompsbw2LfffguVSoVZs2ZZXFfvgQcewN69ewEAJ0+eRN++fZGUlISePXsiNze30fOrqqowatQoJCUlIS0tDUOGDMHp06etfi0qlQoVFRVWP99Wzt6+I/cVERGBd999F7fffrsDW2WZNZ+xXkJCAjp37oy0tDSkpaXh448/Niz729/+hoSEBKhUKqO/LyEE+vfvj4KCAqe+DiJXUVI8bmjOnDmN/iebwpgscUU8Bmz7nO+9916kpKQgLS0N/fv3R05OjmGZuXjNmOwYTJw9SHx8PL744gvD/ZUrVyIjI8OqdQ8cOIArV66gT58+AIApU6YgMzMTJ06cwOzZszF58mST62VmZuL48ePIycnBiBEjkJmZ2fwX4qEuXryIwYMHG91mz57tsvZY+xnrrV+/Hjk5OcjJycHDDz9seHzMmDH48ccf0b59e6Pnq1QqTJ8+HXPmzHFK+4mUzBXxWO/w4cPYt28f4uPj7X8BXsCdY/K6devw888/IycnBzNnzsSkSZOMlpuK14zJjsHEuQk6HVBSIs9Np2teWydNmoSVK1cCADQaDfbt24chQ4ZYte7y5csxduxYAEBxcTEOHz6MRx99FAAwevRoFBQUNOpNDgoKwrBhwwzTy/Tu3RunTp0yu48NGzagc+fO6NOnD+bNm2e07ODBg/jd736HjIwMdOvWDZ9++qlh2aefforOnTsjPT0d8+fPN9uTYGn7lvaxd+9e9O/fH6mpqUhJScHGjRsBAI8++igyMjKQkpKCESNGoLi4uFmvpU2bNtixY4fR7Z///KfZ96spL774IoYMGYJ+/frh9ttvx6BBg3DlyhWr1rX2M7bG3XffjdjYWJPLRo4ciS+//BLl5eU2b5eoIcbjpv9Xq6ur8cQTT+Ctt95qcuovV8ZkS9tnTG76cw4PDzf8rtForJ6ukTHZAYSX02g0AoDQaDSGxyorK0Vubq6orKwUxcVCAPLcioubbm/79u3FL7/8Yvbxzp07C7VaLf73v/+JZ599Vrz88sti5syZFtcVQojExESRm5srhBDi0KFDIjk52Wh5jx49xM6dOy22bdy4ceKpp54yuezixYsiMjJSHDt2TAghxKJFiwQAUV5eLi5fvizS09NFUVGREEKIkpISER8fL86fP29Y78SJE0IIIf79738b1rN2+0IIs/s4d+6caNOmjdi9e7cQQoi6ujpRVlZmeI7ewoULxRNPPNGs19KUKVOmiHbt2okpU6aI/Pz8Jp8/ZMgQMWDAAKHVaoVOpxNDhgwRb7zxhhBCiNGjR4vU1FSTt7Nnz9r8Gbdv316kpKSIrl27ismTJ4tiE3+s5v6+Bg0aJLZu3drk63GG+v/LzmQqjpDtGI8lzYnHs2fPFsuWLWtyH66MyZa2X1ZWpoiYbGs8FkLemCyE9J0bGxsrYmNjxdGjRw2PNxWvPT0mOzse88qBHmbcuHFYtWoVPv/8c3z44Yf48MMPrVpPrVYjJibGcL9hT4UQwuL6CxYswMmTJ5GVlWVy+b59+9CtWzdDzVhmZiaeeeYZAMCePXtw6tQpDB061Gh/x48fh0ajQbdu3dCpUycAwGOPPYbp06fbtH1L+3jnnXfQpUsX9O3bF4B0kYXIyEgAwIcffog1a9aguroalZWVhvfH3tdS//01xdx7Z052dja2bduGkJAQAEBqairKysoASKfpLCkuLrbpM961axfi4+NRU1ODF154ARMmTMCXX35pVTtjYmKgVqutei6RJ5E7Hu/duxcHDx7Ea6+91uQ+XBmTLW2/oqJCETHZ1ngMyBuTAWD16tUAgFWrVuHpp582xOSm4jVjcvMwcfYwEydORLdu3ZCUlGQIbNYIDg5GZWUlIiIiEBcXB7VajdraWvj5+UEIgcLCQrP1cosXL8aGDRuwY8cOs5fEtRQAhBBISUnBrl27Gi3buHGjVVcaairAmNvHli1bsHPnzkbP//HHH7Fs2TLs2bMH0dHR+OKLLzB37txmvRZHOnPmDMrLy5Gammp47MCBA3jqqacASHXH+fn5JtfdtGmTzZ+x/nF/f3889dRTSEpKsrqtVVVVaNGihdXPJ/IUcsfjnTt34tixY+jQoQMAKQG/7777sGLFCqPEEXBtTLa0/S1btphchzHZvAkTJmDq1KkoKytDVFRUk/GaMbl5WOPsYdq2bYuFCxdi0aJFNq2XkpKCY8eOAQBat26N9PR0fPDBBwCkeraEhAQkJCQ0Wm/JkiVYu3Yttm/fblRz1VCfPn1w5MgRnDhxAgCwYsUKw7K+ffvi5MmT+Pbbbw2P5eTk4Pr16+jduzeys7MNAWfVqlU2b9/SPjIyMpCXl4c9e/YAAHQ6HS5duoTLly8jNDQUkZGRuH79OpYvX97s12KP8ePH47PPPmv0+KFDh1BdXW2oKf+///s/lJeXY8SIEQCMB4Y0vMXFxdn0GV+9etWoTm/t2rVIT0+3+jXk5eUZfZkQeQu54/Gzzz6LoqIinD59GqdPn0ZsbCy+/vrrRkkz4NqYbGn7ffv2ZUyG5c9Zq9WiqKjIcP+zzz5DVFQUIiMjrYrXjMnNwx7nJkRFAfXGHzh9X9YYPHgw/PxufnT79u0zWv7YY4/ZvO8xY8Zg69at+N3vfgdAGpwyceJELFiwAKGhoUbBcdiwYZg7dy5iYmIwc+ZMJCYmYtCgQQCAwMBA7N+/v9H2W7dujbfffhsjR45EVFQUxowZY1gWERGBTZs24emnn8b06dNRU1OD+Ph4fP7552jTpg2ysrIwfPhwREVFYeTIkfD392/Us21p+03t47PPPsPMmTNRXl4OlUqFefPmYdiwYfjggw/QuXNnxMbGom/fvvj666+b9VrskZ2djWnTpjV6/NChQ5g2bRqmTp2KCxcuoFOnTtiyZYvVA0QAy58xcPNzjoyMxOjRo1FXVwchBBITEw2nCAHgiSeewMaNG3HhwgUMHjwYLVu2NHyp6ge2dO3a1Y5XT2SM8dh8PLZ2xg49V8ZkS9uPiIhgTG7ic9ZoNBg9ejQqKyvh4+OD6OhobN68GSqVChcvXrQYrxmTHcApldNupKnBKN5Cq9WKO+64Q1RUVLi6KY1otVrD7ytXrhR33XWXC1sjn7KyMjF48GCTywYPHiy+/vprmVtku2eeeUasWLHCZfvn4ED3wngsUXI8FoIx2RTGZOtwcCB5jJCQELzxxhsoKChQ3JHo0qVL8cknn6C2thaRkZF45513XN0kWURGRmL79u0mlx0+fNjmHiZXaNu2rV09bkTeTMnxGGBMNoUx2XuohGhiVJWH02q1CAsLg0ajQWhoKACpcL6goAAdOnRAUFCQi1tIRPaS63/ZVBwh2zEeE3k2Of6fnR2POTiQiIiIiMgKTJyJiIiIiKzAxJmIiIiIyApMnImIiIiIrMDEmYiIiIjICkyciYiIiIiswMSZiIiIiMgKTJyJiIiIiKzAxJmIiIiIyApMnIm8xOXLlzF58mTExcW5uilERF6N8dh9MXF2MwkJCTh69KjTtq9SqVBRUQEA2LhxI5KTk5GWloZffvnFqnXq/27KAw88gL179wIATp48ib59+yIpKQk9e/ZEbm5uk+2bM2cOVCqVTe9BU21qLmdv31H7ioiIwLvvvovbb7/dwa2yzJrPuaqqCqNGjUJSUhLS0tIwZMgQnD59utHzGn7+Qgj0798fBQUFzn4ZRI0wHjMee2I81rv33nuRkpKCtLQ09O/fHzk5OQAsx2tviMlukzhb+qAOHTqEPn36ID09HcnJyfjnP//p2sZ6iKysLMydOxc5OTm48847m729AwcO4MqVK+jTpw8AYMqUKcjMzMSJEycwe/ZsTJ482eL6hw8fxr59+xAfH9/stniyixcvYvDgwUa32bNnu6w91n7OmZmZOH78OHJycjBixAhkZmYaLTf1+atUKkyfPh1z5sxx6msgY4zH8mM8dk/uGo8BYN26dfj555+Rk5ODmTNnYtKkSYZl5uK1V8Rk4SYqKyvFli1bhE6nE0II8eabb4rf//73Qggh0tLSxMaNG4UQQpSVlYno6Gjx66+/WrVdjUYjAAiNRmO0r9zcXFFZWSlEXZ0QxcXy3Orqmmxv+/btxS+//NLo8bFjx4ru3buLO++8UwwfPlxcvHhRCCEEAPHaa6+Jnj17ioSEBLFy5Uqj9T799FNx++23i969e4u5c+cKAKK8vFxMmzZN3HLLLaJDhw6iT58+Te6jvLy80e8NTZo0SaxYsUIIIcTFixdFWFiYqKmpEUIIodPpRJs2bURBQYHJdauqqkTv3r3FqVOnzL4HTb0mIYQ4cOCAGDRokOjevbtIT08X69evF0IIsX79enH77beLtLQ0MW/ePIuvw57tCyHEnj17RL9+/URKSoq48847xeeff27xfW3Oviy55557rHqe3gsvvCDuu+8+cdddd4mkpCQxcOBAcfnyZavWtfVz1jt48KC47bbbDPctff7Xr18X0dHRQqvVNtqO0f+yE5mKI56M8VjCeMx47A3xWAgh3n//fdG9e3eTyxrGa1fHZGfHY7dJnBuq/0GlpaWJVatWCSGEOHv2rGjXrp04f/68VdtpMlAXFwsByHMrLm6yveaCVElJieH3hQsXiieeeEIIIQXON954QwghRG5urmjZsqXhn+bixYsiMjJSHDt2TAghxKJFi4wCwYABA8SmTZus2oc1gToxMVHk5uYKIYQ4dOiQSE5ONlreo0cPsXPnTpPrzp49Wyxbtszie9DUa7p8+bJIT08XRUVFhtcTHx8vzpw5IyIjI8WJEyeEEEL8+9//Nvs67Nn++fPnRVlZmWjTpo3YvXu3EEKIuro6UVZWZvF9tXdflkyZMkW0a9dOTJkyReTn51t8rt6QIUPEgAEDhFarFTqdTgwZMsTwNzV69GiRmppq8nb27FmbP2e9cePGiaeeespwv6nPf9CgQWLr1q2NtsPEWR6Mx8YYj0WTr4nx2D3i8bhx40RsbKyIjY0VR48eNfuc+vFaCNfGZGfHYz/n9WU719KlSzFy5EgAwHvvvYf7778fL7zwAkpKSvD2228jJibG5HrV1dWorq423NdqtbK019k+/PBDrFmzBtXV1aisrDR6/WPHjgUAJCcnw8/PDxcuXEBsbCz27duHbt26GWqsMjMz8cwzz9i1D2uo1WqjdVQqldFyIYTJ9fbu3YuDBw/itddea3Ifll7Tnj17cOrUKQwdOtRon++99x66deuGTp06AQAee+wxTJ8+3WHbP378OCoqKtClSxf07dsXAODj44PIyEgA5t9Xe/dl6XPJyspq8j1sKDs7G9u2bUNISAgAIDU1FWVlZQCA9evXW1y3uLjY6s9Zb8GCBTh58qShrdZ8/jExMVCr1U2+FnIOxmNjjMcSxmP3j8erV68GAKxatQpPP/00vvzyS6PlDeO1nifHZLdMnBt+UK+//jpef/11PPTQQzh16hQGDhyInj17miy6X7hwocfV3vz4449YtmwZ9uzZg+joaHzxxReYO3euYXlQUJDhd19fX9TW1gJo+h/Gln1YIzg4GJWVlYiIiEBcXBzUajVqa2vh5+cHIQQKCwtN1svt3LkTx44dQ4cOHQBIAf++++7DihUrjAJVU69JCIGUlBTs2rXL6PGNGzdi9+7dVr0Ge7YPAFu2bDG5jqX31d59OdKZM2dQXl6O1NRUw2MHDhzAU089BQAYM2YM8vPzTa67adMmmz5nAFi8eDE2bNiAHTt2IDg4GIB1n39VVRVatGjhsNdN1mM8NsZ4fBPjsWPJHY/rmzBhAqZOnYqysjJERUUBMB2v9Tw6JjulH9uJXn/9ddG9e3dDTU9JSYlo0aKF0XPGjBnTqHZMr6qqSmg0GsOtsLDQ7U8NfvHFF6Jbt26itrZWVFdXi2HDhhlqkdDgFFdUVJShnunixYsiKipKHD9+3PDe1n9+/VOD1u6j4f7qu/vuu8U333xjuD9gwADx3nvvCSGE+OSTT0SvXr2afP3m3gM9S6/p0qVLIiYmxqgNR44cERcuXBCRkZHi5MmTQggh/vOf/1g8NWjr9qurqw3LGp4atPS+2rsve4wbN05s2LCh0ePr168XKpXKcBrxww8/FBkZGaLOivpPPWs/53/961+iW7du4tKlSxa3Z+rz79y5s8m/CZZqOBfjMeMx47FnxmONRiPOnTtnuL9hwwbRrl07w7iGpuK1K2Oys+Ox28yqAQBLlizB2rVrsX37doSHhwOQpnQJCgrCzp07AQClpaXYt28funbtanIbgYGBCA0NNbpZFBUFFBfLc7txFNeUwYMHIzY21nBLT09Hx44d0blzZ9x3331IS0uzajutW7fG22+/jZEjR6Jv377w8TH/5zB06FC79lHfmDFjsHXrVsP95cuXY/ny5UhKSsJrr72Gd99917Bs2LBhOHTokM37sPSaIiIisGnTJsybNw+pqano0qULnn32WYSFhSErKwvDhw9H3759cfXqVfj7+zc6grZ3+zqdDhEREfjss8/w9NNPIyUlBenp6fjxxx8tvq/27sse2dnZiI2NbfT4oUOHMG3aNEydOhV33nkn1q9fjy1btlj8W2nIms9ZrVZj5syZuHLlCgYNGoS0tDT06tXLqu3rZ11H7NUAACAASURBVHMw9z9PzsF4LGE8No/x2D3jMQBoNBqMGjUKd955J1JTU/Hf//4XmzdvhkqlajJee3xMdko67gT6nojExERDsXvPnj2FEEJs375ddOvWTaSkpIjk5GRDobw1mhyMQg6j1WrFHXfcISoqKlzdlEbqj/5duXKluOuuu1zYGnmVlZWJwYMHm1w2ePBg8fXXX8vcIts888wzhtkBGmKPs3MwHrs/xmNlcvd4LITrYzIHB94QGxtrtsZo8ODByM7OlrlFZKuQkBC88cYbKCgoUNyR6NKlS/HJJ5+gtrYWkZGReOedd1zdJNlERkZi+/btJpcdPnwYGRkZMrfINm3btsVjjz3m6mZ4FcZj98d4rEzuHo8Bz4/JKmEu+nkJrVaLsLAwaDQaw2nCqqoqFBQUoEOHDkYDOYjIvcj1v2wqjpDtGI+JPJsc/8/OjsduVeNMREREROQqTJyJiIiIiKzAxJmIiIiIyApMnImIiIiIrMDE2QJ752AkImXw8rHPHoXxmMj9eUJMdpvp6OQUEBAAHx8fFBUVITo6GgEBAY2u705EyiaEQElJCVQqFfz9/V3dHLIT4zGRZ/CUmMzE2QQfHx906NAB58+fR1FRkaubQ0R2UqlUiI2Nha+vr6ubQnZiPCbyHJ4Qk5k4mxEQEID4+HjU1tairq7O1c0hIjv4+/u7dYAmCeMxkWfwhJjMxNkC/ekEdz6lQETkCRiPiUgJODiQiIiIiMgKTJyJiIiIiKzAxJmIiIiIyApMnImIiIiIrMDEmYiIiIjICkyciYiIiIiswMSZiIiIiMgKTJyJiIiIiKzAxJmIiIiIyApMnImIiIiIrMDEmYiIiIjICkyciYiIiIiswMSZiIiIiMgKTJyJiIiIiKzAxJmIiIiIyApMnImIiIiIrMDEmYiIiIjICkyciYiIiIiswMSZiIiIiMgKTJyJiIiIiKwge+JcV1dn13pVVVUYNWoUkpKSkJaWhiFDhuD06dMAACEEXnnlFSQlJaFr164YOHCg4xpMROShGI+JiGwjS+K8a9cuPProo7j11lsREBCAoKAg9OzZE4sWLcKlS5es3k5mZiaOHz+OnJwcjBgxApmZmQCApUuX4pdffsHRo0dx9OhRrF271lkvhYjIrTEeExHZz+mJ8/Dhw7FkyRKMHDkSBw4cQFVVFUpKSrBs2TJUV1fjnnvuwVdffdXkdoKCgjBs2DCoVCoAQO/evXHq1CkAwOuvv45FixYhICAAAHDrrbc67wUREbkpxmMiouZRCSGEM3fw888/IyUlxezympoaFBQUICkpyabtjh8/HlFRUZgzZw6io6Mxf/58fPrppwCA6dOn4+GHHza5XnV1Naqrqw33tVot4uLioNFoEBoaalMbiIgAKY6EhYUpPo4wHhORp3N2PPZz+BYbsBSkAcDf39/mIL1gwQKcPHkSWVlZqKysxPXr11FZWYl9+/bh7Nmz6NOnD+644w507dq10boLFy7EnDlzbNofEZEnYDwmImoep/c46z344IOG03p6YWFh6NOnDyZOnAgfH+uqRhYvXoyPPvoIO3bsQHh4OAAgJCQEP/30ExITEwEADz30EIYNG4aJEyc2Wp89HETkaO7S46zHeExEnsrZ8Vi2WTVat26NwsJC9OvXD/369cO5c+fQokULrFu3Dk899ZRV21iyZAnWrl2L7du3G4I0APzxj3801OVdvnwZBw4cMNuzEhgYiNDQUKMbEZE3YTwmIrKPbD3O/fv3x44dOxAYGAhAms5o5MiR2LJlC9LS0pCbm2txfbVajbi4OCQmJiIkJASAFHT379+P0tJSPPbYYygoKAAATJs2DVOmTLGqXe7WU0REyuNucYTxmIg8ldvXOOsVFxcbRlkDUi2dWq1GQECAIXhbEhsbC3M5fqtWrbBp0yaHtZWIyJMxHhMR2Ue2xHnAgAEYPnw4xo0bB5VKhQ8++AD9+vVDRUWFVYGaiIgcg/HYg+l0QH4+oNEAYWFAx46AlTXrRNQ02Uo1ampqkJWVhe+//x5CCAwaNAhTp06Fv7+/HLs3i6cGiai53C2OMB57qCNHgFWrgLw8oKoKCAoCkpOBCROA9HRXt45IFs6OI7Ilznq1tbXw85Oto7tJDNRE1FzuGkcYjz3IkSPA3LkQJaUoD49Fle8tCKq7ipAraqiiWwEvvcTkmbyCx8yq8euvvyItLQ0dOnQAAGRnZ+OZZ56Ra/dERHQD47GH0emAVatQfroUe68k44efQrHvgC9++CkUe68ko/x0KbB6tfQ8ImoW2RLnJ598EsuWLUOrVq0AAN26dcOWLVvk2j0REd3AeOxh8vOh3Z+HIyWxKClTITBQKm8ODARKylQ4UhIL7b5cqfaZiJpFtsS5vLwc/fr1M9xXqVQur6cjIvJGjMeeRXdZg+KzVdDU3oKIcCDAH1CppJ8R4YCmNhjFhVXQXda4uqlEbk+2xNnPzw81NTWGq1Wp1Wqrr05F5DF0OuDECeDgQeknT52SCzAee5YzV8JwpToIkYFXTS6PDLyGK1VBOHMlTOaWEXke2UaFPPnkk/jDH/6A0tJSvPLKK1i9ejUWLFgg1+6JXI8j3t2Xh03xxXjsWUrDO+J0UDJSq7NxoUWy1N2sJwSiq9XICcqACO+IDq5rJpFHkC1xfvTRR5GYmIiNGzfi2rVrWLVqFfr37y/X7olc68aId5SWArGxwC23AFevAtnZwJkzHPGuZB54wMN47FnCInzwXdwEJBSewa1X8nD5llhU+wUjsPYaIq6qcdmvFb6PG4+0CPc92CNSClnnIerbty/69u0r5y6JXO/GiHeUlkoJl743KDRUup+XJ414T011615Mj+TBBzyMx56jY0cgoFc6/lf1Ev5Uuwq3XslD+LUi1PgF4XR0Btb6jUdY73R07OjqlhK5P6cnzj169DDU0Zly4MABZzeByLXy86XkODbW+BQqIN2PjQVyb4x4T0pyTRupMVsOeNwE47Fn8vGRToDMPZOOuSWp6B6fj0hfDS7VhSFb0xFR0T6YNp7H5USO4PTEefHixQCAzZs348SJE5g0aRIA4P3330eqG33hENlNo5FO8d9yi+nlwcFAUZH0PFIOWw54YmJc00YbMR57rvR06QTIqlU+yM1LQlWFVFXUvQcwfrzbnhghUhynJ84DBgwAALzyyiv49ttvDb0dI0aMwODBg529eyLXCwuTvsGuXpV6Kxu6dk1aHsYR74piywGPmyTOjMeeLT1dOgHiQeNYiRRHtn8ntVqNqqoqw/3q6mqo1Wq5dk/kOh07Sqf21Wqg4RXuhZAe79IFLEBUmPoHPKa48QEP47Hn8vGRKr569JB+MmkmcizZBgc+/PDD6NOnDx5++GEAwLp16/DII4/ItXtl87CprqgBfQHimTM3T/0HB0uJl1oNtGolnUvlZ64s+gOe7GzjGmfg5gFPRob0vIoK17XTDozHRET2UQnRsAvMeTZv3ozvvvsOQgjcc889GD58uFy7Nkur1SIsLAwajQahpk6jO5sHTnVFZpj6rLt0YQGikjWcVaPhAc+NWTVcHkfswHhMRJ7I2XFE1sRZiVwaqM1NddXgS5k8CM8uuB8rDniY8DkG30ciai5nxxGnl2o8/vjjeO655xAXF9domRACGzZsQHV1Nf70pz85uynKwrl9vZO+AJHchweNuGI8JiJqHqcnzkOHDsXw4cMRERGBvn37IiYmBpWVlTh27Bh++OEHDB06FHPnznV2M5SHc/t6LvYqex4POeBhPCYiah6nJ84jR47EyJEj8eOPP+L7779HXl4egoODMXDgQCxevBitWrVydhOUiXP7eibWrJOCMR4TETWPbLNq9OvXD/369ZNrd8rHuX09jwdfnpk8C+MxEZF9eP7YVTi3r2dpWLMeGgr4+t6sWS8tlWrWdTpXt5SIiIjsxMTZVfRz+7ZqJZ3W12qB2lrpZ14e5/Z1N7bUrBMREZFbYlbmSunp0un77t2BS5ekpOrSJemiCjyt716sqVmvqmLNOhERkRuTrcYZAIqLi3H8+HH0798ftbW10Ol0CAgIkLMJyuNBU115Ndask5thPCYisp1s2dmGDRvQs2dPjBs3DgDw66+/YtSoUXLtXtn0U1316CH9ZNLsflizTm6E8ZiIyD6yZWgLFixAdnY2IiIiAACpqak4c+aMXLsnci5H1KzrdMCJE8DBg9JPDiQkJ2E8JiKyj2ylGj4+PoiKijJ6jKcFyaPoa9b18zgXFUnlGRkZRpdnNonzP5OMGI+JiOwjW+IcEhKCixcvQnVjxoHvvvvO0NtB5DHsqVnn/M8kM8ZjIiL7yJY4L1q0CMOGDUNBQQEGDhyIkydPYtOmTXLtnkg+tlyeueH8z/qp7PTzP+flSfM/p6ay9p0chvGYiMg+siXOGRkZ+Pbbb7Fnzx4IIdC3b1+Eh4fLtXsiZbJl/mdrk3GiJjAeExHZx+ldWNeuXTPc/P39MWDAAAwcOBABAQG4du2a1dupqqrCqFGjkJSUhLS0NAwZMgSnT582es6qVaugUqmwefNmB78KIifh/M8kI8ZjIqLmcXqPc8uWLQ11dKbU1dVZva3MzEwMHToUKpUKy5YtQ2ZmJrZt2wYAUKvVWL58OXr37t3sNpMH0umUOVc2538mGTEeExE1j9MTZ92NKbXmz5+PwMBAZGZmQgiBFStWwM/P+t0HBQVh2LBhhvu9e/fGG2+8YbifmZmJf//733jmmWcsbqe6uhrV1dWG+1qt1uo2kJtS8owV+vmfs7ONa5yBm/M/Z2Rw/mdyCMZjIqLmka3LbevWrXj66acRFhaG8PBwzJo1C+vXr7d7e0uXLsXIkSMBAP/73/9wxx13oFevXk2ut3DhQoSFhRlucXFxdrfBUTh9rxPpZ6zIzgYiI4FOnaSf2dnS40eOuLZ9jpj/mchGjMdERPaRbXDgpUuXkJ+fj443es7y8/NRWlpq17YWLFiAkydPIisrCwUFBXjnnXewe/duq9Z97rnnMGPGDMN9rVbr0mCt5M5Qt+cuM1ZYM/+zUktNyC0xHhMR2Ue2xPnVV19F79690b17dwDAkSNH8Pbbb9u8ncWLF2PDhg3YsWMHgoODsXfvXhQVFSE5ORkAcOHCBUyePBnz58/HX/7yl0brBwYGIjAwsHkvxhwbkxtO3+tk7jRjhaX5n3l0RQ7mFfGYiMgJVEIIIdfOiouLsX//fggh0KdPH0RHR9u0/pIlS/Dhhx9ix44dZifrHzhwIGbNmoURI0ZYtU2tVouwsDBoNBqEmhqcZS0bkxudDpgxw3xpa16e1OH4r3+xY9FuBw8Cs2ZJ5Rm+vo2X19ZKierixUCPHvK3zxrmjq7UaqmMg0dXiuCwOCIjj47HROS1nB1HZOtxBoDWrVsb6uBspVarMXPmTCQmJmLQoEEApN6K/fv3O7KJ9rGj69idOkPdlrvPWOEupSbkljw2HhMROZFsibOPj4/JaZCsnf4oNjYW1nSOf//997Y2rXnsTG6smb63qIjT99qkYalMYqJ7z1jBoytyEo+Nx0RETiZb4lxeXm74vbKyEqtXr8b169fl2r3z2JncuHtnqOKYK5Xp1Uvq9dd/RsHB0purL3VQ8owVPLoiJ/HYeExE5GSyZQy33HKL4daqVSvMmDEDX331lVy7dx47r/ymn75XrZY6P+vTd4Z26aLczlBFsTTl3Lp1wEMPAd27A5cuSQcwly5JPc1Krw+uf3RlCo+uHMuL5oX02HhMRORkstY413fy5EkUFha6aveOY2fXsX76XnftDFUMa0plDhyA7p+Lcea7U6i8oEGLmDC0v6cjfPwU/uby4ijy8fKZSzwmHhMROZlsiXN0dLShpq6urg61tbX4z3/+I9funacZyY010/fKyh3nCraiVEa7Lxdv/uUUdl1IupkTbXWDnIhHV/LwwnkhPTYeExE5mWyJ86FDh27u1M8PMTEx8DU1RZi7aWZyY2n6Xlm5a49bE6UypZXBuHiyCKcrNYhMdsOcSHFHVx7GS2cu8dh4TETkZLJ9E6xZswbt27dH+/bt0a5dO/j6+mL+/Ply7d659MmNnXW0Pj7SuMEePaSfLkmalXxZakss1AELAZzJu4ardUFokxSG0FBpOmd9TlRaKuVEii9lTU8HliwB3nxTmnP6zTelCb6ZNDefLYN7PYhHx2MiIieSLUXbsGGDVY+5LXdNbhr2uLlbdmlhlKVGIxBQrEZp6y4oDTculXG7nMjlR1ceys7Bve7O4+MxEZGTOL1UY/v27di2bRuKioowe/Zsw+MaD/siAnAzuXEn7j5XsIVSGd/jalz2bYWDyeMhVI0TTc7mRt42L6RXxWMiIidwerdVQEAAWrZsCZVKZTQFUufOndnDoQSe0ONmplSmJi0DH3V6CcdamO7197CcyPXccTo3L5sXkvGYiKh5nN7jPGDAAAwYMACjRo1Camqqs3dHtvKUHjcToyzDEzsiYJYP1JzNzfncdXCpl81cwnhMRNQ8Tk+cP/nkEzz44IPYvXs3du/e3Wj5448/7uwmkCWeNFdwg1IZH3hVTuQ67j6dmxfNXMJ4TETUPE5PnI8ePYoHH3wQBw8ebLRM1bCmluTn4T1uXpQTuYanTOemmHkhnYvxmIioeVRCNCzsc47S0lK0atWqycfkptVqERYWBo1Gg1BTpQrewtSp9i5dPCa7dMdru7iFEyeAadOk6QtN/f9otVLd+ZtvKnNwqYO4WxxhPCYiT+XsOCLbBVDuvfdeHD58uMnHyEU8vMfNHSc8cQhnHzFYM7iUU5coDuMxEZF9nJ4419bW4vr169DpdKisrIS+g1uj0eDatWvO3j3ZwluzS0/tjpZjwJ6nDC71EozHRETN4/TE+dVXX8WcOXMM0x/phYaGYubMmc7evaw8Nf/yaO46G0RT5Bqw50mDS72AN8VjIiJnkK3G+a9//Sv+97//ybErmziqFsZT8y+PZi651A+KVPpsEObodMCMGeaT2bw8KZn9178cc2TX8H1sOLjUXd9HG7hbba6nx2Mi8l7OjiOyJc56+lOFesHBwXLuvhFHvMGemn8pkqO69eVOLuXkigF7Hj64tCnumvB5YjwmIu/mMYMDDx48iEmTJiEvLw/1c/W6ujq5muAUnjIbl1twZLe+u19q3BJXDNjz8MGlnsZT4zERkbPJljhPmzYNK1aswNSpU7Fr1y4sXboULVq0kGv3TuPJ+ZdLNexZLi8H5s93XM2uJ88G4aoBe946uNQNeWo8JiJyNtm6g2pqatCrVy/U1tYiJCQEzz//PL744gu5du801uRfVVXumX+5zJEjUhnFtGnArFnAk08Cf/4zcPq01MMcGgr4+t7s1i8tlbr1dTrr91E/uTTFnWeD0A/YU6ulspP69AP2unThgD0v5qnxmIjI2WRLnP38pM7tqKgo5OTkoLS0FGfOnJFr907jyfmXS+gLxrOzpRrdTp0Af38paS4pAcrKjJ/fsFvfWp6cXOqvBtmqlXQ6RKsFamuln3l5bn81SGo+T43HRETOJts35yOPPIKysjL84x//wN133434+Hg88cQTcu3eaTw5/5Jdw4Jxfc9yQIB09FFbKw18a/hG29Ot7+nJpf5a4927SwMB8/OlnxkZHK1KHhuPiYicTfZZNQDpNGFVVRVCQkLk3nUjzphVwwtn43IMc7NBXLkC7NkjJbE6HXDXXcZd+M2ZJaI5s0G4w8Td7tBGd1FbK9W9V1U1+jtz59kgPC0eE5F3c/tZNXJzcy0u79Kli7Ob4HT6zj19/lVUJOVfGRleMxuXY5grGA8LA8LDpVINAKg3fVazL7Jh72wQ7jJxNwfsWUcI4PJl4OxZ6VZYePP3s2chCguBc+eg0ulwrXs/BB34wS2PP7whHhMROZPTE+fhw4ebXaZSqXDq1ClnN0EWnI3LAczNBqFSScnf5cvSsuvXpd6/+t36zSmrsDW5lOuqfOQ4VVU3k+H6SXH93y1ccrr+hDnlv57FP2Yo7xjJGt4Sj4mInMXpiXNBQYGzd6EY7NxrJkuXb46KAqKjpdv169IRiiu69Zs5cTcrJ5xApwMuXDCfEBcWAsXFDttdq+pzOHKoDmfO+LrdMZI3xWMiImeQbR5noibpB+ydOXNzcuz6BeMJCcALLwAhIa7LPJsxcbe7VHcojkZjOSlWq4GaGtmaU+cTgB4JJdh/JsZwjERERN6BiTMpi9ILxu28cAqrO8y4fh04d858Ynz2rDT4Uy4qFXDrrUD79tBGxOPrX+NwNSoe11rF41LLOFxqGY+rgVGASmV0jBQTI18TiYjIdZg4k/IouWDcjqvyee1l2YWQXnT9JLhhgnz+fOPpBZ3J1xdo2VI6axEUJPVUt2oF/PWvwODBQNu20rzhAI4fBJbNkqYS9/VtvKn6x0hMnImIvIPsiXNtba1h8n1bVFVV4ZFHHkFubi6Cg4MRExODrKwsJCQkYNKkSdi9ezdatGiB0NBQLF26FGlpaU5oPclGqQXjluqwzczw4bGXZb96VUp+LZVRVFXJ1x5/fyAu7uatfXvpZ2ws8NFH0ht8552NP7O8PODoUeCxx4yOXFx15XI5MR4TEdlGtsT5119/xdixY1FWVobCwkJkZ2dj3bp1WLRokdXbyMzMxNChQ6FSqbBs2TJkZmZi27ZtGDVqFN5++234+flh8+bNeOihh3DixAknvhovxFFtkqbqsE3M8GFndYdr1dVJvcENyybqJ8cNr+LobG3aSIlwfLx0q/97fDzQurXpv8kTJ6TBgQkJNh252HKMVFHhnJfsLIzHRET2ke0CKIMGDcK8efMwbdo0HDlyBEII3HnnnTh69Khd2zt06BAeeeQR5De4zHJpaSnatWuHyspK+Jj4Eq2urkZ1dbXhvlarRVxcHCfct4Sj2hqz4cIp5q7rotec67fYRQjpojJNDbirq5OhMTfccovphLh+D3JQkH3bPngQmGWh5qK2VkqaFy8GevQwWmTtxY3c7cIdjMdE5Knc/gIoeuXl5ejXr5/hvkqlgv+NWkJ7LF26FCNHjmz0+H/+8x8MGzbMZJAGgIULF2LOnDl279frcFSbaTbUYdtR3dE81dXSRk0NtNPfl7OL1NcXaNeucQ9x/fvh4Y17gx2lGTUXSh+rai/GYyIi+8iWOPv5+aGmpgaqG1+OarXabDBtyoIFC3Dy5ElkZWUZPf7BBx9g3bp1+OGHH8yu+9xzz2HGjBmG+/oeDjLBa0e1WcnKOmw7qjvM0+mksgNLs1BcvNj812aLyEjj3uH27Y2T41tvBeyoo3WYZh65KHmsqr0Yj4mI7CPbt9mTTz6JP/zhDygtLcUrr7yC1atXY8GCBTZvZ/HixdiwYQN27NiB4OBgw+Mff/wx5syZg2+++QatW7c2u35gYCACAwPteg1ex2NHtcnP6p7L8vLGA+7qJ8eFhcaXHHe2wEDTdcX1f7ZsKV977OGAIxeljlW1F+MxEZF9ZEucH330USQmJmLjxo24du0aVq1ahf79+9u0jSVLlmDt2rXYsWMHwsPDDY+vW7cOL7zwAnbs2IH4+HhHN917ueWoNuVK71qD1L8XQb23EDW/nUWo5ixaXT0L1Uv1kuQrV+RtVEyMcflEw9ri1q2dV0IhJ0+tubAT4zFRM3CwvFeTbXDgypUrMWnSpCYfM0etViMuLg6JiYkICQkBIPVW7N+/H/7+/oiJiUFUVJTh+d98843RfXPcbVCPrBQ3qk3BhJDeC0t1xUVFUsCVS0iI+Rko4uKkumNv6+1z0heeu8URxmMiO3GwvOI5O47Iljh369YNhw8fbvIxuTFQW6DTATNmmK8NzcuTeuz+9S/PP9qurDQulzBVX1xZKV97/PykkgNT5RP6mztPMOxm3C2OMB4T2cHcYPmG0+yQS7n9rBqHDh3C/v37UVpairfeesvwuEajwXU5azXJdtbWhgJS77S7nraqqwMuXDDfU3z2rBQo5RQdbbm2OCbG9NRqRBYwHhPZiYPl6QanJ87nzp3DoUOHcPXqVRw8eNDweGhoKN5//31n797xvK22qanaUEDqlVbyaSuNxvwMFPo5i2tr5WtPixaWSyji4qTnEDmYx8VjIrlwsDzd4PTE+f7778f999+PrVu3YujQoc7enXN5a22Tufm4fvrJ9XM8X78OnDtnubZYq3VuG+rz8QHatrU8C0VUlGcMuCO341HxmEhOHCxPN8g2q0ZJSQlWr17d6PHx+l5LpfP2C4E0nI9LjtNWQgAlJeavbnf2rFRiIU+ZviQ8vHEi3L79zftt2wLNuJCE4nnbGRcP5fbxmEhuzbiQEnkW2RLnTZs2GX6vqqrCjz/+iN69e7tHoGZtU2OOOG119arluuLCQukqeHIJCDA90K7+YzdmEPBK3nrGxQO5dTwmcgXZLwFLSiVb4vzJJ58Y3S8oKMCLL74o1+6bh7VNjTV12iooSJqTeNcuKdCY6i2+dEneNrdpY3xlu4b1xdHR3nPgYytvP+PiYdw6HpNyWDoD5Wlnpxx6CVhyZy67Dm6HDh1w/PhxV+3eNqxtMqYvjaipkWbT0OmAioqbt6tXpZsQwIED8rSpZUvzg+3i46Ug521zFsNB31084+Lx3CoekzJYOgMFeObZKV5IiSBj4vzll18afq+rq8P+/fuhcpcBUt5W21RVJR1BW5qz+OpV+drj6ytdrMNUbbH+9/Bw2QfcmUtKldLR4rDKCp5x8ThuHY/J9SydgfrpJ+k5tbWeeXbK3GB5dhp4DdkS59dff/3mTv38cNttt+Hjjz+Wa/fN40m1TTodUFxsua744kV52xQZaTzArmGC3Lat4uYsNpeU9uoF7N/v+o4Wh1ZWOOKMi1KOJgiAm8djci1LZ6A6dwb0B2XDht38H/e0s1MNB8uTV5Etcf7uu+/k2pXjuVNtU3m5+V5i/f2aGvnaExRkOiHW3/RZnRsxl5Tu3AmsWyeVUnfu7LqOld61ggAAIABJREFUFodXVjT3jAsHFSqOW8djci1LZ6C0WikAAdJ3Uf2YwLNT5CGcnjjn5uZaXN6lSxdnN8ExlFDbVFMj7dfSnMVXrji/HXoqlXQFu/h4qVSiVSup57hbNyAhQXq8VSuPmrPYXFIaEiKdmayokDrQQ0KkZa7oaHF4ZUVzzrg0t+ubPdUO5THxmFzH0hmo69dvjoExdSVKbxsPRPZTcOx3euI8fPhws8tUKhVOnTrl7CY4jjNrm4QAysos1xWfP3/zaF4OoaHmL+QRHy/VHQcEyNceBTCXlGo00jFLRIT0u1Z7s7NF7o4Wh49ltfeMS3O7vtlT7XAeFY+9mSuTCktnoAICbv6fm/pu8LTxQOQcCo/9Tk+cCwoKnL0Ledlb23TtmpQEm0uKCwuBykrHt9ccP7+bl3c2lxwzuDViLim9fl3qcQ4Jkc5QNuxskbOjxSljWe0549Kcrm9Of+cUHhePvZGrkwpLZ6BCQ28m8A3nvHe38UDkGm4Q+2Wdju7QoUP45ptvoFKpcM8996B79+5y7t556uqkK9hZGnBXWipvm1q3Nl9bHBcnFeIqbMCdNVx99sZcUhoQIB2LVFdLPxt2tsjZ0eK0say2nnGxt+ub09/JwmPjsSdTQlLR1Bko/UHwsWPKHg9EyuMmsV+2xPmdd97BvHnz8MADDwAAHnjgAbz44ov485//LFcTHOPjj6XpduonyefOSd2NcgkONl8+oR9w16KFfO2Rias7WgDzSWlYmFTmfeaM9BHUT6rl7mhx6lhWW8642Nv1zenvnM5j4rE3UVJS0dQZKIBzHZPt3CT2y5Y4v/nmm8jOzkZ0dDQA4Pnnn8c999zjfoH6nXeAb75x3vZ9fKTp1+onwg2T5MhIjxpwZw0ldLQAlpNSPz/pOiz+/lK5his7WpQwltXurm9ecMjpPCYeexOlJRVNnYHiXMdkKzeJ/bKWauiDtP53t5xwPz6+eetHRJjuLdZf0KNtWykDcwFXl0FYapdSOloA80npwIFAz54353F2dUeLy+fpt7fr29suOOQiHhGPvYkCkwodfJCPJGgAhAHoCMDw38y5jslWbhL7ZcvQOnbsiOeffx5PPPEEVCoV3n77bdx2221y7d5xLCXOAQGm64nr/96ypXxttUFTZRCuTKqV1tECWE5KH37Y8e+Vve+/xe8uOT5Ue7q+PemCQwrlMfHYmygsqVBC6Rx5GDeJ/bIlzllZWfjb3/6GlJQUAMDvf/97ZGVlybV7x+neHRgzpnFdcVwcEB2tjC5aGzVVBvHQQ/JfDa9+TnfmjDThiII6WgCYT0od3dHilC8oOb/1bO36dmKRtlLPqsjNY+KxN1FQUqGU0jnyMG5ysTmVEPrZyr2TVqtFWFgYNBoNQk0dxXs4nQ6YMcN8LD54UJqT+NZbpWMDfYDU/w07I0A2zOnq6qSJSZKTpWqWhrRa4NIl4M037UtYlZxMmfuCatb775SNOoGp5L5LF7vrXpx5rODtccRR+D42oeH/bsOkQoYLCjX1nZGXJ+Xv//qXcuIouZlmxn5nxxHZepyXL1+ORx55BGFhYXjyySexb98+LFmyBHfffbdcTSATLJVBAFJMvnRJCoT6vz9n1habyukqKoCCAimJDw6WOvb1mtvRouTTjU6p7VZawbglDizSZg+ZMcZjN9WcUb8OCnZKLJ0jD+PyATqWyZY4//e//8WUKVOwe/du/PLLL3j11Vcxa9YsHDhwQK4mkAmWxptoNFLS6ucnXe27PmcESHM5XVgY0KsXsHOnVDLSv7/U3uaevVF6MuWULyhrNvrrr8D27dLsLa4OWA6oe3GnYwW5MB67MXuSCgcGOwWOUSR3ZeEMiMWBpy4mW+Lsd2OmiG+//Rbjx4/Hfffdh+eee06u3ZMZlsabXL8uJcyBgaavnuroAGkpp4uOBnr0kJar1dK1W5ozY4USk6mGMeTyZSd8QTX1rVdZKWXjL78sffBK6oK3E3vIGmM8dnO2HFA6ONgpbIziTUquubOCmzffdhbOgBxBumLPBAMyJs4+Pj746KOP8PHHH2Pz5s0AgOsNr0tMsrM03sTfX0qcIyLkCZBN5XTt2kn7/NvfpFrn5gQXpSVTpmJITIx08OLQLyhL33qlpcChQ9KyVq2kBiipC95O7CFrjPHYizg42ClojOJNSq65s8KRI8Dq93XQZOfD/5oGNcFhCOveEeMn+rhD821n4QzIlZ/OYA1eQnZtuiLPBAMyl2osXLgQf/nLX5CQkIATJ05g0KBBcu2ezLA0iLWoSDpbHxzceD1nBEhrejJatABSUpqfzCopmTIXQ377DSgultrZo4eDvqDMfesJAZw4IY20jI+X5hNXqTyinkGxPWQuxHjsRRwc7BQ38YHSa+6acOQIsGbGEfQ5sQq36/IQhCpUIQjHC5Kx5ucJwJJ0JTffdhbOgIjOySj7Mg+9sBpXh938rlHa15Bsu+7Vqxc+//xz/P3vfwcAJCUl4c0335Rr92SBfrxJ9+7SQMD8fOlnjx7A/PlAQoL0B6vVSlcW12ql+44OkPqcTq2W8rj69Ilily6OSdTrJ1OmyJVMNYwhoaFSGUpoqPRaQ0Ol9zs310Hvv/5br1Ur4w/13DnpDQ4NBW6/3ThLb9gr5Wbk/LtyF4zHXsQJwc7cd0ZGhsx5qqUAmpwsPb56tfQ8BdLpgB2vH8Gon+cirS4b1cGRKA7thOrgSKTVZWPUz3PxzeIjSm2+fSycAdGUq3BGF4vOuly01hp/1yjpa0i2Hme1Wo0pU6bg+++/BwD87ne/w1tvvYW4uDi5mkAWWBpv0rmzPJdulrMnQymnG5s6i9q5M3D6NHDbbcCFCw56/02NzK+ulnpqMjKkN7ohN65nUFwPmQIwHnsRa4NdYqJ01snKIltFTHygtJo7G+Wf0OG23asQ7VOKC+E3P5uqgFBc8E9GdFkeEn9cjfwTqUjq7CEBysIZkOvXgWsiGO1URWhxvfF3jVK+hmRLnMeNG4dhw4bh//7v/yCEwIoVKzB+/Hh89913cjWBmmBuvImcAbI5sy3ZQinJlDVnUQMCgMcfl2rNHfb+N/xQL12SJl5t0cL08928nkGuvyt3wXjsRawJdj17ArNm2Vwj7PKratcLoEIAGq2UfAUEAGGhgEopmZYZVUfzEavNgzbCdOKvCYlF7OVcVB3NBzorL/G3i4XauYAAIFh1DVUIQmVA4+8apXwNyZY4X7lyBU8//bTh/qxZs/Dhhx/KtXvSs3PorlMCpJm2yJWoKyGZsrb+NiLCCe9//Q9VpwO2bnV9F7wTKaKHTCEYj72MpWDXsyewbp171gjfCKCX1Fdx7FwoLl8B6moBXz8gIhzo3O4aIpWQaZkRBg2AKpSoboG/ieXXEIxWKLrxPA9h4QxIWIhAex81DiEDxaHG3zVK+hqSLXG+7bbbkJ+fj443XnF+fj7uuOMOuXZPgLJGHjfRFrl6MlydTCmlZEQxXfBO5vIeMoVgPPZCpoJdYqLU06zkeTktBeSOHVEclYzSr7NR4p+MW1qq4H8LUFMLlJQIhBepUTMkA21cnWmZEdc1DJWhQdBprwKtGvec1JVfQ0BoEOK6KjPxt0u97xqRm4fy8FhU+QYjqO4aQq6oEZXUCvsxHrnHfBT7NeT0xPnBBx+ESqWCVqtFamoq+vXrBwDYvXs3BgwYYPV2qqqq8MgjjyA3NxfBwcGIiYlBVlYWEhISUFxcjPHjx+O3335DYGAgsrKyDPuhG5Q08lhJbYFrkylF5atK6IInp2I89nINg92JE8qqEbaxc0cHH6zCBPTCGSQjD1dELKpFMELFNcRDjfNohS8xHjPgo4yLZzQ4KPDpmIjIu5Kh+zobJy9Lib+fnzRe+2qFQCedGhH9MuCTpMzE327p6Tj20Eu48M9VaHksD341Raj1D0JFXAZiZo/HuM7p0Cn4a8jpifOIESMMv48dO9bw+5/+9Cebt5WZmYmhQ4dCpVJh2bJlyMzMxLZt2/Dss8+id+/e+Oqrr3Dw4EGMGTMGv/32m2GSf6+npKt9KKktCqGofNXVXfAKo9NJV8/Uam/ehAD69HF1y+zDeExG3GFeTgsdKvn5wI6ydFzq8RLuObcKt17JQ/i1ItT4BeFM6wx823Y8Dpem4/8pYWygmYOC1iN7IeD8GQSeyMOZq7GoEMEIVl1DV181opJbIXyWArpYHezIEWDuunSU3ZKK7r3yEeWnQVltGLI1HRG1zgcvvQQsWaLcryGnR7IJEyaYfLyurg6bNm2yejtBQUEYNmyY4X7v3r3xxhtvAADWrVuHgoICAECPHj3Qpk0b/Pjjjxg4cGCj7VRXV6O6utpwX6vVWt0Gt6WkkcdKaouCKCpf9YB6hpoaoLzcOOG1dNNoTD9eUdF42x07AidPyv+aHIHxmIw4cZJzm4bT2Nmhos/7SzqlY318KqI1+WhxXYPKgDCUhHVETZ0PqvIVMDawiYOC8MyHELZvP2Ky81B3tQi+twQhOCMDqgkK6WJ1IKOP+v+zd+fhTZVp/8C/SVda2nSlQNOF0hYKpQsUBpFVECviO76COjMIRZkfODL6qgzoLIIwCm44jtvghhZQRgQRdxT3BaQUimALbSm0jaV0oSTdt9y/P44JTZt9PUnuz3XlgubknDznJLnPfZ7zLGOkaJOkou3XZaNjL3/UmzeL9zTk9CqA06dPY+vWrcjPz0dsbCxuuOEGq7bzzDPP4Prrr0djYyPUajWio6O1yxITE1FVVaV3vU2bNmH9+vVWvafbElOtgpjKIjLukK86ZFrYXzdKl5ToGiSDMjoZqhapTkJrSRKsUgkzhzuKJ+V2HI+9nIM6WVjcncbKChXdvF+KujDdACqKURjMuSg4fBiSzU8iuKJCBDUnjuUJdWdOSZzb2tqwa9cuvPLKK6ioqEB7ezu+++47qzujbNy4EWVlZdiyZQva29sh6Xf0qf8sB3389a9/xX333af9W6VSef7YpWKaOk1MZWEWMXQyXLJECHCWJLaax7DaY7jmQj4S20vg29OBdgpEMdKwDXkogjhrWtw9ceZ4zLQc0MnCqi4sVlaomJ33J6mBUhfdzjM3U6yoEG+maEeeUHfm8MR5+fLl2LNnD6ZNm4Y1a9Zg3rx5SElJsTpIP/nkk3jnnXdw4MABBAUFIejX+aDr6+u1tRyVlZWIj4/Xu35AQAACAgKs2xlXsEcVn2iGbhBZWbxcb6/5NblnzwKHDwvnUx8fYd2uLuDzz4F//9u698/CMdyLDYhEAxSQoxXBCEYrJqAQiajEBqwVXfIcHCxc73V3A376xo8SOY7HbAA7drKwuguLlRUq5uT9KyYdg/QvLhxNyhMyRTvyhLozhyfOO3fuxIQJE7BixQrk5uZCIpEMqJEw11NPPYWdO3fiwIEDCAsL0z5/00034fnnn8dDDz2EgoIC1NbWekYvbnsNHyemoRvEVBY31dXVL7G9pEbv6XJ01inRpJahOuByUwdjD0Mz8DqDBGosQT4i0YASpAEQYkIzQlGCNKShBIuxDceRCbKxP7xEIgTovo+QECEw93/e2LLBgwF379/G8ZjpZadOFlbfhrdhdsPsbKnBvH/FpGMYvcvFIzh5QqZoR55Qd+bw08D58+fx3//+Fxs2bMDy5cuxZMkSdHd3W7wdhUKBVatWISkpCbNmzQIg1Fb8+OOPeOyxx7B48WKkpKTA398f27dvd/8e3PYesk1MQzeIqSxOQiS0u7WmOUP/R5++VMjCMSxBPsagBIPQAQkCUYs0fCjipg4AkIxyjEEJFJBDkzRfJoECcqSjGBPDylEXlmpZkjtYjcimcoSolQgeLsOgccmQ+vKFGMDxmBlhh04WVleumjm7Ia36C9oKS9Db2iF0oJuQBsnSPGRnZw/M+5PUQk2zq0dw8oRM0Y48oe5MQsYaoNnZzz//jK1bt2LHjh0YOXIkbr31Vtx5553Oenu9VCoVZDIZlEolQvVdDbqCWg3cd5/hH1pJifBD27zZ8m+XQ3p3WUlMZTFA33Bk1j56e+1btiwcw1o9TR3kUKARUS5r6iCVCjWzQUHAsGHA0KEDE91UZQGu/vQvaI9NgW+gD/z8AH8/YcpVf3/AT9oDn4pySDY/CUycaP6bu2iSH1HGERM4HjN7Ky0F7roLiIjQX7mqUgEXLwLPPmsgR9f3+x0zBpg0CZde2oXG0gZUquVoo2AESVqRIBUmzAh7Sk9Fks2FsaP+FWH9M0Uxz87oIIY+anvUnTk6jjg1cdbo6enBu+++i61bt+Kjjz5y9tvrEGWgFtMP3o30zcODgoCYGNuT3uZmV++VfhKosRn3YQIKdZo6CAhpKMER5OAv2Gx2U4eQEP21uUTAkSPC34MH/5rY9klyGxqAU6eAuDhg1Cjhda2tJs4JjviOG7pL44STkyjjiJk4HjN7sUudT/8KlaQk1C35Cxr2F6JMMzugrzA7YGsLIaW7BJG5OYjZ3m+jBQXCrIgpKULHjP56eoT3edLCi3NrOTJTFAMrKsIcVXfm6Djikvtnvr6+WLhwIRYuXOiKtxc/L+1MQCQ0Q+ibtJoaZ1fzqK0Fzp8XLuJ7eoQfpCfTNHX4BXIE+Et0klk/fwl8IMc16mJ0zS1HZ4Lppg6DBxsOWMZOhmq1kFQHBgrnHs02TN4NtfX2pZ6TK0+sYx2Ox8xe7HIbvl+TEfWpUlz8vgS/