# https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access import sys import matplotlib.pyplot as plt from matplotlib import gridspec import numpy as np from scipy import stats import csv from datetime import date from datetime import datetime from elevations import Elevations from math import sin, cos, sqrt, atan2, radians PRINT = False BASINTITLE = "ATLANTIC" FILENAME = "ibtracs.NA.list.v04r00.csv" DECREASES = [{'name': 'ABLE', 'year': 1950, 'dec': -35}, {'name': 'DOG', 'year': 1950, 'dec': -35}, {'name': 'FOX', 'year': 1950, 'dec': -35}, {'name': 'EASY', 'year': 1951, 'dec': -30}, {'name': 'BETSY', 'year': 1956, 'dec': -30}, {'name': 'CARRIE', 'year': 1957, 'dec': -35}, {'name': 'ILSA', 'year': 1958, 'dec': -30}, {'name': 'ETHEL', 'year': 1960, 'dec': -30}, {'name': 'ESTHER', 'year': 1961, 'dec': -30}, {'name': 'FRANCES', 'year': 1961, 'dec': -35}, {'name': 'ARLENE', 'year': 1963, 'dec': -35}, {'name': 'BEULAH', 'year': 1963, 'dec': -30}, {'name': 'ALMA', 'year': 1966, 'dec': -30}, {'name': 'INEZ', 'year': 1966, 'dec': -50}, {'name': 'BEULAH', 'year': 1967, 'dec': -55}, {'name': 'GLADYS', 'year': 1975, 'dec': -35}, {'name': 'FRANCES', 'year': 1976, 'dec': -30}, {'name': 'ELLA', 'year': 1978, 'dec': -55}, {'name': 'ALLEN', 'year': 1980, 'dec': -55}, {'name': 'HARVEY', 'year': 1981, 'dec': -35}, {'name': 'DEBBY', 'year': 1982, 'dec': -40}, {'name': 'DIANA', 'year': 1984, 'dec': -30}, {'name': 'GLORIA', 'year': 1985, 'dec': -45}, {'name': 'JOAN:MIRIAM', 'year': 1988, 'dec': -40}, {'name': 'CLAUDETTE', 'year': 1991, 'dec': -30}, {'name': 'EDOUARD', 'year': 1996, 'dec': -30}, {'name': 'HORTENSE', 'year': 1996, 'dec': -30}, {'name': 'GEORGES', 'year': 1998, 'dec': -35}, {'name': 'MITCH', 'year': 1998, 'dec': -55}, {'name': 'CINDY', 'year': 1999, 'dec': -30}, {'name': 'FLOYD', 'year': 1999, 'dec': -30}, {'name': 'LENNY', 'year': 1999, 'dec': -50}, {'name': 'ALBERTO', 'year': 2000, 'dec': -40}, {'name': 'ISAAC', 'year': 2000, 'dec': -30}, {'name': 'KEITH', 'year': 2000, 'dec': -45}, {'name': 'LILI', 'year': 2002, 'dec': -30}, {'name': 'ALEX', 'year': 2004, 'dec': -40}, {'name': 'KARL', 'year': 2004, 'dec': -35}, {'name': 'KATRINA', 'year': 2005, 'dec': -35}, {'name': 'MARIA', 'year': 2005, 'dec': -35}, {'name': 'RITA', 'year': 2005, 'dec': -40}, {'name': 'WILMA', 'year': 2005, 'dec': -30}, {'name': 'FELIX', 'year': 2007, 'dec': -35}, {'name': 'BERTHA', 'year': 2008, 'dec': -40}, {'name': 'OMAR', 'year': 2008, 'dec': -45}, {'name': 'EARL', 'year': 2010, 'dec': -40}, {'name': 'JULIA', 'year': 2010, 'dec': -35}, {'name': 'KATIA', 'year': 2011, 'dec': -35}, {'name': 'OPHELIA', 'year': 2011, 'dec': -50}, {'name': 'RINA', 'year': 2011, 'dec': -35}, {'name': 'SANDY', 'year': 2012, 'dec': -30}, {'name': 'DANNY', 'year': 2015, 'dec': -40}, {'name': 'JOAQUIN', 'year': 2015, 'dec': -45}, {'name': 'MATTHEW', 'year': 2016, 'dec': -30}, {'name': 'NICOLE', 'year': 2016, 'dec': -45}, {'name': 'JOSE', 'year': 2017, 'dec': -30}, {'name': 'FLORENCE', 'year': 2018, 'dec': -40}, {'name': 'DORIAN', 'year': 2019, 'dec': -40}, {'name': 'HUMBERTO', 'year': 2019, 'dec': -30}, {'name': 'LORENZO', 'year': 2019, 'dec': -40}, {'name': 'GENEVIEVE', 'year': 2020, 'dec': -35}] STRONGEST = [{'name': 'ALLEN', 'year': 1980, 'wind': 165}, {'name': 'LBRDAY', 'year': 1935, 'wind': 160}, {'name': 'GILBERT', 'year': 1988, 'wind': 160}, {'name': 'DORIAN', 'year': 2019, 'wind': 160}, {'name': 'WILMA', 'year': 2005, 'wind': 160}, {'name': 'MITCH', 'year': 1998, 'wind': 155}, {'name': 'RITA', 'year': 2005, 'wind': 155}, {'name': 'IRMA', 'year': 2017, 'wind': 155}, {'name': 'CUBA', 'year': 1932, 'wind': 150}, {'name': 'JANET', 'year': 1955, 'wind': 150}, {'name': 'CAMILLE', 'year': 1969, 'wind': 150}, {'name': 'ANITA', 'year': 1977, 'wind': 150}, {'name': 'DAVID', 'year': 1979, 'wind': 150}, {'name': 'ANDREW', 'year': 1992, 'wind': 150}, {'name': 'KATRINA', 'year': 2005, 'wind': 150}, {'name': 'DEAN', 'year': 2007, 'wind': 150}, {'name': 'FELIX', 'year': 2007, 'wind': 150}, {'name': 'MARIA', 'year': 2017, 'wind': 150}] TOP10WETTEST = [{'rank': 1, 'total': 60.58, 'name': 'HARVEY', 'year': 2017, 'location': 'Nederland, TX'}, {'rank': 2, 'total': 48.00, 'name': 'AMELIA', 'year': 1978, 'location': 'Medina, TX'}, {'rank': 3, 'total': 45.20, 'name': 'EASY', 'year': 1950, 'location': 'Yankeetown, FL'}, {'rank': 4, 'total': 45.00, 'name': 'CLAUDETTE', 'year': 1979, 'location': 'Alvin, TX'}, {'rank': 5, 'total': 43.31, 'name': 'IMELDA', 'year': 2019, 'location': 'Jefferson County, TX'}, {'rank': 6, 'total': 40.68, 'name': 'ALLISON', 