SOUID78cJ/z9AP9wCX5pkEP6XTGiS8shHd3nItuctsUBAcIFekGB4+96imqwfjuz8k6fOwzBqg83PBMjN+tMQCQUyVTtrankV6USRivwBv7+5ndO61sTrFkWWaFE+CMdkI4KhkTfr7gnCCivwdg/KQEDlSma3LO21ngMN3YyLCsTPrPx4weua7QzkC1nWH1BeuhQoRY7MdF9BwdlzAPYuwtL9UklulQdkEbor0jyCQlCV1MNqk8qkTC6zwJTF+enTgn/37xZqLFxRrMud80UjbF3fyw3wImzGDmpM0Fvr+GmDOYkuX1rhT29hlcjKMh05zTN34ZGZwgJESo6bCKRAcGBQJt1F1eWVhAYOhmmpAhfSblcfzGN3hyx5gxrKEgfPy78LmJi9B8PD71Lw5gY2bNyVQkZOhCIwdSKTgz8bQehDR0IBNAv1hm7OD91CrhwQYgXkZFekew5hNXjD7o3TpzFyERtnDoyCqr/WYKmStNDjhl76JtO2FMNHgyEhVk3DJnm+ZAQEQ1HZsPFlbUVBPpOhmo18H//Z/rmSEjIgFGkhDhqyRnWWJBOTb38exkyZGCts8ju0jDm6exVuRqYnozy0DSkNRei3n9grJM1K1ASmoP0dD0VSfouzjW1FjExQvsyL0n2HMITpgG0gljSAI9n6XBkzc3ZiGxaiyvP5kN+vAQ+3TVoVQfiRG8OXutdgqJ9nn817OtrOslVqYADB4Dhw4W8qG+nNT8/Ie6dOyfcjXNG/w+nsbKpg60VBP1Phmq16fw9Ph74z3+ESh69tdvmnmGNBemwMCFhrqsTEvA+4wqbe5fGDQZ5YczrJKdKse/KPETtr8TQSyW4FCxHp28QAnraENaqwHl1FCqmLsENqQZ+rP0vzi9eFE4IkZFelew5hBj6Y3V22uEWrmU4cbZQSQlQXe2s4ciy8QwykYxyyKCEEjKUI9nmCSEcLTDQdG2uqeYMoaHCb8HU3AylpcKsdsYGZxg0yEMrGq1o6mDvCgJT+buvr1Csqio7NH8zFqQlEiEbb2wETp8W/m9BryQXjWLHmFW86SJPKgXmrM7G9vNrMbk0H6PaSiCjGnRIAnHMJwc/pi3B4r9km9/hsKBASLa8rPO9Qzi6PxYRUF8vnEAqK/U/YmOBEyds2w8LceJsob//Hdi713nvR5CiDM656h082PpZ1fp2YvP3d0pxATihObjYz1AWNiZ0RAWBofx9wgShAri62k7N30wF6UGDhEbXqalCj0czeyV5Yd8W5sa88SIvOxvAU9nY9nomPiksh1+bEt1BMoTlJGNxntSy/XazzvfGuPz0ZOsJuLdXiNOGkuLKSuFWvTE9PcJ7WTkDqjU4cbaQ2IYWlUr1J7GWJsCDB+sf6lLsHDoLkbucoSxoTOioc4ax9s92a/5mTpCePBl44gmgosKss4mX9m1hbsqbL/KEGCNFeXmqbYmih8zkJ4rTk6kTcHg4MGMG8PnnusmwpgZZoRASX1s0NwOXLgnv5SScOFvIXomzn9/ApNZU5zR9CXBQkFMvtPRy9VWvQ2bw9tAzlCPPGf3z94ICO9dum3uV5Otr9oWEl/ZtYW6IL/Ls1OHQA+Z8Fs3pSakUatxmzAD27QMOHhQK0tUlJMRtbcD77zu+HJWVnDiLWXS07hTC1tb0Orktu8OI4qoXdh5b3oPPUM48ZzikdtvOV0li6NvCmDm85SLPERUxA7aZmQ2pDXHElZVFfU9PY0arMURVjkGNSrT7yxA6OhnFp6T2OT0RCW3tjDWjcFZg9PERvuAJCfofI0Y4pxy/4sTZQg8+KDycxdW1ucaI5qr3V3YbW97Dz1AOqaHXw2G123a8SvKg5o7Mw3nDRZ4jKmIMbzMb2U9ZHkdcXVmkOT1NDT6G2YfyMexSCfx6OtDtG4jzYWn4PDYPR4uzTZ+euruBX34ZmAxrmlFUVQk76AyDBgnDLxlKjIcPF9FYsJw4i5qrf6DGeHClrFecoZwx+6tDa7ftdJXkIc0dmTszs3bE0y/yHFERY3qbUmRnmx9HxFBZpFQCcQ3HcFPDBoR0NqApWI7OoGAE9LQiob4QN6kqUR+1Fs0XRgFqI6NR/PKL82YuCw8fmAz3TZSjo13f5tQCnDiLlBh+oMZ4dKWsp5+hfuWM2V+dVbttLQ9o7sjcmQW1I558keeIihh7b1MslUWyEDXm1b+O4NbzUIUMR3DnRUS0VCKwuwUB3c2QNxbhOcV+BE43MRqFPQ0bZrzGWGyjKtiIE2cREssP1BiPrpT15DOUC5iq3TZW4eaMpkpiT+6Zh7KwdsTRF3mubBboiIoYe2/TqZVFarUwpKaemuKU0jKMVFTAh3qBiza+jzl8fYG4OMNJcVyc53TaMhMnziLkDrW5Hl0py9WQdmeodttYhRvgvKZKzmi6wpiWlbUjjrrIc3WzQEdUxNh7m3bdXleXcC4x1Iyiulp4jR4SAHYdOTYoyHBSnJAg1Ca741i1DsSJswi5Q22ux1fKcjWkwxmrcDt+XHhNT4/zmio5o+kKYwBsqh2x90WeWRXfmY6tjnZERYy9t2nR9lpbjY9GUVMjnCidITLScNvihAT9U48zozhxFiF3qM31ikpZroZ0GGMVbqNHAx99JPw9b97lwy2mpkqM2cTG2hF7XeSZU/H9+ZPHkBWVD8kpx1VHO6Iixt7b1G7vCGFiUiMiWyoR2VKJiOZKRLRUwr+mEkk+lYidUgU0NppfUBsQABoUjJ5Bg4HBg+EXPhiSlhYgPR14+GEgMVGY3cwJxDwCmL1x4ixC7lKb6xWVslwN6RDGKtxUqsudvZubdS8QxdJUiTGbiKR2xFTF99TgY7jisw1oj29AUIrjbv04oiLG6m329gLnzw+oJZZWVmLT6UpIqioR+H2bzftsFn9//Z3uurqAt9+GpLUVkvh4+PfdsREjhM8lPd05ZYTrm/o4GyfOIuROtblcKcusYazCravr8l1Mfc38xNBUiTGbiKR2xNjvUEJqXFWdj5DOBjTL09BFEnRdBPz9QyEbnSbUQNvx1o8jKmL0bTPEvxO5I6twy+RKJB+rAt7V077YwDTQg2zcxwFCQoy3L46JMXxsc3JEUWsl9hHAHIETZws563aEO9XmcqUss5SxCjd//8t5hL//wHXF0FSJMZuIpHbE2O8wWlmOIRdLUOsnR2OxBG3tQG8P4OMLhIdJMDpWjgg73/qxS0WMSqWTCGdXViKrphIdFyrho6iEf2Ot8Lp8uxTZuOho44lxWJj17YtFUGvlDiOAOQInzhZw9u0IEfwuGHMIYxVuoaGXv+MhIbrriampEmM2EUHtiLHfYWCXEj3NHWiSBCNQBQQPBvyCge4eoL4BaFUFYXJUDULsfOvHaEUMEVBfb7zj3aVLA1aTwAG1xVKp7jTQ/ZtUxMcLF0N96FS81QPJMkBqS788F9daucMIYI7AibOZXHU7gmtzmScyVeGm+c6fOmV5ZZw3dVJhbs7FtSPGfocnq2SYpg5EaEArgsMvV0f7+wH+YUBnQxt+aQhEaogMdittT4/+aaD7TgftrGmgfXyEjnWDBwtX8MHBQqeLjAzg8ceF8YstmAbaI9oB9wuuyqZkdHRIRT0CmCNw4mwGb70dwZgjmapwAyyvjPOIkxPzKmpIUY5UKAHIACQD9ktEzWDodzg8PRk1NWnI7ClEPQ1shy2HAsXIgRTJMLtup71dSH6NTQPd2+uI3RxIJhvYdMLPD9i5Uxi7WN800CoVcPEi0N1tcdLs9u2A9QTXkUPTMLYrD02t2aIdAcwROHE2g7fejmDM0UxVuFlSGecRJyfmVcRyoafvd9jUJMXLJ/OQ2lCJYZdK0BQsR6dvEAJ62hDeqkBzUBQ+ilqCuOY+P8hLl4w3o6irc95OxcQIyXBiov5xjPVlcwUFwDvvGB7b2IoqVI+oeDMQXMPPFOKOuko83bEWNDHbKX1cW1uF6yuFQujHqVYDt91mv+2bgxNnM7jDhCQD8P1q5iaMNUcyt6mSR5ycmFcR24Ve/99aaSlQHZWNt6PXYvYv+RjWVIzI5nOQQI2WwCjUBibgf+pfRdr9lUDDr4mxSuWcwvr4CE0l9A3VpkmOAwMt364Dhgl0+4o3I8FVMiYNI1pLcEPzNjxZnInYOKlNfVybm4X1+j6qq3X/bmrSXUcu58RZlEQy5Kb5xFKNAbh9Au/mxfcabn9yYl5FlBd63d0600Ann6vEekUlfGqqEK8WJvnwU18eHzJD859qB5Rl0CDjo1EMH+6YaaDFRh5PAAAgAElEQVQdMEygW1a89WUiuIaMlmPmuWKcGFmOb2pT9TarIxL2z1RSbM111/nzQtN4C1rO2IwTZzOIZMhN85ioxlD/Yy3KQ7KdkwiaSODFnpSK6fqDGef2JyfmVVxyodd/Guj+bY1rai7PPAShnfVkO731ABER+kei0DyiolwzDbQDhgl0u4q3/kwEVxoUhEDUYMEcJaLagLNnhVUuXgRWr76cFLe2OqZ4vb1Aba3wUTkLJ85mEMmQm6aZqMZoLihB4R+34dHoTLR3Sh2bCJpI4E/dvBZbfswWbVIqttuozDi3Pzkxr2L3Cz0iIVMx1r7YSdNAQyIROtf1bTbRt51xfPzAcSbFxM7DBIqy4s3MWiu1GmjqkcG3KxDNpa1QqkPR0gq0tgixtrUVkLS0IUwdiLsOyVDmxF0IDxfOzXK5cLPEmThxNpMIhtw0zUg1RkOjBMX1cgS1FiNteDma41MdlwiakcCXPrgNR4cKbaLElpSK8jYq09E/7iclifDkxNySukeNys/L0V6rxKChMiTMTobU174/dIsv9NRqvdNA6zwcVaXXD/n5AXFxkBhqRiGXAwEBTimLw9hxmEDRVbwdOwZ6PR+tR0rQqepAtzQQKnkaCsfm4RiydZpO/PIL0N2VjM1IwwQUogRpEEbF1iCkQYEjyEE57BdcIyOFJuxyORAbe/n/mkdsrDBKoKu4TeJ8991347333kNlZSVOnDiB9F/nYd+/fz/+9re/Qa1Wo7u7G6tXr0ZeXp5DyiD6CUkMVGMQCR09lD1BGBpYg2h/Jdp8HJgIGkngCRKUtskxtLEY08eXoz5UuA8ppqSU28uKm6EmNL/5jYhOTh5ODPHYEU7tPIbax/MxuLoEvt0daPMLRGVcGoauycPo39vvar5/LaSfuhPhLdWIaKlCRHMl1GcrMTakEil3VF6eBtpZ1WqDBxvucJeQAMmwYd7xQ7LjJArOrnjr6RGus/q3KfY9cQzX/LgBAS0NqFLL0YJgBKMV8p8KEfhRJT7DWhShf2Gk2IY8JKISaSiBAnK0IQhBaIMcCjQgCtuxBGTmIIpDhgjxuW8y3Dc5jo0VmriLmdskzgsXLsSaNWswdepU7XNEhD/84Q/48ssvkZGRgXPnzmH06NG48cYbEeKgW0GinpDEQDWGUgU0XQIiAtrQrQ5Eu//l+9UOSQSN3IdUqoC61iAk+tUgqFv3PqRYklJuLytepprQ3Hwz8OOPIr4r5CHEEo/t6dTOY7i0agPCWxrQEi5HW0AwfDqFIbcurarEKay1PnlubtapHZZWVmJdSSXqfq5ExOEqRHadhxQ0cL1i2/ZJn57wKHTGJEAdn4DBYxIgSeyXIIeHu6Z9sYezV8VbV5cQ2/onxX0f58/rNFcHAEigxmbkwxcNONmn5rgZoShBGtJQgsXYhuPIHJAEFyEbG7AWS5CPMShBLGrQgUAcQQ62YwmKIAxFN3TowKS4b3IcG+v+NyMAN0qcp0+fbnDZpV+n2FSpVIiMjESAJ3wy1jDQmKqrC+jtJkRDgcohOaiX6d5SsXsiaOQ+ZFcX4NfZhl5/3QTeYWWxAreXFSdzmtAcPgw8+SRQUSHSu0IewtPisbpHjdrH8xHe0gDl8Mtfrt6gUCgHpUFWU4LaJ7Yh9abMgc02iIQvpbFmFP3H0AIQ/uvDrqRSYcQJPTXGxa0JeP2LeBwvDxbu1ABIIyBvOl9UOoupireOjstjFBsafeLCBeveOxnlGPNrjbFucwsAkEABOcaiGMkoR1m/KW18fIDG4dnYHZuJCbJyjIhQQpYggzwzGc/HSyGXC83a/fysK5u7cZvEWR+JRIJdu3bhxhtvRHBwMJqamvDOO+/A39/f4DqdnZ3o7OzU/q1y1riTzmCgMVVgVxtGdCpwKTgKP6YsAUl0A7/dE0EjvSH8/QgxPQpURQxM4B1SFiuIsjMHM7sJTUWFiO8KeTB3jseVn5djcHUJWsL1fLlA6AoJx9Az36Phb5sxRNalOyJFVZUQuJwhIMDwSBQJCUKVnp7shTs7u56m2ZixpLihwXHvL4MSgehAK3RvpUogDOXW6xOEUHUNFucqETr7cvOJuDhhLhlh9D8pYP48kR7LrRPnnp4ebNq0Cfv27cOVV16JgoIC3HDDDThx4gQiIiL0rrNp0yasX7/eySV1Ij2NqUICAtGYmINtWALfyGzdpv2OSASN9IaQ/aJAVUQUdgctgS+kji+LFUTXmYMB4CY0Yue28bijA71FPyGs/TzUPm0IaG1FYHcLArpbENjdjICeVkg0zSieKHBsWUJDjY9fPGSIxYGHOzs7nr6JO/onxXpuOjhcQMDlBDhZLQMOByJhcCu6B4XCz1dImDXjH/t3tkHaFIjfLpEhY6Hzy+pO3DpxLioqQk1NDa688koAwMSJEzF8+HAcP34cs2bN0rvOX//6V9x3333av1UqFeLi4pxSXqfp15hKIpNB1pyMpoelaHBWImigN4RkYg78Jy1B065s55XFfsXn9rIuxE1oxE208VipNN6M4sKFy+MBOLrieMgQ44lxWJjd35I7O1vP0MQd/ZNjV9woGTTI+MgTcXG6M4eXnkrGyWvSkNZWiPrggbdSZc0KlITmID2db6Wa4taJc1xcHBQKBU6fPo1Ro0ahvLwcZ86cQaqRX39AQIBbtLmzWb/GVNlwQSJooDfEaKkUa0eLPykV/SgqXoab0IibS+IxkdDo01hi7MxpoGNjBybDmjGM4+JcMlwA36nRTzP0tamaYieN8qcjOPhyMwl9I0/I5Zb34UxOlWLflXmI2l+JoZdKcClYjk7fIAT0tCGsVYHz6ihUTF2CG1L5BGeK2yTOK1euxL59+1BbW4s5c+Zg8ODBKC8vx4svvoiFCxdCKpWCiPDCCy8gNjbW1cUVJZckggZ6Q7hLUirqUVQcRKwzOnITGvEQTTz+3/8F9u1z3Pb7UPsHQjrCxDTQzpz310zeeKdG01+z/5TO/R/t7c4vm0ym235Y3+gTMpkdBjbpF8ilycmYszob28+vxeTSfIxqK4GMatAhCcQxnxz8mLYEi/+SzfHTDBIi0jMGjvdQqVSQyWRQKpUI1RdVGPMi7jDNuL4yjhnj2rsVHEfsw+LjuHIl8MIL9nnz8HB0hA5BS1M3ert60EO+aPcLgXJ4GkLv/SNSVlzllsO0qdXAffcZvlNTUiLcqdm82T0uOnt7gbo64wmxQiGM4ORskZGGa4g1zzllZEYjgfwYsrHtdTWUheXwa1OiO0iGsJxkLM6TiibG28rR8Vh8l8eMMZdwl5737nK3gjlBfLz5r+07DbS+R0gIAgH495s5MNsBMwc6kzvdqenpAWprjdcU19QIr3M2zcQd/RPhvu2Mg4KcX64BTATy7LVrkfmvbJSXp3L8tBInzoy5OXs0rXC3nvfe2ISG6ZGQIPzr6ytkMIaS4rg4s2dekPpKMeIaz/pyiaGzc1eX/tns+ibJ+ibucDSJRBhuTZMA66sldpuJO8wM5NLNmUjltsxW48SZMTdmr6YV3POeuaXrrhMyr2HDNAPNMgMceaems1N34g59NcYXLghNQ5xJMx+Mvhpizb8eNXFHn0BOkECpFC5Y/P0BWagEEg7kdsGJM2Nuyp5NK7jnPXNLISFOajTqGay5U9PWpn82u74Jcn29Y8prjK+vkAgbaj6hmbhDhP01HefXQN7QHozSk0DTJaC3B/DxBcLDgNSRQYjq4EBuK2/6SjHmMezdtMIbe94z5u1aWkwPx3bxovPL5e9veGxiTXIcEyOOZmOiIpNB1RWI4iOtaOoNRXAw4BcMdPcA9Q1AT1MbsuICEcqB3CacODPmhuzdtILHSGbMcxAJw1cbazqhULim4nHQIP21w30T5Kgo5wxgItahN62lTkrG0Y40hKsKgeGXA7m/H+AvI8hqFDjamYPpSclw4910OU6cPZSnBQSmy95NK9yp5z1j3oxImL7ZUA2x5tHS4vyyaSbuMDTyRFyc5RN3OIo7DL1pqfIKKXYF5uG20EoMu1SCpj6TnIS3KtAUGoVdAUswvELKTZxtwImzB/LEgMB0OaJphRh63jPmzTQTd5hqPuHqiTv6jz6heT40VBxJsSnuMvSmpZRK4Gf/bHyUsxZXnsnHsEslCGurQbdvIM4NycEPSUvwc1M2N3G2ESfOHsZTAwLT5aimFTxGMmOOoVYLE3eYms3OFRN3REQYbzrhtIk7nMDdht60hKZC5dSgbJy/IhPRynIM6lKi3V+GelkylM1SBLZzXxVbceLsQTw5IDBdjmxawWMkM2aZ3l5h4g5jTSdqaoDubueXLTra+MgTopm4w0k8eejNvhUqIWlS1IVd3gHuq2I/nDh7EE8OCGwgblrBmON1dwu/LWO1xOfPC8mzM/WfuENfchwbK8QEdpknD73JfVWcgxNnD+LJAYHpx00rGLNe/4k79D1qa10zccewYQOHYev7GDZMGLaNWcbTh97kChXH48TZg3h6QGD6cdMKxgZqbxeSYkNtiqurxTNxR/8xi4cO9bKJO5zIG4be5AoVx+KfpgfxhoDAGGMaR44AJ0/qT4wbG51fHkMTd/R98MQdruUtzRlEU6HigWPjcuLsLE748ogxIHjgb4YxJhJPPgm89ZZz3iswUP8sdv0n7uD4Jn4Obc7AJ73LPHRsXE6cncGJXx4xtW/y0N8MY0wk5HL7bCcoaOD4xP1rjiMi3GOMYmYehzRn4JPeZR48Ni4nzo7mgi+PGNo3efBvhjEmEnFxpl8TEqKbAOubuEMm46TYG9m1OQOf9C7z8LFxOXF2JBd+eVzZvsnDfzOMMZEYORIYN85w04nYWP0dpRmzKy856ZndCsXDx8blxNmRPPzLY4iX7jZjzMnmzxcejNmbRU2VveCkZ1ErFA8fG5cTZ0fy8C+PIV6624wxxjyAxU2VPfykZ3ErFA8fG9d97xm4g75fHn3c/MtjiJfuNmOMMTenSRILC4UOoSkpwr+FhcLzx47pWanPSY8IuKQE6uqFf4ng1ie9/q1QQkMBH5/LrVAaGoRWKGp1n5U0Y+MqFANnD9KMjTtmjNuOjcuJsyN5+JfHEC/dbcYYY27MqiQR0J70mk8pcPAHwvffA4cOAt9/Dxz8gdB8yn1Pepa0QtHSjI0bFSWsrFIBPT3CvyUlbj9YtnuW2l14+JfHEC/dbcYYY27MqiQRAKRSnPpNHn6+EIXgqhKESVUIC+lBmFSF4KoS/HwhCqcmuedJz5xWKB0delqhaMbGnTABuHhROGgXLwpj47r5CCPcxtnRxDSwshN56W4zxhhzU9Y2VVargS0/ZkMVsxZ/iMzHsEsl8GuuQbdvIGoScrDTdwlkh7Ox+Rb3y51taq4shrFxHYATZ2fw0C+PKV6624wxpotnk3ML1iaJmprqiNHZ2B2SiWhlOQZ1KdHuL0O9LBntzVL84qaDamiaXhYW6o60B1xuepmTY6QVimjm/rYfTpydxQO/PObw0t1mjDEBzybnNqxNEvvWVJNEirow3ZOeOw+qoWl6WVl5uRlLUJBwEaFQeGfTSy/aVcYYY8yJrBqigbmKtf1zPH0kKQ9urmwVrnFmjDHG7M1LZpPzNNb0z7G5OYMb4KaXl3HizBhjjNmbF8wm56ksTRK9pTkDN70UcOLsJNw3hDHGvIiHzybn6SxNEnkkKe/hNonz3Xffjffeew+VlZU4ceIE0tPTAQCdnZ1YtWoV9u/fD39/f2RnZ2PHjh0uLq0u7hvCGPMk7hyPncbDpx1mA3FzBu/gNonzwoULsWbNGkydOlXn+QceeABSqRSlpaWQSCQ4f/68i0qon8VzvDPGmMi5azx2Km9o+MoG4OYMns9tEufp06cPeK61tRWvvfYaFAoFJL8GpWHDhjm7aAZx3xDGmCdyx3jsdN7S8JUxL+M2ibM+Z86cQWRkJB5++GEcOHAAgwYNwkMPPYTZs2cbXKezsxOdnZ3av5W/ti9TqVR2L195OXDiBBATA3R3D1weEwP89BNQVMSVDoy5M038ICIXl8R1xB6PXWLkSOC++4A33wRKSy+31Rs3Dvj974XlnrKvjImEw+MxuZmEhAQ6ceIEEREdOXKEAFB+fj4RERUVFVFUVBTV1dUZXH/dunUEgB/84Ac/7P44c+aMU+KgWHA85gc/+CHWh6PisYTIvapIEhMT8cEHHyA9PR0NDQ2IiYlBV1cXfHx8AACTJk3C448/jpkzZ+pdv38Nx6VLl5CQkICqqirIXNhJQ6VSIS4uDtXV1QjV15HEy8ohprJwObgcpiiVSsTHx6OpqQlhYWEuLYszcTz2jnKIqSxcDnGWQ0xlcXQ8duumGlFRUZg9ezb279+PefPmobKyEmfPnsWoUaMMrhMQEICAgIABz8tkMpd/6QAgNDSUy9GPWMrC5eBymCL14vaqHI89vxyAeMrC5RBnOQDxlMVR8dhtovzKlSshl8uhUCgwZ84cJP/aKHjLli14/PHHMW7cOPz2t7/FSy+95N0dUhhjzME4HjPGvJXb1Dg///zzeP755wc8n5SUhK+++sr5BWKMMS/F8Zgx5q18HnrooYdcXQhX8/HxwcyZM+Hr69rrCC6HeMvC5eByuFNZ3JlYjiOXQ7xl4XKIsxxiKosjy+F2nQMZY4wxxhhzBbdp48wYY4wxxpgrceLMGGOMMcaYGThxZowxxhhjzAwelzjffffdSExMhEQiwcmTJ7XPf/LJJ8jJyUFGRgYmT56M48ePa5cdOXIEV1xxBbKzs5GWlobHH39cu6ytrQ2///3vkZycjNTUVLzzzjsuKcfSpUshl8uRlZWFrKwsrF692mHlKCgowJVXXomMjAxkZWXhiy++sPl4OKIs1h6Tjo4O3HDDDUhNTUVWVhZyc3Nx7tw5AEBdXR1yc3ORkpKC9PR0fPfdd9r1rF3mzHLMnDkTSUlJ2mPyr3/9y2Hl2LhxI0aNGgWpVIoPPvhAZ5vOPB7GyuHM43H77bdj1KhRyMrKwvTp01FUVKRdZsvvxp1xPLa9HByPOR57czy2pSwOi8kOmY/Qhb7++muqrq7WmQr24sWLFBkZScXFxURE9NVXX9HYsWO162RlZdG+ffuIiKixsZGio6Pp559/JiKi9evXU15eHhERVVRUUExMDF28eNHp5cjLy6Nnn33W4cdDrVZTbGwsffHFF0REVFJSQnK5nNra2mw6Ho4oi7XHpL29nT788ENSq9VERPTss8/S1VdfTUREt912G61bt46IiA4fPkzx8fHU3d1t0zJnlmPGjBn0/vvvO+V4HDp0iMrLy/W+pzOPh7FyOPN47Nu3T/v/999/n1JSUrTbtOV34844HttWDo7HHI+9PR7bUhZHxWSPS5w1+gaDgoICSktL01k+ePBgKiwsJCIhQObn5xMRUVVVFcXGxtL58+eJiGjMmDF0+PBh7Xo33XQTvfbaa04vh7VBydJy1NfX06BBg3SWpaen0549e4jI9uNhz7LYekw0CgoKaOTIkUREFBwcTHV1ddplEydOpC+//NKmZc4sh7WByZpyaOh7T2ceD2PlcMXxICKqr68nf39/6u3tJSL7/G7cGcdj68rB8ZjjMcdj28tCZN+Y7HFNNfRJSUlBfX09Dh06BADYu3cvWlpatFX9r732Gh588EHEx8cjNTUVmzZtwtChQwEAVVVVSEhI0G4rMTERVVVVTi8HADz11FPIyMjA/PnzdW452LMcUVFRiImJwZ49ewAAP/74I0pLS7VltOfxsLUsgH2OyTPPPIPrr78ejY2NUKvViI6OHrB/1i5zZjk0Vq9ejXHjxuGWW25BRUWFRWUwtxzGOPN4mMMVx+Pf//435s2bp53y1d6/G3fG8dj8cnA85njM8dg+ZbFnTHb9aNlOIJPJsGfPHjzwwANobm7G1KlTMWbMGPj5+QEAnnjiCTzxxBO4+eabUVFRgZkzZ2LSpEkYNWoUAEAikWi3RTYMe21LOR555BEMGzYMUqkUe/fuxbXXXouysjIMHjzY7uXYt28f7r//fjzyyCMYN24cpk6dql1mz+Nha1nscUw2btyIsrIybNmyBe3t7Tr71n//rF3mzHJs374dcXFxICI8//zzmD9/PoqLix1SDmOceTyMccXx2LFjB3bt2oVvv/1W53l7/m7cGcdjy8rB8fgyjsfeHY+tLYvdY7JZ9dJuqO/tp/46OjooLCyMysrK9N5+WrhwIW3dupWI7Htr0JZy9JeamkpHjhyxezn0GT16NB04cICI7H9r0Jay9GfpMXniiSdowoQJ1NTUpH0uKCjI4G0fa5c5sxz9BQQEUENDg0PKoaHv9pszj4excvTn6OPx3//+l5KTk6myslJnW9xUg+OxNeXQh+PxlzYtc2Y5+uN4rMuS42FtWRwRk70mca6pqdH+/+9//zvdeOONRETU09ND4eHh9NVXXxGR0A5GLpdrD+i6det0GpAPGTKEGhsbnV6O6upq7XoHDx6kyMhIunTpkt3LQUTadnxERC+99BJNmDBB2yjf1uNhz7LYckw2b95M48ePH9AZIC8vT6ejQVxcnLZzgbXLnFWO7u5uqq2t1W5j9+7dFB8f77DjoaEvQDrzeBgqh7OPx1tvvUXJycl07ty5Aduzx+/GnXE8tq4cRByPOR6bVw4NT4zH1pbFUTHZ4xLnO++8k2JjY8nHx4diYmK0DciXLVtGo0aNopEjR9Ktt96qc8Xy2Wef0fjx4ykjI4PS0tLo6aef1i5raWmhm2++mUaOHEkpKSn09ttvu6Qcs2fPpvT0dMrMzKTJkydrezY7ohwPPfQQpaSkUHJyMl1//fVUVVVl8/FwRFmsPSbV1dUEgJKSkigzM5MyMzNp0qRJRERUW1tLV199NSUnJ9OYMWO0J05bljmrHC0tLTRhwgRKT0+njIwMuuqqq6ioqMhh5di4cSPFxsaSv78/RUZGUmxsrPbK35nHw1A5nH08fH19SS6Xa9fJzMzU1qbY8rtxZxyPbS8Hx2OOx94cj20pi6NisoTIixvbMcYYY4wxZiavGFWDMcYYY4wxW3HizBhjjDHGmBk4cWaMMcYYY8wMnDgzxhhjjDFmBk6cGWOMMcYYMwMnzowxxhhjjJmBE2fmlRYtWoS///3vOs9dc8012Lx5s4tKxBhj3onjMXMnPI4z80pNTU3IysrC7t27MXHiRLz88svYvn07vvrqK0il1l9P9vT0wNfX144lZYwxz8bxmLkTrnFmXik8PBwvvvgili5ditLSUqxfvx6PPfYYfve732HSpEnIyMjA2rVrta9fvXo1Jk6ciKysLMyYMQNlZWUAgHPnziEqKgobNmzAtGnT8Oyzz7pqlxhjzC1xPGZuxew5BhnzQCtWrCCZTEYvv/wyzZ07l77++msiIuru7qZrrrmG3nnnHSIiqq+v166zc+dOuu6664iI6OzZswSA3njjDecXnjHGPAjHY+YOuKkG82pnzpzBxIkTUV1djbCwMIwdO1a7rKWlBcuWLcNf//pXvPnmm3j22WfR3NwMtVoNlUoFhUKBc+fOIS0tDW1tbZBIJC7cE8YYc28cj5k74MY/zKv5+PhAKpVCrVZDIpGgoKAAfn5+Oq+pqqrC3XffjcOHDyMpKQk//fQTrrrqKu3y4OBgDtKMMWYjjsfMHXAbZ8YAhISEYNq0aXj00Ue1z9XU1EChUECpVMLf3x9Dhw4FEeG5555zYUkZY8yzcTxmYsaJM2O/euONN1BSUoJx48Zh3LhxWLBgARobGzFu3DjcdNNNGDt2LGbOnIn4+HhXF5Uxxjwax2MmVtzGmTHGGGOMMTNwjTNjjDHGGGNm4MSZMcYYY4wxM3DizBhjjDHGmBk4cWaMMcYYY8wMnDgzxhhjjDFmBk6cGWOMMcYYMwMnzowxxhhjjJmBE2fGGGOMMcbMwIkzY4wxxhhjZuDEmTHGGGOMMTNw4swYY4wxxpgZOHFmjDHGGGPMDJw4M8YYY4wxZgZOnBljjDHGGDMDJ86MMcYYY4yZgRNnxhhjjDHGzMCJM2OMMcYYY2bgxJkxxhhjjDEzcOLMGGOMMcaYGThxZowxxhhjzAycODPGGGOMMWYGTpwZY4wxxhgzAyfOjDHGGGOMmYETZ8YYY4wxxszAiTNjjDHGGGNm4MSZMcYYY4wxM3DizBhjjDHGmBk4cWaMMcYYY8wMnDgzxhhjjDFmBk6cGWOMMcYYMwMnzoy5gaamJixbtgxxcXGuLgpjjHk9jsneixNnJjqJiYk4efKk2c9bY+7cucjIyEBWVhamTZuGoqIis5a5Snh4OF599VWMGjXK1UVhjHkZZ8TkxMREjB49GllZWcjKysJbb72lXVZWVoYpU6YgNTUVkyZNQnFxsV3e0xYck72Xr6sLwJitPvroI0yYMAExMTFmr7Nr1y6EhYUBAN59913cfvvtOHr0qMlljnThwgUsWrRI57nx48fj8ccfd/h7M8aYvVgTkwFg9+7dSE9PH/D8ihUrsHz5cixduhS7d+/GsmXLcPDgQXsV1yCOyUwfTpyZ2ysoKMDdd9+NsLAwzJs3D9deey1+85vfQCo1fENFkxgDgFKp1HmtsWX9SSQSbNy4EXv37kVDQwNeeuklfP755/jkk0/Q1dWFXbt2YezYsTrrtLe3Y+nSpThx4gT8/PwQExODTz/9FDExMThw4IA1h4AxxkTDmphsSF1dHY4ePYpPP/0UALBgwQL8+c9/xrlz55CYmDjg9RyTmaNxUw3m9tatW4fy8nLs2LED4eHhWLt2LeLi4rBo0SIolUqD6y1ZsgRxcXH4xz/+gfz8fLOX9RcaGorDhw/jsccew29/+1tMnToVx44dQ15eHh555JEBr//kk0/Q1NSE4uJiHD9+HP/973/N2s877rgDp06dwh133IEzZ86YtQ5jjDmbtTF50aJFGDduHP74xz+ivr4eAFBdXY3hw4fD11eo55NIJIiPj0dVVZXB7XBMZo7Eib9PQksAACAASURBVDPzGJ2dndoHAPj4+Bh9/bZt21BdXY2HH34Yq1evNntZf7fccgsA4RaeVCrFddddBwCYMGECKioqBrw+MzMTp06dwp133om33noLfn5+Zu3fli1boFAosGXLFowcOdKsdRhjzFUsicnffPMNjh8/jqNHjyIyMhJ5eXnaZRKJROe1RGT0fTkmM0fixJm5vb/97W9ISEjAn/70J3R3d+Opp56CQqHAtm3bIJPJTK6fl5eHL7/8Eo2NjRYt0wgMDAQgnBQCAgK0z/v4+KCnp2fA65OSklBcXIzc3Fx8//33SE9PR1NTkzm7yhhjomdNTI6PjwcA+Pn54Z577sG3334LAIiLi4NCodDGUiJCdXW19vX6cExmjmRx4tzY2IjQ0FCEhoYaveXSn1Kp1K5nLAlhzFKzZ8/GsWPH8MMPP+DBBx9ETk7OgBqKvlQqFWpqarR/7927F5GRkYiIiDC6zF4UCgUkEgn+53/+B08++aT2RMCYpayNxxrTp0+HRCLBZ5995oDSMW9laUxubW3FpUuXtH/v3LkT2dnZAIAhQ4YgOzsbO3bsAADs2bMHiYmJets3W4tjMrOExZ0Dn3jiCTQ3N+P//u//dK4cv/jiCzz33HM4ePAgGhsbER0djYyMDCxfvhz/+7//C5lMhmXLluHpp5/Gk08+iU2bNtl1R5hnmTNnjrZNGwAcOnTI4GsfeughlJSU6F127NixAeNsKpVKLFiwAO3t7ZBKpYiOjsYHH3wAiURidJm9nDhxAg888ACICGq1GosXL0ZGRobdts+8R/94/Prrr+O2224DINzeDggIQEREBMaMGYNFixZhyZIlOh20Vq1ahW+//RZr167F1Vdf7ardYG7AkTH5woULWLBgAXp7e0FESEpKwrZt27TLX3zxRSxduhQbN25EaGioyX4nluKYzCwhIVONhfro7u7G8OHD0dDQgIKCAuTk5AAQfiTr168HINwKGTlyJNrb26FQKDB9+nR89dVXAIAjR45g4sSJiIqKQk1NjdntiBhjjOnSF4/7Js5paWmQSCQ4c+aMto1pbm4u9u3bB39/f+02YmJi0NTUhKNHj2pr+RhjjOlnUVONzz77DA0NDRg2bJg2af7ss8+0SXN2djbKy8tx+vRpVFVV4ezZs7j11lu16+fk5GDo0KFoaGjgIV4YY8wG+uJxXy+88AJ+/vlnXLhwAX/4wx8ACKMHbNiwQfsaPz8/XHPNNQBg9kgCjDHmzSxKnDWN9SdOnKh97qWXXtL+/9VXX9Vpd5SQkIA//vGPOtuYNGmSzrYYY4xZTl881kcmk+GVV17BsGHDAAAvv/yyzqgEHJMZY8x8FiXOZWVlAKCTHGumvgwJCTHrNl9CQoLOthhjjFlOXzw2ZNCgQdpa6bq6OjQ0NGiXcUxmjDHzWZQ4a3pth4SEaJ+zoIk0AGFg8r7bYowxZjl98dgYtVqt93mOyYwxZj6LEmdNgG1padE+p5m6srm5GcePHze5DZVKpbOtuXPnIiMjA1lZWZg2bRqKiooACLUfU6ZMQWpqKiZNmqSt2dbn4YcfxsiRIzFy5Eg8+OCDluwSY4y5JX3x2JD29nYUFhYCAKKjoxEVFaVd1jcmczxmjDHjLEqcU1JSAACVlZXa55YvX679/7Jly3SWKRQKvPrqqzrb0CxPTk4GAOzatQs//fQTioqKsGrVKtx+++0AgBUrVmD58uUoLS3FmjVrsGzZMr1l+uabb7Bz50789NNPKC4uxscff4z9+/dbsluMMeZ29MVjfZRKJf74xz+itrYWAPD//t//0xlesW9M5njMGGPGWZQ4T506FQC0NRcAcPXVV2Pt2rXa55OTkzF69GgkJiYiISEB27dv19lGQUEBAGDatGkAgLCwMO0ypVIJqVSKuro6HD16VDsix4IFC3D27FmcO3duQJneeustLF26FMHBwQgICMDtt9+OnTt3WrJbjDHmdvTF477uvPNOjB07FjExMXjzzTcBCMPRrVu3Tud1hw8fBiDEZI7HjDFmnEUToMydOxeRkZGorq5GUVERsrKyAADr16/H9OnTtROgnDlzBpGRkbjqqqvwpz/9Sbt+YWEhzp8/j8jISJ3B9pcsWYIvv/wSgDBcUnV1NYYPH64dbF0ikSA+Ph5VVVUDOsJUVVVhxowZ2r8TExOxe/dug/vQ2dmpHdMUENr9Xbx4EZGRkXad5IIx5j2ICM3NzRg+fLjOBCOOZCgea5SUlMDf3x+RkZFIS0vDokWLsHTpUp3ydXd349NPPwUA/O53vwPA8Zgx5t4cHo/JQmvWrCEAdN9991m6Kt17770EgO6//369y19//XW69tpr6ciRIzRmzBidZTk5OfT1118PWGf+/Pm0a9cu7d8ffPABzZo1y2AZ1q1bRwD4wQ9+8MPuj+rqaovjoi1sicdERPv27SMA9Jvf/GbAMo7H/OAHP9z54ah4bNHMgQDQ2NiIESNGQCKRoKqqSmfabWOUSqV2ms2zZ88iMjJS7+sGDRqEc+fOITU1FY2NjfD19QURYdiwYTh06NCAGo6VK1ciMTERq1evBiAM+n/48GG8/vrrerffv4ZDqVQiPj4e1dXV2s42jDFmCZVKhbi4OFy6dMnsmGgP1sZjjenTp+Pbb7/F/v37MXfu3AHLOR4zxtyNo+OxRU01ACAyMlLbC9sSMplswHoqlQotLS0YPnw4AGDv3r2IjIzEkCFDkJ2djR07dmDp0qXYs2cPEhMT9Y5XetNNN+HPf/4z7rzzTvj6+mLr1q14+OGHDZYjICAAAQEBA54PDQ3lQM0Ys4mzmxdYG481vvnmG+3/OR4zxjyJo+KxxYmzPSmVSixYsADt7e2QSqWIjo7GBx98AIlEghdffBFLly7Fxo0bERoaivz8fO168+bNw4YNG5CTk4OZM2fi5ptvxrhx4wAI7fRyc3NdtUuMMeaWOB4zxphpFjfV8DQqlQoymQxKpVJvDUdvby+6u7tdUDLGmK38/f2d0lnPVBxh5uF4zJhnc0ZMdnQ8dmmNs5gREWpra3Hp0iVXF4UxZiWpVIoRI0bA39/f1UVhNuB4zJhn8ISYzImzAZogPWTIEAQFBfHQSIy5GbVajZqaGpw/fx7x8fH8G3ZjHI8Zc3+eEpM5cdajt7dXG6QNjf7BGBO/6Oho1NTUoKenB35+fq4uDrMCx2PGPIcnxGTnjNTvZjRt6IKCglxcEsaYLTS3A3t7e11cEmYtjseMeQ5PiMmcOBvhrrcRGGMC/g17Dv4sGXN/nvA75sSZMcYYY4wxM3DizBhjjDHGmBk4cWZMBJqamrBs2TLttPSMMcZcg+MxM4YTZzeTmJiIkydP6n1+yJAhOpMDfPHFF5BIJPjLX/5idF2NG2+8EQcPHgQAlJWVYcqUKUhNTcWkSZNQXFw84PUdHR244YYbkJqaiqysLOTm5uLcuXNm74tEIkFLS4vZr7eUo7dvz/cKDw/Hq6++ilGjRtmxVMaZ8xlrJCYmYvTo0cjKykJWVhbeeust7bK7774biYmJkEgkOt8vIsK0adNw9uxZh+4HY64ipnjc3/r16wf8Jk3hmCxwRTwGLPuc586di4yMDGRlZWHatGkoKirSLjMUrzkm2wcnzh4kPj4e7733nvbvrVu3Iicnx6x1Dx8+jEuXLuGKK64AAKxYsQLLly9HaWkp1qxZg2XLluldb/ny5Th9+jSKioowf/58LF++3PYd8VAXLlzAnDlzdB5r1qxxWXnM/Yw1du/ejaKiIhQVFeGWW27RPr9w4UJ89913SEhI0Hm9RCLBvffei/Xr1zuk/IyJmSviscbRo0dx6NAhxMfHW78DXsCdY/KuXbvw008/oaioCKtWrcLtt9+us1xfvOaYbB+cOJugVgP19c55qNW2lfX222/H1q1bAQBKpRKHDh1Cbm6uWeu++OKLWLRoEQCgrq4OR48exa233goAWLBgAc6ePTugNjkwMBDz5s3T9pKdPHkyKioqDL7HO++8g9GjR+OKK67AP//5T51lBQUFuOqqq5CTk4Px48djz5492mV79uzB6NGjkZ2djYcffthgTYKx7Rt7j4MHD2LatGnIzMxERkYG9u3bBwC49dZbkZOTg4yMDMyfPx91dXU27UtMTAwOHDig83j88ccNHi9THnzwQeTm5mLq1KkYNWoUZs2aZfbMauZ+xuaYPn065HK53mXXX389PvroIzQ3N1u8Xcb643hs+rfa2dmJlStX4oUXXjA5goErY7Kx7XNMNv05h4WFaf+vVCrNnsaaY7IdkJdTKpUEgJRKpfa59vZ2Ki4upvb2dqqrIwKc86irM13ehIQEOnHihMHnR48eTQqFgv7zn//QAw88QOvWraNVq1YZXZeIKCkpiYqLi4mI6MiRI5SWlqazfOLEifT1118bLdvixYvpnnvu0bvswoULFBERQadOnSIioscee4wAUHNzMzU1NVF2djbV1NQQEVF9fT3Fx8fT+fPnteuVlpYSEdG//vUv7Xrmbp+IDL7HL7/8QjExMfT9998TEVFvby81NjZqX6OxadMmWrlypU37YsqKFSsoNjaWVqxYQeXl5SZfn5ubSzNmzCCVSkVqtZpyc3Pp6aefJiKiBQsWUGZmpt5HVVWVxZ9xQkICZWRkUHp6Oi1btozq9HxZDX2/Zs2aRR9//LHJ/XGEvr9lR9IXR5jlOB4LbInHa9asoeeee87ke7gyJhvbfmNjoyhisqXxmMi5MZlIOOfK5XKSy+V08uRJ7fOm4rWnx2RHx2OeOdDDLF68GPn5+Xj33Xfxxhtv4I033jBrPYVCgaFDh2r/7l9TQURG19+4cSPKysqwZcsWvcsPHTqE8ePHa9uMLV++HPfffz8A4IcffkBFRQWuvfZanfc7ffo0lEolxo8fj5SUFADAbbfdhnvvvdei7Rt7j5dffhljxozBlClTAABSqRQREREAgDfeeAPbt29HZ2cn2tvbtcfH2n3pe3z1MXTsDCksLMSnn36KkJAQAEBmZiYaGxsBCLfpjKmrq7PoM/7mm28QHx+P7u5u/OMf/0BeXh4++ugjs8o5dOhQKBQKs17LmCdxdjw+ePAgCgoK8Oijj5p8D1fGZGPbb2lpEUVMtjQeA86NyQCwbds2AEB+fj5Wr16tjcmm4jXHZNtw4uxhli5divHjxyM1NVUb2MwRFBSE9vZ2hIeHIy4uDgqFAj09PfD19QURobq62mB7uSeffBLvvPMODhw4YHB2L2MBgIiQkZGBb775ZsCyffv2mTVguqkAY+g9PvzwQ3z99dcDXv/dd9/hueeeww8//IDo6Gi899572LBhg037Yk+VlZVobm5GZmam9rnDhw/jnnvuASC0Oy4vL9e77vvvv2/xZ6x53s/PD/fccw9SU1PNLmtHRwcGDRpk9usZ8xTOjsdff/01Tp06hREjRgAQEvBrrrkGr7zyik7iCLg2Jhvb/ocffqh3HY7JhuXl5eGOO+5AY2MjIiMjTcZrjsm24TbOHmb48OHYtGkTHnvsMYvWy8jIwKlTpwAAQ4YMQXZ2Nnbs2AFAaM+WmJiIxMTEAes99dRT2LlzJz777DOdNlf9XXHFFTh27BhKS0sBAK+88op22ZQpU1BWVoYvvvhC+1xRURG6urowefJkFBYWagNOfn6+xds39h45OTkoKSnBDz/8AABQq9W4ePEimpqaEBoaioiICHR1deHFF1+0eV+ssWTJEuzdu3fA80eOHEFnZ6e2Tfmbb76J5uZmzJ8/H4Bux5D+j7i4OIs+49bWVp12ejt37kR2drbZ+1BSUqJzMmHMWzg7Hj/wwAOoqanBuXPncO7cOcjlcuzfv39A0gy4NiYb2/6UKVM4JsP456xSqVBTU6P9e+/evYiMjERERIRZ8Zpjsm24xtmEyEigT/8Dh7+XOebMmQNf38sf3aFDh3SW33bbbRa/98KFC/Hxxx/jqquuAiB0Tlm6dCk2btyI0NBQneA4b948bNiwAUOHDsWqVauQlJSEWbNmAQACAgLw448/Dtj+kCFD8NJLL+H6669HZGQkFi5cqF0WHh6O999/H6tXr8a9996L7u5uxMfH491330VMTAy2bNmC6667DpGRkbj++uvh5+c3oGbb2PZNvcfevXuxatUqNDc3QyKR4J///CfmzZuHHTt2YPTo0ZDL5ZgyZQr2799v075Yo7CwEHfdddeA548cOYK77roLd9xxB2pra5GSkoIPP/zQ7A4igPHPGLj8OUdERGDBggXo7e0FESEpKUl7ixAAVq5ciX379qG2thZz5szB4MGDtSdVTceW9PR0K/aeMV0cjw3HY3NH7NBwZUw2tv3w8HCOySY+Z6VSiQULFqC9vR1SqRTR0dH44IMPIJFIcOHCBaPxmmOyHTik5bQbMdUZxVuoVCoaO3YstbS0uLooA6hUKu3/t27dSldeeaULS+M8jY2NNGfOHL3L5syZQ/v373dyiSx3//330yuvvOKy9+fOge6F47FAzPGYiGOyPhyTzcOdA5nHCAkJwdNPP42zZ8+K7kr0mWeewdtvv42enh5ERETg5ZdfdnWRnCIiIgKfffaZ3mVHjx61uIbJFYYPH25VjRtj3kzM8RjgmKwPx2TvISEy0avKw6lUKshkMiiVSoSGhgIQGs6fPXsWI0aMQGBgoItLyBizlrN+y/riCLMcx2PGPJszfs+OjsfcOZAxxhhjjDEzcOLMGGOMMcaYGThxZowxxhhjzAycODPGGGOMMWYGTpwZY4wxxhgzAyfOjDHGGGOMmcGliXNHRwduuOEGpKamIisrC7m5udpZbaZMmYKsrCxkZWUhPT0dEokEP/3004BtnDt3Dr6+vtrXZmVl4cyZM07eE8YYc28cjxljzDSXT4CyfPlyXHvttZBIJHjuueewfPlyfPrpp9p56gFhjvf169cjIyND7zbCwsJQVFTkrCIzxphH4njMGGPGubTGOTAwEPPmzYNEIgEATJ48GRUVFQNet3XrVixbtszZxWOMMa/B8ZgxxkwTVRvnZ555Btdff73Oc7/88gu++uor3HrrrQbXU6lUmDhxIsaPH48NGzagt7fX4Gs7OzuhUql0Hox5g6amJixbtgxxcXGuLgpzAxyPGXMcjsfuSzSJ88aNG1FWVoZHHnlE5/nXX38d8+fPR1RUlN71hg0bBoVCgYKCAhw4cADffvstNm/ebPB9Nm3aBJlMpn2425c2MTERJ0+edNj2JRIJWlpaAAD79u1DWloasrKycOLECbPW6ft/fW688UYcPHgQAFBWVoYpU6YgNTUVkyZNQnFxscnyrV+/HhKJxKJjYKpMtnL09u31XuHh4Xj11VcxatQoO5fKOHM+Z2Pta/vq//kTEaZNm4azZ886eje8Csdj83A85njsifFYY+7cucjIyEBWVhamTZumbYJlLF57RUwmEXjiiSdowoQJ1NTUpPO8Wq2mkSNH0ieffGL2tt58802aP3++weUdHR2kVCq1j+rqagJASqVS+5r29nYqLi6m9vZ2y3fGwRISEujEiRMO2z4Aam5uJiKi3Nxc2rVrl0Xr9P1/fz/++CPNmjVL+/esWbPotddeIyKit99+myZPnmz0fQoLCyk3N5fi4+MtOgbGymQPjt6+pe9VW1tLs2fP1nmsXr1au3z27NmOLqYOcz7n9vZ2+vDDD0mtVhMR0bPPPktXX321zmsMff579uyhvLw8ve/trN+yUqkcEEfcFcdj83E85njsifFYo28M2Lt3L2VnZxOR6Xjt6pjs6Hjs8sR58+bNNH78eLp48eKAZV9++SXFxcVRb2+vwfUvXLhAXV1dRCQE4YULF9KDDz5o9vvrO8A6H2xvL1FdnXMeRvZTw1CgXrRoEU2YMIHGjRtH1113HV24cIGIhB/2o48+SpMmTaLExETaunWrznp79uyhUaNG0eTJk2nDhg3aQHDXXXdRcHAwjRgxgq644gqT72FOoL799tvplVdeISLhc5PJZNTd3U1Ewkk5JiaGzp49q3fdjo4Omjx5MlVUVJg8WRnaJyKiw4cP06xZs2jChAmUnZ1Nu3fvJiKi3bt306hRoygrK4v++c9/Gt0Pa7ZPRPTDDz/Q1KlTKSMjg8aNG0fvvvuu0eNqy3sZY2mg/sc//kHXXHMNXXnllZSamkozZ84ckFQZYunnrFFQUEAjR47U/m3s8+/q6qLo6GhSqVQDtsOJs2U4HnM8JuJ4zPF4oNdff50mTJigd1n/eO3qmOzRibOmdiEpKYkyMzMpMzOTJk2apF1+66230tq1awes9+CDD9J//vMfIhK+yGPHjqWMjAwaM2YM/fnPf6aOjg6zy2AyUNfVEQHOedTVmSyvoSBVX1+v/f+mTZto5cqVRCQEzqeffpqIiIqLi2nw4MHaH82FCxcoIiKCTp06RUREjz32mE4gmDFjBr3//vtmvYc5gTopKYmKi4uJiOjIkSOUlpams3zixIn09ddf6113zZo19Nxzzxk9Bqb2qampibKzs6mmpka7P/Hx8VRZWUkRERFUWlpKRET/+te/DO6HNds/f/48NTY2UkxMDH3//fdERNTb20uNjY1Gj6u172XMihUrKDY2llasWEHl5eVGX6uRm5tLM2bMIJVKRWq1mnJzc7XfqQULFmh/u/0fVVVVFn/OGosXL6Z77rlH+7epz3/WrFn08ccfD9gOJ87m43jM8bgvjsccj4mEWCyXy0kul9PJkycNvqZvvCZybUx2dDx26XB0crkcRGRw+fbt2/U+v2HDBu3/b7zxRtx44412L5u7eeONN7B9+3Z0dnaivb0dQ4cO1S5btGgRACAtLQ2+vr6ora2FXC7HoUOHMH78eG0bq+XLl+P++++36j3MoVAodNbR9N7XMPRdOHjwIAoKCvDoo4+afA9j+/TDDz+goqIC1157rc57vvbaaxg/fjxSUlIAALfddhvuvfdeu23/9OnTaGlpwZgxYzBlyhQAgFQqRUREBADDx9Xa9zL2uWzZssXkMeyvsLAQn376KUJCQgAAmZmZaGxsBCAMTWZMXV2d2Z+zhqZ9raas5nz+Q4cOhUKhMLkvzDCOx/bD8VjA8dj94/G2bdsAAPn5+Vi9ejU++ugjneX947WGJ8dkl4/jzP4/e2ce31Sd7v/PSdM0TZd0SUuBFllKp+2oJVAEEQWVuY4g6sy4MDNSRseLXhXnXkRn/KkdZdwF7gyiMo46A3oHReVeN3QU1wEdhFpBpVDCUggFm25JaZu0ac7vj2/TJc16luSc5Hm/XnllP/menHOe7+f7fJ/n+Ypn+/btWLduHT7//HPk5eXhzTffHNaZ6fX6gcdJSUlwu90AQl8wkfxGOBgMBnR3dyM7OxtFRUWwWq1wu93QarXgeR7Hjx/HuHHjRnzv008/xf79+zFhwgQAzOBfcskleO6554YZqlD7xPM8zj77bHz22WfDXn/jjTewY8eOsPZByPYB4J133vH7nWD/q9DfkpKGhgZ0dHSgoqJi4LUvv/wS//mf/wkAuOqqq2CxWPx+96233oroOAPAqlWrsGXLFmzbtg0GgwFAeMff6XQiNTVVsv0mCKGQPR6E7LG0RNseD2XJkiW4+eab0dLSgtzcXAD+7bWXuLbJsvixVUQ8TA2++eab/NSpU3m32827XC5+/vz5A7FI8Jniys3NHYhn+v777/nc3Fz+wIEDPM+zpKChnx86NRjub/j+3lAuuOAC/sMPPxx4PmfOnGFJCjNmzAi5/4H+Ay/B9qm1tZUvKCgY1oba2lr+1KlTfE5ODn/w4EGe53n+T3/6U9CpwUi373K5Bt7znRoM9r8K/S0hLF68mN+yZcuI11977TWe47iBacT/+Z//4SsrK4PGufoS7nEOFl87FH/Hv7S01O85QaEa6oLsMdljssfKsMd2u50/ceLEwPMtW7bwY8eOHUgIDGWvY2mT5bbHiilHp1hyc4Gmpujc+kdxoZg3bx4KCwsHbmazGcXFxSgtLcUll1yCKVOmhLWd/Px8PPvss1i4cCFmzZoFjSbw6XDppZcK+o2hXHXVVXj33XcHnv/5z3/Gn//8Z5SUlODRRx/F888/P/De/PnzsXv37oh/I9g+ZWdn46233sIf/vAHVFRUoLy8HL/73e9gNBqxfv16LFiwALNmzUJnZyeSk5NHjKCFbt/j8SA7Oxv/+7//izvvvBNnn302zGYztm/fHvR/FfpbQqipqUFhYeGI13fv3o1ly5bh5ptvxllnnYXXXnsN77zzTtBzxZdwjrPVasUdd9yB9vZ2XHjhhZgyZQpmzJgR1va9ZZDOPPPMsNtEqBSyxwDIHovZPtnj8I6z3W7HlVdeibPOOgsVFRV46qmn8Pbbb4PjuJD2Ou5tsixyXEWE9HAQkuFwOPgf/vCH/OnTp2PdlBEMzf594YUX+PPOOy+GrYkuLS0t/Lx58/y+N2/ePP4f//hHlFsUGb/97W8HqgP4Qh5ndUH2OHqQPVYmarfHPB97mxzXyYFEYpGRkYE//vGPOHLkiOJGomvXrsWrr74Kt9uNnJwc/OUvf4l1k6JGTk4OPvjgA7/vffXVV6isrIxyiyJjzJgxuP7662PdDIJQFWSPlYna7TEQ/zaZ4/kIMhLiEIfDAaPRCLvdjszMTAAsqP3IkSOYMGHCsEQOgiDURbSuZX92hIgcsscEEd9E43qW2x5TjDNBEARBEARBhAEJZ4IgCIIgCIIIAxLOBEEQBEEQBBEGJJwJgiAIgiAIIgxIOAdBaA1GgiCUQYLnPscVZI8JQv3Eg02mcnR+0Ol00Gg0aGxsRF5eHnQ63Yj13QmCUDY8z8Nms4HjOCQnJ8e6OYRAyB4TRHwQLzaZhLMfNBoNJkyYgJMnT6KxsTHWzSEIQiAcx6GwsBBJSUmxbgohELLHBBE/xINNJuEcAJ1Oh3HjxsHtdqOvry/WzSEIQgDJycmqNtAEg+wxQcQH8WCTSTgHwTudoOYpBYIgh6HaJQAAIABJREFUiHiA7DFBEEqAkgMJgiAIgiAIIgxIOBMEQRAEQRBEGFCoBkEQBEHECx4PYLEAdjtgNALFxYCGfGQEIRUknAmCIAgiHqitBTZsAOrqAKcT0OuBsjJgyRLAbI516wgiLiDhTBAEQRBqp7YWWLkSvK0ZHVmFcOrToO/rRMbuGnANDUB1NYlngpAAEs4EQRAEoWY8HmDDBnQcbcY3vWVoa+DQ5waStJnINpbhrM46ZGzcCFRUUNgGQYiEriCCIAiCUDMWCxw761BrK4SthUNKCgtvTkkBbC0cam2FcPxrH4t9JghCFORxJohoQok76oSOG6FgPG12NB1zwt6Xhuzswdd1yYAuC7C3GdB0vBHpbXbylhGESEg4E0S0oMQddULHjVA4De1GtLv0yEnrRB8yR7yfk9KF9k49GtqNmBCD9hFEPEGDT4KIBv2JO6ipAXJygMmT2X1NDXu9tjbWLST8QceNUAHNWcU4oi9DnssK8PzwN3keeS4rDuvL0ZxVHJsGEkQcQcKZIOSmP3EHzc3MU5mZCSQlsfuyMvb6xo3sc4RyoONGqARjtgYfFy2BXWvC6PY66Hsc4Dxu6HscGN1eh3atCZ8UVcGYTV0+QYglpleR0+nElVdeiZKSEkyZMgU//vGPcfToUQDA3LlzMXHiREyZMgVTpkzBf//3fwfczvPPP4/Jkydj0qRJWLp0Kdxud5T2gCDCwGJh0/yFhQDHDX+P49jr+yhxR3Ek2HEje6xeiosB3QwznsmrRkPeNKT1tGKUw4K0nlYczavE+rxqpMw0o5gczgQhmpjHOC9duhSXXnopOI7DunXrsHTpUrz//vsAgLVr1+Kyyy4L+v0jR47gvvvuQ21tLfLz83HFFVfg+eefx0033RSN5hNEaOx2Fhublub/fYMBaGxknyOUQyTHraAgum2TCbLH6kSjYSH3KxvMWGmrwLRxFuQk2dHaZ0SNvRi5eRosq6J8VoKQgpheRnq9HvPnzwfX782ZOXMmDh8+HNE2XnvtNfzkJz/BqFGjwHEcbr75ZmzatCng510uFxwOx7BbzPF4gPp6YNcudk9Tv/GF0cgSyjo7/b/f1cXeNxqj2y4iOAl23Mgeqxuzma1xMrVSg33uEmyzT8c+dwmmTdfQ2icEISGKGn+uXbsWCxcuHHh+55134qyzzsK1114b0IAfO3YMZ5xxxsDz8ePH49ixYwF/45FHHoHRaBy4FRUVSbcDQqitBZYvB5YtA1asYPfLl1PSUTxRXMxiYq3+E3dgtQLl5aB5VIWR4MctIe2xyjGbgTVrgCefBFatYverV5NoJggpUYxwfvjhh3Hw4EE89NBDAIAXX3wRdXV12Lt3L84///ygU4TckPhD3reD8+Huu++G3W4fuB0/flyaHRACZezHJ74zCACbRzWZWMyswwG43ey+ro69XkXzqIrDO/+dgMctIe1xnKDRACUlwPTp7D4OT0+CiCkxj3EGgFWrVmHLli3Ytm0bDAYDAAx4HjiOw2233YYVK1agpaUFubm5w747bty4gQQWAGhoaMC4ceMC/lZKSgpSUlKk34lI8c3Y93Y23oz9ujqWsU9LpKqLYDV/q6sH32tsZO9VVjLxRS4hZeKd/06g45aQ9pggCCJMYi6c16xZg02bNmHbtm3IysoCALjdbrS0tGDUqFEAgNdffx2jRo0aYaQB4Gc/+xlmz56N6upq5OfnY/369Vi0aFFU90EQkWTsl5TEpo1EZHhnEJqb2fFLS2PxsTU1QEMDE2Br1tAKdGrDbGYD2AQ4bglrjwmCIMIkpsLZarXijjvuwMSJE3HhhRcCYB6Ijz76CAsWLIDL5YJGo4HJZMKbb7458L0bb7wRl19+OS6//HJMnDgRDzzwAM477zx4PB5cdNFF+PWvfx2rXQofqrQQX4Q7g7B6NQ2E1Ih3/juOSWh7TBAEESYcHyoILc5xOBwwGo2w2+3IzBy5VKls1NezRMCcHCauRjYMaG1l2R1x3mHHBXQ8E5qY2ZE4g/5HgiDEIrcdkWyusa+vT6pNJQYJnrEfd4Qzg+B00gwCERXIHhMEQciDKOH82Wef4brrrsPo0aOh0+mg1+txzjnn4LHHHkNra6tUbYxPEjhjPy6RouYv1fMmRED2mCAIQn4Eq7IFCxZgzZo1WLhwIb788ks4nU7YbDasW7cOLpcLF198Md577z0p2xp/eDP2p01j0/gWC7uvrARVrFcZYmcQqJ43IQKyxwRBENFBcIzz3r17cfbZZwd8v7e3F0eOHEGJwuM5FRFT5/EkRMZ+3ONbVcNgYJ5mq5XNIAQaDAWqxhHqe4RiiLUdIXtMEATBkNuOUHIgGWpCSvzVcS4vD1zz1+NhnuWamuHVOADmqa6rYzMQq1fTYErBkB2RBvofCYIQi9x2RHQ5uquvvnrYSlEAYDQace655+JXv/oVNNTZE0ogWl79SGv+Uj1vQkLIHhMEQciLaOGcn5+Pr776Cj//+c8BAK+88goKCgqwefNmfP3111i7dq3oRhKEKIKt5idHCEQkNX+pnjchIWSPCYIg5EW0cN67dy8++eSTgWVTly5dioULF+Kdd97BlClTRDcwEaAQZxkJZzW/WMYPD63G4W9KyVuNIyODVdqgk4QIAtljgiAIeREtnJuamqDT6QaeJycnw2q1QqfTDRjvhEGAAo62MzShCHc1v4qK2IlQbzWOQDHOViswbhzwzDPA/v10khBBIXtMEAQhL6KF85w5c7BgwQIsXrwYHMfhpZdewuzZs3H69OnEMtQCFLDSnaGqRw3xw9563g0Ng20dWo1Dq2WhGseO0UlChITsMUEQhLyIrqrR29uL9evX45NPPgHP87jwwgtx8803Izk5Wao2yook2ZcCyolRMQUZ8PX4t7UBd90FTJ4MJCWN/LzbzT6/ahUwfXr02zuUQAOvpibg+HE6SRSOUqpBkD0mCCLRUU05OrfbDa1WtAM76oj+gwUq4Pp6tsZFTo7/0FaHg62F8uSTVEwhLPwJz4IC9kePH6+OP9lX+Hs8wG9+QydJtBCRbKA0wZew9pggiIRHbjsi2k313XffYcqUKZgwYQIAoKamBr/97W9FN0w1RBIOMIRwiik4nVRMISy8Hv+aGiYyJ09m94cOMY/t/v3gPTza7UCTDWi3A7wnjNX8oo23Gsf06ey+o4NOkmgRJys3Jrw9JgiCkBnRwvm2227DunXrYDKZAABTp07FO++8I7phqkGgAh5aTMEf3mIKRqPE7Q2Gx8M8tLt2sXuPJ4o/LhDfBMDMTBaWkZnJRHFmJpw2Bw5v3YevPnXgy8/d+OpTBw5vrUO71sQWJlFqmIMiT5I4JNDAq6aGva4i8Zzw9pggCEJmRM/ldXR0YPbs2QPPOY5TTTydJIRbTsxH3IRTTKGyMorOULWW9wjh8W8dVYqmo0dxRDcJYzSnMJZrhBN67EYldqIKi2GGYvdOcSdJHKKGyisRkPD2mCAIQmZEC2etVove3t6B1aqsVmtirU4lUNyEKqZgiqYzVM3lPYJ4/HkeOHDcgFSPDl+dews+T8lGao8d3TojmjKLsW+/BrySNZGiTpI4RQ2VVyIg4e0xQRCEzEgSqvGTn/wEzc3NuP/++3HBBRfgzjvvlKJt6sArbkwm1gE7HKxag8PBngcRN2Yz06TTprEcL4uF3VdWRlGrBgt1KCtjr2/cqNywjSDhDHYH0N3aBc6ghzMlG01ZJWjIn46mrBJAowkUfq4sFHGShIkaQ33iLNkg4e0xQRCEzIj2OF933XWYOHEi3njjDXR1dWHDhg04//zzpWibevCKG2+oQ2MjE3OVlUw0BxE3ZjPzeMZs5UC1e9yCePx7XDxMXVacKqqEzTgynEE1q1nH/CQJA7WG+ggMtVIqZI8JgiDkRZJ6RbNmzcKsWbOk2JR6ESFuvMUUYkI4Hjclq8sg4Qzpx63oSDHh48Iq8NzI46AqTRTTkyQEag71icM4crLHBEEQ8iFYOE+fPn0gjs4fX375pdBNqxcli5tAxIPHLYDHP3V2JT5vqsL242aU8XGhiZSH2pPr4iSOnOwxQRBEdBAsnFetWgUAePvtt1FfX48bbrgBAPC3v/0NFRUV0rSOkJ948bj58fhzxcWYt0eDz1eG0ETwAPUKDoMQg4hFPcJC7aE+gKhQK6VA9pggCCI6CBbOc+bMAQDcf//9+Oijjwa8HZdddhnmzZsnTetUhtwaRRbixOMGwK/HP6QmQi2wXIWxueEQjbhjtYf6eFFDHHkQyB4TBEFEB9ExzlarFU6nE6mpqQAAl8sFq9UqumFqQ625UQDiwuMWjICaaI+KY3NDEa2443gI9fGixlArH8geEwRByIto4Xzttdfi3HPPxbXXXgsA2Lx5MxYtWiS6YWpCzblRA6jF4ybQrT9CE6k9NjcY0dy3eAn1iRPIHhMEQciLaOH84IMPYubMmfj444/B8zwefPBBLFiwQIq2qYK40l9K8rj5E8h79kjn1o+H2NxARHPf4inUJw5IdHtMEAQhN5KUo7vssstw2WWXRfw9p9OJRYsWYd++fTAYDCgoKMD69esxfvx43HDDDdixYwdSU1ORmZmJtWvXYsqUKSO2cfToURQXF+PMM88ceO3111/HpEmTRO1TuMSz/ooZ/uJecnOBkyfZ4jJSuPXjJTbXH9HetzgP9VEbiWyPCYIg5EawcL7llltw9913o6ioaMR7PM9jy5YtcLlc+MUvfhF0O0uXLsWll14KjuOwbt06LF26FO+//z6uvPJKPPvss9BqtXj77bdxzTXXoL6+3u82srKy8PXXXwvdFVHEs/6KCf7iXk6fBj79lP3Rc+YMxtKKcevHU2yuL7HYN7WE+sQpZI8JgiCig2DhfOmll2LBggXIzs7GrFmzUFBQgO7ubuzfvx///Oc/cemll2LlypVBt6HX6zF//vyB5zNnzsQf//hHAMDll18+7PWGhgZ4PB5oFNYRx7P+ijqB4l74/iLMGg1w8CCb/ve+J9StL0VsrlLLqMQq7lhJoT4So9RD7YXsMUEQRHQQLJwXLlyIhQsXYvv27fjkk09QV1cHg8GAuXPnYtWqVTCZTBFvc+3atVi4cOGI1//0pz9h/vz5AY20w+HA9OnT0dfXhyuvvBL33HMPkpKS/H7W5XLB5XIN+64YKDdKQgLFvfT0AH19bGTS3g44HMNHIkLc+mJjc5VcRoXijiPH42Hnls02/NbcjKbvbDhWYwOabTC6bGhJLcLTv3hDEYfaC9ljgiCI6CA6xnn27NmYPXu26IY8/PDDOHjwINavXz/s9ZdeegmbN2/GP//5T7/fGz16NKxWK/Lz89Ha2oprr70Wq1evxl133eX384888ggeeOAB0e31QhpFQgLFveh0gFbLRiJuNxPSQxHq1hcamyuijErUPJeJHnfc28uOT3PzcCHc1MQOwPffsxCgzk72eksLG5z5Ib//5uV7vl2xFXMS3R4TBEHIDcfzPB/rRqxatQovv/wytm3bhqysrIHXX3nlFdx777348MMPMW7cuLC2tWnTJvz973/HW2+95fd9fx6OoqIi2O12ZPqLtQgTfw7I8vLE0CiSUV8PLFsG5OQMj3vheeCLL4BTp4CUFGD27EGRzPPsT6+sBFavFqZCI1GzHg+wfHngKYYgbYmJk1rpMQbh0tXl1xsc8Hl7u2xNcSan4/ZfdQw71KdPO2A0GkXbESUQD/aYIIjExeGQ1x5LUlVDDGvWrMGmTZtGGOnNmzfj3nvvxbZt24Ia6aamJmRnZyM5ORkulwtbtmyBOYgKSUlJQUpKiqT7AFBulCQEinvhOGDyZOY19Y7z3G7p3PqRxOYKLKMSs1rfSow75vnBsIhg4nfo866uWLd6AH3vaST3OVFYqB841AUFsW6VNMSLPSYIgpCLmApnq9WKO+64AxMnTsSFF14IgBnSnTt34pe//CUKCgpwxRVXDHz+ww8/RG5uLqqrqzFmzBjcfPPN2L59O6qrq5GUlAS3242LLroI99xzT0z2R4kaRTKi4bkMFvdiswFnnw2MHs2m1U+ejE3ogYAyKnFV69sfbjc7JsEE8NBbSwv7jlJJTwfy8nA61YRvTuXBk5uHztQ8nNab0KHPw+nUPPCcZtihjgfhHG/2mCAIQg4kCdVoamrCgQMHcP7558PtdsPj8UCn00nRPtmR26UfF0Q7xiBY3Eus3fqBwkm8OBxAayvw5JMDoygBX4kt3d3Bha+vMG5ri3WLg5OVxeLiDQYmivV6dktNZfceD+ByAY8+CsyYwV5DZMetoEA5doTsMUEQiYziQzW2bNmC5cuXA2DF77/77jvcfffd2Lp1q+jGEQogFjEGoeJeYqkuBZRRiWmtb55nCi+UN3joa52dMjREIrRaIC9v8GYyBX5uMrGFc2prgRUrWLiPv+oObjc71wyGAdEMRHaoT5+Owr6HAdljgiAIeREtnB9++GHU1NRg3rx5AICKigo0NDSIbhihAGIZY6DUuBcBZVQkrfXd1zcYFhEqPtj7Wm+vdPsvNQbDcOEbSgwbjSNjy0Mh8ACosWIO2WOCIAh5ES2cNRoNcnNzh72mlmlBIgS0nrh/Iiz1FsxzmeR2wXXIhh9NtqH4sA3YFUIEt7YOJkgqkezskcLXnxj2vmYwyN8mEcXW1VbVj+wxQUSBeKlWRAhCtHDOyMjA999/D66/M/r444+RnZ0tumGEAqD1xAPjL5xk0iTm1Tx0aJjY1dhsuLvVhm+PNSP1OxvyORuMPTakO21IdffP8e8C8PeY7tFIkpL8C95AXuHcXCA5OdatHolI17GaKuaQPSYImVHy4ldEVBAtnB977DHMnz8fR44cwdy5c3Hw4MGANTsJlRHuFHdGBsukUrqqiBSPh3l4QyXHDX3Nd3GWfkb132KKXu9fBAcSw0ZjfBxHQLTrWKmRQ76QPSYIGYlZXVFCSUhSVcNut+Pzzz8Hz/OYNWvWsPqfSifi7MtEmqIJZ7GPceOY4Nq/X/mj756e8GODbTYmmj2eWLc6MEZjeOEQ3seBZg4SCZmuXyVVg0goe0wQ0ULE4ldEdFFsVY2uIQsSJCcnY86cOcPeM0QjdjHaJNoUTagpbq2Wee6OHYv+6JvnB5dLDrdihMMhT1ukQKNhoQ7hJsqZTGwp8mij9oGjWlzHEZKQ9pggognl/BD9CBbO6enpA3F0/ujr6xO6aWWSqFM0gaa4p00DmpqA48elqbjh8bB6wOEuqWyzsdq7SiUlJfxKEXl5LKlO6QI00QaOKiLh7DGhDIINpNU+yPaFcn6IfgQLZ0//FPaDDz6IlJQULF26FDzP47nnnoNWG/OVvKUl7pd+C4G/7CiPB/jNbwKPvkePZkLr7beZoQklhFtaWKk1pZKZOULw8rkmNHN56EjNQ8rYPIw+Ow+aUf2fSU+PvGyaDEjWdyXqwFElJJQ9JpRBsIE0EH+DbEnrihJqRnSM83nnnYcdO3YMe2327NnYvn27qIZFi7BiYVS39JsMDA2LaG4Gdu4EXnyRCUSXi6025713OgMmySkCb1hEKC/w0LCIlJRhmwjWZyilAoNkDmIpYvvizfvkg1JicxPCHhOxJ9BA2hvCB7CFhXzfM5nUO8imGGfVoNgYZy+tra2wWCwo7q+BarFY0NzcLLphiiLepmg8HqC9PfwFNGw2JoiVik4XOklu6OvZ2f5XkAuTYM7XPXuYs72lJbaOFkkdxGJj+yjEI2okhD0mYkuwGdjSUsC7SuX8+YMCMh5mZ9W4IhIhC6KF80MPPYSZM2di2rRpAIDa2lo8++yzohumKJQ+ReN2MyMWqDqEryhublZ2WERGRngC2HuLYlhEsD4jLw/49FM2QXH++axZsYhmkDyySMzAUayCj3NPtdQkhD0mYkuwgbTDMViJqKNjeJ8YDwl0alsRSc0o2PaLFs4//elPMXv2bOzcuRM8z+Pcc89FXl6eFG1TDiJWHhNEV1dob/DQ521t0vyuHHAcC3EJVwibTMwQKZRAfQbPAwcPsuva+3pSUmwcLZInfwsdOIpV8OSpjpiEsMdEbEVFsIF0T8/gyqb+wvXUNjvrDzWtiKRWFG77Jckayc/Px8KFC6XYlDIRM0XD8+ziiqRaxJDSUoojOXm42NVqgVOngNOnmTAyGJgaW7SIuV1zcgZj3iQg1oPQQH2G3c6iXzIyWFTL0D4j2o4WySOLhA4cxSh4SkYUTNzb40Qn1qIi2EBapxu81v2Vy4z17KxUxGlZS0WgAtsvWtFoNBq/ZZDirvyRd4rmr38F9u4dFIr5+Syu6803gRde8B8i4XbHuvWBSUvznwwXaHW5zMyRIihKajbW/QUQuM/o6WGHWadj4wTfPiOajhbJI4uEDhyFKvhEr2IjgoSxx4mKEkRFsIF0ZubgNZmRMfx7cszOEvGFSmy/aOHc0dEx8Li7uxsbN25Ej5IrKojBbAbuuosFsg7FmwyhBHJywo8NNpmA1NSBrwrWv1EYfSuhvwAC9xk6HQvNcDiAgoKRgjWajhZZIouExPYJVfC00IBgEsoeJxpKERVDBtL8vjp0ZBXCmWSAvq8LGe1WcN5rcv9+SqAjIkMltl+0cE4b4k1KS0vD8uXLMXfuXPzud78Tu2llEs14Qa02eMk03/dycwWHRYTy5sYyREIp/QUQ2PnKcUyUejzA5MnSh8FH8v+H7SCGB6iP4KBGGtsnVMHHWxWbKJJw9jiRUJKoMJux/5pqnHp8A9L310Hb2wh3sh6niypRcFcVSktBCXRE5KjE9kteGf/gwYM4fvy41JtVDmKEs8Hg3xscyENsNEalWkQob+4117CyzdEMkRgqFFtbWX8gV38R6aAgkPN17lz22GZjZZ+lcrQICVEJ6SBGLbBcQNxLJLMLQkM8ZKxiE+sY+WgT9/Y4kZBCVEh0AdTWAis3m9GSVoFpMyzI1drR4jaixl6M3M0aNgO4hhLoiAhRegWzfkQL57y8vIGYur6+PrjdbvzpT38S3TDFYjINPs7KCi8cwvvYYIhduwMQypu7axdw772sNnFRUXRCJHyFosvFVvaePt3/tSRmECo0bjqQ83XPHmkdLWJCVAI6iPdEMe5FSIiHTFVslBAjLzcJZ48TCbGiQqILYFifUa5BF1cCbzp76dghM4CrNdBQKBURCdGuYCYQ0cJ59+7dgxvTalFQUIAkEYtLKJ5bbwVuvJEJ4uTkWLdGNMFm/wBmi1tb2bnqtdVyhkj4E4onTwIHDgC7dwMzZgwfu3jbKGQQKjZu2p/zVUylIl9n0MSJ4kNURrQxnLiXDRtY7Lu3DqtYT1Gkf4oMCw0oJUZebhLOHicSYkSFhBeAkiJGhqHy6SSVN18Y/nY6WPx8njJi5EUL5xdffBH33nvvsNcefPDBEa/FDTk5sW6BpASb/bPbWfEQrRbo7R3+nhwGMpCmGzuW/VZDAxPQubmD7wkdhMoZNy0kV9KfM6iggC2mMn68hB1UqF4vLQ343/9lo5SkJOncspH+KRIuNKCkGHm5STh7nEgIHVBKfAEoMgxV5dNJtbXAxr95YK+xILnLjl6DEcZpxaj6lUYNzRdGkGMWMH7+1iqUKuAPES2ct2zZMsIo+3uNUCbBZv96ephgTknxX5JTagMZSNNxHNNcbW3AiRPsN0eNEhc/rCSvSSBn0J49bP9GjZIwRCVYr9fczP6U9nZWYrGoKLZuWYkWGlDSsZYbssdxjpABpcQXgOLCUFU+nVRbC7y4vBbn1m/ADzx10MMJJ/Q4cKQML+5dAqwxK7n5wghyzNr3NOBZVGN32hr/8fOlsT+cgoXzBx98gPfffx+NjY246667Bl63U6a7qgg2+5eczIRzdnZ0DGQwTWcysb5h9252rXV0iIsfVorXJJgzqKRk0LmUnz+yzxP0/wfq9Xieube7ulj91ezs2C19OBQJSh0q5VjLCdnjBCLSAWW4F0BbG7MBIbapqDBUlU8neTzAtidqceXelShIbkZ7WiEc2jSkuDsxpbMGBXsb8OGqalS8aFZi84UR5JjxpWVo2VqHGdiIzvmr0aUJED8f48MpWDjrdDqkp6eD47hhJZBGjx6Nu+++W5LGETLgE1OkKS7GkiUav7N/jY0sMsVfTqMcBjKUJyM1FSgvB+64g7VLTByYUrwmwZxBWVlMMDc1scOVlTX4nuD/P1CvZ7cPLt3uO1JSuVtWKcdaTsgeJxiRDCjDuQB6eoCnn2arwIYIdZAhBUE4QwwoDw52O9sVnQ4wZnLgFG63LPUeTNqxAXmaZpzKGrTHTl0mTiWXIa+lDhO3b4SlvgIlpXGinIN0evYODg2eQpRiH751WNCUNXjMlNQNCRbOc+bMwZw5c3DllVeioqJCyjYRchEgpsi8ZAmqq80jZv+mTwfOOQfYvFkmA+kj4osnFqOsTBPSk/GjH4k3ykrxmgRzBnEca1tLC4vtLiuT4P8P1Ou1tTE3fnY2s0i+Kl7FblmlHGs5IXtMBCTUBbB/P1u5KSkp7NJJEqYgjCSSLLl+A9rcnYb6b4G2dqDPDSRpgewsoGSSASancu2W81sLCh11cGT7D6OxZxSisG0fnN9agFLlCX9BDOn0eB6wOwYHOy4X0MUbMJZrRGrPyGOmlG5IsHB+9dVXcfXVV2PHjh3YsWPHiPdvueWWkNtwOp1YtGgR9u3bB4PBgIKCAqxfvx7jx49HU1MTqqqqcOjQIaSkpGD9+vWYPXu23+08//zzePTRR+HxeHDxxRfj6aefhlbgQiBxS4g4MHN1NSrWmP3aq9JSGQykHxGvKSvDzTOW4O4Gs+yeDKV4TcLxsk+ezLTsqVMS/f/+er2+PubSLi0dWbYEULVbVinHWk7IHhMBCXYBHD/ORHNmJpvOiyDUQaIUhOFEmuRnNMLRo8e+3Z1o68tEWhqQnAb0ugFbM+Bu68KUIj0ylWK3fAYFRr4NgBM2Lg3+anR1wQATGmGEMoW/IPpPHlvmAAAgAElEQVQ7vVZrJ/afyBw22DGkAjp3F5zJenTrRh4zpXRDgq3Zt99+i6uvvhq7du0a8R7nO3IKwtKlS3HppZeC4zisW7cOS5cuxfvvv4/f/e53mDlzJt577z3s2rULV111FQ4dOjTCAB85cgT33XcfamtrkZ+fjyuuuALPP/88brrpJqG7Fn+EGQemWV2BkpKRVk9yAxlExJc2NOCRa6qxfqdZ9kWnZPWahEk43tCZM4EnngAOH5awg/I9qBkZwDPPAF99xX44ztyyUh5rj0d5IpvsMRGUQBdAcTEbNAss3RMsYiTi8moCkvw8E4vxlbMM2Y4aYMygAdUlAzojD2OjFV+5KnHBxGLE/JL1MygYN6oAR/U9+N7RCZhGek76Orqgy9Sj6EyFCH8pKC5GU24Zmv9RA1tyGdLSuYHBjsPOY8xpK77JrERT5vC+RkndEMfzPC9mA83NzTD5eKj8vRYOu3fvxqJFi2CxWJCeno4jR44gr3+lvnPOOQePP/445s6dO+w7TzzxBI4ePYqnnnoKALB161Y8/vjj+OSTT8L6TYfDAaPRCLvdjkx/Lr94oL4eWLaMBQb720eHgxVrfvJJ+QOHPB5g+fLASrGuDqishOeJ1bAc1kSlpmWs62f69he+3tCoJYWH0RBPhf9ZCVFIdAB6e4HubtZkf7fublZesaFhMJzbYBj+Hd/v+3s+Zgxw5Mjw31aKHSF7TATF91prawPuuotNa/mr9+12s8+vWsVi98Ik4upwYfYLWL16mG2orwf+uKQW1x9fiSx3M9rSCuHSGpDi7kJ2pxVtWhP+VlSN//xrBUo0CjLy3kHB8eNwHj2JE6cz8a1+OtLSOWi17G/vPM1jcm8dcn9ciVEvrlbeaF0gHg+w+rpazPjHYEKk95hldVph7TbhvzOq0TbejKIiYf2h3HZE9PzZv/3bv+Grr74K+Vo4rF27FgsXLkRLSws8Hs+AkQaA8ePH49ixYyO+c+zYMZxxxhkhP+fF5XLB5XINPHc4HBG3M6pIISqUVFYgzNJImsMWlEQp+l+Cwg2iUILnO5yG1MKMDcsjK5XK80zQBhKh2m9qkf/eBqQdqwPndKJHo8f3uWXYVbYEhzLNYYlZ783tjs7f1N0dnd8RAtljIii+xq6+XvLMWUHV4QSWzLPbge90ZmytrMZ5hzZgdHsdsroa0avV42h+JT6fWIWe40DmA8uB1hjVeA4241teDn1nJ8bCAfD70NBZhNO8AQauC2cmWZFbZkLWCpXHkflgsQDbWsxonV6Ni08MP2YN+ZX4aEwVmu1mlEySMDxRYgQLZ7fbjZ6eHng8HnR3d8PruLbb7ejq6grx7ZE8/PDDOHjwINavX4/u7u4R04vBHONDPxvKgf7II4/ggQceiLh9MUGqou5KKiugJBGvIESFwwgYXPE8S8gYIUhdZnRdVgGuzIK+Vjs6NEacSi/GwRc1ePdd5rE1GJjtdzpZZ7hpE+vXNBr/4ravz38bpqAW1VgJD5phRSE6kYY0dKIQNRj7RQOeRzW+hgKspA8CzJvskD0mBCFx5qzg6nACS+YZM4qh12uwP9WMk+dWIM9uQWqPHd06I2zGYmQf24Prj69EJtcMTI5RjedQg4LSUuj1RzGxZBIKDp1CX2cjktL0MFRWgluiEKUoId5DbZtsxmvjRh6z3j4NdN3ALbewXHUlrqQoWDg/9NBDeOCBB0aUP8rMzMQdd9wR0bZWrVqFLVu2YNu2bTAYDDD01z+z2WwDXo6GhgaMGzduxHfHjRuHo0ePDjwP9Dkvd999N5YvXz7w3OFwoKioKKL2RgUpi7orqayAkkS8wtBo2Iypy8X+hpMn/YcbDH2efrAWJf/agJxTddD0OOHi9DiWVoYPxizBdzr/HlvvLbCm0QCIzAXf1BTZvnLwoAobkItm1KEMADsnO5CJOpShDHVYjI3YgwrwEUYncvCgGBYYYYcdRlhQHPE2gqFE4Uz2mBCExJmzgtdaCbNkHv/00+g6dAp9nU4kpelRPLUM83KX4M3jZmSUaYaVL4PHA/M3G1CQ3IxUcxmgiVGN53AGBToduFtuQZpSlaKEDD/UPscMgxLAW+BJiQgWzr///e/x+9//Hv/xH/+BZ555RnAD1qxZg02bNmHbtm3IGlKo9uqrr8ZTTz2F+++/H7t27cKpU6f8ZnH/7Gc/w+zZs1FdXY38/HysX78eixYtCvh7KSkpSElJEdzeqCB1UXcllRWQUcTLGavM88z2BYqdDRVKEM5r3ufhZh14PbY9aMZuH4/t+fUN+FihHlsAKIYF5aiDFYXwiuZBOFhRiB9iH4phwcEIRPwU1KIKG1AOtgJXr0YPi64MbxiX4EiWGamp7NRPTWX9mPexwTB4833ue0tNHZk/GWvIHhOCkTBWTPCEYhgl85w2B04cS0KDpwhdfBoMXCfOOFKDRaMbcDKjGrvqzMO6Nu6gBZN765A9tRCcJhIVLzHhOouUrBTDIcwOWEl+PKGIjnH2GmnvVKEXr5ciGFarFXfccQcmTpyICy+8EAAzpDt37sRjjz2GxYsXY/LkydDpdHjxxRcHMrhvvPFGXH755bj88ssxceJEPPDAAzjvvPPg8Xhw0UUX4de//rXY3YotcqwRrJRAWolFvMfDDPW//gW89BKbxevuZnkuY8cCF1wAjB4tTMj6vqck5PTYRoNRKXZk9DnRmpKGLC0rRZSsBbT9j3UaA8Z2NeLfL7DDNp51xMMErd6DPLsF6X12JJuM0JQUI6thD/LXr0SyvRlcUSGSMtPAdXVinrUGN5uUv/SuFJA9JgQhUekkwROKIUrmOW2O/gS6cqSlc8jQAr3uTHx7ugyTj9Rh6TkbwZdUYN9+zUDXdslkO4p5JzIKg6j4EyeAvXvl9fLGg1IMRQRhpUry4wlFdFWNXbt24YYbbkBdXd2weLa+QIGNCkORWdy7dgErVkie6QwgaiUk+vqY2AwkUJO/rcXoDzYg/VgdOBdLCjuZU46dJVWwZISfFOZ0St50VTAZ9XgSy9CCHHRg5HmbAQdy0YpleDJsjy3H+fe8chxw9Cizhamp/eI2id1rtcwAdnYCZ57JOsukJLZce1IScOwY649Xrhz08Or1AHdQRKUXf0a6tBSw2Vhd2giy8qVCKXaE7DERSwQWxxjEz7XNF4zG0fcPwOIejxQ/JdtczQ4UGVpR/O6TOKwtGezaPPXQ/CaIjfEqt6IiZqzkTBpUTOkkGQgUVhpi3/yZ8fJyafx4iq+qsWzZMjz33HO4+eab8dlnn2Ht2rVITU2Vom2Ji4xxwH28Bt1jStCV1S8+90cWdhDMKzv0/SGJ8gEwg0PFyFjUnQoeZsYIjWakmK3k7RjV6ASfkQZTMvPSaofckjUG5LU14uGr7egoHRSt3ltDA/Duu6ysWm8ve7+8HPjVr0YarWCdYVsbcOgQ63umTPG/4KA3VtvpZLWojUageGIxNEK8MIGM9I4dTKVPnSrdLI0KIXtMxBLR3kQ/nu9je9rQ8eZd0OT49xwnZRjQ09aIE/vsKLlqyBueIJ5em405qPR61sj0dHmTBpUy4ys1IsJKZVlAJ0qIFs69vb2YMWMG3G43MjIycM8992Du3LnDEj6I0LjdQwSophiZ+WVI+aYGHUVlcPdxcLvZ6jruXh6ZJ6ywjq7EOxuL0RmkBq2/14bM3sYcHpqI4leVRlIS6xRGhBH0C9yhcbS+sbPe9/zF0/q+lpzsJ5623ggs0wM5AQZXji6gVY+iXxtH5PnV1gIvvzxSe371FdOevn1GsM6wvp79D779kheDgX3mgQeYA3lwFk+Dm2csQWkkPWwwI11UBBw8yDzO48YJWjI81vW8pYDsMRFrRGtEn5J59r31cEKPdL4TLj+zawZ0wQk9AB9HUiDD1dkJ7NzJPjNjxqADSu6kQTUrxUCIDCuNdSlYoYgWzt44t9zcXHz99dcoLCxEQ0OD6IYplaNHWQUBKTy0Q9/r7R36KxpMwRJUowG5e1gCVRcMMKALhbCiDib84UgVvv5cxRecjHDcYKiAVsvCRiZMYFrMV8wGEqsDz/UeZLdYkOa2Q5dnRHJZMQzpmgFBGzMExs0JdRAE6gynTBkcJPjjxAmmZTmORR4NLQ5zd4MZj1xTjdKdYfawwYy0TscOWGsrC/PwnY0JMUsjVeXHWJNo9jhRUfogT0qNqD+zGJbMMpR11MCmG2nrjB1W1GVW4swz/cQI+zNcfX2sY5g+HRhSmxyA/LNTEivFmJ8HcpeX9XjYtGZTE7vZbCMfJycDf/+78H0QgGjhvGjRIrS0tOD//b//hwsuuAButxsrV66Uom2K5Le/BTZvlv93voYZK1E9UCFgLBrhhB67UYkXUaXYagnB8GqbocJUSHWDod7cpibg0UeB3FyWlKzVDjccohZFVLKaEjgnKsZB4K8znDiRheP70+8eD/DNN8yumc2DTRkq0v/8pRmrV1VAczgM6x/MSBuNLJbRah0ZJxQiAUfKyo+xJtHscSKiZLM0FKk0YnGJBm+ctwSmfzSgoL1uxEpzJz0mHJ5dhStLAihGX8PV0ACsXcsudn+oZC0BRZwHkYaV8jz7X72i158QHvq4uTnwQgBeYpALIVo4/9d//RcAtjpVS0sLnE4nMjIyRDdMqYSRnC4ZX8OMPf7igCWukpCSEn45Ln/vpaQwgdrXx/RacTELGfMVyFrRZ9tIPB7ggw+YyMnPlzBhWQ1qSsCcqFgHgb/OMJB+P3iQzaRMnTpSBw8T6Yc14a0SGcxIcxwL1/AmCOr1YQ0kpK78GGsSzR4nGmowS2Lx50Wdd6cZL56sxsz6DfhBVx2MfCOcnB61SZXYWVaFxSvMQa9PDzSwoAR2AKYMI8brU8GJyCGKtafXex602DyYZrQgN8uOFrcRNbuL0dCgid55UFzMbl9+ycpYOZ1sKt1bu/X771nnf+21g2J4+PS6eByOwZFDlBAsZfbt2xf0/fLycqGbVjTRFM6s79fAkVwCVwozkjOzIvfKBvPmpqb6L9wRLsFGvdE4BWQpbaMmNRXhnKgceaeB9PvkyWzwIpljJ1R4Smcn8KMfsYO+f39YAwk5Kj/GgkS1x4mEmsySUIL1J4vXmLHxbxV4r8aC5C47eg1GZFUWY/ESTVCR6LvN1JRi/M5Whmm2GmRMj7w8XKw9vd7zIPtoLW51b8Dohjoku53o1eqxIKsMf+9cgo0bzcLPA6czfI9wU9NgrdbduwNv89gxQfsaNjYbc5xECcHCecGCBQHf4zgOhw8fFrppRRMoESwSj204yWR6PbBnjwKmYoKgFO+H5AnLalNTEcyJylVS1J9+93iA3/xGQpEezihpxYqIBhLxsgJ8otrjuCBM96XazFKkhNOfrP5vDSyWkrA9vf63qcFG2xLov2/AD3fVIaM0fG+LEvo8iwXo2VmL/7CtRJa7GW1phXAZ0pDi7sQZthr8h7YBf/1XNSwWMzsPentZg30FbyAx3NEh7w4IxWhkMen5+ezmfZyXx6a4o4hg4XzkyBEp26EaHn+c3eRGCRdoMJTm/ZA0YTle1JQf5Cw+76vfPZ7wRPrEiazyhr/jNkJTVJihCWeUFKZyiJcV4BPVHqueCNyX0cjDilX4Qbj9yerV4Q8Kgm2Tn27Gs7uqsQQbcEFLHbgwvC1K6fPszb245OgzyOs9BrthNNKdzcjuOw6duxvJfd0oaK/DA7aFSP+3dKDDxpJ8lEha2nDx6xXEvs/z8thNQSuMyhB1SohFKRdoMJTo/ZAsYTle1FQAolVSNByRfs45zEnsTzcAgTSFGeY10oySEmFRL0KhROgdkdMsxTr8QI7+JNQ2u0vNeKSlAuPusGBCTmg7Ilufx/NAe/twr28Qz3BlSws4j4d9t8X/JjMAINrFdFJSwhPAo0ax+2jGvUoMCWcFokRR6kscO2UTQk1Fq6RoMJF+zjmsQo0/3bBnD/u+2x1IU2hgNos/+eNh+VdChQjwjshllsLS7xXyuqPl6E/C26YGzTklmBDGArxht7GdBzpOhx8j3NwcUcKcn3L58pCU5D80IoCH2JOWAcshTrElEqWEhLMCUYMojWunbIKoqWgVnw9Wxs6fbigtBbZuZc/nz/dfxk7KGZd4XdSLUDACvCNymKVw9PuHq2oxxbQB3H753NFy9CeSbrO7G7mdNpzlakLhQRtGaZqQ2d2EjO4mpDttyOhuQtrpJqR3N8F0gQ1wOcNvaLTgOFa31Z9H2J8ozsoK+2SK9YxFtJFMOLvd7oHi+/FMNOLA1CBK494pS2pKUnxFen19YN3gcLDrDGB5KkPPc7lmXOJtUa9EsceqRaB3RGqzFEq/z06rxbkfrER3UTPcowvh1KdB39eJjN014CRMtpGjPwm2TY27B92HmjFvchOKDzcBX/oJk7DZ2ApOLS1AVxcmAlgnek+lxa3RwalJhYvTozcpFUjVI1vXiZSyScDSpYNhEaNGMdEspoRWAJSejyUHoi3rd999h1/+8pdoaWnB8ePHUVNTg82bN+Oxxx6Ton2KIlqjKjWI0oRwysabmlIQwXRDTw87z72PfZFrxkWty78OJZHssaoR4R2R0iwFuw453oOLjm+AoasZn7eXwdXEoc8NJGkzkW0sw1mddciQaOpHdH/S18cE7hDhq2lqwm+7bNhrbYK+rgmjOBuyephXOL23nX1vF4DoLjoXnPT04Mly3vumJuCpp5DU0gpPViH6kgww9HUho90KLq9UPrXq4zn0TCzGhg0aRedjyYFo4Xzbbbdh3bp1WLZsGQBg6tSpWLJkSdwZ6miOqtQiShPCKRsPakqBBNMNOt2gAdbpRn431IxLrBcniCWJYo9VzxDvCF9aBnsHh54edr4bM3hwIbwjUpmlYNdhnt2CnO/rcKS3EHBwyDQCyWlArxuwtXCoTSrElH/tQ6ZEUz9D+5P9+zzoaGhHgaYJ14xuwoIZNkz8VxPwZoBY4ZaWwdH2EEb332KGXj8gfu0peTjQmo+Grjw0a/Jx2pCPzOJ8zLk6D6Xn94vi1NTwt52XB27DBmTW1SGzo7/znS5j5+vHc9heUIae+iUoHG9WbD6WHIgWzh0dHZg9e/bAc47jkJycLHaziiIWVS5Ei9IoqQdyyhJCCDarkpk5eP74LnoXasYl0WLtfEkEexwX9HtH2vc0oGVrHRo8hejiDTBwXThDY0VuiQlZUfCOBLsO9S47ejqccCalYaxpMClNlwzosgB7mwFNxxuR3mYPvZYtz7MYrBALa5htNkzpf40butTyaxLvuFA4DjAYwE8qRldGPpzpedCMyoPxB6OgGeUnVjg9HeC4QccbBxRWDDrerFbgvdeA6nLAPC7CtkSz8w3gOUzeU4OfWxvw0ahqNGaONLBKyMeSA9HCWavVore3F1z/FWe1WqGJM9UUqyoXgq+LKKuHeHbKJrL3Uk5Czap4z6f9+8OfcUnEWDtfEsEexwu1MONFVGMmNuAHqMNYrhFO6LEbldiJKiyGGXKfrsGuwxqLEbN4PUYbO8FhZDiJKdmBrtM8Tu5swNhmP9UifJ/7i7vyQ9SqRgDsD8jNHYwDTk0FvvmGGfusrMGVyfR6du90Am1t4J58EmklJQgQoT4MWR1v0eh8g+xA3w/KkNVQh+l1G/FmfgV4bvgOiMnH4nnW7zY3s1tLy+Djoc+1WladKZpIEqrxk5/8BM3Nzbj//vuxceNGPPzww1K0TTHEsspFxNdFCPXgubcalgxz9IRgEOWpdFGa6N5LuQk1qwKEP+Oihtrn0SAR7HE84D1fa9xmdM6vwDcOC1J77OjWGdGUWYx9+zXgo3S+eq/Dl5534dQ3NnCnbZiAJhSmn4JR143xju3o6TIguc+J5L5u6PqcSHZ3I4l3sw385p/yNjBSsrNDV47wPs/JGZ4wt2sXK/czebL/RLqkJODkyYg6ezWUlw1KkB0wGjkcyi+EqWkfTO0W2LIHd8B3kau2tuGCd6gQ9ve4pYWFrociyosGApBAOF933XWYOHEi3njjDXR1dWHDhg04//zzpWibYlBDlQsAIdVDx6461Ny4EY/mVaDbpZFfCAZRnrUwK1qUkvcyOoSaVQl3xkX1nZNEJII9lhuP24OGDy3oPmVHaoERZ1xcDI1WWvU69HyFRoOmrOEnpSTnq9vNDJi/sAif18xNTTArdT49MzO8esJ5eWw6SkxokgydvRLLy0Z0jvvsAM+zyQOnk91ScgxwNTbim+12HMxiYreri0Xm8Dw7j598MjwRLITTpwGXK7oLC4oWzi+88AJuuOEGzJo1a8Rr8YIaqlwACKoemls47LMVwtC5D2VjLOgYVyKvEAyiPNv3NOBFVKPGbVakKCXvZXQJNqsS6D3f2Yq2NuV1TrEgEeyxnOzfVItTj29A+vE6aHud6ErWo6GoDAV3LUHpz6UzSoLElMfDlk8OZ1GNpiblLrWcmhr2ohrIy2NCNVrI0NkrzfHme453JulRX1CGnkVLwE01j/D+Jh8xYslePWy1nWjpzYTTNTwPMwNdyIUedaeNOHgyOvvgS0sLMGZM9H5PtHBet27dCKPs7zU1o5YqF4GsMc+zurl2twEF+kbk6ezoSpJRCAZRnnxpGVq21mEGNqJz/uAPKkmUkvdS2fibyCgoYF4QpXROsSIR7LFc7N9Ui/Y7ViL7dDNOZxeiKyUNSa5OZB+qQfsdDdiPasnEs9EI6FN48G125CezBTQyupuQ0b+YRoq9CSkdNvzw9ibgdL8obm6Wz20nhuTkwRjhcEqpBRotKAEZOvtoOd48nsGY4EBhENzXtbhiz0pkuZtxCIXo4NNgQCcK22rQ9fsGrEQ1vvaJrOdQjGKUYRpqYEUZhkeh8yiEFbtRCQvk8xx6V/M2mVhIusk0/HG0V+8WLJx3796NnTt3orm5GU8//fTA63a7HT1hJgGoCVWUXgswtLU7gLZ2ICelC70ePbp1g+pBFiEYRHnaOzg0eApRin341mEZNkWpFFGqxKk1ghFoIuPQIeZkczqB6dMVPCskE4lmj6XG4/bg1OMbkH26GfYxg+qmz5AJe2oZjI11OPXERpRcXRF4Spvn2ckYJCTC+3iyzYZ3TjZB6wmx1PIpiXc0HJKSmBrpF76tyfn45lQejpzORzOXh9Np+cguycePfpGH8rn5rK/x9TCoGYk7eyFafKgIDiaEfWOCvQtH+YODB6uxARloxrdDBHAHMlGHMpShDouxEXtQAX5InRQeGmzEEoxHA8pQBysK0QUDDOhCIaxohgkvomrYd4Kh1w8KX6/49eZneoWwrzg2GJR1igkWzidOnMDu3bvR2dmJXbt2DbyemZmJv/3tb1K0TXEovvRagKFtTw/Q18sjD1Y05FfCZhyuHiQXgkGUZ08P0MUbMJZrRGrPyB9UgihV2tQawQgWQlNezo6Xw8EGXkVFCp0VkolEtMdS0vChBenH63A6e/hgn/O4oetzgk8zYIzlU7TcvQp5ozSBq0d0d4f1exwkXLY35I9xcBtzcLIvH81cPlzGPHSn5aFFOwoN3Xnoy8nHlUvzUXJev0c4O3vYhZID4HwPMFqp/Z4cSNjZezzAGWcw8bxpE6sU1NXF3vP2L15nQLgiWAjFsKC8X/iOrF3CwYpC/BD7UAwLDmK41+prmLES1ajCBpSjDmPBKsDsSarEVlMVuDFmzAsgemPtHZYDwdfuFVdcgSuuuALvvvsuLr30UinbpGgUXXotwNBW39OFCS4r2tNM2Dm5StKSMX4Jojx1OsDAdcGJ4Z5v2doiANXEtCcYoUJoSkuBo0eBSZOAU6cUOiskE4lqjyOmt5cpEx/Bm/HhHmQ69oJ36qBrYtUjkvucIz3Cq2pj025fsrIGY4C9yyoHCI3wZOdixV3agPasrg44+R2w+obAulDR/Z5c+Nlpjwdob/fv7Q30WigRvHu3zPvRjxF26OFEZ4Aiet0wwMA14uwiO84oGekFNpnMMOVUYEyXBblaOzKLjLjkrGIsi+sRlH9ED3ptNhs2btw44vUqbz0pIrr4mWbKSNGjZXwlNqIK2lzz8AglOYRgEOVpzOBxhobFRDVlDv9BpYhS1cS0y4RSywSGE0Kj0wG33MKcZkprfzRIOHvc18eS4EJUjRh43NbmdzMm74MQkRNy4TGkodeYB09ePvRF+eBG+QjgoeLYZIqohIClnnI2/BFIBAcLj2htld4TLAepqUBergfmDAvGptuhyzMiOSkD3DY9ivSdcKdmIimJReUkadl9cncXUjr1eOJZIyZcEmjLGgAJdJIEQLRwfuuttwYeO51ObN++HTNnzoxfQ60GfKaZOKMRxo5itD2oQXM0hGAQ5clZ2apYO1GFffs1ihWlqohplwEl164ON4QmOzuxBMBQEs4eL17M5r+VRkpK6NJpQx5rDAbIVU0rEXI2gongQI/VJIK9oQ7DPcBBYoIP+Bhytx78pFLUFOXCePI47Bkjpx7S26xoK67E1ItpKjUUooXzq6++Ouz5kSNHcN9994X9/dtvvx1vvvkmGhoa8M033+DMM89Ee3s75s6dO/CZrq4uHD58GE1NTcjJyRn2/U8++QTz589HyZCe8osvvkBqJGu+xyM+00xmRFkIBlGeWVVsVSyPwkWp4mPa+5HKQ6z02tUUQhOahLPHeXnybNcHHhz6snKhHTcmeOUI7+OMDMVkM6ktZ8NXBIfjDVaLCDYYQscAD02YExQTHMCQc7Vf4QcZWhxt0cLYyOL5+1IMSHJ1Ib3Niu50EwrurJK8Znk8Inl+woQJE3DgwIGwP3/VVVfhrrvuwuzZswdey8rKwtdffz3wfNWqVfj0009HGGkv5eXl2B2tQCEVE3UhGOQHzVCHKFV6bJ9UHmI11K5O9BAaIcS9Pc7PF/Y9jhu+1HK/8LV934e2fx2Apr0VvIeHS5eB9qKzYPrtjSj95TRp2x4lYjng9HhYdEww4esbD6wmERyoKkQgISx7Yv1WXccAACAASURBVNwQQ86XlsHewaGnFdDpMmEsLUPG/jqMmT4O354yIe34fmjbG+FO1qOtuBIFd1ZJWqs8nhEtnLdu3TrwuK+vDzt37gQXwUj7ggsuCPmZv/71r3jooYcEtY8YTtSFYJAfVLooVTpSeojVUrs6UUNowiXh7PFQ4ZydHTRJbtjj3Fy/SyrnAcgdsqpaWoER5TKsHBhNpBpwekVwuKEQahTB4VSF8D5W5KR2vyFvTSvE/n9xaGsH+twsjjk7i0Pp2ELkeppx/v/ch4bjGnSfssNYYMRUlZ/j0Ua0cH7iiScGN6bVYtKkSXjllVfEbnaAL774Ai0tLbjssssCfubAgQOYOnUqkpKScP311+OWW24J+FmXywWXyzXw3OFwSNZWJaHUBC9CGqT2EKspDlItITSxIOHs8bXXAgsWMDWj00X