'year': 2001, 'location': 'NW Jefferson County, TX'}, {'rank': 7, 'total': 38.46, 'name': 'GEORGES', 'year': 1998, 'location': 'Munson, FL'}, {'rank': 8, 'total': 36.71, 'name': 'DANNY', 'year': 1997, 'location': 'Dauphin Island Sea Lab, AL'}, {'rank': 9, 'total': 35.93, 'name': 'FLORENCE', 'year': 2018, 'location': 'Elizabethtown, NC'}, {'rank': 10, 'total': 29.76, 'name': 'UNNAMED-01', 'year': 1960, 'location': 'Port Lavaca #2, TX'}] META = [{'year': 1950, 'name': 'EASY', '24h': ' ', 'total': 45.2, 'location': 'FL', 'notes': 'state record'}, {'year': 1960, 'name': 'UNNAMED-01', '24h': 22, 'total': ' ', 'location': ' ', 'notes': 'KY state record (11.25)'}, {'year': 1962, 'name': 'TD3', '24h': 22, 'total': ' ', 'location': 'LA', 'notes': 'state 24h record'}, {'year': 1963, 'name': 'FLORA', '24h': 28.39, 'total': 100.39, 'location': 'Cuba', 'notes': '2nd highest in western hemisphere'}, {'year': 1972, 'name': 'AGNES', '24h': '', 'total': ' ', 'location': '', 'notes': 'PA TC record (19.0)'}, {'year': 1979, 'name': 'CLAUDETTE', '24h': 42, 'total': ' ', 'location': '', 'notes': 'US 24h record'}, {'year': 1979, 'name': 'FREDERIC', '24h': '', 'total': ' ', 'location': '', 'notes': 'OH TC record (8.67)'}, {'year': 1980, 'name': 'HERMINE', '24h': '', 'total': 31.15, 'location': 'Mexico', 'notes': ''}, {'year': 1982, 'name': 'CHRIS', '24h': '', 'total': ' ', 'location': '', 'notes': 'TN TC record (13.6)'}, {'year': 1994, 'name': 'ALBERTO', '24h': ' ', 'total': 27.85, 'location': 'GA', 'notes': 'state record'}, {'year': 1997, 'name': 'DANNY', '24h': 32.52, 'total': 36.71, 'location': 'AL', 'notes': 'state record'}, {'year': 1998, 'name': 'MITCH', '24h': ' ', 'total': 62.87, 'location': 'Honduras, Nicaragua', 'notes': '11,000 fatalities'}, {'year': 1998, 'name': 'GEORGES', '24h': ' ', 'total': 38.46, 'location': 'FL/MS', 'notes': 'MS state record (32.21)'}, {'year': 2000, 'name': 'KEITH', '24h': ' ', 'total': 32.67, 'location': 'Belize', 'notes': 'record'}, {'year': 2001, 'name': 'ALLISON', '24h': ' ', 'total': '', 'location': '', 'notes': 'LA 2nd highest'}, {'year': 2005, 'name': 'DENNIS', '24h': ' ', 'total': 42.99, 'location': 'Cuba', 'notes': '2nd highest'}, {'year': 2005, 'name': 'WILMA', '24h': 64.33, 'total': ' ', 'location': '', 'notes': 'record 24h Western Hemisphere'}, {'year': 2007, 'name': 'NOEL', '24h': ' ', 'total': 29.43, 'location': 'Bahamas', 'notes': 'record'}, {'year': 2017, 'name': 'HARVEY', '24h': ' ', 'total': 60.58, 'location': 'TX', 'notes': 'TX and US record'}, {'year': 2018, 'name': 'FLORENCE', '24h': ' ', 'total': 35.93, 'location': 'NC/SC', 'notes': 'state records'}, {'year': 2019, 'name': 'BARRY', '24h': ' ', 'total': ' ', 'location': '', 'notes': 'AR state record (16.59)'}, {'year': 2019, 'name': 'DORIAN', '24h': ' ', 'total': 22.84, 'location': 'Bahamas', 'notes': '2nd highest'}] # NOTE: all labels below need to be unique text since they are used as keys # 6 storm categories STORMS6 = {'min': [35, 55, 70, 85, 105, 125], 'max': [54, 69, 84, 104, 124, 999], # Near-land trends since 1920 for correcting counts and ACE 'trends': [-0.00230, 0.000785, -0.00209, -0.001615, -0.001824, -0.001067], 'colors': ['green', 'aqua', 'blue', 'purple', 'red', 'black'], 'labels': ['35-54', '55-69', '70-84', '85-104', '105-124', '>=125']} # 4 storm categories STORMS4 = {'min': [35, 69, 91, 120], 'max': [68, 90, 119, 999], # Near-land trends since 1920 for correcting counts and ACE 'trends': [-0.001996, -0.00179, -0.000775, -0.001012], 'colors': ['green', 'blue', 'red', 'black'], 'labels': ['35-68', '69-90', '91-119', '>=120']} RI24 = {'title': "Percent of all hurricanes with rapid 24h intensification ", 'min': [30, 40, 50, 60], 'colors': ['blue', 'purple', 'red', 'black'], 'labels': ['RI>=30', 'RI>=40', 'RI>=50', 'RI>=60']} RD24 = {'title': "Percent of all hurricanes with rapid 24h decreases over water ", 'min': [-10, -20, -30, -40], 'colors': ['blue', 'purple', 'red', 'black'], 'labels': ['D>=-10', 'D>=-20', 'D>=-30', 'D>=-40']} STARTINGWINDS = {'min': [0, 35, 50, 65, 80, 105], 'max': [34, 49, 64, 79, 104, 999], 'colors': ['orange', 'lightgray', 'darkgray', 'gray', 'dimgray', 'black'], 'labels': ['<35', '35-49', '50-64', '65-79', '80-104', '>=105']} SLOWING = {'title': "Percent of landfalling storms with slow motion ", 'max': [3, 6, 9, 12], 'colors': ['black', 'purple', 'blue', 'green'], 'labels': ['0-3', '0-6', '0-9', '0-12']} elevations = Elevations(); elevations.loadFiles(); # HURDAT2 has two types of rows, first for each storm, second for each storm report COLUMNS1 = {"desig": 0, "name": 