23QBotBpMuCS+RvS+A84TJ9i4YeJEYO5c4NgxNhAPJobb2tQnggPFAXuf5+SwsBCXK07siN2OjmYndrekodvF7HlyGtDrBmzNQKfDgJmmRmR0dmDCJdNj3VrVIlo4f/zxx1K0IyAvvPACqqqqoNX6b+rUqVNhtVphNBphtVoxf/58mEwmXHPNNX4//8gjj+CBBx6Qs8kxR8kJXoQ0SO0hVlscJM1W+Cfh7HFmpv8TNoHo6xvpCQ7mBf7+ezbg9C6b7KcIi2JISwsc9uAbGuF9L9wVumtrgTVr4quf9GQYcaJZD66rE9mmwetClwzosgBXcxdONOtRkmEMc7kSwh+ChfO+ffuCvl9eXi500wN0dnbilVdewZdffhnwM5lDjGZhYSF+/vOf45///GdAQ3333Xdj+fLlA88dDgeKiopEt1UpKD3Bi5AGqT3ElHinbsgexwf+RHCokIi2tkERrGS8IjiUF1iICI6UeO0nLSjGPpShDDWw8SMNeSGs2IdKaFBMReVEIFg4L1iwIOB7HMfh8OHDQjc9wKuvvoqzzz4bpaWlAT9z8uRJjBo1ChqNBh0dHXj77bfx61//OuDnU1JSkBJBDUw1oYYEL0IapPYQU+KduiF7rDwCieBQMcFqE8HhVoeQSwRHSjz3k/YODbaalmB8cwNGt9ehLa0QLq0BKe4uZHda0WEwYaupCkUdKtsxhSFYOB85ckSSBtx666144403cOrUKcybNw/p6emwWCwAgOeff96v0b3xxhtx+eWX4/LLL8frr7+OZ555BlqtFm63G1dffTWuv/56SdqmNtSS4EWIRw4PMSXeqReyx/LiXWclkOj1J4LV7AkO9FhpIlgI8dxPGo3AcZMZr+ZV4+ITGzC6vQ5ZXY3o1epxNL8SH42pwnHerJiQO7XC8bz4S3v37t348MMPwXEcLr74Ykybpp6yPQ6HA0ajEXa7fdg0oxrZtQtYsQKYPNlvwjjcbmYMVq0CplNegOrxnW709RALnW6kxNLIUZIdIXscnFAiOFBinBpEcHp64DjgQEJYzSJYCPHcT3o8wPLlzKFSXupBvsOC1B47unVGNGUWY99+DSorgdWr49umy21HRCcH/uUvf8Ef/vAH/PSnPwUA/PSnP8V9992HG2+8UXTj4oooqBElJniRCJMPuTzElHinXhLNHieKCA4lgL2P4zTqRVJk7ydj2OkNDbnbt18DR2EJDDn9DpX9MQq5i0MRINrjfPbZZ+PDDz9EXv8KTjabDRdffDH27t0rSQPlJiqeoiiVuRg62vQ3fV9Xh6iONqm6R3SIQ7ukOpTicU40e7x4MfDSS1FomEjS08NfMplEsLzI2k8qpNPz14zy8hiE3MXo/1C8xxnAgJH2Po6k4H7cE8X0XSUleMVr1rISIQ8xMZREsscmU/R/MyMjuPglEaxsZOsnFdTpyVXrPiInjYL+D6kRLZyLi4txzz334NZbbwXHcXj22WcxadIkKdqmfmKQvquEBK94zlomCCWTaPZYrHDOyAhfAJtMbMEMEsHqJ5x+MiKRqMBOT2qHSkTOYwX+H1IiWjivX78et99+O84++2wAwI9+9COsX79edMPighil78Z6ZbV4zlomCCWTaPY4N3fwsT8RHKxWcG6uZIsNEiokWD8ZcYRBnHd6ETuP4/z/EC2c8/Pz8fLLL0vRlvgjhusYx3L6Xk3LNxNEPJFo9vgXvwCuuIJEMCEMf/2koAiDIZ0ezwN2B9DTw85JYybAqbjTE+Q8jnMRINoH+ec//xn2/p2/7bbbUFlZic8++0x0w+KCoem7/lDaOsYSkaC7TRAxJ9HscWYmMHo0iWZCGnxFYmYmK1nnFYnNzUwkejw+X+zv9FqtnfjiC2DHDuBf/fdffAG0nlBvpxeJ83iAOBcBooXzU089BaPRiB07duCbb77BQw89hBUrVkjRNvXjXaXCah1Z/8i7SkV5edytY5ygu00QMYfssULxeID6elZEuL7ej/IilIAgkQgAxcVoyi3DqV1W2Gw8UlKYJkxJAWw2Hqd2WfG9SZ2dXjjOY6fTx3kc5yJAtHDWalm0x0cffYSqqipccsklcLvdohsWF3jTd00mdjU6HKy6usPBnsfpOsYJutsEEXPIHiuQ2lpW/2zZMrbyxrJl7HltbaxbRvggSCQC8ECDDViCZphQhjpk8g5oeDcyeQfKUAcbTHgRVfCIl1xRR5DzOM5FgOhWazQavPzyy3jllVdw8cUXAwB6enpENyxu8KbvTpvGKvVbLOy+slLV5VhCkaC7TRAxheyxwvAGzNbUsJIckyez+5oa9jqJZ0UhNMLAYgG2tZjx7vRqHMubhrSeVoxyWJDW04qG/Eq8N70aHzSbR3qqVYBg53EciwDRyYFPPfUUHnnkEfz7v/87xo8fj/r6elx44YVStC1+iHWZixiRoLtNEDGD7LGCiPOSXPGIVyQGWhzFamW6z1ckej3VtslmvDauAnn2waWubcZi9PZp4LSoMxdOVN3rOBUBolcOVDvRWvGLVncjiPhFKSsHqp24+h/r61lYRk6O/7WdHQ7mgXvySVWW5IpXfKtq+IpEf87SRDjUilmNMAwUv3Kg1WrFTTfdhE8++QQAcNFFF+Hpp59GUVGR2E3HDQpZhZMgiDiH7LGCiPOSXPGKkEXEhHqq1UScOo8FIVo4L168GPPnz8ff//538DyP5557DlVVVfj444+laJ/qieNVJwmCUBhkjxXE0IBZf14vlZfkimciFYmyLeOtMGK5PoSSEH0Y29vbceedd8JoNCIrKwsrVqxAe3u7FG1TPYJrQhIEQQiA7LGCiPOSXPGOVyROn87uQ4neOM6FI3wQ7XGeNGkSLBYLivsvfovFgh/+8IeiGxYPxPmqkwRBKAyyxwpiiBuS31eHjqxCOJMM0Pd1IaPdCi4vTtyQxAAUzpAYCBbOV199NTiOg8PhQEVFBWbPng0A2LFjB+bMmSNZA9UMhbgRBBENyB4rFLMZ+6+pxqnHNyB9fx20vY1wJ+txuqgSBbdWoZTckHGH6sMZqJJBSAQL58suu2zg8S9/+cuBx7/4xS/EtSiOoBA3giCiAdljZVJbC6zcbEZLWgWmzbAgV2tHi9uIGnsxcjdrUF1KU/iEgqBKBmEhWDgvWbLE7+t9fX146623BDconkiETFuCIGIP2WPlMSzHpVyDLq4EXf3vlY5N8DLO5NVUHlTJIGxExzh7OXDgAF544QVs2LABY8eOxZVXXinVplVLomTaEgShLMgexx7KcQkAeTWVh9oW64nxwEuUcO7q6sLmzZvx3HPP4fDhw+ju7sb27dspGWUIQmpCEgRBRArZY2VBOS5+IK+mMlHTKE8BAy/Bwnnp0qV4/fXXcf755+Ouu+7C/PnzMXnyZDLSfpAt05amuwiCANljJUI5Lj6ozauZSKhllKeQgZdg4bxp0yZMmzYNN910E3784x+D4zhwviMVYgDJM20VMOoiCEIZkD1WHpTj4oOavJqJhhpGeZEMvGRG8LDu5MmTuO6667By5UqMGzcO99xzD3p7e6VsGxEI76irpgbIyQEmT2b3NTXs9draWLeQIIgoQvZYeXhzXEwm1qc7HIDbze7r6hIwxyUcr6bTGXuvZiIi42I9Hg9QXw/s2sXuBS/4FsnAS2YEX7Lp6em48cYb8cUXX+C9996D0+lET08PZs2ahaefflrKNhJDoeUICYLwgeyxMqHV5IYwxKvJ80C7HWiysXuehzK8momKTKO82lpg+XJg2TJgxQp2v3y5QN+eggZeHM/7Di+E43a78X//93944YUXsHXrVqk2KysOhwNGoxF2ux2Z/qYolEZ9PTv7cnL8T6k4HMwyP/kkTXcRRJRQoh0he6wcKB0F7E9Yvhwdn9bgm94ytNk59LmBJC2QbeRxVnIdMuZWAqtXJ+CfoxD8hYCWlwuqZBAoHNlbUSzigWME2sdRUCCrHZH07NRqtbjqqqsiMtK33347xo8fD47j8O233w68Pn78eJSWlmLKlCmYMmUKXnnllYDbePDBBzFp0iRMmjQJ9913n6h9UDwKGnURBKFcyB4rB2+Oy/Tp7D4hdaFGg/0zluC7701IO1aHLI0DWRluZGkcSPv/7d17UJTX3Qfw767IIigLAqJxubwCa0CBXQFjGqMk1WiNzmTqJfomI0Yzpo19nY6taZ1MTGQSTeulaexFk6kZzYWaqhmb9E0vGWOMMzUiwRgDjRDlsq9a7rsKsor7e/94ZCsKZtnrs7vfzwyDu2efh9+ecX/8OM95zmmoxlf/TsS/JofT3BUVMpuBbduUgbctW5TvW7cOumj2yYVxH04nGSyvrePsrgULFuCZZ55xbhF7s3379mHixIl3PP7IkSMoKyvDqVOnEBERgfvuuw9Tp07FrFmzfBVyYN18uWtELKw24OpVIDIS0McCGl7uIiI3MR+TrzgcwI7PzLAlr8d/J+zGmI5qDL10HtcionA+rRBlEUuhP27G1kdZOweUF1Yy8Ml9oCraGCPghfO0adM8On7v3r1YtmwZYm6MwC5fvhxlZWUDJmq73Q673e58bLPZPPr5fnfjr66BL3dZlMtdYXOrNhF5C/Mx+UpvMTXybjP2jchHkrUWw65acSVSj2Z9Jq5c0uL/uKhGSPDZ6nYq2RhD1X/XPfbYY8jNzcWTTz6J5ubmfl/T0NCAtLQ05+P09HQ0NDQMeM5NmzZBr9c7v1JSUrwet0/xchcRBQDzMXni5mJKNFo0xRlRP6oITXFGiEbLWYYh5ObV7frj0YVxsxmOLdtwbs12VC3fgnNrtsOxefDTSTyh2urqyJEj+OKLL/D5558jISEBJSUlA7725vVKv+1ex3Xr1sFqtTq/GhsbvRazP/Re7noteT2a0woQ52jD6Eu1iHO0oSmtEK8lr8fO42YuqkFEXsN8TJ7yaTFFquLL6ciVlcCan2rxg21G/HBXEX6wzYg1P9X6dRXegE/VGEhqaioAYOjQofjxj38M4wDXblJTU1FXV+d8XF9f7zy2PzqdDjqdzqux+hMvdxGRvzEfk6e4IUxw8MYKML6ajqySjQPVOeLc2dmJjo4O5+OysjKYB+iNhQsXYvfu3ejs7ITdbseuXbuwePFif4Xqd7zcRUT+xHxM3sANYdTPm+sue3sNczVtYRHwEedVq1bh4MGDuHjxImbMmIHhw4fj73//O+bPn4/r169DRDBu3Djs2bPHecycOXNQWlqKwsJCFBcXY9GiRcjNzQUALF68GLNnzw7U2/G5YNgZk4iCE/Mx+ZJK7u2ifvhiNNdsVnbA9sYa5oNZqWP06MGffzC8ugFKMAq2BfdvrCE/4OWu6molCXENeSL/CbY8olbsx/DADWHUJRjqivJyZRQ8K0sZab5VT4/yf2rLFmD8eN/mkYCPONPgqGgpQyIiokHzwlLB5EU+WXfZy9R0tZ3lVRDy9twhIiIiCk/BsCGxijYO5IhzsPLm3CEiIiIKT2oazR2Imq62s3AOYrzcRURERJ4IlqUC1XJzKQtnIiIiojClptHcb6OGq+0snImIiIjCmFpGc10R6KvtLJyJiIiIwpwaRnODAQtnIiIiIgr4aG4w4N8RREREREQuYOFMREREROQCFs5ERERERC5g4UxERERE5AIWzkRERERELmDhTERERETkAhbOREREREQuYOFMREREROQCFs5ERERERC7gzoFERERq43Bw72MiFWLhTEREpCaVlcDu3UB1NdDdDURFAdnZQEkJYDYHOjqisMbCmYiIyFPeGiGurARKS4GWFsBgAGJigM5OoKICqK8H1q9n8UwUQCyciYiIPOGtEWKHQzlPS4tyvEajPB8bqzyurgb27AHy8zltgyhA+MkjIiJy140RYjlRAdvQkWiKy4Jt6EjIiQpl5Liy0vVz1dYqxbHB8J+iuZdGozxfVaW8jogCgiPORERE7rgxQnyprgVfXstGe70G13uAIRGxiNdnI7ezGiMGM0JstSoj1jEx/bdHRwPnzyuvI6KA4IgzERGRO2prYfusGpXNBjS3aqDTKdObdTqguVWDymYDbMcGMUKs1yvTPDo7+2/v6lLa9XrvvQciGhQWzkRERG5wtFvR1NANa08M4uOAyKHKjIrIoUB8HGDtiUZTYzcc7S6OEGdmKnOZLRZApG+biPJ8To7yOiIKiIAXzqtXr0Z6ejo0Gg1Onz4NAOju7sYjjzwCo9EIk8mE2bNno66urt/jDx8+jOjoaJhMJufXlStX/PgOiIhCA/Px4NR36NFhj8JIXf8jxCN1XejojkJ9h4sjxFqtckNhYqIy19lmA3p6lO/V1crzS5fyxkCiAAr4p2/BggU4evQo0tLS+jy/cuVKfP311zh58iTmzp2LlStXDniOnJwcnDx50vk1bNgwX4dNRBRywiYfOxzAmTNAebny3eFw6zQtcZk4F5WNJHv/I8RJdgvORuWgJW4QI8Rms7LkXEEB0NamTPNoawMKC7kUHZEKBPzmwGnTpt32XFRUFObMmeN8PGXKFLzyyite+Xl2ux12u9352GazeeW8RETBLizysRc3F9HHa/FxSgnSG+sxpqMa7TEG2COioevpQnynBe0RiTicshSm+EGOUZnNyg2F3DmQSHWC4lP46quvYt68eQO2f/3115g0aRKKiorwu9/97o7n2rRpE/R6vfMrJSXF2+ESEYWsoM7HvZuLVFQAI0cCWVnK9wo3lo6DUstG3mPG75PWoz6pADFX25Bsq0XM1TbUJRViR9J66KaY3ZuSrNUCRiNQVKR8Z9FMpAoBH3H+Nhs3bkRNTQ127NjRb/ukSZNgsVig1+thsVgwZ84cJCYmYtGiRf2+ft26dVizZo3zsc1mY/FMROSCoM7HPthcpHdKcmm9GaXN+ShIrcXIIVa0XdejwpqJhCQt/odTkolCiqo/zlu2bMGBAwfw4YcfIjo6ut/XxMbGQn9jaR6DwYAlS5bg008/HfCcOp0OsbGxfb6IiOjOgj4f37S5iECDDivQ1Ax0WAGB+5uL9E5JnlSoRVWPER9Zi1DVY0RBkZZTkolCkGpHnLdt24aysjJ89NFHiIuLG/B1Fy5cQHJyMrRaLS5duoQPPvgAK1as8GOkREShLSTy8Y3NRVquxODMaaC9Azc2K1GWjjNmRCOx273NRTglmSh8BPxjvWrVKhgMBlgsFsyYMQOZmZmwWCz4yU9+go6ODjzwwAMwmUy45557nMc8+eST+POf/wwA2L9/P3Jzc5Gfn48pU6Zg5syZeOKJJwL1doiIglZI52O9HrarUag60YnmFvTdrKQFqDrRBdtV9zcX4ZRkovCgEbl1DZ3wYrPZoNfrYbVaOW2DiNzCPOIdvuxHR48DR4rWIP6bCljvummOMwCIQH++Gu2ZhZh2fCu0Eax6iYKVr/MxswMREYW82rNavBtVgquxiRjTUY2oqzZoHD2IumrDmI5q2GMT8a5uKWrP8tciEQ2MGYKIiEKe1Qp8FWnG/xb2s3TcqEJ8WLgeX0Wa3ZniTERhRLU3BxIREXmLXq/sdfKvYWZcuDcfSdZaDLtqxZVIPZr1mbBe0iLqittTnIkoTLBwJiKikJeZqSzXXFEBjMjWoinO6GwTASwWZVdrtzYrIaKwwcKZiIhCXu9mJfX1zuWcER0NdHUpRXNiIrBURZuVOBxc3o5IjVg4ExFRWOjdrGT3bqV4Pn9emb5RWKgUzWrZrKSy8j8xdncrMWZnK4W/WmIkClcsnImIKGyofbOSykqgtFTZGdxgAGJigM5OZYpJfT24GyFRgLFwJiKisNK7WYnaOBzKSHNLizLC3LvUdGys8ri6GtizRyn81VLoE4UbfvSIiIhUoLb2P/Ovb96fBVAeGwxAVZXyOiIKDBbOREREKmC1KnOaY2L6b4+OVtq51jRR4LBwJiIiUoHetaY7O/tv7+pS2rnWNFHgsHAmIiJSgd61pi0WZW3pm/WuNZ2Tw7WmiQKJhTMREZEK9K41nZiozHW22YCeHuV7dbX61pomCkf8+BEREalE71rTBQVAW5tyI2Bbm7LWNJeiIwo8LkdHRESkImpfa5oonLFwJiIiUhm1rjVNFO749ysRERERkQs44kxEREHL4eCUBiLyHxbOREQUlCorlS2qq6uVjUGiopTl3EpKeBMdEfkGC2ciIgo6lZVAaSnQ2uxAgb4WCXFWtPboUXEiE/X1Wq5AQUQ+wcKZiIiCisOhjDTH11ViVc9ujKmvxtCeblyLiMLDcdl4p7MEe/aYkZ/PaRtE5F1MKUREFFRqa4Grn1Xih82lSGuuQGfkSPw7NgudkSOR1lyBHzaXwn6sErW1gY6UiEINC2ciIgoq1nYHHmjcDX1PCy7EZaM7MhaiHYLuyFhciMuGvqcFxY17YG13BDpUIgoxLJyJiCioJHbU4r+6q9GsMwAaTd9GjQbNOgPGdVchsYNDzkTkXSyciYgoqKTFWRGn60abPabf9jZ7NOKiupEWZ/VzZEQU6gJeOK9evRrp6enQaDQ4ffq08/mamhp85zvfgdFoxOTJk1FVVTXgOV588UVkZGQgIyMDzz33nD/CJiIKOcGSj7XxeoxKjYI+ohPtHcDVa4BDlO/tHYA+ogujUqKgjdf75OcTUfgKeOG8YMECHD16FGlpaX2ef+qpp7By5UqcOXMGzzzzDFasWNHv8UeOHEFZWRlOnTqFqqoqfPjhh/jb3/7mj9CJiEJK0OTjzEzE3pMNc5IFSQkCux2wWQG7HUhKEJiTLIidkqPshkJE5EUBL5ynTZsGg8HQ57mmpiZ8/vnnePzxxwEA8+fPx7lz51BXV3fb8Xv37sWyZcsQExMDnU6H5cuXo6yszB+hExGFlKDJx1otUFKCEemJuDeuGvfn2zBlcg/uz7fh3rhqjEhPBJYu5Vp0ROR1qlzHubGxEXfddRciIpTwNBoNUlNT0dDQgPT09D6vbWhowPTp052P09PTsW/fvgHPbbfbYbfbnY+tVmUOnM1m8+I7IKJw0ps/RCTAkXifavNxRgawZg3wzjvAmTOIurF14KW8XGDJEqWdeZ0o7Pg6H6uycAaU5HyzO3XAza/9to7atGkTNmzYcNvzKSkpg4yQiKiv1tZW6PWhN682qPLxoUPA73/v/vFEFBJ8lY9VWTinpKTAYrGgp6cHEREREBE0NjYiNTX1ttempqb2uWRYX1/f7+t6rVu3DmvWrHE+7ujoQFpaGhoaGgL6C89msyElJQWNjY2IjY0N+zjUFAvjYBzfxmq1IjU1FSNHjgxoHL7AfMz/42qKhXGoMw41xeLrfKzKwnnUqFEwm8146623sGzZMuzfvx/p6em3XRYEgIULF+JHP/oRnn76aURERGDXrl148cUXBzy3TqeDTqe77Xm9Xh/w/3QAEBsbyzhuoZZYGAfj+DbaEJxTy3zMOG6mllgYhzrjANQTi6/yccCz/KpVq2AwGGCxWDBjxgxk3rgLeufOndi5cyeMRiNefvll/OEPf3AeM2fOHJw4cQIAUFxcjEWLFiE3NxfZ2dl46KGHMHv27IC8FyKiYMZ8TER0ZxoJxbtZBsFms0Gv18NqtQb8cgvjUGcsjINxBFMswUwt/cg41BsL41BnHGqKxddxDHnhhRde8PpZg8yQIUNQXFzsvGuccagjDjXFwjgYRzDFEszU0o+MQ72xMA51xqGmWHwZR9iPOBMRERERuSLgc5yJiIiIiIIBC2ciIiIiIhewcCYiIiIickHIFc6rV69Geno6NBoNTp8+7Xz+r3/9KwoLC5GXl4cpU6bgiy++cLadOHEC9957L8xmM7Kzs/HLX/7S2dbV1YUlS5YgMzMTRqMRBw4cCEgcy5Ytg8FggMlkgslkwtq1a30WR3l5Oe677z7k5eXBZDLh0KFDHveHL2Jxt0+6u7vxyCOPwGg0wmQyYfbs2c5NG5qamjB79mxkZWVh4sSJOHr0qPM4d9v8GUdxcTHGjRvn7JNf/epXPotj48aNGD9+PLRaLT744IM+5/Rnf9wpDn/2x/LlyzF+/HiYTCZMmzYNJ0+edLZ58rkJZszHnsfBfMx8HM752JNYfJaTJcR88skn0tjYKGlpafLll1+KiEhbW5skJCRIVVWViIgcPnxYJkyY4DzGZDLJwYMHRUSktbVVkpKS5KuvvhIRkQ0bNkhJSYmIiJw9e1aSk5Olra3N73GUlJTI9u3bfd4fDodDxo4dK4cOHRIRkerqajEYDNLV1eVRf/giFnf75MqVK/KXv/xFHA6HiIhs375dZs6cKSIiTzzxhDz//PMiInL8+HFJTU2Va9euedTmzzimT58u77//vl/649ixY1JbW9vvz/Rnf9wpDn/2x8GDB53/fv/99yUrK8t5Tk8+N8GM+dizOJiPmY/DPR97EouvcnLIFc69bk4G5eXlkp2d3ad9+PDhUlFRISJKgty9e7eIiDQ0NMjYsWPlwoULIiKSk5Mjx48fdx63cOFCeeONN/weh7tJabBxNDc3y7Bhw/q0TZw4Ufbv3y8inveHN2PxtE96lZeXS0ZGhoiIlL2RLQAABwZJREFUxMTESFNTk7OtqKhIPv74Y4/a/BmHu4nJnTh69fcz/dkfd4ojEP0hItLc3CyRkZFy/fp1EfHO5yaYMR+7FwfzMfMx87HnsYh4NyeH3FSN/mRlZaG5uRnHjh0DALz33nu4fPmyc6j/jTfewHPPPYfU1FQYjUZs2rQJo0ePBgA0NDQgLS3Nea709HQ0NDT4PQ4A2LZtG/Ly8jB37tw+lxy8GUdiYiKSk5Oxf/9+AMBnn32GM2fOOGP0Zn94GgvgnT559dVXMW/ePLS2tsLhcCApKem29+dumz/j6LV27Vrk5ubi0UcfxdmzZwcVg6tx3Ik/+8MVgeiPX//615gzZ45zy1dvf26CGfOx63EwHzMfMx97JxZv5uTAr5btB3q9Hvv378fPf/5zXLp0CVOnTkVOTg6GDh0KANi8eTM2b96MRYsW4ezZsyguLsbkyZMxfvx4AIBGo3GeSzxY9tqTOF566SWMGTMGWq0W7733Hr73ve+hpqYGw4cP93ocBw8exM9+9jO89NJLyM3NxdSpU51t3uwPT2PxRp9s3LgRNTU12LFjB65cudLnvd36/txt82ccb775JlJSUiAi+O1vf4u5c+eiqqrKJ3HciT/7404C0R9vvfUW3n33XXz66ad9nvfm5yaYMR8PLg7m4/9gPg7vfOxuLF7PyS6NSwehmy8/3aq7u1vi4uKkpqam38tPCxYskF27domIdy8NehLHrYxGo5w4ccLrcfTn7rvvlo8++khEvH9p0JNYbjXYPtm8ebMUFBRIe3u787no6OgBL/u42+bPOG6l0+mkpaXFJ3H06u/ymz/7405x3MrX/fHHP/5RMjMzpb6+vs+5OFWD+didOPrDfPyxR23+jONWzMd9DaY/3I3FFzk5bArn8+fPO//97LPPyve//30REenp6ZH4+Hg5fPiwiCjzYAwGg7NDn3/++T4TyEeNGiWtra1+j6OxsdF53D//+U9JSEiQjo4Or8chIs55fCIir732mhQUFDgn5XvaH96MxZM+2bp1q0yaNOm2mwFKSkr63GiQkpLivLnA3TZ/xXHt2jW5ePGi8xz79u2T1NRUn/VHr/4SpD/7Y6A4/N0fe/fulczMTKmrq7vtfN743AQz5mP34hBhPmY+di2OXqGYj92NxVc5OeQK56efflrGjh0rQ4YMkeTkZOcE8hUrVsj48eMlIyNDHn/88T5/sfzjH/+QSZMmSV5enmRnZ8srr7zibLt8+bIsWrRIMjIyJCsrS/70pz8FJI7vfve7MnHiRMnPz5cpU6Y472z2RRwvvPCCZGVlSWZmpsybN08aGho87g9fxOJunzQ2NgoAGTdunOTn50t+fr5MnjxZREQuXrwoM2fOlMzMTMnJyXH+4vSkzV9xXL58WQoKCmTixImSl5cnDz74oJw8edJncWzcuFHGjh0rkZGRkpCQIGPHjnX+5e/P/hgoDn/3R0REhBgMBucx+fn5ztEUTz43wYz52PM4mI+Zj8M5H3sSi69yskYkjCfbERERERG5KCxW1SAiIiIi8hQLZyIiIiIiF7BwJiIiIiJyAQtnIiIiIiIXsHAmIiIiInIBC2ciIiIiIhewcKaw9Nhjj+HZZ5/t89ysWbOwdevWAEVERBSemI8pmHAdZwpL7e3tMJlM2LdvH4qKivD666/jzTffxOHDh6HVuv/3ZE9PDyIiIrwYKRFRaGM+pmDCEWcKS/Hx8di5cyeWLVuGM2fOYMOGDfjFL36BxYsXY/LkycjLy8P69eudr1+7di2KiopgMpkwffp01NTUAADq6uqQmJiI0tJS3H///di+fXug3hIRUVBiPqag4vIeg0Qh6KmnnhK9Xi+vv/66PPTQQ/LJJ5+IiMi1a9dk1qxZcuDAARERaW5udh5TVlYmDz/8sIiInDt3TgDI22+/7f/giYhCCPMxBQNO1aCw9s0336CoqAiNjY2Ii4vDhAkTnG2XL1/GihUrsG7dOrzzzjvYvn07Ll26BIfDAZvNBovFgrq6OmRnZ6OrqwsajSaA74SIKLgxH1Mw4OQfCmtDhgyBVquFw+GARqNBeXk5hg4d2uc1DQ0NWL16NY4fP45x48bh1KlTePDBB53tMTExTNJERB5iPqZgwDnORABGjBiB+++/Hy+//LLzufPnz8NiscBqtSIyMhKjR4+GiOA3v/lNACMlIgptzMekZiyciW54++23UV1djdzcXOTm5mL+/PlobW1Fbm4uFi5ciAkTJqC4uBipqamBDpWIKKQxH5NacY4zEREREZELOOJMREREROQCFs5ERERERC5g4UxERERE5AIWzkRERERELmDhTERERETkAhbOREREREQuYOFMREREROQCFs5ERERERC5g4UxERERE5AIWzkRERERELmDhTERERETkgv8HMTwjx7OrLm4AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(2,2); fig.set_size_inches(7,7); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=64):\n",
" tmp.extend([lat_LMI_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))]) #\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[0][0].plot(X,Y,'o',color='b',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][0].plot(x_trend,y_trend,'-',lw=3,color='b',label='LMI ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C0', alpha=.2, edgecolor='none', interpolate=True)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=64):\n",
" tmp.extend([lat_LI_inverse[CCC]])\n",
" #tmp = np.asarray(tmp)\n",
" #tmp [(tmp>=33)] = np.nan\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))])#\n",
"tmp_LI = np.asarray(Y)\n",
"_=ax[0][0].plot(X,Y,'o',color='r',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][0].plot(x_trend,y_trend,'-',lw=3,color='r',label='Landfall ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C1', alpha=.2, edgecolor='none', interpolate=True)\n",
"_=ax[0][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][0].legend(fontsize=fontsize,loc=2)\n",
"_=ax[0][0].set_xticks(range(1980,2021,5))\n",
"_=ax[0][0].set_ylim([16,30])\n",
"_=ax[0][0].set_xlim([1980,2020])\n",
"_=ax[0][0].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[0][0].set_ylabel('Absolute latitude (deg)',fontsize=fontsize)\n",
"_=ax[0][0].text(1973,30,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[0][0].set_title('LMI ≥ 33 m s$^{-1}$',fontsize=fontsize)\n",
"\n",
"#----------------------------------------------------------------------------------------------------------\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=96):\n",
" tmp.extend([lat_LMI_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))]) #\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[0][1].plot(X,Y,'o',color='b',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][1].plot(x_trend,y_trend,'-',lw=3,color='b',label='LMI ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C0', alpha=.2, edgecolor='none', interpolate=True)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=96):\n",
" tmp.extend([lat_LI_inverse[CCC]])\n",
" #tmp = np.asarray(tmp)\n",
" #tmp [(tmp>=33)] = np.nan\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))])#\n",
"tmp_LI = np.asarray(Y)\n",
"_=ax[0][1].plot(X,Y,'o',color='r',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][1].plot(x_trend,y_trend,'-',lw=3,color='r',label='Landfall ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C1', alpha=.2, edgecolor='none', interpolate=True)\n",
"_=ax[0][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][1].legend(fontsize=fontsize,loc=2)\n",
"_=ax[0][1].set_xticks(range(1980,2021,5))\n",
"_=ax[0][1].set_ylim([16,30])\n",
"_=ax[0][1].set_xlim([1980,2020])\n",
"_=ax[0][1].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[0][1].set_ylabel('Absolute latitude (deg)',fontsize=fontsize)\n",
"_=ax[0][1].text(1973,30,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[0][1].set_title('LMI ≥ 50 m s$^{-1}$',fontsize=fontsize)\n",
"\n",
"#----------------------------------------------------------------------------------------------------------\n",
"\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=64):\n",
" tmp.extend([lat_LMI_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))]) #len(tmp)\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1][0].plot(X,Y,'o',color='b',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][0].plot(x_trend,y_trend,'-',lw=3,color='b',label='LMI ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C0', alpha=.2, edgecolor='none', interpolate=True)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=64):\n",
" tmp.extend([lat_LI_inverse[CCC]])\n",
" #tmp = np.asarray(tmp)\n",
" #tmp [(tmp>=33)] = np.nan\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))])#sum(~np.isnan(tmp))\n",
"tmp_LI = np.asarray(Y)\n",
"_=ax[1][0].plot(X,Y,'o',color='r',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][0].plot(x_trend,y_trend,'-',lw=3,color='r',label='Landfall ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C1', alpha=.2, edgecolor='none', interpolate=True)\n",
"_=ax[1][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][0].legend(fontsize=fontsize,loc=2)\n",
"_=ax[1][0].set_xticks(range(1980,2021,5))\n",
"_=ax[1][0].set_ylim([10,30])\n",
"_=ax[1][0].set_xlim([1980,2020])\n",
"_=ax[1][0].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[1][0].set_ylabel('Absolute latitude (deg)',fontsize=fontsize)\n",
"_=ax[1][0].text(1972,30,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[1][0].set_title('LI ≥ 33 m s$^{-1}$',fontsize=fontsize)\n",
"\n",
"#----------------------------------------------------------------------------------------------------------\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=96):\n",
" tmp.extend([lat_LMI_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))]) #len(tmp)\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1][1].plot(X,Y,'o',color='b',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][1].plot(x_trend,y_trend,'-',lw=3,color='b',label='LMI ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C0', alpha=.2, edgecolor='none', interpolate=True)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][1][-1]>=96):\n",
" tmp.extend([lat_LI_inverse[CCC]])\n",
" #tmp = np.asarray(tmp)\n",
" #tmp [(tmp>=33)] = np.nan\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([sum(~np.isnan(tmp))])#sum(~np.isnan(tmp))\n",
"tmp_LI = np.asarray(Y)\n",
"_=ax[1][1].plot(X,Y,'o',color='r',lw=1,alpha=0.6)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][1].plot(x_trend,y_trend,'-',lw=3,color='r',label='Landfall ('+str(round(slope*10,1))+' deg decade$^{-1}$, $p$='+str(round(p,2))+')')\n",
"#_=ax.fill_between(x_trend, ci_down, ci_up, facecolor='C1', alpha=.2, edgecolor='none', interpolate=True)\n",
"_=ax[1][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][1].legend(fontsize=fontsize,loc=2)\n",
"_=ax[1][1].set_xticks(range(1980,2021,5))\n",
"_=ax[1][1].set_ylim([10,30])\n",
"_=ax[1][1].set_xlim([1980,2020])\n",
"_=ax[1][1].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[1][1].set_ylabel('Absolute latitude (deg)',fontsize=fontsize)\n",
"_=ax[1][1].text(1972,30,'(D)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[1][1].set_title('LI ≥ 50 m s$^{-1}$',fontsize=fontsize)\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp_8.png',dpi=600,bbox_inches='tight')\n",
"#plt.savefig('/home/sw1013/www/FIGURES/Figure_1.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 "
]
}
],
"source": [
"#fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"#Afig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"THRESs = np.arange(64,117)\n",
"\n",
"MEAN_LI = []\n",
"CI95_LI = []\n",
"MEAN_LI_P = []\n",
"CI95_LI_P = []\n",
"for THRES in THRESs:\n",
" print(THRES,end=' ')\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=THRES)])\n",
" sample_weight.extend([1])\n",
" tmp_LMI = np.asarray(Y)\n",
" x_trend, y_trend,slope,ci = get_trend_and_ci_weight_CITHRES(X,Y,sample_weight,0.95)\n",
" MEAN_LI.extend([round(slope*10,2)])\n",
" CI95_LI.extend([round(ci *10,2)])\n",
" pred, mean, ci, p = poisson_reg(X,Y)\n",
" MEAN_LI_P.extend([round(np.log(2)/mean,2)])\n",
" if abs(mean)=THRES)])\n",
" sample_weight.extend([1])\n",
" tmp_LMI = np.asarray(Y)\n",
" x_trend, y_trend,slope,ci = get_trend_and_ci_weight_CITHRES(X,Y,sample_weight,0.95)\n",
" MEAN_LMI.extend([round(slope*10,2)])\n",
" CI95_LMI.extend([round(ci *10,2)])\n",
" pred, mean, ci,p = poisson_reg(X,Y)\n",
" MEAN_LMI_P.extend([round(np.log(2)/mean,2)])\n",
" if abs(mean)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"#+++++++++++++++++++\n",
"THRESs = np.arange(64,117)\n",
"THRESs = THRESs+1\n",
"#+++++++++++++++++++\n",
"\n",
"_=ax.plot(THRESs*0.5144444,MEAN_LI_P,':',color='k' ,ms=3,lw=2,zorder=10)\n",
"tmp_X = copy.copy(THRESs.astype(float))\n",
"tmp_X[CI95_LI_P==False]=np.nan\n",
"tmp_Y = copy.copy(MEAN_LI_P)\n",
"tmp_Y[CI95_LI_P==False]=np.nan\n",
"lns3=ax.plot(tmp_X*0.5144444, tmp_Y,'-',color='k',ms=3,lw=2,label='LI',zorder=10,alpha=1)\n",
"\n",
"_=ax.plot(THRESs*0.5144444,MEAN_LMI_P,':',color='b' ,ms=3,lw=2,zorder=10)\n",
"tmp_X = copy.copy(THRESs.astype(float))\n",
"tmp_X[CI95_LMI_P==False]=np.nan\n",
"tmp_Y = copy.copy(MEAN_LMI_P)\n",
"tmp_Y[CI95_LMI_P==False]=np.nan\n",
"lns4=ax.plot(tmp_X*0.5144444, tmp_Y,'-',color='b',ms=3,lw=2,label='LMI',zorder=10,alpha=1)\n",
"\n",
"_=ax.legend(loc=1,fontsize=fontsize)#,bbox_to_anchor=(1.02, 1.28))\n",
"_=ax.set_yticks(np.arange(20,161,20))\n",
"_=ax.set_ylim([20,160])\n",
"_=ax.set_xlim([35,60])\n",
"#_=ax.set_xlim([(THRESs*0.5144444).min(),(THRESs*0.5144444).max()])\n",
"_=ax.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.set_ylabel('Doubling time of annual TC landfall count (years)',fontsize=fontsize)\n",
"_=ax.set_xlabel('LI or LMI ≥ intensity threshold (m s$^{-1}$)',fontsize=fontsize)\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_2.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"all_LMI=newbt_lf.groupby('SID').apply(lambda x: np.nanmax(x.USA_WIND))\n",
"all_YEAR = newbt_lf.groupby('SID').apply(lambda x: x.SEASON.iloc[np.nanargmax(x.USA_WIND)])\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(all_LMI)):\n",
" if (all_YEAR[CCC] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]\n",
"\n",
"\n",
" \n",
" \n",
" | \n",
" 1 | \n",
" 3 | \n",
" 5 | \n",
" 6 | \n",
" 7 | \n",
" 9 | \n",
" 11 | \n",
"
\n",
" \n",
" \n",
" \n",
" r | \n",
" 0.233788 | \n",
" 0.365739 | \n",
" 0.357108 | \n",
" 0.353495 | \n",
" 0.342061 | \n",
" 0.317649 | \n",
" 0.347358 | \n",
"
\n",
" \n",
" p | \n",
" 0.157748 | \n",
" 0.0282631 | \n",
" 0.038143 | \n",
" 0.0471762 | \n",
" 0.0553321 | \n",
" 0.0871686 | \n",
" 0.0701237 | \n",
"
\n",
" \n",
"
\n",
""
],
"text/plain": [
" 1 3 5 6 7 9 11\n",
"r 0.233788 0.365739 0.357108 0.353495 0.342061 0.317649 0.347358 \n",
"p 0.157748 0.0282631 0.038143 0.0471762 0.0553321 0.0871686 0.0701237"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp_LMI_LF = pd.DataFrame(data=[scipy.signal.detrend(tmp_LMI),scipy.signal.detrend(tmp_LI)],index=['LMI','LI'],columns=INDEX_year) .transpose()\n",
"TABLE = pd.DataFrame(index=['r','p'],columns=[1,3,5,6,7,9,11])\n",
"_,_,TABLE.iloc[0,0],TABLE.iloc[1,0],_=scipy.stats.linregress(tmp_LMI_LF.LMI,tmp_LMI_LF.LI)\n",
"for POS, LLL in enumerate ([3,5,6,7,9,11]):\n",
" tmp1 = tmp_LMI_LF.LMI.rolling(window=LLL,center=True).mean().iloc[int(LLL/2):-int(LLL/2)]\n",
" tmp2 = tmp_LMI_LF.LI .rolling(window=LLL,center=True).mean().iloc[int(LLL/2):-int(LLL/2)]\n",
" _,_,TABLE.iloc[0,POS+1],TABLE.iloc[1,POS+1],_=scipy.stats.linregress(tmp1,tmp2)\n",
"TABLE"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.38556060487722554, intercept=0.051855244807418974, rvalue=0.35496786702335237, pvalue=0.028754074187917467, stderr=0.16924176063012317)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def low_pass_weights(window, cutoff):\n",
" \"\"\"Calculate weights for a low pass Lanczos filter.\n",
"\n",
" Args:\n",
"\n",
" window: int\n",
" The length of the filter window.\n",
"\n",
" cutoff: float\n",
" The cutoff frequency in inverse time steps.\n",
"\n",
" \"\"\"\n",
" order = ((window - 1) // 2 ) + 1\n",
" nwts = 2 * order + 1\n",
" w = np.zeros([nwts])\n",
" n = nwts // 2\n",
" w[n] = 2 * cutoff\n",
" k = np.arange(1., n)\n",
" sigma = np.sin(np.pi * k / n) * n / (np.pi * k)\n",
" firstfactor = np.sin(2. * np.pi * cutoff * k) / (np.pi * k)\n",
" w[n-1:0:-1] = firstfactor * sigma\n",
" w[n+1:-1] = firstfactor * sigma\n",
" return w[1:-1]\n",
"\n",
"tmp_year = 9\n",
"wt = low_pass_weights(30,1/tmp_year)\n",
"tmp_LMI_LF['LMI_DECADAL'] = np.convolve(wt, tmp_LMI_LF['LMI'], mode='same')\n",
"tmp_LMI_LF['LI_DECADAL' ] = np.convolve(wt, tmp_LMI_LF['LI' ], mode='same')\n",
"\n",
"scipy.stats.linregress(tmp_LMI_LF['LMI_DECADAL'],tmp_LMI_LF['LI_DECADAL' ])\n",
"\n",
"_=plt.figure(figsize=(5,3),dpi=100); fontsize=8\n",
"_=plt.plot(tmp_LMI_LF['LMI'] ,'--k',label='Annual LMI')\n",
"_=plt.plot(tmp_LMI_LF['LMI_DECADAL'],'-k' ,label='Filtered LMI')\n",
"_=plt.plot(tmp_LMI_LF['LI'] ,'--r',label='Annual ')\n",
"_=plt.plot(tmp_LMI_LF['LI_DECADAL'] ,'-r' ,label='')\n",
"_=plt.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=plt.legend(fontsize=fontsize,loc=2)\n",
"_=plt.xticks(range(1980,2021,5))\n",
"_=plt.ylim([-10,10])\n",
"_=plt.xlim([1980,2020])\n",
"_=plt.xlabel('Year',fontsize=fontsize)\n",
"_=plt.ylabel('Annual TC counts',fontsize=fontsize)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - Trend**"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def basin_LI_trend (AREA):\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]])\n",
" Y.extend([sum(np.asarray(tmp)>=96)]) #96\n",
" sample_weight.extend([1])\n",
" pred, mean, ci, p = poisson_reg(X,Y)\n",
" STR = ' ($p$='+str(round(p,2))+')'\n",
" if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
" output1 = str(round(np.log(2)/mean).astype(int))+STR\n",
" count = np.nansum(sample_weight)\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]])\n",
" Y.extend([sum(np.asarray(tmp)>=96)]) #96\n",
" sample_weight.extend([1])\n",
" pred, mean, ci, p = poisson_reg(X,Y)\n",
" STR = ' ($p$='+str(round(p,2))+')'\n",
" if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
" output2 = str(round(np.log(2)/mean).astype(int))+STR\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([dur_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
" x_trend, y_trend, slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
" p_str = '='+str('%.2f' %np.round(p,2))\n",
" if p<0.05:\n",
" p_str = '<0.05'\n",
" output3 = str(round(slope*10))+' ($p$'+p_str+')'\n",
" \n",
" Ref_B = 2.3e-7\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([trajectory_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
" x_trend, y_trend, slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
" p_str = '='+str('%.2f' %np.round(p,2))\n",
" if p<0.05:\n",
" p_str = '<0.05'\n",
" output4 = str(round(slope*10))+' ($p$'+p_str+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([speed_inverse[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
" x_trend, y_trend, slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
" p_str = '='+str('%.2f' %np.round(p,2))\n",
" if p<0.05:\n",
" p_str = '<0.05'\n",
" output5 = str(round(slope*10))+' ($p$'+p_str+')'\n",
" \n",
" return output1, output2,output3,output4,output5, count\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"TABLE = pd.DataFrame(index=['Major landfall doubling time (year)','Major LMI doubling time (year)','$T$ (hr per decade)','$d$ (km per decade)','$c$ (m s$^{-1}$ per decade)'],\n",
" columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5,output6 = basin_LI_trend(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
"TABLE\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - Epoch**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def basin_LI_epoch (AREA):\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" else:\n",
" tmp_after .extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output1 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" else:\n",
" tmp_after .extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output2 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([dur_inverse[CCC]])\n",
" else:\n",
" tmp_after .extend([dur_inverse[CCC]])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output3 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" Ref_B = 1.0e-7\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([B_inverse[CCC]/Ref_B*10])\n",
" else:\n",
" tmp_after .extend([B_inverse[CCC]/Ref_B*10])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output4 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([B_inverse[CCC]*dur_inverse[CCC]*3600*1000])\n",
" else:\n",
" tmp_after .extend([B_inverse[CCC]*dur_inverse[CCC]*3600*1000])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output5 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([1./(VMAX_PMIN_SERIES[CCC][1][0]*.514444)*1000])\n",
" else:\n",
" tmp_after .extend([1./(VMAX_PMIN_SERIES[CCC][1][0]*.514444)*1000])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output6 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([trajectory_inverse[CCC]])\n",
" else:\n",
" tmp_after .extend([trajectory_inverse[CCC]])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output7 = str(int(round(mean)))+'±'+str(int(round((ci1+ci2)/2)))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([speed_inverse[CCC]])\n",
" else:\n",
" tmp_after .extend([speed_inverse[CCC]])\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output8 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" return output1, output2,output3,output4,output5,output6,output7,output8\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"TABLE = pd.DataFrame(index=['LMI','LI','$\\Delta$t','$\\kappa$','$\\Delta$t${\\times}\\kappa$','1/LMI','$\\Delta$d','c'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5,output6,output7,output8 = basin_LI_epoch(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
" TABLE.iloc[5,BBB] = output6\n",
" TABLE.iloc[6,BBB] = output7\n",
" TABLE.iloc[7,BBB] = output8\n",
"TABLE[['Global','WP','EP','NA','NI','SI','SP']].reindex(['LI','LMI','1/LMI','$\\Delta$t${\\times}\\kappa$','$\\Delta$d','$\\Delta$t','$\\kappa$','c'])\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - Epoch - counts**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def basin_LI_epoch (AREA):\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" Y.extend([sum(np.asarray(tmp)>=96*.514444)])\n",
" Y = np.asarray(Y)\n",
" tmp_before = Y[:19]\n",
" tmp_after = Y[20:]\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output1 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
"\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" Y.extend([sum(np.asarray(tmp)>=-99999)])\n",
" Y = np.asarray(Y)\n",
" tmp_before = Y[:19]\n",
" tmp_after = Y[20:]\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output2 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" Y.extend([sum(np.asarray(tmp)>=96*.514444)])\n",
" Y = np.asarray(Y)\n",
" tmp_before = Y[:19]\n",
" tmp_after = Y[20:]\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output3 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" Y.extend([sum(np.asarray(tmp)>=-99999)])\n",
" Y = np.asarray(Y)\n",
" tmp_before = Y[:19]\n",
" tmp_after = Y[20:]\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output4 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" #Y.extend([sum(np.asarray(tmp)>=113*.514444)]) # CAT5 135 kt\n",
" Y.extend([sum(np.asarray(tmp)>=np.nanpercentile(A_inverse,90))]) # CAT5 135 kt\n",
" Y = np.asarray(Y)\n",
" tmp_before = Y[:19]\n",
" tmp_after = Y[20:]\n",
" mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
" output5 = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" return output1, output2,output3,output4,output5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"TABLE = pd.DataFrame(index=['Major LI','All LI','Major LMI','All LMI','CAT5 LMI'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5 = basin_LI_epoch(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
"TABLE[['Global','WP','EP','NA','NI','SI','SP']].reindex(['Major LI','All LI','Major LMI','All LMI','CAT5 LMI'])\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Epochal LI, terciles**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"AREA = ['WP','EP','NA','NI','SI','SP']\n",
"for YEAR in X:\n",
" tmp_before = []; tmp_after = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)): \n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" if (VMAX_PMIN_SERIES[CCC][2] <= 2000) :\n",
" tmp_before.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" else:\n",
" tmp_after .extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
"tmp_after = np.asarray(tmp_after)\n",
"tmp_before = np.asarray(tmp_before)\n",
"tmp_after = tmp_after [(tmp_after >np.nanpercentile(tmp_after ,66))&(tmp_after <=np.nanpercentile(tmp_after ,100))]\n",
"tmp_before = tmp_before[(tmp_before>np.nanpercentile(tmp_before,66))&(tmp_before<=np.nanpercentile(tmp_before,100))]\n",
"\n",
"mean = np.nanmean(tmp_after) - np.nanmean(tmp_before)\n",
"ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(tmp_before,tmp_after,95)\n",
"str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Major landfall proportion**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"fig,ax=plt.subplots(1,2); fig.set_size_inches(7,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"#newbt_lf[(newbt_lf.SEASON==YEAR)&(newbt_lf.BASIN==BASIN)].groupby('SID').ngroups\n",
"#X = INDEX_year[:]\n",
"#Y = []\n",
"#sample_weight = []\n",
"#for YEAR in X:\n",
"# tmp = []\n",
"# for CCC in range(len(VMAX_PMIN_SERIES)):\n",
"# if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=33)])\n",
"# sample_weight.extend([1]) #[len(tmp)]\n",
"#tmp_LMI = np.asarray(Y)\n",
"#_=ax[0].plot(X,Y,'-',color='C0',lw=1)\n",
"#x_trend, y_trend,slope,ci = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"#_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='C0',label='All landfall ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=64*.514444)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[0].plot(X,Y,'-',color='C0',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='C0',label='All landfall ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96*.514444)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[0].plot(X,Y,'-',color='C1',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='C1',label='Major landfall ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=64*.514444))])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[0].plot(X,Y,'-',color='C2',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0].plot(x_trend,y_trend,'-',lw=3,color='C2',label='Minor landfall ('+str(round(slope*10,1))+'±'+str(round(ci*10,1))+' decade$^{-1}$)')\n",
"\n",
"_=ax[0].legend(fontsize=fontsize)\n",
"_=ax[0].set_ylim([0,25])\n",
"_=ax[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0].legend(fontsize=fontsize,loc=2)\n",
"_=ax[0].set_xticks(range(1980,2021,5))\n",
"_=ax[0].set_ylim([0,25])\n",
"_=ax[0].set_xlim([1980,2020])\n",
"_=ax[0].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[0].set_ylabel('Global landfall counts',fontsize=fontsize)\n",
"#-------------------------------------------------------------------------------------------------------------------------------------------\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=50)/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1].plot(X,Y,'-',color='C1',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1].plot(x_trend,y_trend,'-',lw=3,color='C1',label='Major landfall ('+str(round(slope*10*1e2,1))+'±'+str(round(ci*10*1e2,1))+'% decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=64*.514444))/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax[1].plot(X,Y,'-',color='C2',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1].plot(x_trend,y_trend,'-',lw=3,color='C2',label='Minor landfall ('+str(round(slope*10*1e2,1))+'±'+str(round(ci*10*1e2,1))+'% decade$^{-1}$)')\n",
"\n",
"_=ax[1].legend(fontsize=fontsize)\n",
"_=ax[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1].legend(fontsize=fontsize,loc=2)\n",
"_=ax[1].set_xticks(range(1980,2021,5))\n",
"_=ax[1].set_ylim([0,1])\n",
"_=ax[1].set_xlim([1980,2020])\n",
"_=ax[1].set_xlabel('Year',fontsize=fontsize)\n",
"_=ax[1].set_ylabel('Global landfall fraction',fontsize=fontsize)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"#-------------------------------------------------------------------------------------------------------------------------------------------\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96*.514444)/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax.plot(X,Y,'-',color='C1',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend,'-',lw=3,color='C1',label='Major LMI ('+str(round(slope*10*1e2,1))+'±'+str(round(ci*10*1e2,1))+'% decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96*.514444)/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax.plot(X,Y,'-',color='C2',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend,'-',lw=3,color='C2',label='Major LI ('+str(round(slope*10*1e2,1))+'±'+str(round(ci*10*1e2,1))+'% decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=135*.514444)/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"_=ax.plot(X,Y,'-',color='C3',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend,'-',lw=3,color='C3',label='CAT5 LMI ('+str(round(slope*10*1e2,1))+'±'+str(round(ci*10*1e2,1))+'% decade$^{-1}$)')\n",
"\n",
"_=ax.legend(fontsize=fontsize)\n",
"_=ax.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.legend(fontsize=fontsize,loc=2)\n",
"_=ax.set_xticks(range(1980,2021,5))\n",
"_=ax.set_ylim([0,1.2])\n",
"_=ax.set_yticks(np.arange(0,1.1,.2))\n",
"_=ax.set_xlim([1980,2020])\n",
"_=ax.set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.set_ylabel('Global landfall fraction',fontsize=fontsize)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"#-------------------------------------------------------------------------------------------------------------------------------------------\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96*.514444)/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LMI = np.asarray(Y)\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]=96*.514444)/len(tmp)])\n",
" sample_weight.extend([1])\n",
"tmp_LI = np.asarray(Y)\n",
"\n",
"Y = tmp_LI/tmp_LMI\n",
"\n",
"_=ax.plot(X,Y,'-',color='C2',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend,'-',lw=3,color='C2',label='Major LI / Major LMI ('+str(round(slope*10*1e2,1))+'±'+str(round(ci*10*1e2,1))+'% decade$^{-1}$)')\n",
"\n",
"_=ax.legend(fontsize=fontsize)\n",
"_=ax.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.legend(fontsize=fontsize,loc=2)\n",
"_=ax.set_xticks(range(1980,2021,5))\n",
"_=ax.set_ylim([0,.8])\n",
"_=ax.set_yticks(np.arange(0,.9,.1))\n",
"_=ax.set_xlim([1980,2020])\n",
"_=ax.set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.set_ylabel('Global count fraction',fontsize=fontsize)\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp_editor.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation between decay factors and LI/LMI (FIGURE 3)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,3); fig.set_size_inches(7,2.5); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.35,hspace=0.3); fontsize = 8\n",
"\n",
"interp_vmax_in_inverse = np.asarray(interp_vmax_in_inverse )\n",
"interp_vmax_pred_inverse = np.asarray(interp_vmax_pred_inverse)\n",
"\n",
"dis_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" dis_inverse.extend([VMAX_PMIN_SERIES[CCC][3]])\n",
"dis_inverse=np.asarray(dis_inverse)\n",
"\n",
"# tmp_LMI and tmp_LF of individual cases with B_inverse case sequence\n",
"tmp_LILMI = []\n",
"for CCC in range(len(B_inverse)):\n",
" tmp_LILMI.extend([interp_vmax_in_inverse[CCC][-1]/interp_vmax_in_inverse[CCC][ 0]])\n",
"tmp_LILMI=np.asarray(tmp_LILMI)\n",
"#\n",
"_=ax[2].plot(trajectory_inverse/1e3,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[2].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax[2].set_xlabel('$d$ (10$^3$ km)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,trajectory_inverse)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax[2].text(7*.99,1*.99,'$r$='+str('%.2f' %np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[2].text(-1.98,1,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[2].set_ylim([0,1])\n",
"_=ax[2].set_xlim([0,7])\n",
"_=ax[2].set_xticks(np.arange(0,8,1))\n",
"#\n",
"_=ax[1].plot(dur_inverse,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax[1].set_xlabel('$T$ (hr)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,dur_inverse)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax[1].text(400*.99,1*.99,'$r$='+str('%.2f' %np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[1].text(-113,1,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[1].set_ylim([0,1])\n",
"_=ax[1].set_xlim([0,400])\n",
"_=ax[1].set_xticks(np.arange(0,401,100))\n",
"#\n",
"Ref_B = 1.0e-7\n",
"B_inverse=np.asarray(B_inverse)\n",
"_=ax[0].plot(B_inverse/Ref_B,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax[0].set_xlabel('$\\kappa/\\kappa_o$',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI[~np.isnan(B_inverse)],B_inverse[~np.isnan(B_inverse)])\n",
"p_str = str('%.2f' %np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax[0].text(7*.99,1*.99,'$r$='+str('%.2f' %np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[0].text(-1.98,1,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[0].set_ylim([0,1])\n",
"_=ax[0].set_xlim([0,7])\n",
"_=ax[0].set_xticks(np.arange(0,7.1,1))\n",
"#\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_3.png',dpi=600,bbox_inches='tight')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_3.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**supp. Figure**"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,2); fig.set_size_inches(7,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.3); fontsize = 8\n",
"\n",
"interp_vmax_in_inverse = np.asarray(interp_vmax_in_inverse )\n",
"interp_vmax_pred_inverse = np.asarray(interp_vmax_pred_inverse)\n",
"\n",
"dis_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" dis_inverse.extend([VMAX_PMIN_SERIES[CCC][3]])\n",
"dis_inverse=np.asarray(dis_inverse)\n",
"\n",
"# tmp_LMI and tmp_LF of individual cases with B_inverse case sequence\n",
"tmp_LILMI = []\n",
"for CCC in range(len(B_inverse)):\n",
" tmp_LILMI.extend([interp_vmax_in_inverse[CCC][-1]/interp_vmax_in_inverse[CCC][ 0]])\n",
"tmp_LILMI=np.asarray(tmp_LILMI)\n",
"#\n",
"_=ax[0].plot(speed_inverse,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax[0].set_xlabel('$c$ (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI[~np.isnan(speed_inverse)],speed_inverse[~np.isnan(speed_inverse)])\n",
"p_str = str('%.2f' %(np.round(p,2)))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax[0].text(16*.99,1*.99,'$r$='+str(np.round(r,2))+'\\n$p$='+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[0].text(-3.2,1,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[0].set_ylim([0,1])\n",
"_=ax[0].set_xlim([0,16])\n",
"_=ax[0].set_xticks(np.arange(0,17,2))\n",
"#\n",
"_=ax[1].plot(dis_inverse/1e3,dur_inverse,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1].set_ylabel('$T$ (hr)',fontsize=fontsize)\n",
"_=ax[1].set_xlabel('$d$ (10$^3$ km)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(dur_inverse,dis_inverse)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax[1].text(0.45*.99,400*.99,'$r$='+str(np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[1].text(-.5,400,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[1].set_ylim([0,400])\n",
"_=ax[1].set_xlim([0,2.5])\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp5.png',dpi=600,bbox_inches='tight')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp5.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - std**"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def basin_mean_std (AREA):\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" output1 = str(round(np.nanmean(tmp)).astype(int))+'±'+str(round(np.nanstd(tmp)).astype(int))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" output2 = str(round(np.nanmean(tmp)).astype(int))+'±'+str(round(np.nanstd(tmp)).astype(int))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([dur_inverse[CCC]])\n",
" output3 = str(round(np.nanmean(tmp)).astype(int))+'±'+str(round(np.nanstd(tmp)).astype(int))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" Ref_B = 1.0e-7\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([B_inverse_dis[CCC]/Ref_B])\n",
" output4 = str(round(np.nanmean(tmp),1))+'±'+str(round(np.nanstd(tmp),1))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([B_inverse[CCC]*dur_inverse[CCC]*3600*1000])\n",
" output5 = str(round(np.nanmean(tmp),1))+'±'+str(round(np.nanstd(tmp),1))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([1/(VMAX_PMIN_SERIES[CCC][1][0]*.514444)*1000])\n",
" output6 = str(round(np.nanmean(tmp),1))+'±'+str(round(np.nanstd(tmp),1))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([trajectory_inverse[CCC]])\n",
" output7 = str(int(round(np.nanmean(tmp))))+'±'+str(int(round(np.nanstd(tmp))))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([speed_inverse[CCC]])\n",
" output8 = str(round(np.nanmean(tmp),1))+'±'+str(round(np.nanstd(tmp),1))+'('+str(round(np.nanstd(tmp)/np.nanmean(tmp)*100).astype(int))+'%)'\n",
" \n",
" return output1, output2,output3,output4,output5,output6,output7,output8\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" NH | \n",
" SH | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" LMI | \n",
" 53±14(26%) | \n",
" 53±14(27%) | \n",
" 53±13(25%) | \n",
" 54±14(26%) | \n",
" 52±13(24%) | \n",
" 52±15(29%) | \n",
" 50±14(27%) | \n",
" 53±13(25%) | \n",
" 54±13(24%) | \n",
"
\n",
" \n",
" LI | \n",
" 38±13(36%) | \n",
" 37±13(36%) | \n",
" 40±14(35%) | \n",
" 37±13(34%) | \n",
" 32±12(38%) | \n",
" 40±14(36%) | \n",
" 38±14(38%) | \n",
" 41±14(35%) | \n",
" 39±14(35%) | \n",
"
\n",
" \n",
" $\\Delta$t | \n",
" 35±46(132%) | \n",
" 36±45(124%) | \n",
" 30±51(170%) | \n",
" 37±43(115%) | \n",
" 44±48(111%) | \n",
" 34±52(154%) | \n",
" 20±28(139%) | \n",
" 30±54(183%) | \n",
" 31±43(138%) | \n",
"
\n",
" \n",
" $\\kappa$ | \n",
" 1.4±1.3(94%) | \n",
" 1.3±1.3(97%) | \n",
" 1.7±1.4(82%) | \n",
" 1.1±1.1(99%) | \n",
" 1.6±1.5(93%) | \n",
" 1.5±1.4(93%) | \n",
" 1.9±1.4(75%) | \n",
" 1.8±1.4(79%) | \n",
" 1.4±1.2(85%) | \n",
"
\n",
" \n",
" $\\Delta$t${\\times}\\kappa$ | \n",
" 9.2±10.8(118%) | \n",
" 9.3±10.1(109%) | \n",
" 8.7±13.6(157%) | \n",
" 8.5±8.3(98%) | \n",
" 13.4±13.6(101%) | \n",
" 9.0±11.0(122%) | \n",
" 10.1±13.4(133%) | \n",
" 8.7±15.2(174%) | \n",
" 8.5±8.1(96%) | \n",
"
\n",
" \n",
" 1/LMI | \n",
" 20.1±5.5(27%) | \n",
" 20.2±5.5(27%) | \n",
" 20.1±5.4(27%) | \n",
" 19.7±5.4(27%) | \n",
" 20.2±4.8(24%) | \n",
" 21.0±5.8(28%) | \n",
" 21.5±5.8(27%) | \n",
" 20.2±5.6(28%) | \n",
" 19.8±4.9(25%) | \n",
"
\n",
" \n",
" $\\Delta$d | \n",
" 642±826(129%) | \n",
" 690±855(124%) | \n",
" 414±622(150%) | \n",
" 732±769(105%) | \n",
" 729±873(120%) | \n",
" 738±1204(163%) | \n",
" 287±355(124%) | \n",
" 387±621(160%) | \n",
" 479±620(129%) | \n",
"
\n",
" \n",
" c | \n",
" 5.7±3.2(55%) | \n",
" 5.9±3.3(55%) | \n",
" 4.6±2.0(44%) | \n",
" 6.0±2.1(36%) | \n",
" 5.2±2.1(40%) | \n",
" 6.6±6.4(98%) | \n",
" 5.0±2.4(47%) | \n",
" 4.4±1.7(40%) | \n",
" 5.2±2.5(49%) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global NH SH \\\n",
"LMI 53±14(26%) 53±14(27%) 53±13(25%) \n",
"LI 38±13(36%) 37±13(36%) 40±14(35%) \n",
"$\\Delta$t 35±46(132%) 36±45(124%) 30±51(170%) \n",
"$\\kappa$ 1.4±1.3(94%) 1.3±1.3(97%) 1.7±1.4(82%) \n",
"$\\Delta$t${\\times}\\kappa$ 9.2±10.8(118%) 9.3±10.1(109%) 8.7±13.6(157%) \n",
"1/LMI 20.1±5.5(27%) 20.2±5.5(27%) 20.1±5.4(27%) \n",
"$\\Delta$d 642±826(129%) 690±855(124%) 414±622(150%) \n",
"c 5.7±3.2(55%) 5.9±3.3(55%) 4.6±2.0(44%) \n",
"\n",
" WP EP NA \\\n",
"LMI 54±14(26%) 52±13(24%) 52±15(29%) \n",
"LI 37±13(34%) 32±12(38%) 40±14(36%) \n",
"$\\Delta$t 37±43(115%) 44±48(111%) 34±52(154%) \n",
"$\\kappa$ 1.1±1.1(99%) 1.6±1.5(93%) 1.5±1.4(93%) \n",
"$\\Delta$t${\\times}\\kappa$ 8.5±8.3(98%) 13.4±13.6(101%) 9.0±11.0(122%) \n",
"1/LMI 19.7±5.4(27%) 20.2±4.8(24%) 21.0±5.8(28%) \n",