1, "rows": 2} COLUMNS2 = {"ymd": 0, "time": 1, "landfall": 2, "stormType": 3, "lat": 4, "lon": 5, "wind": 6, "pressure": 7} # IBTRACS has only one row type: COLUMN_NAMES_IB = ['SID','SEASON','NUMBER','BASIN','SUBBASIN','NAME','ISO_TIME','NATURE','LAT','LON','WMO_WIND','WMO_PRES','WMO_AGENCY','TRACK_TYPE','DIST2LAND','LANDFALL','IFLAG','USA_AGENCY','USA_ATCF_ID','USA_LAT','USA_LON','USA_RECORD','USA_STATUS','USA_WIND','USA_PRES','USA_SSHS','USA_R34_NE','USA_R34_SE','USA_R34_SW','USA_R34_NW','USA_R50_NE','USA_R50_SE','USA_R50_SW','USA_R50_NW','USA_R64_NE','USA_R64_SE','USA_R64_SW','USA_R64_NW','USA_POCI','USA_ROCI','USA_RMW','USA_EYE','TOKYO_LAT','TOKYO_LON','TOKYO_GRADE','TOKYO_WIND','TOKYO_PRES','TOKYO_R50_DIR','TOKYO_R50_LONG','TOKYO_R50_SHORT','TOKYO_R30_DIR','TOKYO_R30_LONG','TOKYO_R30_SHORT','TOKYO_LAND','CMA_LAT','CMA_LON','CMA_CAT','CMA_WIND','CMA_PRES','HKO_LAT','HKO_LON','HKO_CAT','HKO_WIND','HKO_PRES','NEWDELHI_LAT','NEWDELHI_LON','NEWDELHI_GRADE','NEWDELHI_WIND','NEWDELHI_PRES','NEWDELHI_CI','NEWDELHI_DP','NEWDELHI_POCI','REUNION_LAT','REUNION_LON','REUNION_TYPE','REUNION_WIND','REUNION_PRES','REUNION_TNUM','REUNION_CI','REUNION_RMW','REUNION_R34_NE','REUNION_R34_SE','REUNION_R34_SW','REUNION_R34_NW','REUNION_R50_NE','REUNION_R50_SE','REUNION_R50_SW','REUNION_R50_NW','REUNION_R64_NE','REUNION_R64_SE','REUNION_R64_SW','REUNION_R64_NW','BOM_LAT','BOM_LON','BOM_TYPE','BOM_WIND','BOM_PRES','BOM_TNUM','BOM_CI','BOM_RMW','BOM_R34_NE','BOM_R34_SE','BOM_R34_SW','BOM_R34_NW','BOM_R50_NE','BOM_R50_SE','BOM_R50_SW','BOM_R50_NW','BOM_R64_NE','BOM_R64_SE','BOM_R64_SW','BOM_R64_NW','BOM_ROCI','BOM_POCI','BOM_EYE','BOM_POS_METHOD','BOM_PRES_METHOD','NADI_LAT','NADI_LON','NADI_CAT','NADI_WIND','NADI_PRES','WELLINGTON_LAT','WELLINGTON_LON','WELLINGTON_WIND','WELLINGTON_PRES','DS824_LAT','DS824_LON','DS824_STAGE','DS824_WIND','DS824_PRES','TD9636_LAT','TD9636_LON','TD9636_STAGE','TD9636_WIND','TD9636_PRES','TD9635_LAT','TD9635_LON','TD9635_WIND','TD9635_PRES','TD9635_ROCI','NEUMANN_LAT','NEUMANN_LON','NEUMANN_CLASS','NEUMANN_WIND','NEUMANN_PRES','MLC_LAT','MLC_LON','MLC_CLASS','MLC_WIND','MLC_PRES','USA_GUST','BOM_GUST','BOM_GUST_PER','REUNION_GUST','REUNION_GUST_PER','USA_SEAHGT','USA_SEARAD_NE','USA_SEARAD_SE','USA_SEARAD_SW','USA_SEARAD_NW','STORM_SPEED','STORM_DIR'] col = 0 COLUMNS_IB = {} for name in COLUMN_NAMES_IB: COLUMNS_IB[name] = col col = col + 1 # slow moving landfall table class SpeedTable(): def __init__(self, lower, upper): self.lowerThresh = lower self.upperThresh = upper self.table = "" def addYear(self, year): stormCount = 0 for stormNum in year.storms: storm = year.storms[stormNum] if storm['slowestSpeedNearLand'] <= self.upperThresh: stormCount = stormCount + 1 if stormCount == 0: return firstRow = True for stormNum in year.storms: storm = year.storms[stormNum] if storm['slowestSpeedNearLand'] <= self.upperThresh: if firstRow: self.table = self.table + "\n\t" firstRow = False else: self.table = self.table + "\t" if storm['slowestSpeedNearLand'] <= self.lowerThresh: self.table = self.table + "" foundTop10 = False for top10 in TOP10WETTEST: if top10['year'] == year.year and top10['name'] == storm['name']: foundTop10 = True break if foundTop10: self.table = self.table + "" else: self.table = self.table + "" foundMeta = False for row in META: if row['year'] == year.year and row['name'] == storm['name']: foundMeta = True break if foundMeta: self.table = self.table + "" self.table = self.table + "" self.table = self.table + "" else: self.table = self.table + "" self.table = self.table + "\n" self.table = self.table + "\n" def fini(self): self.table = self.table + "
YearNamespeed
mph		rank
US		Rain
24 hr		Rain
total		Notes
" + str(year.year) + "
" else: self.table = self.table + "" #Year Name speed USrank 24hrRain totalRain Notes self.table = self.table + storm['name'] + "" + str(storm['slowestSpeedNearLand']) + "" + str(top10['rank']) + "" + str(row['24h']) + "" + str(row['total']) + "" + str(row['location']) + ' ' + str(row['notes']) + "