"$\\Delta$d 732±769(105%) 729±873(120%) 738±1204(163%) \n",
"c 6.0±2.1(36%) 5.2±2.1(40%) 6.6±6.4(98%) \n",
"\n",
" NI SI SP \n",
"LMI 50±14(27%) 53±13(25%) 54±13(24%) \n",
"LI 38±14(38%) 41±14(35%) 39±14(35%) \n",
"$\\Delta$t 20±28(139%) 30±54(183%) 31±43(138%) \n",
"$\\kappa$ 1.9±1.4(75%) 1.8±1.4(79%) 1.4±1.2(85%) \n",
"$\\Delta$t${\\times}\\kappa$ 10.1±13.4(133%) 8.7±15.2(174%) 8.5±8.1(96%) \n",
"1/LMI 21.5±5.8(27%) 20.2±5.6(28%) 19.8±4.9(25%) \n",
"$\\Delta$d 287±355(124%) 387±621(160%) 479±620(129%) \n",
"c 5.0±2.4(47%) 4.4±1.7(40%) 5.2±2.5(49%) "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TABLE = pd.DataFrame(index=['LMI','LI','$\\Delta$t','$\\kappa$','$\\Delta$t${\\times}\\kappa$','1/LMI','$\\Delta$d','c'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5,output6,output7,output8 = basin_mean_std(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
" TABLE.iloc[5,BBB] = output6\n",
" TABLE.iloc[6,BBB] = output7\n",
" TABLE.iloc[7,BBB] = output8\n",
"TABLE\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - Q3-Q1 / Q3+Q1**"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def basin_mean_std (AREA):\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" output1 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" output2 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([dur_inverse[CCC]])\n",
" output3 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" Ref_B = 1.0e-7\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([B_inverse_dis[CCC]/Ref_B*10])\n",
" output4 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([B_inverse[CCC]*dur_inverse[CCC]*3600*1000])\n",
" output5 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([1/(VMAX_PMIN_SERIES[CCC][1][0]*.514444)*1000])\n",
" output6 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([trajectory_inverse[CCC]])\n",
" output7 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([speed_inverse[CCC]])\n",
" output8 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((np.nanquantile(tmp,.75)-np.nanquantile(tmp,.25)) / (np.nanquantile(tmp,.75)+np.nanquantile(tmp,.25)) * 100).astype(int))+')'\n",
" \n",
" return output1, output2,output3,output4,output5,output6,output7,output8\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" LI | \n",
" 36(24) | \n",
" 36(23) | \n",
" 33(28) | \n",
" 39(19) | \n",
" 34(23) | \n",
" 41(25) | \n",
" 37(24) | \n",
"
\n",
" \n",
" LMI | \n",
" 51(22) | \n",
" 54(24) | \n",
" 51(17) | \n",
" 49(25) | \n",
" 45(24) | \n",
" 57(24) | \n",
" 51(16) | \n",
"
\n",
" \n",
" 1/LMI | \n",
" 19(22) | \n",
" 19(24) | \n",
" 19(18) | \n",
" 21(25) | \n",
" 22(24) | \n",
" 18(24) | \n",
" 19(16) | \n",
"
\n",
" \n",
" $\\Delta$t${\\times}\\kappa$ | \n",
" 5(62) | \n",
" 6(60) | \n",
" 8(69) | \n",
" 5(61) | \n",
" 4(59) | \n",
" 4(45) | \n",
" 6(79) | \n",
"
\n",
" \n",
" $\\Delta$d | \n",
" 282(82) | \n",
" 455(80) | \n",
" 383(75) | \n",
" 168(87) | \n",
" 164(69) | \n",
" 185(75) | \n",
" 213(81) | \n",
"
\n",
" \n",
" $\\Delta$t | \n",
" 15(78) | \n",
" 21(80) | \n",
" 24(85) | \n",
" 9(89) | \n",
" 8(78) | \n",
" 9(70) | \n",
" 12(86) | \n",
"
\n",
" \n",
" $\\kappa$ | \n",
" 10(53) | \n",
" 8(50) | \n",
" 10(48) | \n",
" 10(61) | \n",
" 13(42) | \n",
" 13(53) | \n",
" 10(40) | \n",
"
\n",
" \n",
" c | \n",
" 5(27) | \n",
" 6(22) | \n",
" 5(31) | \n",
" 6(32) | \n",
" 5(28) | \n",
" 4(28) | \n",
" 5(22) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global WP EP NA NI \\\n",
"LI 36(24) 36(23) 33(28) 39(19) 34(23) \n",
"LMI 51(22) 54(24) 51(17) 49(25) 45(24) \n",
"1/LMI 19(22) 19(24) 19(18) 21(25) 22(24) \n",
"$\\Delta$t${\\times}\\kappa$ 5(62) 6(60) 8(69) 5(61) 4(59) \n",
"$\\Delta$d 282(82) 455(80) 383(75) 168(87) 164(69) \n",
"$\\Delta$t 15(78) 21(80) 24(85) 9(89) 8(78) \n",
"$\\kappa$ 10(53) 8(50) 10(48) 10(61) 13(42) \n",
"c 5(27) 6(22) 5(31) 6(32) 5(28) \n",
"\n",
" SI SP \n",
"LI 41(25) 37(24) \n",
"LMI 57(24) 51(16) \n",
"1/LMI 18(24) 19(16) \n",
"$\\Delta$t${\\times}\\kappa$ 4(45) 6(79) \n",
"$\\Delta$d 185(75) 213(81) \n",
"$\\Delta$t 9(70) 12(86) \n",
"$\\kappa$ 13(53) 10(40) \n",
"c 4(28) 5(22) "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TABLE = pd.DataFrame(index=['LMI','LI','$\\Delta$t','$\\kappa$','$\\Delta$t${\\times}\\kappa$','1/LMI','$\\Delta$d','c'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5,output6,output7,output8 = basin_mean_std(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
" TABLE.iloc[5,BBB] = output6\n",
" TABLE.iloc[6,BBB] = output7\n",
" TABLE.iloc[7,BBB] = output8\n",
"TABLE[['Global','WP','EP','NA','NI','SI','SP']].reindex(['LI','LMI','1/LMI','$\\Delta$t${\\times}\\kappa$','$\\Delta$d','$\\Delta$t','$\\kappa$','c'])\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - median absolute deviation / median**"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def basin_mean_std (AREA):\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]*.514444])\n",
" output1 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
" output2 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([dur_inverse[CCC]])\n",
" output3 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" Ref_B = 1.0e-7\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([B_inverse_dis[CCC]/Ref_B*10])\n",
" output4 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([B_inverse[CCC]*dur_inverse[CCC]*3600*1000])\n",
" output5 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([1/(VMAX_PMIN_SERIES[CCC][1][0]*.514444)*1000])\n",
" output6 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([trajectory_inverse[CCC]])\n",
" output7 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([speed_inverse[CCC]])\n",
" output8 = str(round(np.nanmedian(tmp)).astype(int))+'('+str(round((scipy.stats.median_absolute_deviation(tmp)) / (np.nanmean(tmp)) * 100).astype(int))+')'\n",
" \n",
" return output1, output2,output3,output4,output5,output6,output7,output8\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" LI | \n",
" 36(34) | \n",
" 36(31) | \n",
" 33(48) | \n",
" 39(29) | \n",
" 34(48) | \n",
" 41(38) | \n",
" 37(35) | \n",
"
\n",
" \n",
" LMI | \n",
" 51(36) | \n",
" 54(35) | \n",
" 51(29) | \n",
" 49(37) | \n",
" 45(34) | \n",
" 57(29) | \n",
" 51(28) | \n",
"
\n",
" \n",
" 1/LMI | \n",
" 19(33) | \n",
" 19(31) | \n",
" 19(25) | \n",
" 21(38) | \n",
" 22(38) | \n",
" 18(20) | \n",
" 19(29) | \n",
"
\n",
" \n",
" $\\Delta$t${\\times}\\kappa$ | \n",
" 5(-9223372036854775808) | \n",
" 6(-9223372036854775808) | \n",
" 8(-9223372036854775808) | \n",
" 5(-9223372036854775808) | \n",
" 4(-9223372036854775808) | \n",
" 4(-9223372036854775808) | \n",
" 6(-9223372036854775808) | \n",
"
\n",
" \n",
" $\\Delta$d | \n",
" 282(54) | \n",
" 455(74) | \n",
" 383(58) | \n",
" 168(34) | \n",
" 164(61) | \n",
" 185(52) | \n",
" 213(51) | \n",
"
\n",
" \n",
" $\\Delta$t | \n",
" 15(51) | \n",
" 21(71) | \n",
" 24(71) | \n",
" 9(33) | \n",
" 8(45) | \n",
" 9(30) | \n",
" 12(43) | \n",
"
\n",
" \n",
" $\\kappa$ | \n",
" 10(54) | \n",
" 8(47) | \n",
" 10(39) | \n",
" 10(61) | \n",
" 13(48) | \n",
" 13(57) | \n",
" 10(45) | \n",
"
\n",
" \n",
" c | \n",
" 5(-9223372036854775808) | \n",
" 6(-9223372036854775808) | \n",
" 5(-9223372036854775808) | \n",
" 6(-9223372036854775808) | \n",
" 5(-9223372036854775808) | \n",
" 4(-9223372036854775808) | \n",
" 5(-9223372036854775808) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global WP \\\n",
"LI 36(34) 36(31) \n",
"LMI 51(36) 54(35) \n",
"1/LMI 19(33) 19(31) \n",
"$\\Delta$t${\\times}\\kappa$ 5(-9223372036854775808) 6(-9223372036854775808) \n",
"$\\Delta$d 282(54) 455(74) \n",
"$\\Delta$t 15(51) 21(71) \n",
"$\\kappa$ 10(54) 8(47) \n",
"c 5(-9223372036854775808) 6(-9223372036854775808) \n",
"\n",
" EP NA \\\n",
"LI 33(48) 39(29) \n",
"LMI 51(29) 49(37) \n",
"1/LMI 19(25) 21(38) \n",
"$\\Delta$t${\\times}\\kappa$ 8(-9223372036854775808) 5(-9223372036854775808) \n",
"$\\Delta$d 383(58) 168(34) \n",
"$\\Delta$t 24(71) 9(33) \n",
"$\\kappa$ 10(39) 10(61) \n",
"c 5(-9223372036854775808) 6(-9223372036854775808) \n",
"\n",
" NI SI \\\n",
"LI 34(48) 41(38) \n",
"LMI 45(34) 57(29) \n",
"1/LMI 22(38) 18(20) \n",
"$\\Delta$t${\\times}\\kappa$ 4(-9223372036854775808) 4(-9223372036854775808) \n",
"$\\Delta$d 164(61) 185(52) \n",
"$\\Delta$t 8(45) 9(30) \n",
"$\\kappa$ 13(48) 13(57) \n",
"c 5(-9223372036854775808) 4(-9223372036854775808) \n",
"\n",
" SP \n",
"LI 37(35) \n",
"LMI 51(28) \n",
"1/LMI 19(29) \n",
"$\\Delta$t${\\times}\\kappa$ 6(-9223372036854775808) \n",
"$\\Delta$d 213(51) \n",
"$\\Delta$t 12(43) \n",
"$\\kappa$ 10(45) \n",
"c 5(-9223372036854775808) "
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TABLE = pd.DataFrame(index=['LMI','LI','$\\Delta$t','$\\kappa$','$\\Delta$t${\\times}\\kappa$','1/LMI','$\\Delta$d','c'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5,output6,output7,output8 = basin_mean_std(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
" TABLE.iloc[5,BBB] = output6\n",
" TABLE.iloc[6,BBB] = output7\n",
" TABLE.iloc[7,BBB] = output8\n",
"TABLE[['Global','WP','EP','NA','NI','SI','SP']].reindex(['LI','LMI','1/LMI','$\\Delta$t${\\times}\\kappa$','$\\Delta$d','$\\Delta$t','$\\kappa$','c'])\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Major TC percentile**"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" LI | \n",
" 57(91) | \n",
" 57(93) | \n",
" 53(97) | \n",
" 59(91) | \n",
" 57(89) | \n",
" 57(86) | \n",
" 57(89) | \n",
"
\n",
" \n",
" LMI | \n",
" 67(80) | \n",
" 69(81) | \n",
" 68(93) | \n",
" 67(80) | \n",
" 67(84) | \n",
" 64(77) | \n",
" 64(77) | \n",
"
\n",
" \n",
" 1/LMI | \n",
" 15(21) | \n",
" 14(19) | \n",
" 15(7) | \n",
" 15(20) | \n",
" 15(16) | \n",
" 16(24) | \n",
" 16(26) | \n",
"
\n",
" \n",
" $\\Delta$t${\\times}\\kappa$ | \n",
" 2(17) | \n",
" 2(14) | \n",
" 3(18) | \n",
" 2(22) | \n",
" 2(23) | \n",
" 3(24) | \n",
" 1(20) | \n",
"
\n",
" \n",
" $\\Delta$d | \n",
" 122(32) | \n",
" 183(33) | \n",
" 332(45) | \n",
" 116(45) | \n",
" 65(32) | \n",
" 81(32) | \n",
" 91(35) | \n",
"
\n",
" \n",
" $\\Delta$t | \n",
" 9(39) | \n",
" 9(34) | \n",
" 12(38) | \n",
" 4(39) | \n",
" 4(38) | \n",
" 9(47) | \n",
" 4(32) | \n",
"
\n",
" \n",
" $\\kappa$ | \n",
" 6(31) | \n",
" 5(29) | \n",
" 7(37) | \n",
" 6(27) | \n",
" 6(18) | \n",
" 7(33) | \n",
" 6(38) | \n",
"
\n",
" \n",
" c | \n",
" 6(54) | \n",
" 6(56) | \n",
" 7(72) | \n",
" 7(65) | \n",
" 4(39) | \n",
" 4(48) | \n",
" 7(73) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global WP EP NA NI SI \\\n",
"LI 57(91) 57(93) 53(97) 59(91) 57(89) 57(86) \n",
"LMI 67(80) 69(81) 68(93) 67(80) 67(84) 64(77) \n",
"1/LMI 15(21) 14(19) 15(7) 15(20) 15(16) 16(24) \n",
"$\\Delta$t${\\times}\\kappa$ 2(17) 2(14) 3(18) 2(22) 2(23) 3(24) \n",
"$\\Delta$d 122(32) 183(33) 332(45) 116(45) 65(32) 81(32) \n",
"$\\Delta$t 9(39) 9(34) 12(38) 4(39) 4(38) 9(47) \n",
"$\\kappa$ 6(31) 5(29) 7(37) 6(27) 6(18) 7(33) \n",
"c 6(54) 6(56) 7(72) 7(65) 4(39) 4(48) \n",
"\n",
" SP \n",
"LI 57(89) \n",
"LMI 64(77) \n",
"1/LMI 16(26) \n",
"$\\Delta$t${\\times}\\kappa$ 1(20) \n",
"$\\Delta$d 91(35) \n",
"$\\Delta$t 4(32) \n",
"$\\kappa$ 6(38) \n",
"c 7(73) "
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ref_B = 1e-7\n",
"\n",
"LI_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" LI_inverse.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
"LI_inverse = np.asarray(LI_inverse)\n",
"A_inverse = np.asarray(A_inverse)\n",
"\n",
"def find_var_percentile (x,AREA):\n",
" x = np.asarray(x)\n",
" \n",
" tmp_AREA = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp_AREA.extend([True])\n",
" else:\n",
" tmp_AREA.extend([False])\n",
" \n",
" tmp_TOP_LI = LI_inverse>=96*.514444 # np.percentile(LI_inverse[tmp_AREA],90)\n",
" \n",
" return str(round(np.nanmedian(x[tmp_TOP_LI & tmp_AREA])).astype(int)) +'('+str(round(scipy.stats.percentileofscore(x[tmp_AREA],np.nanmedian(x[tmp_TOP_LI & tmp_AREA]))))+')'\n",
" \n",
"TABLE = pd.DataFrame(index=['LI','LMI','1/LMI','$\\Delta$t${\\times}\\kappa$','$\\Delta$d','$\\Delta$t','$\\kappa$','c'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" dur_inverse = np.asarray(dur_inverse)\n",
" TABLE.iloc[0,BBB] = find_var_percentile(LI_inverse ,AREA)\n",
" TABLE.iloc[1,BBB] = find_var_percentile(A_inverse ,AREA)\n",
" TABLE.iloc[2,BBB] = find_var_percentile(1/A_inverse *1000,AREA)\n",
" TABLE.iloc[3,BBB] = find_var_percentile(dur_inverse*3600*B_inverse *1000,AREA)\n",
" TABLE.iloc[4,BBB] = find_var_percentile(trajectory_inverse ,AREA)\n",
" TABLE.iloc[5,BBB] = find_var_percentile(dur_inverse ,AREA)\n",
" TABLE.iloc[6,BBB] = find_var_percentile(B_inverse /Ref_B*10,AREA)\n",
" TABLE.iloc[7,BBB] = find_var_percentile(speed_inverse ,AREA) \n",
"\n",
"TABLE[['Global','WP','EP','NA','NI','SI','SP']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Total landfall TCs, LMI, LI, t (>0 ie LMI not LI time), κ distribution**"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(2,2); fig.set_size_inches(7,7); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.3); fontsize = 8\n",
"\n",
"A_inverse = np.asarray(A_inverse)\n",
"X= A_inverse[dur_inverse>0]\n",
"_=ax[0][0].hist(X,density=True,bins=10)\n",
"_=ax[0][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][0].set_xticks(np.arange(0,101,10))\n",
"_=ax[0][0].set_xlabel('LMI (m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax[0][0].set_ylabel('Probability density function',fontsize=fontsize)\n",
"_=ax[0][0].set_ylim(0,0.04)\n",
"_=ax[0][0].set_xlim(0,100)\n",
"_=ax[0][0].text(-28,0.04,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"#_=ax[0][0].plot([np.nanpercentile(X,50),np.nanpercentile(X,50)],[0,10],'-k')\n",
"#_=ax[0][0].plot([np.nanpercentile(X,25),np.nanpercentile(X,25)],[0,10],'--k')\n",
"#_=ax[0][0].plot([np.nanpercentile(X,75),np.nanpercentile(X,75)],[0,10],'--k')\n",
"\n",
"X=LI_inverse[dur_inverse>-1]\n",
"_=ax[0][1].hist(X,density=True,bins=10)\n",
"_=ax[0][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][1].set_xticks(np.arange(0,101,10))\n",
"_=ax[0][1].set_xlabel('LI (m s$^{-1}$)',fontsize=fontsize)\n",
"_=ax[0][1].set_ylabel('Probability density function',fontsize=fontsize)\n",
"_=ax[0][1].set_ylim(0,0.035)\n",
"_=ax[0][1].set_xlim(0,100)\n",
"_=ax[0][1].text(-28,0.035,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"#_=ax[0][1].plot([np.nanpercentile(X,50),np.nanpercentile(X,50)],[0,10],'-k')\n",
"#_=ax[0][1].plot([np.nanpercentile(X,25),np.nanpercentile(X,25)],[0,10],'--k')\n",
"#_=ax[0][1].plot([np.nanpercentile(X,75),np.nanpercentile(X,75)],[0,10],'--k')\n",
"\n",
"X=dur_inverse[dur_inverse>-1]\n",
"_=ax[1][0].hist(X,density=True,bins=10)\n",
"_=ax[1][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][0].set_xticks(np.arange(0,401,50))\n",
"_=ax[1][0].set_xlabel('$T$ (hr)',fontsize=fontsize)\n",
"_=ax[1][0].set_ylabel('Probability density function',fontsize=fontsize)\n",
"_=ax[1][0].set_ylim(0,0.02)\n",
"_=ax[1][0].set_xlim(0,400)\n",
"_=ax[1][0].text(-112,0.02,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"#_=ax[1][0].plot([np.nanpercentile(X,50),np.nanpercentile(X,50)],[0,10],'-k')\n",
"#_=ax[1][0].plot([np.nanpercentile(X,25),np.nanpercentile(X,25)],[0,10],'--k')\n",
"#_=ax[1][0].plot([np.nanpercentile(X,75),np.nanpercentile(X,75)],[0,10],'--k')\n",
"\n",
"X=B_inverse[dur_inverse>-1]/Ref_B\n",
"_=ax[1][1].hist(X,density=True,bins=10)\n",
"_=ax[1][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][1].set_xticks(np.arange(0,6.1,1))\n",
"_=ax[1][1].set_xlabel('$\\kappa/\\kappa_o$',fontsize=fontsize)\n",
"_=ax[1][1].set_ylabel('Probability density function',fontsize=fontsize)\n",
"_=ax[1][1].set_ylim(0,.9)\n",
"_=ax[1][1].set_xlim(0,7)\n",
"_=ax[1][1].text(-1.96,.9,'(D)',fontsize=fontsize+2,fontweight='bold')\n",
"#_=ax[1][1].plot([np.nanpercentile(X,50),np.nanpercentile(X,50)],[0,10],'-k')\n",
"#_=ax[1][1].plot([np.nanpercentile(X,25),np.nanpercentile(X,25)],[0,10],'--k')\n",
"#_=ax[1][1].plot([np.nanpercentile(X,75),np.nanpercentile(X,75)],[0,10],'--k')\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp7.png',dpi=600,bbox_inches='tight')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp7.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Top 10 percentile**"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" LI | \n",
" 62(95) | \n",
" 59(95) | \n",
" 51(96) | \n",
" 64(95) | \n",
" 59(95) | \n",
" 64(96) | \n",
" 61(95) | \n",
"
\n",
" \n",
" LMI | \n",
" 71(86) | \n",
" 69(81) | \n",
" 59(69) | \n",
" 77(93) | \n",
" 64(77) | \n",
" 67(84) | \n",
" 67(82) | \n",
"
\n",
" \n",
" 1/LMI | \n",
" 14(14) | \n",
" 14(19) | \n",
" 17(32) | \n",
" 13(8) | \n",
" 16(23) | \n",
" 15(17) | \n",
" 15(22) | \n",
"
\n",
" \n",
" $\\Delta$t${\\times}\\kappa$ | \n",
" 2(12) | \n",
" 2(11) | \n",
" 3(21) | \n",
" 2(19) | \n",
" 2(14) | \n",
" 2(12) | \n",
" 1(20) | \n",
"
\n",
" \n",
" $\\Delta$d | \n",
" 105(29) | \n",
" 159(30) | \n",
" 266(42) | \n",
" 188(53) | \n",
" 47(23) | \n",
" 32(11) | \n",
" 91(35) | \n",
"
\n",
" \n",
" $\\Delta$t | \n",
" 6(30) | \n",
" 9(34) | \n",
" 12(38) | \n",
" 9(50) | \n",
" 3(27) | \n",
" 3(16) | \n",
" 4(32) | \n",
"
\n",
" \n",
" $\\kappa$ | \n",
" 6(32) | \n",
" 6(37) | \n",
" 9(49) | \n",
" 4(22) | \n",
" 5(14) | \n",
" 6(24) | \n",
" 6(35) | \n",
"
\n",
" \n",
" c | \n",
" 6(55) | \n",
" 6(59) | \n",
" 6(67) | \n",
" 7(67) | \n",
" 5(62) | \n",
" 3(30) | \n",
" 7(73) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global WP EP NA NI SI \\\n",
"LI 62(95) 59(95) 51(96) 64(95) 59(95) 64(96) \n",
"LMI 71(86) 69(81) 59(69) 77(93) 64(77) 67(84) \n",
"1/LMI 14(14) 14(19) 17(32) 13(8) 16(23) 15(17) \n",
"$\\Delta$t${\\times}\\kappa$ 2(12) 2(11) 3(21) 2(19) 2(14) 2(12) \n",
"$\\Delta$d 105(29) 159(30) 266(42) 188(53) 47(23) 32(11) \n",
"$\\Delta$t 6(30) 9(34) 12(38) 9(50) 3(27) 3(16) \n",
"$\\kappa$ 6(32) 6(37) 9(49) 4(22) 5(14) 6(24) \n",
"c 6(55) 6(59) 6(67) 7(67) 5(62) 3(30) \n",
"\n",
" SP \n",
"LI 61(95) \n",
"LMI 67(82) \n",
"1/LMI 15(22) \n",
"$\\Delta$t${\\times}\\kappa$ 1(20) \n",
"$\\Delta$d 91(35) \n",
"$\\Delta$t 4(32) \n",
"$\\kappa$ 6(35) \n",
"c 7(73) "
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LI_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" LI_inverse.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
"LI_inverse = np.asarray(LI_inverse)\n",
"A_inverse = np.asarray(A_inverse)\n",
"\n",
"def find_var_percentile (x,AREA):\n",
" x = np.asarray(x)\n",
" \n",
" tmp_AREA = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp_AREA.extend([True])\n",
" else:\n",
" tmp_AREA.extend([False])\n",
" \n",
" tmp_TOP_LI = LI_inverse>=np.percentile(LI_inverse[tmp_AREA],90)\n",
" \n",
" return str(round(np.nanmedian(x[tmp_TOP_LI & tmp_AREA])).astype(int)) +'('+str(round(scipy.stats.percentileofscore(x[tmp_AREA],np.nanmedian(x[tmp_TOP_LI & tmp_AREA]))))+')'\n",
" \n",
"TABLE = pd.DataFrame(index=['LI','LMI','1/LMI','$\\Delta$t${\\times}\\kappa$','$\\Delta$d','$\\Delta$t','$\\kappa$','c'],columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" dur_inverse = np.asarray(dur_inverse)\n",
" TABLE.iloc[0,BBB] = find_var_percentile(LI_inverse ,AREA)\n",
" TABLE.iloc[1,BBB] = find_var_percentile(A_inverse ,AREA)\n",
" TABLE.iloc[2,BBB] = find_var_percentile(1/A_inverse *1000,AREA)\n",
" TABLE.iloc[3,BBB] = find_var_percentile(dur_inverse*3600*B_inverse *1000,AREA)\n",
" TABLE.iloc[4,BBB] = find_var_percentile(trajectory_inverse ,AREA)\n",
" TABLE.iloc[5,BBB] = find_var_percentile(dur_inverse ,AREA)\n",
" TABLE.iloc[6,BBB] = find_var_percentile(B_inverse /Ref_B*10,AREA)\n",
" TABLE.iloc[7,BBB] = find_var_percentile(speed_inverse ,AREA) \n",
"\n",
"TABLE[['Global','WP','EP','NA','NI','SI','SP']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__PLOT:__ full track and filtered track check"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.close(); plt.figure(figsize=(7,2),dpi=200)\n",
"ax=[]\n",
"gs0 = gridspec.GridSpec(1, 1)\n",
"gs0.update(left=0.05, right=0.98, bottom=0.1, top=0.9, hspace=0.06, wspace=0.1)\n",
"ax.append(plt.subplot(gs0[0, 0]))\n",
"fontsize = 7\n",
"#### density map plot\n",
"# draw base maps\n",
"m = Basemap(projection='cyl', llcrnrlat=-40, urcrnrlat=40,llcrnrlon=0, urcrnrlon=361,resolution='i',ax=ax[0])\n",
"_ = m.drawcoastlines(linewidth=.5,color='k',zorder=10)\n",
"_ = m.drawmapboundary(fill_color='w')\n",
"_ = m.fillcontinents(color='none',lake_color='none')\n",
"_ = m.drawmeridians(np.arange(0,361,45),labels=[0,0,0,1],linewidth=0.,fontsize=fontsize)\n",
"_ = m.drawparallels(np.arange(-40,41,20),labels=[1,0,0,0],linewidth=0.,fontsize=fontsize)\n",
"#\n",
"def plot_track(x):\n",
" tmp_lon = x.USA_LON.values\n",
" tmp_lat = x.USA_LAT.values\n",
" tmp_lon [tmp_lon<0] = tmp_lon [tmp_lon<0] + 360\n",
" _=ax[0].plot(x.USA_LON,x.USA_LAT,lw=.3,color='b',zorder=1)\n",
" #\n",
" RRR_LMI= x.iloc[::-1].USA_WIND.idxmax()\n",
" tmp_x=x[x.LF_MARKER==1]\n",
" RRR_LF = tmp_x.USA_WIND.idxmax()\n",
" pick = x[(x.index>=RRR_LMI)&(x.index<=RRR_LF)]\n",
" tmp_lat = pick.USA_LAT.values[:]\n",
" tmp_lon = pick.USA_LON.values[:]\n",
" _=ax[0].plot(tmp_lon,tmp_lat,lw=.3,color='red',zorder=2)\n",
" #\n",
" #if pick.USA_WIND.values[-1]*0.5144444>=np.nanpercentile(LI_inverse,90):\n",
" if pick.USA_WIND.values[-1]*0.5144444>=50:\n",
" _=ax[0].plot(tmp_lon[-1],tmp_lat[-1],'oy',lw=.3,ms=3,zorder=3,alpha=1)\n",
" \n",
"_=newbt_lf.groupby('SID').apply(lambda x: plot_track(x))\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp.png',bbox_inches='tight', format='pdf',dpi=600)\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp.pdf',bbox_inches='tight', format='pdf',dpi=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Figure 4**"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.3); fontsize = 8\n",
"#\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (year_inverse[CCC] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]>=CAT_KT.CAT3.LOWER):\n",
" tmp.extend([dur_inverse[CCC]])\n",
" Y.extend([np.nanmedian(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax.plot(X,Y,'-C0',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"lns1=ax.plot(x_trend,y_trend,'-',lw=3,color='C0',label='Duration ('+str(int(round(slope*10)))+'±'+str(int(round(ci*10)))+' hr decade$^{-1}$)')\n",
"_=ax.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"#_=ax.legend(fontsize=fontsize,loc=2,handlelength=1.6)\n",
"_=ax.set_xticks(range(1980,2021,5))\n",
"_=ax.set_ylim([0,100])\n",
"_=ax.set_yticks(np.arange(0,70,10))\n",
"_=ax.set_yticklabels(np.arange(0,70,10),fontsize=fontsize,color='C0')\n",
"_=ax.tick_params(axis='y', colors='C0')\n",
"_=ax.set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.set_ylabel('Duration of decay (hr)',fontsize=fontsize,color='C0')\n",
"#_=ax.text(1975,110,'a',fontsize=fontsize,fontweight='bold')\n",
"\n",
"bx=ax.twinx()\n",
"_=bx.plot([-100000,-200000],[-100000,-200000],'-',lw=3,color='k',label='Duration ('+str(int(round(slope*10)))+'±'+str(int(round(ci*10)))+' hr decade$^{-1}$)')\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]>=CAT_KT.CAT3.LOWER):\n",
" tmp.extend([trajectory_inverse[CCC]])\n",
" Y.extend([np.nanmedian(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=bx.plot(X,Y,'-C1',lw=1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"lns2=bx.plot(x_trend,y_trend,'-',lw=3,color='C1',label='Distance ('+str(int(round(slope*10)))+'±'+str(int(round(ci*10)))+' km decade$^{-1}$)')\n",
"_=bx.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"#_=bx.legend(fontsize=fontsize,loc=2,handlelength=1.6)\n",
"_=bx.set_xticks(range(1980,2021,5))\n",
"_=bx.set_ylim([-500,2200])\n",
"_=bx.set_xlim([1980,2020])\n",
"_=bx.set_yticks(np.arange(0,2001,400))\n",
"_=bx.set_yticklabels(np.arange(0,2001,400),fontsize=fontsize,color='C1')\n",
"_=bx.tick_params(axis='y', colors='C1')\n",
"_=bx.set_xlabel('Year',fontsize=fontsize)\n",
"_=bx.set_ylabel('LMI location to landfall (km)',fontsize=fontsize,color='C1')\n",
"\n",
"_=bx.yaxis.set_label_coords(1.15,0.62)\n",
"_=ax.yaxis.set_label_coords(-0.1,0.31)\n",
"\n",
"lns = lns1+lns2\n",
"labs = [l.get_label() for l in lns]\n",
"_=ax.legend(lns, labs, fontsize=fontsize,loc=2,bbox_to_anchor=[-.01,1.01])\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp.png',dpi=600,bbox_inches='tight')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1160.8390621546816\n",
"49.090909090909086\n",