" return self.table # RI Table class RITable(): def __init__(self, lower, upper): self.lowerThresh = lower self.upperThresh = upper self.table = "" def addYear(self, year): stormCount = 0 for stormNum in year.storms: storm = year.storms[stormNum] if storm['max24Inc'] >= self.lowerThresh: stormCount = stormCount + 1 if stormCount == 0: return firstRow = True for stormNum in year.storms: storm = year.storms[stormNum] if storm['max24Inc'] >= self.lowerThresh: if firstRow: self.table = self.table + "\n\t" firstRow = False else: self.table = self.table + "\t" if storm['max24Inc'] >= self.upperThresh: self.table = self.table + "" self.table = self.table + "" self.table = self.table + "" foundStrongest = False for row in STRONGEST: if row['year'] == year.year and row['name'] == storm['name']: foundStrongest = True break if foundStrongest: self.table = self.table + "" else: self.table = self.table + "" foundDecrease = False for row in DECREASES: if row['year'] == year.year and row['name'] == storm['name']: foundDecrease = True break if foundDecrease: self.table = self.table + "" else: self.table = self.table + "" self.table = self.table + "\n" self.table = self.table + "\n" def fini(self): self.table = self.table + "
YearName24h
inc		36h
inc		6h
inc		peak
wind		24h
decr
" + str(year.year) + "
" else: self.table = self.table + "" #Year Name ri24 ri36 ri6 peak self.table = self.table + storm['name'] + "" + str(storm['max24Inc']) + "" + str(storm['max36Inc']) + "" + str(storm['max6Inc']) + "" + str(row['wind']) + "" + str(row['dec']) + "
" return self.table class LFTable(): def __init__(self): self.table = "" def addYear(self, year): stormCount = 0 for stormNum in year.storms: storm = year.storms[stormNum] if storm['maxInc6LF'] >= 10 or storm['maxInc12LF'] >= 15: stormCount = stormCount + 1 if stormCount == 0: return firstRow = True for stormNum in year.storms: storm = year.storms[stormNum] if storm['maxInc6LF'] >= 10 or storm['maxInc12LF'] >= 15: if firstRow: self.table = self.table + "\n\t" firstRow = False else: self.table = self.table + "\t" self.table = self.table + "" self.table = self.table + "" self.table = self.table + "" self.table = self.table + "\n" self.table = self.table + "\n" def fini(self): self.table = self.table + "
YearName12h
inc		6h
inc
" + str(year.year) + "
" #Year Name 12inc 6inc self.table = self.table + storm['name'] + "" + str(storm['maxInc12LF']) + "" + str(storm['maxInc6LF']) + "
" return self.table def RDList(years): rdlist = [] for year in years: for stormNum in year.storms: storm = year.storms[stormNum] if storm['max24Dec'] <= -30: rdlist.append({'name': storm['name'], 'year': year.year, 'dec': storm['max24Dec']}) print(rdlist) class Year(): def __init__(self, yearNum): self.year = yearNum self.storms = {} self.stormNum = 0 def addStorm(self, storm): self.storms[self.stormNum] = storm.summary self.stormNum = self.stormNum + 1 # FROM STORMS: #{'desig': 'AL141990', 'madeTS': True, 'madeHU': True, 'firstWind': 45, 'lastWind': 40, 'maxW': 65, 'ace': 5.7875, 'n24HU': 6, 'n24RI': 0, 'max6Inc': 5, 'max24Inc': 15, 'max36Inc': 15, 'max6Dec': -10, 'max24Dec': -15, 'windAtLandfall': 0, 'wind6AfterLandfall': 0, 'wind24AfterLandfall': 0, 'slowestSpeedNearLand': 999} #{'desig': 'AL151990', 'madeTS': True, 'madeHU': False, 'firstWind': 25, 'lastWind': 15, 'maxW': 55, 'ace': 1.4475, 'n24HU': 0, 'n24RI': 0, 'max6Inc': 5, 'max24Inc': 20, 'max36Inc': 25, 'max6Dec': -10, 'max24Dec': -20, 'windAtLandfall': 25, 'wind6AfterLandfall': 0, 'wind24AfterLandfall': 0, 'slowestSpeedNearLand': 6.990032819711232} #{'desig': 'AL161990', 'madeTS': True, 'madeHU': True, 'firstWind': 25, 'lastWind': 20, 'maxW': 75, 'ace': 6.1725, 'n24HU': 6, 'n24RI': 0, 'max6Inc': 10, 'max24Inc': 30, 'max36Inc': 40, 'max6Dec': -10, 'max24Dec': -35, 'windAtLandfall': 0, 'wind6AfterLandfall': 0, 'wind24AfterLandfall': 0, 'slowestSpeedNearLand': 999} # TO YEAR: #1990 {'numTS': 14, 'numHU': 8, 'total24HU': 76, 'total24RI': 0, 'ACE': 91.1675, '35-54': 3, '55-69': 4, '70-84': 4, '85-104': 2, '105-124': 1, '>=125': 0, '>=30': 4, '>=40': 0, '>=50': 0, '>=60': 0, '<35': 15, '35-49': 1, '50-64': 0, '65-79': 0, '80-104': 0, '>=105': 0, '0-3': 0, '0-6': 0, '0-9': 0, '0-12': 0} def summarize(self): self.summary = {'numTS': 0, 'numHU': 0, 'numLF': 0, 'total24HU': 0, 'total24RI': 0, 'ACE': 0} self.summary['avgWeakening'] = 999 self.summary['extremeInc6LF'] = 0 self.summary['avgInc6LF'] = 999 self.summary['avgInc12LF'] = 999 for label in STORMS4['labels']: self.summary[label] = 0 self.summary['nl' + label] = 0 self.summary['ace' + label] = 0 for label in RI24['labels']: self.summary[label] = 0 for label in RD24['labels']: self.summary[label] = 0 for label in STARTINGWINDS['labels']: self.summary[label] = 0 for label in SLOWING['labels']: self.summary[label] = 0 totalWeakening = 0 countOfWeakening = 0 totalStrengthening6 = 0 countOfStrengthening6 = 0 totalStrengthening12 = 0 countOfStrengthening12 = 0 for stormNum in self.storms: storm = self.storms[stormNum] if storm['madeTS']: self.summary['numTS'] = self.summary['numTS'] + 1 if storm['madeHU']: self.summary['numHU'] = self.summary['numHU'] + 1 if storm['windAtLandfall'] > 0: self.summary['numLF'] = self.summary['numLF'] + 1 self.summary['ACE'] = self.summary['ACE'] + storm['ace'] self.summary['total24HU'] = self.summary['total24HU'] + storm['n24HU'] self.summary['total24RI'] = self.summary['total24RI'] + storm['n24RI'] if storm['landWeakening'] != 999: totalWeakening = totalWeakening + storm['landWeakening'] countOfWeakening = countOfWeakening + 1 if storm['madeLF'] and storm['madeHU']: #if storm['maxInc6LF'] > 0: #print(self.year, storm['name'], storm['maxInc6LF']) totalStrengthening6 = totalStrengthening6 + storm['maxInc6LF'] countOfStrengthening6 = countOfStrengthening6 + 1 #if storm['maxInc12LF'] > 0: #print(self.year, storm['name'], storm['maxInc12LF']) totalStrengthening12 = totalStrengthening12 + storm['maxInc12LF'] countOfStrengthening12 = countOfStrengthening12 + 1 if storm['maxInc6LF'] > self.summary['extremeInc6LF']: self.summary['extremeInc6LF'] = storm['maxInc6LF'] for windMin, windMax, label in zip(STORMS4['min'], STORMS4['max'], STORMS4['labels']): if storm['maxW'] >= windMin and storm['maxW'] <= windMax: self.summary[label] = self.summary[label] + 1 self.summary['ace' + label] = self.summary['ace' + label] + storm['ace'] if storm['madeLF']: self.summary['nl' + label] = self.summary['nl' + label] + 1 for riMin, label in zip(RI24['min'], RI24['labels']): if storm['max24Inc'] >= riMin: self.summary[label] = self.summary[label] + 1 for rdMin, label in zip(RD24['min'], RD24['labels']): if storm['max24Dec'] <= rdMin: self.summary[label] = self.summary[label] + 1 for swMin, swMax, label in zip(STARTINGWINDS['min'], STARTINGWINDS['max'], STARTINGWINDS['labels']): if storm['firstWind'] >= swMin and storm['firstWind'] <= swMax and storm['madeTS']: self.summary[label] = self.summary[label] + 1 for slowMax, label in zip(SLOWING['max'], SLOWING['labels']): if storm['slowestSpeedNearLand'] <= slowMax: self.summary[label] = self.summary[label] + 1 if countOfWeakening > 0: self.summary['avgWeakening'] = totalWeakening / countOfWeakening if countOfStrengthening6 > 0: self.summary['avgInc6LF'] = totalStrengthening6 / countOfStrengthening6 if countOfStrengthening12 > 0: self.summary['avgInc12LF'] = totalStrengthening12 / countOfStrengthening12 if PRINT: print(self.year, self.summary) class Storm(): def __init__HURDAT2(self, line): self.desig = line[COLUMNS1["desig"]] self.name = line[COLUMNS1["name"]].strip() if self.name == "UNNAMED": self.name = self.name + '-' + self.desig[2:4] self.nrows = int(line[COLUMNS1["rows"]]) self.rows = {} self.rowNum = 0 def __init__(self, line): self.desig = line[COLUMNS_IB["SID"]] self.name = line[COLUMNS_IB["NAME"]].strip() if self.name == "NOT_NAMED": self.name = self.desig self.rows = {} self.rowNum = 0 self.addStormRow(line) # https://stackoverflow.com/questions/19412462/getting-distance-between-two-points-based-on-latitude-longitude def getDistance(self, lat1, lon1, lat2, lon2): # approximate radius of earth in miles R = 3960 lat1 = radians(lat1) lon1 = radians(lon1) lat2 = radians(lat2) lon2 = radians(lon2) dlon = lon2 - lon1 dlat = lat2 - lat1 a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2 c = 2 * atan2(sqrt(a), sqrt(1 - a)) distance = R * c return distance def convertLatitude(self, latString): if latString[-1] == 'N': return float(latString[0:-1]) else: return float(latString[0:-1]) * -1.0 def convertLongitude(self, lonString): if lonString[-1] == 'W': return float(lonString[0:-1]) * -1.0 else: return float(lonString[0:-1]) def addStormRow_HURDAT2(self, line): wind = int(line[COLUMNS2["wind"]]) if wind == -99: return time = int(line[COLUMNS2["time"]]) if time not in [0, 600, 1200, 1800]: return lat = self.convertLatitude(line[COLUMNS2["lat"]]) lon = self.convertLongitude(line[COLUMNS2["lon"]]) ele = elevations.getElevation(lon, lat) overland = False if ele != None and ele > -500: overland = True stormType = line[COLUMNS2["stormType"]].strip() self.rows[self.rowNum] = {'lat': lat, 'lon': lon, 'ele': ele, 'overland': overland, 'wind': wind, 'type': stormType} self.rowNum = self.rowNum + 1 def addStormRow(self, line): windstr = line[COLUMNS_IB["USA_WIND"]] # ignore rows with negative or non-numeric wind if windstr.isdigit(): wind = int(windstr) else: return datetime_str = line[COLUMNS_IB["ISO_TIME"]] # Format is YYYY-MM-DD HH:mm:ss datetime_obj = datetime.strptime(datetime_str, '%Y-%m-%d %H:%M:%S') if datetime_obj.time().hour not in [0, 6, 12, 18]: return lat = float(line[COLUMNS_IB["LAT"]]) lon = float(line[COLUMNS_IB["LON"]]) ele = elevations.getElevation(lon, lat) overland = False if ele != None and ele > -500: overland = True stormType = line[COLUMNS_IB["USA_STATUS"]].strip() self.rows[self.rowNum] = {'lat': lat, 'lon': lon, 'ele': ele, 'overland': overland, 'wind': wind, 'type': stormType} self.rowNum = self.rowNum + 1 #{'lat': 19.1, 'lon': -85.7, 'overland': False, 'wind': 45, 'type': 'TS'} #{'lat': 19.7, 'lon': -85.5, 'overland': False, 'wind': 50, 'type': 'TS'} #{'lat': 20.2, 'lon': -85.4, 'overland': False, 'wind': 60, 'type': 'TS'} #{'lat': 20.9, 'lon': -85.1, 'overland': False, 'wind': 65, 'type': 'HU'} #{'lat': 21.7, 'lon': -85.1, 'overland': False, 'wind': 75, 'type': 'HU'} #{'lat': 22.7, 'lon': -85.2, 'overland': False, 'wind': 85, 'type': 'HU'} #{'lat': 23.7, 'lon': -85.8, 'overland': False, 'wind': 85, 'type': 'HU'} #{'lat': 24.6, 'lon': -86.2, 'overland': False, 'wind': 90, 'type': 'HU'} #{'lat': 25.6, 'lon': -86.4, 'overland': False, 'wind': 100, 'type': 'HU'} #{'lat': 26.6, 'lon': -86.5, 'overland': False, 'wind': 110, 'type': 'HU'} #{'lat': 27.7, 'lon': -86.6, 'overland': False, 'wind': 120, 'type': 'HU'} #{'lat': 29.0, 'lon': -86.3, 'overland': False, 'wind': 125, 'type': 'HU'} #{'lat': 30.2, 'lon': -85.4, 'overland': True, 'wind': 135, 'type': 'HU'} #{'lat': 31.5, 'lon': -84.5, 'overland': True, 'wind': 80, 'type': 'HU'} #{'lat': 32.8, 'lon': -83.2, 'overland': True, 'wind': 50, 'type': 'TS'} #{'lat': 34.1, 'lon': -81.7, 'overland': True, 'wind': 45, 'type': 'TS'} #{'lat': 35.6, 'lon': -80.0, 'overland': True, 'wind': 45, 'type': 'TS'} #{'lat': 36.5, 'lon': -77.7, 'overland': True, 'wind': 50, 'type': 'EX'} def summarizeStorm(self): if len(self.rows) == 0: return False self.summary = {'desig': self.desig, 'name': self.name, 'madeTS': False, 'madeHU': False, 'madeLF': False, 'firstWind': 0, 'lastWind': 0, 'maxW': 0, 'ace': 0, 'n24HU': 0, 'n24RI': 0, 'max6Inc': 0, 'max24Inc': 0, 'max36Inc': 0, 'max6Dec': 0, 'max24Dec': 0, 'maxInc6LF': 0, 'maxInc12LF': 0, 'windAtLandfall': 0, 'wind6AfterLandfall': 0, 'wind24AfterLandfall': 0, 'landWeakening': 999, 'slowestSpeedNearLand': 999} # point to the last 6 rows agos = [36, 30, 24, 18, 12, 6] rowsAgo = {} for ago in agos: rowsAgo[ago] = None for rownum in self.rows: row = self.rows[rownum] if PRINT: print(row) if self.summary['firstWind'] == 0: self.summary['firstWind'] = row['wind'] if row['wind'] > self.summary['maxW']: self.summary['maxW'] = row['wind'] if row['type'] == 'HU': self.summary['madeHU'] = True if row['type'] == 'TS': self.summary['madeTS'] = True if row['overland']: self.summary['madeLF'] = True # Official ACE: only use 6 hour intervals with TS or HU # validated with https://weather.fandom.com/wiki/1992_Atlantic_hurricane_season if row['type'] == 'HU' or row['type'] == 'TS': self.summary['ace'] = self.summary['ace'] + (row['wind'] * row['wind']) if row['type'] != 'EX': # Rapid Intensification, for any storm type #if row['type'] == 'HU' or row['type'] == 'TS': # Rapid Intensification, to hurricane only if row['type'] == 'HU': if rowsAgo[6] != None and row['wind'] - rowsAgo[6]['wind'] > self.summary['max6Inc']: self.summary['max6Inc'] = row['wind'] - rowsAgo[6]['wind'] if rowsAgo[24] != None and row['wind'] - rowsAgo[24]['wind'] > self.summary['max24Inc']: self.summary['max24Inc'] = row['wind'] - rowsAgo[24]['wind'] if rowsAgo[36] != None and row['wind'] - rowsAgo[36]['wind'] > self.summary['max36Inc']: self.summary['max36Inc'] = row['wind'] - rowsAgo[36]['wind'] if self.summary['max36Inc'] < self.summary['max24Inc']: self.summary['max36Inc'] = self.summary['max24Inc'] # find the highest 6 and 12 hour increases before landfall if rowsAgo[6] != None and not rowsAgo[6]['overland'] and row['overland'] and row['wind'] - rowsAgo[6]['wind'] > self.summary['maxInc6LF']: self.summary['maxInc6LF'] = row['wind'] - rowsAgo[6]['wind'] if rowsAgo[12] != None and not rowsAgo[12]['overland'] and not rowsAgo[6]['overland'] and row['overland'] and row['wind'] - rowsAgo[12]['wind'] > self.summary['maxInc12LF']: self.summary['maxInc12LF'] = row['wind'] - rowsAgo[12]['wind'] # 24h weakening over land, starting as HU if rowsAgo[18] != None and rowsAgo[18]['type'] == 'HU' and row['overland'] and rowsAgo[6]['overland'] and rowsAgo[12]['overland'] and rowsAgo[18]['overland']: if self.summary['landWeakening'] == 999: #print(self.desig, self.summary['name'], rowsAgo[18]['wind'], row['wind']) self.summary['landWeakening'] = row['wind'] - rowsAgo[18]['wind'] # Rapid Weakening, excluding any over land, hurricane only if row['type'] == 'HU': if rowsAgo[6] != None and not row['overland'] and not