"1.3752445850759576\n",
"0.009233803314944776\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,1); fig.set_size_inches(3,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.3); fontsize = 8\n",
"#\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]>=CAT_KT.CAT3.LOWER):\n",
" tmp.extend([trajectory_inverse[CCC]])\n",
" Y.extend([np.nanmedian(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax.plot(X,Y/np.max(Y),'-C1',lw=1,zorder=4)\n",
"print(np.max(Y))\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend/np.max(Y),'-',lw=3,color='C1',label='$d$ ('+str(int(round(slope*10)))+' km decade$^{-1}$, $p$='+str(round(p,2))+')',zorder=5)\n",
"#\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (year_inverse[CCC] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]>=CAT_KT.CAT3.LOWER):\n",
" tmp.extend([dur_inverse[CCC]])\n",
" Y.extend([np.nanmedian(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax.plot(X,Y/np.max(Y)/1.1,'-C0',lw=1,zorder=4)\n",
"print(np.max(Y)/1.1)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend/np.max(Y)/1.1,'-',lw=3,color='C0',label='$T$ ('+str(int(round(slope*10)))+' hr decade$^{-1}$, $p$='+str(round(p,2))+')',zorder=5)\n",
"#\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]>=CAT_KT.CAT3.LOWER):\n",
" tmp.extend([B_inverse[CCC]/Ref_B])\n",
" Y.extend([np.nanmedian(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax.plot(X,Y/np.max(Y)/1.2,'-C2',lw=1,zorder=4)\n",
"print(np.max(Y)/1.2)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend/np.max(Y)/1.2,'-',lw=3,color='C2',label='$\\kappa$ ('+str(round(slope*10*1e2).astype(int))+'×10$^{-2}$ $\\kappa_o$ decade$^{-1}$, $p$='+str(round(p,2))+')',zorder=5)\n",
"#\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR):# & (VMAX_PMIN_SERIES[CCC][0]>=CAT_KT.CAT3.LOWER):\n",
" tmp.extend([B_inverse[CCC]*dur_inverse[CCC]*3600])\n",
" Y.extend([np.nanmedian(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax.plot(X,Y/np.max(Y)/1.3,'-C3',lw=1,zorder=4)\n",
"print(np.max(Y)/1.3)\n",
"x_trend, y_trend,slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax.plot(x_trend,y_trend/np.max(Y)/1.3,'-',lw=3,color='C3',label='$\\kappa{d/c}$ ('+str(round(slope*10*1e4).astype(int))+'×10$^{-4}$ s m$^{-1}$ decade$^{-1}$, $p$='+str(round(p,2))+')',zorder=5)\n",
"#\n",
"_=ax.tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.legend(fontsize=fontsize,loc=2,bbox_to_anchor=(0.0,1.3))\n",
"_=ax.set_xticks(range(1980,2021,5))\n",
"#_=ax.set_ylim([0,100])\n",
"#_=ax.set_yticks(np.arange(0,70,10))\n",
"#_=ax.set_yticklabels(np.arange(0,70,10),fontsize=fontsize,color='C0')\n",
"_=ax.set_xlabel('Year',fontsize=fontsize)\n",
"_=ax.set_ylabel('',fontsize=fontsize)\n",
"#_=ax.text(1975,110,'a',fontsize=fontsize,fontweight='bold')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp3.png',dpi=600,bbox_inches='tight')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp3.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"51.444"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"36.0108"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"15.0"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmptmp_LMI = []\n",
"tmptmp_LI = []\n",
"for CCC in range(len(B_inverse)):\n",
" tmptmp_LMI.extend([interp_vmax_in_inverse[CCC][ 0]])\n",
" tmptmp_LI .extend([interp_vmax_in_inverse[CCC][-1]])\n",
"tmptmp_LMI=np.asarray(tmptmp_LMI)\n",
"tmptmp_LI =np.asarray(tmptmp_LI )\n",
"np.nanmedian(tmptmp_LMI)\n",
"np.nanmedian(tmptmp_LI )\n",
"np.nanmedian(dur_inverse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ERA5 - case mean annual trend - from LMI to LF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**I/O: ERA5 read in**"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"ERA5_U_00_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/u_00061218.nc' )\n",
"ERA5_U_03_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/u_03091521.nc' )\n",
"ERA5_V_00_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/v_00061218.nc' )\n",
"ERA5_V_03_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/v_03091521.nc' )\n",
"#ERA5_R_00_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/r_00061218.nc' )\n",
"#ERA5_R_03_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/r_03091521.nc' )\n",
"ERA5_SST_00_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/sst_00061218.nc')\n",
"ERA5_SST_03_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/sst_03091521.nc')\n",
"ERA5_TD2M_00_fh= Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/td2m_00061218.nc' )\n",
"ERA5_TD2M_03_fh= Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/td2m_03091521.nc' )\n",
"ERA5_T2M_00_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/t2m_00061218.nc' )\n",
"ERA5_T2M_03_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/t2m_03091521.nc' )\n",
"ERA5_SP_00_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/sp_00061218.nc' )\n",
"ERA5_SP_03_fh = Dataset('/net/wrfstore7-10/disk1/sw1013/ERA5/sp_03091521.nc' )\n",
"\n",
"TIME_00_SINCE_1900 = ERA5_U_00_fh.variables['time'][:]\n",
"TIME_03_SINCE_1900 = ERA5_U_03_fh.variables['time'][:]\n",
"\n",
"LAT=ERA5_U_00_fh.variables['latitude']\n",
"LON=ERA5_U_00_fh.variables['longitude']\n",
"LON,LAT = np.meshgrid(LON,LAT)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: find_shear_one_record(x)**"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def find_shear_one_record(x):\n",
" C_LAT = x.USA_LAT\n",
" C_LON = x.USA_LON\n",
" TIME = x.ISO_TIME\n",
" #print(x.SEASON.values[0])\n",
" tmp_time = datetime.strptime(TIME, \"%Y-%m-%d %H:%M:%S\") - datetime.strptime(\"1900-01-01 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
" TIME_SPOT = int(tmp_time.days*24+tmp_time.seconds/3600)\n",
" TIME_READ_00 = np.nanargmin(abs(TIME_SPOT-TIME_00_SINCE_1900))\n",
" TIME_READ_03 = np.nanargmin(abs(TIME_SPOT-TIME_03_SINCE_1900))\n",
" \n",
" if np.min(abs(TIME_SPOT-TIME_00_SINCE_1900))<=np.min(abs(TIME_SPOT-TIME_03_SINCE_1900)):\n",
" TIME_READ = TIME_READ_00\n",
" U200=ERA5_U_00_fh .variables['u' ][TIME_READ,0].data\n",
" U850=ERA5_U_00_fh .variables['u' ][TIME_READ,1].data\n",
" V200=ERA5_V_00_fh .variables['v' ][TIME_READ,0].data\n",
" V850=ERA5_V_00_fh .variables['v' ][TIME_READ,1].data\n",
" #R700=ERA5_R_00_fh .variables['r' ][TIME_READ,0].data\n",
" #R850=ERA5_R_00_fh .variables['r' ][TIME_READ,1].data\n",
" SST =ERA5_SST_00_fh.variables['sst'][TIME_READ ].data\n",
" else:\n",
" TIME_READ = TIME_READ_03\n",
" U200=ERA5_U_03_fh .variables['u' ][TIME_READ,0].data\n",
" U850=ERA5_U_03_fh .variables['u' ][TIME_READ,1].data\n",
" V200=ERA5_V_03_fh .variables['v' ][TIME_READ,0].data\n",
" V850=ERA5_V_03_fh .variables['v' ][TIME_READ,1].data\n",
" #R700=ERA5_R_03_fh .variables['r' ][TIME_READ,0].data\n",
" #R850=ERA5_R_03_fh .variables['r' ][TIME_READ,1].data\n",
" SST =ERA5_SST_03_fh.variables['sst'][TIME_READ ].data\n",
" \n",
" DIST = np.sqrt((C_LAT-LAT)**2+(C_LON-LON)**2)\n",
" DIST[(DIST<2) | (DIST>8)]=np.nan\n",
" U200[np.isnan(DIST)]=np.nan\n",
" U850[np.isnan(DIST)]=np.nan\n",
" V200[np.isnan(DIST)]=np.nan\n",
" V850[np.isnan(DIST)]=np.nan\n",
" #R700[np.isnan(DIST)]=np.nan\n",
" #R850[np.isnan(DIST)]=np.nan\n",
" tmp_u200 = np.nanmean(U200)\n",
" tmp_u850 = np.nanmean(U850)\n",
" tmp_v200 = np.nanmean(V200)\n",
" tmp_v850 = np.nanmean(V850)\n",
" #tmp_r700 = np.nanmean(R700)\n",
" #tmp_r850 = np.nanmean(R850)\n",
" SHEAR=np.sqrt((tmp_u200-tmp_u850)**2+(tmp_v200-tmp_v850)**2)\n",
" UPPERWIND = np.sqrt((tmp_u200)**2+(tmp_v200)**2)\n",
" LOWERWIND = np.sqrt((tmp_u850)**2+(tmp_v850)**2)\n",
" #LOWERHUMID= (tmp_r700+tmp_r850)/2\n",
" \n",
" DIST = np.sqrt((C_LAT-LAT)**2+(C_LON-LON)**2)\n",
" DIST[DIST>8]=np.nan\n",
" SST[np.isnan(DIST)]=np.nan\n",
" SST[SST==-32767.0]=np.nan\n",
" tmp_sst = np.nanmean(SST)\n",
" SSTUNDER= tmp_sst\n",
" \n",
" return [SHEAR, UPPERWIND, LOWERWIND, np.nan,SSTUNDER]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: download_ERA5_calc_MPI(x)**\n",
"Only run this stript if you want to download the ERA5 data"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def download_ERA5_calc_MPI(x):\n",
" # YEAR,MONTH,DAY,HR are strings. e.g. YEAR:'2008'; MONTH:'06'; DAY:'09'; HR:'03'\n",
" # LAT, LON are floating. e.g. LAT:21.2; LON:120.3\n",
" \n",
" OUTPUT_TAG = x['SID']+'_'+x['ISO_TIME'][0:4]+x['ISO_TIME'][5:7]+x['ISO_TIME'][8:10]+x['ISO_TIME'][11:13]+x['ISO_TIME'][14:16]+x['ISO_TIME'][17:19]\n",
" utc = x['ISO_TIME']\n",
" time0 = datetime.strptime(utc, \"%Y-%m-%d %H:%M:%S\")\n",
" delta_sec = -3600*24*3\n",
" lst = datetime.strftime(time0+timedelta(0,delta_sec),\"%Y-%m-%d %H:%M:%S\")\n",
" utc = lst\n",
" \n",
" YEAR = utc[ 0: 4]\n",
" MONTH = utc[ 5: 7]\n",
" DAY = utc[ 8:10]\n",
" HR = utc[11:13]\n",
" LAT = x['USA_LAT']\n",
" LON = x['USA_LON']\n",
" \n",
" if np.isnan(LAT) or np.isnan(LON):\n",
" MPI = np.nan\n",
" \n",
" else:\n",
" AREA_RADIUS = 0\n",
"\n",
" c = cdsapi.Client()\n",
" c.retrieve(\n",
" 'reanalysis-era5-pressure-levels',\n",
" {\n",
" 'product_type': 'reanalysis',\n",
" 'format': 'netcdf',\n",
" 'variable': ['specific_humidity', 'temperature'],\n",
" 'pressure_level': [\n",
" '1', '2', '3',\n",
" '5', '7', '10',\n",
" '20', '30', '50',\n",
" '70', '100', '125',\n",
" '150', '175', '200',\n",
" '225', '250', '300',\n",
" '350', '400', '450',\n",
" '500', '550', '600',\n",
" '650', '700', '750',\n",
" '775', '800', '825',\n",
" '850', '875', '900',\n",
" '925', '950', '975',\n",
" '1000',\n",
" ],\n",
" 'year': YEAR,\n",
" 'month': MONTH,\n",
" 'day': DAY,\n",
" 'time': HR+':00',\n",
" 'area': [\n",
" LAT+AREA_RADIUS, LON-AREA_RADIUS, LAT-AREA_RADIUS,\n",
" LON+AREA_RADIUS,\n",
" ],\n",
" },\n",
" '/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_AIR.nc')\n",
"\n",
" c.retrieve(\n",
" 'reanalysis-era5-single-levels',\n",
" {\n",
" 'product_type': 'reanalysis',\n",
" 'format': 'netcdf',\n",
" 'variable': [\n",
" 'mean_sea_level_pressure', 'sea_surface_temperature',\n",
" ],\n",
" 'year': YEAR,\n",
" 'month': MONTH,\n",
" 'day': DAY,\n",
" 'time': HR+':00',\n",
" 'area': [\n",
" LAT+AREA_RADIUS, LON-AREA_RADIUS, LAT-AREA_RADIUS,\n",
" LON+AREA_RADIUS,\n",
" ],\n",
" },\n",
" '/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_SURF.nc')\n",
"\n",
" fh = Dataset('/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_AIR.nc')\n",
" Q =np.flip ( fh.variables['q' ][:].data.squeeze() * 1.e3 ) # unit g/kg\n",
" T =np.flip ( fh.variables['t' ][:].data.squeeze() - 273.15 ) # unit C\n",
" P =np.flip ( fh.variables['level' ][:].data ) # unit hPa\n",
"\n",
" fh = Dataset('/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_SURF.nc')\n",
" MSL =np.flip ( fh.variables['msl' ][:].data.squeeze() / 1.e2 ) # unit hPa\n",
" SST =np.flip ( fh.variables['sst' ][:].data.squeeze() - 273.15 ) # unit C\n",
"\n",
" #tmp = PMIN,VMAX,TO,IFL,RAT,CAPEMS,CAPEM,FAC\n",
" tmp = pcmin(SST,MSL, P, T, Q)\n",
" \n",
" #x['MPI'] = MPI\n",
" if ~np.isnan(tmp[1]):\n",
" return tmp\n",
" else:\n",
" return np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: use_ERA5_calc_MPI(x)**\n",
"this stript only uses downloaded ERA5 data"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def use_ERA5_calc_MPI(x):\n",
" # YEAR,MONTH,DAY,HR are strings. e.g. YEAR:'2008'; MONTH:'06'; DAY:'09'; HR:'03'\n",
" # LAT, LON are floating. e.g. LAT:21.2; LON:120.3\n",
" \n",
" OUTPUT_TAG = x['SID']+'_'+x['ISO_TIME'][0:4]+x['ISO_TIME'][5:7]+x['ISO_TIME'][8:10]+x['ISO_TIME'][11:13]+x['ISO_TIME'][14:16]+x['ISO_TIME'][17:19]\n",
" utc = x['ISO_TIME']\n",
" time0 = datetime.strptime(utc, \"%Y-%m-%d %H:%M:%S\")\n",
" delta_sec = -3600*24*3\n",
" lst = datetime.strftime(time0+timedelta(0,delta_sec),\"%Y-%m-%d %H:%M:%S\")\n",
" utc = lst\n",
" \n",
" YEAR = utc[ 0: 4]\n",
" MONTH = utc[ 5: 7]\n",
" DAY = utc[ 8:10]\n",
" HR = utc[11:13]\n",
" LAT = x['USA_LAT']\n",
" LON = x['USA_LON']\n",
" \n",
" if np.isnan(LAT) or np.isnan(LON):\n",
" return np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan\n",
"\n",
" else:\n",
" if []==glob.glob('/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_AIR.nc'):\n",
" AREA_RADIUS = 0\n",
"\n",
" c = cdsapi.Client()\n",
" c.retrieve(\n",
" 'reanalysis-era5-pressure-levels',\n",
" {\n",
" 'product_type': 'reanalysis',\n",
" 'format': 'netcdf',\n",
" 'variable': ['specific_humidity', 'temperature'],\n",
" 'pressure_level': [\n",
" '1', '2', '3',\n",
" '5', '7', '10',\n",
" '20', '30', '50',\n",
" '70', '100', '125',\n",
" '150', '175', '200',\n",
" '225', '250', '300',\n",
" '350', '400', '450',\n",
" '500', '550', '600',\n",
" '650', '700', '750',\n",
" '775', '800', '825',\n",
" '850', '875', '900',\n",
" '925', '950', '975',\n",
" '1000',\n",
" ],\n",
" 'year': YEAR,\n",
" 'month': MONTH,\n",
" 'day': DAY,\n",
" 'time': HR+':00',\n",
" 'area': [\n",
" LAT+AREA_RADIUS, LON-AREA_RADIUS, LAT-AREA_RADIUS,\n",
" LON+AREA_RADIUS,\n",
" ],\n",
" },\n",
" '/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_AIR.nc')\n",
"\n",
" c.retrieve(\n",
" 'reanalysis-era5-single-levels',\n",
" {\n",
" 'product_type': 'reanalysis',\n",
" 'format': 'netcdf',\n",
" 'variable': [\n",
" 'mean_sea_level_pressure', 'sea_surface_temperature',\n",
" ],\n",
" 'year': YEAR,\n",
" 'month': MONTH,\n",
" 'day': DAY,\n",
" 'time': HR+':00',\n",
" 'area': [\n",
" LAT+AREA_RADIUS, LON-AREA_RADIUS, LAT-AREA_RADIUS,\n",
" LON+AREA_RADIUS,\n",
" ],\n",
" },\n",
" '/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_SURF.nc')\n",
" \n",
" fh = Dataset('/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_AIR.nc')\n",
" Q =np.flip ( fh.variables['q' ][:].data.squeeze() * 1.e3 ) # unit g/kg\n",
" T =np.flip ( fh.variables['t' ][:].data.squeeze() - 273.15 ) # unit C\n",
" P =np.flip ( fh.variables['level' ][:].data ) # unit hPa\n",
"\n",
" fh = Dataset('/net/wrfstore7-10/disk1/sw1013/MPI_ERA5_BYCASE/'+OUTPUT_TAG+'_SURF.nc')\n",
" MSL =np.flip ( fh.variables['msl' ][:].data.squeeze() / 1.e2 ) # unit hPa\n",
" SST =np.flip ( fh.variables['sst' ][:].data.squeeze() - 273.15 ) # unit C\n",
"\n",
" #tmp = PMIN,VMAX,TO,IFL,RAT,CAPEMS,CAPEM,FAC\n",
" tmp = pcmin(SST,MSL, P, T, Q)\n",
"\n",
" #x['MPI'] = MPI\n",
" if ~np.isnan(tmp[1]):\n",
" return tmp\n",
" else:\n",
" return np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: CALC_MOVING_SPEED(tmp_lat,tmp_lon)**"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def CALC_MOVING_SPEED(tmp_lat,tmp_lon):\n",
" tmp_uv = np.nan * np.zeros(shape=tmp_lat.shape)\n",
" for RRR in range(len(tmp_lat)-1):\n",
" coords_1 = (tmp_lat[RRR ], tmp_lon[RRR ])\n",
" coords_2 = (tmp_lat[RRR+1], tmp_lon[RRR+1])\n",
" tmp_uv[RRR] = geopy.distance.vincenty(coords_1, coords_2).m/6./3600.\n",
" return tmp_uv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**DEF: keep_one_from_lmi_to_lf_fraction(x,FRAC)**"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def keep_one_from_lmi_to_lf_fraction(x,FRAC):\n",
" #print(x.SID.values[0])\n",
" RRR_LMI= len(x)-np.nanargmax(x.iloc[::-1].USA_WIND.values)-1\n",
" RRR_LF = np.nanargmax(x.LF_MARKER.values)\n",
" if RRR_LMI + FRAC <= RRR_LF :\n",
" RRR = RRR_LMI + FRAC\n",
" output = x.iloc[int(RRR)]\n",
" tmp = find_shear_one_record(output)\n",
" output['HOUR_TO_LF'] = 3*(RRR_LF-RRR)\n",
" output['SHEAR' ] = tmp[0]\n",
" output['UPPERWIND' ] = tmp[1]\n",
" output['LOWERWIND' ] = tmp[2]\n",
" output['LOWERHUMID'] = tmp[3]\n",
" output['SST' ] = tmp[4]\n",
" tmp = CALC_MOVING_SPEED(x.iloc[int(RRR-1):int(RRR+1)].USA_LAT.values,x.iloc[int(RRR-1):int(RRR+1)].USA_LON.values)\n",
" output['TRANSLATION' ] = tmp[0]\n",
" # PMIN,VMAX,TO,IFL,RAT,CAPEMS,CAPEM,FAC\n",
" tmp = use_ERA5_calc_MPI(output)\n",
" #tmp = download_ERA5_calc_MPI(output)\n",
" output['MPI3DAY' ] = tmp[1]/0.514444\n",
" output['TOUT3DAY' ] = tmp[2]\n",
" output['SST3DAY' ] = tmp[2] * tmp[4] \n",
" output['CAPEMS' ] = tmp[5]\n",
" output['CAPEM' ] = tmp[6]\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"SPLIT_PERIOD = np.arange(0,131,1)\n",
"for tmp,FRAC in enumerate(SPLIT_PERIOD):\n",
" print(FRAC)\n",
" if tmp == 0:\n",
" VAR = newbt_lf.groupby('SID').apply(lambda x: keep_one_from_lmi_to_lf_fraction(x,FRAC))\n",
" else:\n",
" VAR = VAR.append(newbt_lf.groupby('SID').apply(lambda x: keep_one_from_lmi_to_lf_fraction(x,FRAC)))\n",
"VARbackup = copy.copy(VAR)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"VAR = copy.copy(VARbackup)\n",
"VAR=VAR.reset_index(level=0, drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"SHEAR = VAR.groupby('SID').apply(lambda x:np.nanmean(x.SHEAR))\n",
"LOWERHUMID = VAR.groupby('SID').apply(lambda x:np.nanmean(x.LOWERHUMID))\n",
"SST = VAR.groupby('SID').apply(lambda x:np.nanmean(x.SST3DAY))\n",
"SEASON = VAR.groupby('SID').apply(lambda x:np.nanmean(x.SEASON))\n",
"MPI = VAR.groupby('SID').apply(lambda x:np.nanmean(x.MPI3DAY))\n",
"CLAT = VAR.groupby('SID').apply(lambda x:np.nanmean(np.abs(x.LAT)))\n",
"TRANSLATION= VAR.groupby('SID').apply(lambda x:np.nanmean(x.TRANSLATION))\n",
"Sterm = VAR.groupby('SID').apply(lambda x:np.nanmean( np.sqrt(abs(x.CAPEMS-x.CAPEM)) / np.sqrt(abs(x.SST3DAY-x.TOUT3DAY)) )) \n",
"Tterm = VAR.groupby('SID').apply(lambda x:np.nanmean( np.sqrt(abs(x.SST3DAY/x.TOUT3DAY*(x.SST3DAY-x.TOUT3DAY))) ))\n",
"TOUTFLOW = VAR.groupby('SID').apply(lambda x:np.nanmean( x.TOUT3DAY))\n",
"LMI = VAR.groupby('SID').apply(lambda x:x.USA_WIND.values[0])\n",
"LI = VAR.groupby('SID').apply(lambda x:x.USA_WIND.values[-1])\n",
"MPILMI = VAR.groupby('SID').apply(lambda x:x.MPI3DAY.values[0])\n",
"SHEARLMI = VAR.groupby('SID').apply(lambda x:x.SHEAR.values[0])\n",
"TtermLMI = VAR.groupby('SID').apply(lambda x:np.sqrt(abs(x.SST3DAY.values[0]/x.TOUT3DAY.values[0]*(x.SST3DAY.values[0]-x.TOUT3DAY.values[0]))) )\n",
"StermLMI = VAR.groupby('SID').apply(lambda x:np.sqrt(abs(x.CAPEMS.values[0]-x.CAPEM.values[0])) / np.sqrt(abs(x.SST3DAY.values[0]-x.TOUT3DAY.values[0])) )\n",
"SSTLMI = VAR.groupby('SID').apply(lambda x:x.SST3DAY.values[0])\n",
"TOUTFLOWLMI= VAR.groupby('SID').apply(lambda x:x.TOUT3DAY.values[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"fig,ax=plt.subplots(3,3); fig.set_size_inches(10,10); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.3); fontsize = 8\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([SHEAR[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[0][0].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][0].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' m s$^{-1}$/decade')\n",
"_=ax[0][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][0].legend(fontsize=fontsize)\n",
"_=ax[0][0].set_xticks(range(1980,2021,5))\n",
"#_=ax[0][0].set_ylim([50,90])\n",
"_=ax[0][0].set_xlabel('Year')\n",
"_=ax[0][0].set_ylabel('Shear m s$^{-1}$')\n",
"Y_SHEAR = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([LOWERHUMID[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[0][1].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][1].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' %/decade')\n",
"_=ax[0][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][1].legend(fontsize=fontsize)\n",
"_=ax[0][1].set_xticks(range(1980,2021,5))\n",
"#_=ax[0][1].set_ylim([50,90])\n",
"_=ax[0][1].set_xlabel('Year')\n",
"_=ax[0][1].set_ylabel('Low-level RH %')\n",
"Y_RH = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([CLAT[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[0][2].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[0][2].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' %/decade')\n",
"_=ax[0][2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0][2].legend(fontsize=fontsize)\n",
"_=ax[0][2].set_xticks(range(1980,2021,5))\n",
"#_=ax[0][2].set_ylim([50,90])\n",
"_=ax[0][2].set_xlabel('Year')\n",
"_=ax[0][2].set_ylabel('Latitude (deg)')\n",
"Y_RH = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([TRANSLATION[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[1][0].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][0].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' m s$^{-1}$/decade')\n",
"_=ax[1][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][0].legend(fontsize=fontsize)\n",
"_=ax[1][0].set_xticks(range(1980,2021,5))\n",
"#_=ax[1][0].set_ylim([50,90])\n",
"_=ax[1][0].set_xlabel('Year')\n",
"_=ax[1][0].set_ylabel('Translation m s$^{-1}$')\n",
"Y_MPI = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([MPI[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[1][1].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][1].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' kt/decade')\n",
"_=ax[1][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][1].legend(fontsize=fontsize)\n",
"_=ax[1][1].set_xticks(range(1980,2021,5))\n",
"#_=ax[1][1].set_ylim([50,90])\n",
"_=ax[1][1].set_xlabel('Year')\n",
"_=ax[1][1].set_ylabel('MPI (3 days before) kt')\n",
"Y_MPI = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([SST[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[1][2].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[1][2].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' K/decade')\n",
"_=ax[1][2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1][2].legend(fontsize=fontsize)\n",
"_=ax[1][2].set_xticks(range(1980,2021,5))\n",
"#_=ax[1][2].set_ylim([50,90])\n",
"_=ax[1][2].set_xlabel('Year')\n",
"_=ax[1][2].set_ylabel('MPI: $T_s$ K')\n",
"Y_SST = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([Tterm[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[2][0].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[2][0].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' /decade')\n",
"_=ax[2][0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[2][0].legend(fontsize=fontsize)\n",
"_=ax[2][0].set_xticks(range(1980,2021,5))\n",
"#_=ax[2][0].set_ylim([50,90])\n",
"_=ax[2][0].set_xlabel('Year')\n",
"_=ax[2][0].set_ylabel(r'MPI: $\\sqrt{(T_s-T_o)\\frac{T_s}{T_o}}$')\n",
"Y_MPI = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([Sterm[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[2][1].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[2][1].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' /decade')\n",
"_=ax[2][1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[2][1].legend(fontsize=fontsize)\n",
"_=ax[2][1].set_xticks(range(1980,2021,5))\n",
"#_=ax[2][1].set_ylim([50,90])\n",
"_=ax[2][1].set_xlabel('Year')\n",
"_=ax[2][1].set_ylabel('MPI: $\\sqrt{s^*-s}$')\n",
"Y_MPI = Y\n",
"\n",
"X = INDEX_year[:]\n",
"Y = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([TOUTFLOW[CCC]])\n",
" Y.extend([np.nanmean(tmp)])\n",
" sample_weight.extend([len(tmp)])\n",
"_=ax[2][2].plot(X,Y,'-k',lw=.5)\n",
"x_trend, y_trend,slope,ci,_ = get_trend_and_ci_weight(X,Y,sample_weight)\n",
"_=ax[2][2].plot(x_trend,y_trend,'-',color='C1',label='p='+str(np.round(p,2))+'\\nslope='+str(np.round((y_trend[-1]-y_trend[0])/(x_trend[-1]-x_trend[0])*10,1))+' K/decade')\n",
"_=ax[2][2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[2][2].legend(fontsize=fontsize)\n",
"_=ax[2][2].set_xticks(range(1980,2021,5))\n",
"#_=ax[2][2].set_ylim([50,90])\n",
"_=ax[2][2].set_xlabel('Year')\n",
"_=ax[2][2].set_ylabel('MPI: $T_o$ K')\n",
"Y_MPI = Y\n",
"_=ax[2][2].text(1900,190,r'$MPI=0.8\\sqrt{\\frac{C_k}{C_d}}\\sqrt{(T_s-T_o)\\frac{T_s}{T_o}}\\sqrt{s^*-s}$',fontsize=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Correlation bewteen decay factors and environment**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Individual case correlation**"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" LMI | \n",
" Lat | \n",
" Shear | \n",
" SST | \n",
" MPI | \n",
"
\n",
" \n",
" \n",
" \n",
" k | \n",
" -0.47(0.0) | \n",
" 0.01(0.79) | \n",
" 0.09(0.02) | \n",
" -0.01(0.8) | \n",
" -0.01(0.7) | \n",
"
\n",
" \n",
" D | \n",
" 0.3(0.0) | \n",
" 0.23(0.0) | \n",
" 0.17(0.0) | \n",
" -0.35(0.0) | \n",
" -0.3(0.0) | \n",
"
\n",
" \n",
" T | \n",
" 0.29(0.0) | \n",
" 0.1(0.01) | \n",
" 0.08(0.04) | \n",
" -0.22(0.0) | \n",
" -0.2(0.0) | \n",
"
\n",
" \n",
" kT | \n",
" -0.08(0.05) | \n",
" 0.08(0.05) | \n",
" 0.25(0.0) | \n",
" -0.38(0.0) | \n",
" -0.3(0.0) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" LMI Lat Shear SST MPI\n",
"k -0.47(0.0) 0.01(0.79) 0.09(0.02) -0.01(0.8) -0.01(0.7)\n",
"D 0.3(0.0) 0.23(0.0) 0.17(0.0) -0.35(0.0) -0.3(0.0) \n",
"T 0.29(0.0) 0.1(0.01) 0.08(0.04) -0.22(0.0) -0.2(0.0) \n",