rowsAgo[6]['overland'] and row['wind'] - rowsAgo[6]['wind'] < self.summary['max6Dec']: self.summary['max6Dec'] = row['wind'] - rowsAgo[6]['wind'] if rowsAgo[24] != None and not row['overland'] and not rowsAgo[6]['overland'] and not rowsAgo[12]['overland'] and not rowsAgo[18]['overland'] and not rowsAgo[24]['overland'] and row['wind'] - rowsAgo[24]['wind'] < self.summary['max24Dec']: self.summary['max24Dec'] = row['wind'] - rowsAgo[24]['wind'] if self.summary['windAtLandfall'] == 0 and row['overland']: self.summary['windAtLandfall'] = row['wind'] # allow one TS reading before HU periods as a compromise for RI ratio if rowsAgo[24] != None and (rowsAgo[24]['type'] == 'HU' or rowsAgo[24]['type'] == 'TS') and rowsAgo[18]['type'] == 'HU' and rowsAgo[12]['type'] == 'HU' and rowsAgo[6]['type'] == 'HU' and row['type'] == 'HU': # no TS allowed. Doesn't make much difference. #if rowsAgo[24] != None and (rowsAgo[24]['type'] == 'HU') and rowsAgo[18]['type'] == 'HU' and rowsAgo[12]['type'] == 'HU' and rowsAgo[6]['type'] == 'HU' and row['type'] == 'HU': self.summary['n24HU'] = self.summary['n24HU'] + 1 # to match nature paper, "above 30 knots": #if row['wind'] - rowsAgo[24]['wind'] > 30: # to match "at least 30 knots in 24 hours" if row['wind'] - rowsAgo[24]['wind'] >= 30: self.summary['n24RI'] = self.summary['n24RI'] + 1 # find the slowest 6 hour movement near landfall, but leave out TD only if self.summary['madeTS']: previousLat = 0 previousLon = 0 minDistance = 999 nearLand = False for ago in agos: if rowsAgo[ago] != None: lat = rowsAgo[ago]['lat'] lon = rowsAgo[ago]['lon'] if rowsAgo[ago]['overland']: nearLand = True distance = self.getDistance(previousLat, previousLon, lat, lon) if distance < minDistance: minDistance = distance previousLat = lat previousLon = lon if nearLand and minDistance < 999: if self.summary['slowestSpeedNearLand'] > minDistance / 6: self.summary['slowestSpeedNearLand'] = round(minDistance / 6, 2) # shift the pointers to the last six rows and point to the current row for ago in agos: if ago > 6: rowsAgo[ago] = rowsAgo[ago - 6] rowsAgo[6] = row self.summary['ace'] = self.summary['ace'] / 10000 self.summary['lastWind'] = row['wind'] if PRINT: print(self.summary) return True def read_data_HURDAT2(filename, startYear, endYear): years = [] f = open(filename, 'r') csv_reader = csv.reader(f, delimiter=',') current_year = startYear year = Year(current_year) for line in csv_reader: firstToken = line[0] if firstToken.startswith(BASIN1) or firstToken.startswith(BASIN2): rows = int(line[COLUMNS1["rows"]]) row = 0 storm = Storm(line) yearNum = int(firstToken[4:9]) # done with years we want if yearNum > endYear: break # skip over years we don't want if yearNum < current_year: continue # finished the current year if yearNum > current_year: year.summarize() years.append(year) current_year = yearNum year = Year(current_year) elif yearNum == current_year: storm.addStormRow(line) row = row + 1 if row == rows: gotData = storm.summarizeStorm() # some storms are all wind=-99 if gotData: year.addStorm(storm) year.summarize() years.append(year) return years # ibtracs def read_data(filename, startYear, endYear): years = [] f = open(filename, 'r') csv_reader = csv.reader(f, delimiter=',') current_year = startYear current_storm = None year = Year(current_year) for line in csv_reader: # skip the two header lines if line[0] == 'SID' and line[1] == 'SEASON': continue if line[1] == 'Year': continue yearNum = int(line[COLUMNS_IB["SEASON"]]) # skip over years until the starting year if yearNum < current_year: continue stormID = line[COLUMNS_IB["SID"]] if current_storm == None: current_storm = stormID storm = Storm(line) if stormID != current_storm: # done with storm gotData = storm.summarizeStorm() # some storms are all wind=-99 if gotData: year.addStorm(storm) # done with years we want if yearNum > endYear: break # finished the current year if yearNum > current_year: year.summarize() years.append(year) current_year = yearNum year = Year(current_year) # start new storm current_storm = stormID storm = Storm(line) else: storm.addStormRow(line) year.summarize() years.append(year) return years def plotSpeed(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) table = SpeedTable(3, 6) for year in years: table.addYear(year) html = table.fini() f = open("slowstorms.html", "w") f.write(html) f.close() fig = plt.figure(figsize=(9,3)) for slowMax, color, label in zip(SLOWING['max'], SLOWING['colors'], SLOWING['labels']): validYears = [] validPs = [] for year in years: p = year.summary[label] / year.summary['numLF'] * 