"kT -0.08(0.05) 0.08(0.05) 0.25(0.0) -0.38(0.0) -0.3(0.0) "
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp_B = np.asarray(B_inverse ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_DIS = np.asarray(trajectory_inverse) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_DUR = np.asarray(dur_inverse ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_LMI = np.asarray(A_inverse ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_LI = np.asarray(LI ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_SHEAR = np.asarray(SHEAR ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_LAT = np.asarray(CLAT ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_V = np.asarray(speed_inverse ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_BDUR = np.asarray(B_inverse*dur_inverse*3600)[(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_MPI = np.asarray(MPI ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"tmp_SST = np.asarray(SST ) [(~np.isnan(SST))&(~np.isnan(B_inverse))]\n",
"\n",
"\n",
"TABLE = pd.DataFrame(index=['k','D','T','kT'],columns=['LMI','Lat','Shear','SST','MPI'])\n",
"\n",
"def r_and_p(X,Y):\n",
" _,_,r,p,_ = scipy.stats.linregress(scipy.signal.detrend(X),scipy.signal.detrend(Y))\n",
" return str(round(r,2))+'('+str(round(p,2))+')'\n",
"\n",
"TABLE.Lat.D = r_and_p(tmp_LAT,tmp_DIS)\n",
"TABLE.Lat.T = r_and_p(tmp_LAT,tmp_DUR)\n",
"TABLE.Lat.k = r_and_p(tmp_LAT,tmp_B )\n",
"TABLE.Lat.kT = r_and_p(tmp_LAT,tmp_BDUR )\n",
"TABLE.Shear.D = r_and_p(tmp_SHEAR,tmp_DIS)\n",
"TABLE.Shear.T = r_and_p(tmp_SHEAR,tmp_DUR)\n",
"TABLE.Shear.k = r_and_p(tmp_SHEAR,tmp_B )\n",
"TABLE.Shear.kT = r_and_p(tmp_SHEAR,tmp_BDUR )\n",
"TABLE.SST.D = r_and_p(tmp_SST,tmp_DIS)\n",
"TABLE.SST.T = r_and_p(tmp_SST,tmp_DUR)\n",
"TABLE.SST.k = r_and_p(tmp_SST,tmp_B )\n",
"TABLE.SST.kT = r_and_p(tmp_SST,tmp_BDUR )\n",
"TABLE.LMI.D = r_and_p(tmp_LMI,tmp_DIS)\n",
"TABLE.LMI.T = r_and_p(tmp_LMI,tmp_DUR)\n",
"TABLE.LMI.k = r_and_p(tmp_LMI,tmp_B )\n",
"TABLE.LMI.kT = r_and_p(tmp_LMI,tmp_BDUR )\n",
"TABLE.MPI.D = r_and_p(tmp_MPI,tmp_DIS)\n",
"TABLE.MPI.T = r_and_p(tmp_MPI,tmp_DUR)\n",
"TABLE.MPI.k = r_and_p(tmp_MPI,tmp_B )\n",
"TABLE.MPI.kT = r_and_p(tmp_MPI,tmp_BDUR )\n",
"TABLE"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(1,2); fig.set_size_inches(7,3); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.4,hspace=0.3); fontsize = 8\n",
"#\n",
"_=ax[0].plot(tmp_MPI*0.514444,tmp_B/Ref_B,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[0].set_ylabel('$\\kappa/\\kappa_o$',fontsize=fontsize)\n",
"_=ax[0].set_xlabel('Potential intensity (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_MPI,tmp_B)\n",
"p_str = str(np.round(p,2))\n",
"r_str = str('='+'%.2f' %np.round(r,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"else:\n",
" p_str = '='+str(round(p,2))\n",
"if r<0.01:\n",
" r_str = '<0.01'\n",
"_=ax[0].text(100*.99,7*.99,'$r$'+r_str+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[0].text(-20,7,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[0].set_ylim([0,7])\n",
"_=ax[0].set_xlim([0,100])\n",
"#_=ax[0].set_xticks(np.arange(0,17,2))\n",
"#\n",
"_=ax[1].plot(tmp_SHEAR,tmp_B/Ref_B,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax[1].set_ylabel('$\\kappa/\\kappa_o$',fontsize=fontsize)\n",
"_=ax[1].set_xlabel('Vertical wind shear (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_SHEAR,tmp_B)\n",
"p_str = '='+str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax[1].text(30*.99,7*.99,'$r$='+str(np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax[1].text(-6,7,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax[1].set_ylim([0,7])\n",
"_=ax[1].set_xlim([0,30])\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp4.png',dpi=600,bbox_inches='tight')\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_supp4.pdf',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(2,2); fig.set_size_inches(7,7); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"interp_vmax_in_inverse = np.asarray(interp_vmax_in_inverse )\n",
"interp_vmax_pred_inverse = np.asarray(interp_vmax_pred_inverse)\n",
"\n",
"dis_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" dis_inverse.extend([VMAX_PMIN_SERIES[CCC][3]])\n",
"dis_inverse=np.asarray(dis_inverse)\n",
"\n",
"# tmp_LMI and tmp_LF of individual cases with B_inverse case sequence\n",
"tmp_LILMI,tmp_LI,tmp_LMI = [],[],[]\n",
"for CCC in range(len(B_inverse)):\n",
" tmp_LILMI.extend([interp_vmax_in_inverse[CCC][-1]/interp_vmax_in_inverse[CCC][ 0]])\n",
" tmp_LI .extend([interp_vmax_in_inverse[CCC][-1] * 0.51444444 ])\n",
" tmp_LMI .extend([interp_vmax_in_inverse[CCC][ 0] * 0.51444444 ])\n",
"tmp_LILMI,tmp_LI,tmp_LMI=np.asarray(tmp_LILMI),np.asarray(tmp_LI),np.asarray(tmp_LMI)\n",
"\n",
"tmp_DIS = np.asarray(trajectory_inverse) [(~np.isnan(SST))]\n",
"tmp_DUR = np.asarray(dur_inverse ) [(~np.isnan(SST))]\n",
"tmp_LILMI = np.asarray(tmp_LILMI ) [(~np.isnan(SST))]\n",
"tmp_LI = np.asarray(tmp_LI ) [(~np.isnan(SST))]\n",
"tmp_LMI = np.asarray(tmp_LMI ) [(~np.isnan(SST))]\n",
"tmp_SHEAR = np.asarray(SHEAR ) [(~np.isnan(SST))]\n",
"tmp_MPI = np.asarray(MPI ) [(~np.isnan(SST))]\n",
"tmp_C = np.asarray(speed_inverse ) [(~np.isnan(SST))]\n",
"#---------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[0].plot(tmp_DIS/1e3,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[0].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[0].set_xlabel('$d$ (10$^3$ km)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,tmp_DIS)\n",
"p_str = '='+str('%.2f' %np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[0].text(7*.99,1*.99,'$r$='+str('%.2f' %np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax.flatten()[0].text(-1.5,1,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[0].set_ylim([0,1])\n",
"_=ax.flatten()[0].set_xlim([0,7])\n",
"_=ax.flatten()[0].set_xticks(np.arange(0,8,1))\n",
"#-------------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[1].plot(tmp_DUR,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[1].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[1].set_xlabel('$T$ (hr)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,tmp_DUR)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[1].text(400*.99,1*.99,'$r$='+str('%.2f' %np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax.flatten()[1].text(-85.7,1,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[1].set_ylim([0,1])\n",
"_=ax.flatten()[1].set_xlim([0,400])\n",
"_=ax.flatten()[1].set_xticks(np.arange(0,401,100))\n",
"# -------------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[2].plot(tmp_C,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[2].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[2].set_xlabel('$c$ (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI[~np.isnan(tmp_C)],tmp_C[~np.isnan(tmp_C)])\n",
"p_str = '='+str('%.2f' %np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[2].text(16*.99,0*.99,'$r$='+str('%.2f' %np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='bottom',ha='right')\n",
"_=ax.flatten()[2].text(-3.43,1,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[2].set_ylim([0,1])\n",
"_=ax.flatten()[2].set_xlim([0,16])\n",
"_=ax.flatten()[2].set_xticks(np.arange(0,17,2))\n",
"\n",
"_=ax.flatten()[3].plot(tmp_DIS/1e3,tmp_DUR,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[3].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[3].set_ylabel('$T$ (hr)',fontsize=fontsize)\n",
"_=ax.flatten()[3].set_xlabel('$d$ (10$^3$ km)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(dur_inverse,dis_inverse)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[3].text(7*.99,400*.99,'$r$='+str(np.round(r,2))+'\\n$p$'+p_str,fontsize=fontsize,va='top',ha='right')\n",
"_=ax.flatten()[3].text(-1.5,400,'(D)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[3].set_ylim([0,400])\n",
"_=ax.flatten()[3].set_xlim([0,7])\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_3.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots(3,2); fig.set_size_inches(7,10); fig.set_facecolor('w'); fig.set_edgecolor('k');fig.set_dpi(100); \n",
"fig.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3,hspace=0.3); fontsize = 8\n",
"\n",
"interp_vmax_in_inverse = np.asarray(interp_vmax_in_inverse )\n",
"interp_vmax_pred_inverse = np.asarray(interp_vmax_pred_inverse)\n",
"\n",
"dis_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" dis_inverse.extend([VMAX_PMIN_SERIES[CCC][3]])\n",
"dis_inverse=np.asarray(dis_inverse)\n",
"\n",
"# tmp_LMI and tmp_LF of individual cases with B_inverse case sequence\n",
"tmp_LILMI,tmp_LI,tmp_LMI = [],[],[]\n",
"for CCC in range(len(B_inverse)):\n",
" tmp_LILMI.extend([interp_vmax_in_inverse[CCC][-1]/interp_vmax_in_inverse[CCC][ 0]])\n",
" tmp_LI .extend([interp_vmax_in_inverse[CCC][-1] * 0.51444444 ])\n",
" tmp_LMI .extend([interp_vmax_in_inverse[CCC][ 0] * 0.51444444 ])\n",
"tmp_LILMI,tmp_LI,tmp_LMI=np.asarray(tmp_LILMI),np.asarray(tmp_LI),np.asarray(tmp_LMI)\n",
"\n",
"tmp_DIS = np.asarray(trajectory_inverse) [(~np.isnan(SST))]\n",
"tmp_DUR = np.asarray(dur_inverse ) [(~np.isnan(SST))]\n",
"tmp_LILMI = np.asarray(tmp_LILMI ) [(~np.isnan(SST))]\n",
"tmp_LI = np.asarray(tmp_LI ) [(~np.isnan(SST))]\n",
"tmp_LMI = np.asarray(tmp_LMI ) [(~np.isnan(SST))]\n",
"tmp_SHEAR = np.asarray(SHEAR ) [(~np.isnan(SST))]\n",
"tmp_MPI = np.asarray(MPI ) [(~np.isnan(SST))]\n",
"tmp_C = np.asarray(speed_inverse ) [(~np.isnan(SST))]\n",
"#---------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[0].plot(tmp_DIS/1e3,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[0].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[0].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[0].set_xlabel('$d$ (10$^3$ km)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,tmp_DIS)\n",
"p_str = '='+str('%.2f' %np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[0].set_title('$r^2$='+str('%.0f' %np.round(r**2*100,0))+'%, $p$'+p_str,fontsize=fontsize)\n",
"_=ax.flatten()[0].text(-1.5,1,'(A)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[0].set_ylim([0,1])\n",
"_=ax.flatten()[0].set_xlim([0,7])\n",
"_=ax.flatten()[0].set_xticks(np.arange(0,8,1))\n",
"#-------------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[1].plot(tmp_DUR,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[1].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[1].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[1].set_xlabel('$T$ (hr)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,tmp_DUR)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[1].set_title('$r^2$='+str('%.0f' %np.round(r**2*100,0))+'%, $p$'+p_str,fontsize=fontsize)\n",
"_=ax.flatten()[1].text(-85.7,1,'(B)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[1].set_ylim([0,1])\n",
"_=ax.flatten()[1].set_xlim([0,400])\n",
"_=ax.flatten()[1].set_xticks(np.arange(0,401,100))\n",
"# -------------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[2].plot(tmp_C,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[2].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[2].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[2].set_xlabel('$c$ (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI[~np.isnan(tmp_C)],tmp_C[~np.isnan(tmp_C)])\n",
"p_str = '='+str('%.2f' %np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[2].set_title('$r^2$='+str('%.0f' %np.round(r**2*100,0))+'%, $p$'+p_str,fontsize=fontsize)\n",
"_=ax.flatten()[2].text(-3.43,1,'(C)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[2].set_ylim([0,1])\n",
"_=ax.flatten()[2].set_xlim([0,16])\n",
"_=ax.flatten()[2].set_xticks(np.arange(0,17,2))\n",
"#-----------------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[3].plot(tmp_DIS/1e3,tmp_DUR,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[3].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[3].set_ylabel('$T$ (hr)',fontsize=fontsize)\n",
"_=ax.flatten()[3].set_xlabel('$d$ (10$^3$ km)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(dur_inverse,dis_inverse)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[3].set_title('$r^2$='+str('%.0f' %np.round(r**2*100,0))+'%, $p$'+p_str,fontsize=fontsize)\n",
"_=ax.flatten()[3].text(-1.5,400,'(D)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[3].set_ylim([0,400])\n",
"_=ax.flatten()[3].set_xlim([0,7])\n",
"#---------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[4].plot(tmp_MPI*.51444444,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[4].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[4].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[4].set_xlabel('MPI (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,tmp_MPI)\n",
"p_str = '='+str('%.2f' %np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[4].set_title('$r^2$='+str('%.0f' %np.round(r**2*100,0))+'%, $p$'+p_str,fontsize=fontsize)\n",
"_=ax.flatten()[4].text(-21.4,1,'(E)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[4].set_ylim([0,1])\n",
"_=ax.flatten()[4].set_xlim([0,100])\n",
"#_=ax.flatten()[4].set_xticks(np.arange(0,8,1))\n",
"#-------------------------------------------------------------------------------------------------\n",
"_=ax.flatten()[5].plot(tmp_SHEAR,tmp_LILMI,'o',color='k',ms=6,alpha=.5,mec='none')\n",
"_=ax.flatten()[5].tick_params(axis='both', which='major', labelsize=fontsize)\n",
"_=ax.flatten()[5].set_ylabel('LI/LMI',fontsize=fontsize)\n",
"_=ax.flatten()[5].set_xlabel('Shear (m s$^{-1}$)',fontsize=fontsize)\n",
"_,_,r,p,_ = scipy.stats.linregress(tmp_LILMI,tmp_SHEAR)\n",
"p_str = str(np.round(p,2))\n",
"if p<0.05:\n",
" p_str = '<0.05'\n",
"_=ax.flatten()[5].set_title('$r^2$='+str('%.0f' %np.round(r**2*100,0))+'%, $p$'+p_str,fontsize=fontsize)\n",
"_=ax.flatten()[5].text(-5.36,1,'(F)',fontsize=fontsize+2,fontweight='bold')\n",
"_=ax.flatten()[5].set_ylim([0,1])\n",
"_=ax.flatten()[5].set_xlim([0,25])\n",
"#_=ax.flatten()[5].set_xticks(np.arange(0,401,100))\n",
"\n",
"plt.savefig('/home/sw1013/www/FIGURES/Figure_3.png',dpi=600,bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Annual trend coreraltion**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"X = INDEX_year\n",
"\n",
"tmp_B = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(B_inverse)):\n",
" if (year_inverse[CCC] == YEAR):# & (A_inverse[CCC] >= CAT_KT.CAT3.LOWER) :\n",
" tmp.extend([B_inverse[CCC]])\n",
" tmp=np.asarray(tmp)\n",
" tmp_B.extend([np.nanmean(tmp)])\n",
"tmp_B = np.asarray(tmp_B)\n",
"\n",
"tmp_DUR = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (year_inverse[CCC] == YEAR):\n",
" tmp.extend([3*len(vmax_in_inverse[CCC])])\n",
" tmp_DUR.extend([np.nanmean(tmp)])\n",
"tmp_DUR = np.asarray(tmp_DUR)\n",
"\n",
"tmp_LMI = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (year_inverse[CCC] == YEAR):\n",
" tmp.extend([A_inverse[CCC]])\n",
" tmp_LMI.extend([np.nanmean(tmp)])\n",
"tmp_LMI = np.asarray(tmp_LMI)\n",
"\n",
"tmp_SHEAR = []\n",
"sample_weight = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([SHEAR[CCC]])\n",
" tmp_SHEAR.extend([np.nanmean(tmp)])\n",
"tmp_SHEAR=np.asarray(tmp_SHEAR)\n",
"\n",
"tmp_LAT = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([CLAT[CCC]])\n",
" tmp_LAT.extend([np.nanmean(tmp)])\n",
"tmp_LAT=np.asarray(tmp_LAT)\n",
"\n",
"tmp_SST = []\n",
"for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(SEASON)):\n",
" if SEASON[CCC]==YEAR:\n",
" tmp.extend([SST[CCC]])\n",
" tmp_SST.extend([np.nanmean(tmp)])\n",
"tmp_SST=np.asarray(tmp_SST)\n",
"\n",
"TABLE = pd.DataFrame(index=['LMI','Duration','k'],columns=['Lat','Shear','SST'])\n",
"\n",
"def r_and_p(X,Y):\n",
" _,_,r,p,_ = scipy.stats.linregress(scipy.signal.detrend(X),scipy.signal.detrend(Y))\n",
" return str(round(r,2))+'('+str(round(p,2))+')'\n",
"\n",
"TABLE.Lat.LMI = r_and_p(tmp_LAT,tmp_LMI)\n",
"TABLE.Lat.Duration = r_and_p(tmp_LAT,tmp_DUR)\n",
"TABLE.Lat.k = r_and_p(tmp_LAT,tmp_B )\n",
"TABLE.Shear.LMI = r_and_p(tmp_SHEAR,tmp_LMI)\n",
"TABLE.Shear.Duration = r_and_p(tmp_SHEAR,tmp_DUR)\n",
"TABLE.Shear.k = r_and_p(tmp_SHEAR,tmp_B )\n",
"TABLE.SST.LMI = r_and_p(tmp_SST,tmp_LMI)\n",
"TABLE.SST.Duration = r_and_p(tmp_SST,tmp_DUR)\n",
"TABLE.SST.k = r_and_p(tmp_SST,tmp_B )\n",
"TABLE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Major TC decay epcohal change and major percentiles**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"LI_inverse = []\n",
"for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" LI_inverse.extend([VMAX_PMIN_SERIES[CCC][1][-1]*.514444])\n",
"LI_inverse = np.asarray(LI_inverse)\n",
"A_inverse = np.asarray(A_inverse)\n",
"B_inverse = np.asarray(B_inverse)\n",
"year_inverse = np.asarray(year_inverse)\n",
"\n",
"tmp_TOP_LI = LI_inverse>=50.\n",
"tmp_before = year_inverse<=2000\n",
"tmp_after = year_inverse>=2001\n",
"\n",
"def find_var_percentile (x,AREA):\n",
" x = np.asarray(x)\n",
" \n",
" tmp_AREA = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp_AREA.extend([True])\n",
" else:\n",
" tmp_AREA.extend([False])\n",
" \n",
" output_first_row = str(round(np.nanmedian(x[tmp_TOP_LI & tmp_AREA])).astype(int)) +'('+str(round(scipy.stats.percentileofscore(x[tmp_AREA],np.nanmedian(x[tmp_TOP_LI & tmp_AREA]))))+')'\n",
" \n",
" mean = np.nanmean(x[tmp_TOP_LI & tmp_AREA & tmp_after]) - np.nanmean(x[tmp_TOP_LI & tmp_AREA & tmp_before])\n",
" ci1,ci2,mean_check = bootstrapping_compare_mean_CI_star(x[tmp_TOP_LI & tmp_AREA & tmp_after],x[tmp_TOP_LI & tmp_AREA & tmp_before],95)\n",
" output_second_row = str(round(mean,1))+'±'+str(round((ci1+ci2)/2,1))\n",
" \n",
" return output_first_row,output_second_row\n",
" \n",
"TABLE = pd.DataFrame(index=['LI','LMI','1/LMI','$\\Delta$t${\\times}\\kappa$','$\\Delta$d','$\\Delta$t','$\\kappa$','c','MPI'],columns=['%tiles','epoch diff'])#,'NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" #['WP','EP','NA','NI'],\n",
" #['SI','SP'],\n",
" #['WP'],\n",
" #['EP'],\n",
" #['NA'],\n",
" #['NI'],\n",
" #['SI'],\n",
" #['SP'],\n",
" ]):\n",
" dur_inverse = np.asarray(dur_inverse)\n",
" TABLE.iloc[0,0],TABLE.iloc[0,1] = find_var_percentile(LI_inverse ,AREA)\n",
" TABLE.iloc[1,0],TABLE.iloc[1,1] = find_var_percentile(A_inverse ,AREA)\n",
" TABLE.iloc[2,0],TABLE.iloc[2,1] = find_var_percentile(1/A_inverse *1000,AREA)\n",
" TABLE.iloc[3,0],TABLE.iloc[3,1] = find_var_percentile(dur_inverse*3600*B_inverse *1000,AREA)\n",
" TABLE.iloc[4,0],TABLE.iloc[4,1] = find_var_percentile(trajectory_inverse ,AREA)\n",
" TABLE.iloc[5,0],TABLE.iloc[5,1] = find_var_percentile(dur_inverse ,AREA)\n",
" TABLE.iloc[6,0],TABLE.iloc[6,1] = find_var_percentile(B_inverse /Ref_B*10,AREA)\n",
" TABLE.iloc[7,0],TABLE.iloc[7,1] = find_var_percentile(speed_inverse ,AREA) \n",
" TABLE.iloc[8,0],TABLE.iloc[8,1] = find_var_percentile(MPI ,AREA) \n",
"\n",
"TABLE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Basin breakdown - Trend**"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [],
"source": [
"def basin_LI_trend (AREA):\n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]])\n",
" Y.extend([sum(np.asarray(tmp)>=96)]) #96\n",
" sample_weight.extend([1])\n",
" pred, mean, ci, p = poisson_reg(X,Y)\n",
" STR =''# ' ($p$='+str(round(p,2))+')'\n",
" if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
" output1 = str(round(np.log(2)/mean).astype(int))+STR\n",
" count = np.nansum(sample_weight)\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]])\n",
" Y.extend([sum(np.asarray(tmp)>=96)]) #96\n",
" sample_weight.extend([1])\n",
" pred, mean, ci, p = poisson_reg(X,Y)\n",
" STR =''# ' ($p$='+str(round(p,2))+')'\n",
" if abs(ci)<=abs(mean):\n",
" STR = ' ($p$<0.05)'\n",
" output2 = str(round(np.log(2)/mean).astype(int))+STR\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA) & (VMAX_PMIN_SERIES[CCC][1][0]>=96):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][0]])\n",
" Y.extend([np.nanmean(tmp)*.5144444])\n",
" sample_weight.extend([len(tmp)])\n",
" x_trend, y_trend, slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
" p_str =''# '$p$='+str('%.2f' %np.round(p,2))+')'\n",
" if p<0.05:\n",
" p_str = '($p$<0.05)'\n",
" output3 = str(round(slope*10,1))+p_str\n",
" \n",
" X = INDEX_year[:]\n",
" Y = []\n",
" sample_weight = []\n",
" for YEAR in X:\n",
" tmp = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][2] == YEAR) & (VMAX_PMIN_SERIES[CCC][7] in AREA) & (VMAX_PMIN_SERIES[CCC][1][0]>=96):\n",
" tmp.extend([VMAX_PMIN_SERIES[CCC][1][-1]])\n",
" Y.extend([np.nanmean(tmp)*.51444444])\n",
" sample_weight.extend([len(tmp)])\n",
" x_trend, y_trend, slope,ci,p = get_trend_and_ci_weight(X,Y,sample_weight)\n",
" p_str =''# '($p$='+str('%.2f' %np.round(p,2))+')'\n",
" if p<0.05:\n",
" p_str = '($p$<0.05)'\n",
" output4 = str(round(slope*10,1))+p_str\n",
" \n",
" Y = []\n",
" sample_weight = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA) & (VMAX_PMIN_SERIES[CCC][1][0]>=96):\n",
" Y.extend([VMAX_PMIN_SERIES[CCC][1][0]])\n",
" output5 = str(round(np.nanstd(Y)).astype(int))\n",
" \n",
" Y = []\n",
" sample_weight = []\n",
" for CCC in range(len(VMAX_PMIN_SERIES)):\n",
" if (VMAX_PMIN_SERIES[CCC][7] in AREA) & (VMAX_PMIN_SERIES[CCC][1][0]>=96):\n",
" Y.extend([VMAX_PMIN_SERIES[CCC][1][-1]])\n",
" output6 = str(round(np.nanstd(Y)).astype(int))\n",
" \n",
" return output1, output2,output3,output4,output5, output6\n"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Global | \n",
" NH | \n",
" SH | \n",
" WP | \n",
" EP | \n",
" NA | \n",
" NI | \n",
" SI | \n",
" SP | \n",
"
\n",
" \n",
" \n",
" \n",
" Major landfall doubling time (year) | \n",
" 34 ($p$<0.05) | \n",
" 31 ($p$<0.05) | \n",
" 42 | \n",
" 44 | \n",
" 18 | \n",
" 19 | \n",
" 33 | \n",
" 68 | \n",
" 18 | \n",
"
\n",
" \n",
" Major LMI doubling time (year) | \n",
" 50 ($p$<0.05) | \n",
" 50 ($p$<0.05) | \n",
" 47 | \n",
" 53 ($p$<0.05) | \n",
" 67 | \n",
" 42 | \n",
" 37 | \n",
" 45 | \n",
" 54 | \n",
"
\n",
" \n",
" LMI (m s $^{-1}$ per decade) | \n",
" 1.0($p$<0.05) | \n",
" 1.2($p$<0.05) | \n",
" -0.4 | \n",
" 1.0 | \n",
" 2.5 | \n",
" 1.8 | \n",
" -0.3 | \n",
" -0.9 | \n",
" 0.1 | \n",
"
\n",
" \n",
" LI (m s $^{-1}$ per decade) | \n",
" 0.5 | \n",
" 0.3 | \n",
" 1.3 | \n",
" 0.3 | \n",
" 1.0 | \n",
" 0.8 | \n",
" -2.8 | \n",
" -1.0 | \n",
" 3.6 | \n",
"
\n",
" \n",
" LMI σ (m s$^{-1}$) | \n",
" 16 | \n",
" 17 | \n",
" 14 | \n",
" 16 | \n",
" 17 | \n",
" 19 | \n",
" 13 | \n",
" 13 | \n",
" 16 | \n",
"
\n",
" \n",
" LI σ (m s$^{-1}$) | \n",
" 28 | \n",
" 28 | \n",
" 26 | \n",
" 27 | \n",
" 28 | \n",
" 22 | \n",
" 27 | \n",
" 25 | \n",
" 26 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Global NH SH \\\n",
"Major landfall doubling time (year) 34 ($p$<0.05) 31 ($p$<0.05) 42 \n",
"Major LMI doubling time (year) 50 ($p$<0.05) 50 ($p$<0.05) 47 \n",
"LMI (m s $^{-1}$ per decade) 1.0($p$<0.05) 1.2($p$<0.05) -0.4 \n",
"LI (m s $^{-1}$ per decade) 0.5 0.3 1.3 \n",
"LMI σ (m s$^{-1}$) 16 17 14 \n",
"LI σ (m s$^{-1}$) 28 28 26 \n",
"\n",
" WP EP NA NI SI SP \n",
"Major landfall doubling time (year) 44 18 19 33 68 18 \n",
"Major LMI doubling time (year) 53 ($p$<0.05) 67 42 37 45 54 \n",
"LMI (m s $^{-1}$ per decade) 1.0 2.5 1.8 -0.3 -0.9 0.1 \n",
"LI (m s $^{-1}$ per decade) 0.3 1.0 0.8 -2.8 -1.0 3.6 \n",
"LMI σ (m s$^{-1}$) 16 17 19 13 13 16 \n",
"LI σ (m s$^{-1}$) 27 28 22 27 25 26 "
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TABLE = pd.DataFrame(index=['Major landfall doubling time (year)','Major LMI doubling time (year)',\n",
" 'LMI (m s $^{-1}$ per decade)','LI (m s $^{-1}$ per decade)',\n",
" 'LMI σ (m s$^{-1}$)','LI σ (m s$^{-1}$)',],\n",
" columns=['Global','NH','SH','WP','EP','NA','NI','SI','SP'])\n",
"for BBB,AREA in enumerate( [\n",
" ['WP','EP','NA','NI','SI','SP'],\n",
" ['WP','EP','NA','NI'],\n",
" ['SI','SP'],\n",
" ['WP'],\n",
" ['EP'],\n",
" ['NA'],\n",
" ['NI'],\n",
" ['SI'],\n",
" ['SP'],\n",
" ]):\n",
" output1,output2,output3,output4,output5,output6 = basin_LI_trend(AREA)\n",
" TABLE.iloc[0,BBB] = output1\n",
" TABLE.iloc[1,BBB] = output2\n",
" TABLE.iloc[2,BBB] = output3\n",
" TABLE.iloc[3,BBB] = output4\n",
" TABLE.iloc[4,BBB] = output5\n",
" TABLE.iloc[5,BBB] = output6\n",
"TABLE\n",
"#TABLE.to_clipboard() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"toc-autonumbering": true,
"toc-showcode": false,
"toc-showmarkdowntxt": false
},
"nbformat": 4,
"nbformat_minor": 4
}