100 validYears.append(year.year) validPs.append(p) axes = plt.gca() plt.scatter(validYears, validPs, color=color, label=label, alpha=0.6) if len(validYears) > 0: slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validPs) print(slowMax, slope) points = [slope * y + intercept for y in validYears] plt.plot(validYears, points, color=color, alpha=0.5) plt.legend(ncol = 4, loc='upper left') plt.title(SLOWING['title'] + str(yearStart) + "-" + str(yearEnd)) pngFile = "slow" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotRI(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) table = RITable(30, 50) for year in years: table.addYear(year) html = table.fini() f = open("ristorms.html", "w") f.write(html) f.close() fig = plt.figure(figsize=(9,3)) for min, color, label in zip(RI24['min'], RI24['colors'], RI24['labels']): validYears = [] validPs = [] for year in years: # use percent of TS #p = year.summary[label] / year.summary['numTS'] * 100 # use percent of HU p = year.summary[label] / year.summary['numHU'] * 100 validYears.append(year.year) validPs.append(p) axes = plt.gca() plt.scatter(validYears, validPs, color=color, label=label, alpha=0.6) if len(validYears) > 0: slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validPs) print(min, slope) points = [slope * y + intercept for y in validYears] plt.plot(validYears, points, color=color, alpha=0.5) plt.legend(ncol = 4, loc='upper right') plt.title(RI24['title'] + str(yearStart) + "-" + str(yearEnd)) pngFile = "ri" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotRIRatio(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) fig = plt.figure(figsize=(9,3)) validYears = [] validRatios = [] for year in years: r = year.summary['total24RI'] / year.summary['total24HU'] validYears.append(year.year) validRatios.append(r) plt.bar(validYears, validRatios, color='red', alpha=0.6) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validRatios) points = [slope * y + intercept for y in validYears] print('slope', slope) plt.plot(validYears, points, color='red', alpha=0.5) plt.title('RI ratio for ' + str(yearStart) + "-" + str(yearEnd) + " using >= 30 kt (24 hour)") pngFile = "riratio" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotRD(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) RDList(years) fig = plt.figure(figsize=(9,3)) for min, color, label in zip(RD24['min'], RD24['colors'], RD24['labels']): validYears = [] validPs = [] for year in years: p = year.summary[label] / year.summary['numTS'] * 100 validYears.append(year.year) validPs.append(p) axes = plt.gca() plt.scatter(validYears, validPs, color=color, label=label, alpha=0.6) if len(validYears) > 0: slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validPs) print(min, slope) points = [slope * y + intercept for y in validYears] plt.plot(validYears, points, color=color, alpha=0.5) plt.legend(ncol = 4, loc='upper right') plt.title(RD24['title'] + str(yearStart) + "-" + str(yearEnd)) pngFile = "rd" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotSW(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) fig = plt.figure(figsize=(9,3)) for color, label in zip(STARTINGWINDS['colors'], STARTINGWINDS['labels']): validYears = [] validPs = [] for year in years: p = year.summary[label] / year.summary['numTS'] * 100 validYears.append(year.year) validPs.append(p) axes = plt.gca() plt.bar(validYears, validPs, color=color, label=label, alpha=0.8) plt.legend(ncol = 3, loc='upper right') plt.title("starting winds " + str(yearStart) + "-" + str(yearEnd)) pngFile = "sw" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotNearLand(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) fig = plt.figure(figsize=(9,3)) for color, label in zip(STORMS4['colors'], STORMS4['labels']): validYears = [] validPercents = [] for year in years: if year.summary[label] > 0: percent = year.summary['nl' + label] / year.summary[label] * 100 validYears.append(year.year) validPercents.append(percent) axes = plt.gca() plt.scatter(validYears, validPercents, color=color, alpha=0.5) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validPercents) points = [slope * y + intercept for y in validYears] print(label, slope) plt.plot(validYears, points, color=color, label=label, alpha=0.9) plt.legend(loc='upper right') plt.title("percent of storms near land " + str(yearStart) + "-" + str(yearEnd)) pngFile = "nl" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotCounts(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) fig = plt.figure(figsize=(9,3)) for color, label, trend in zip(STORMS4['colors'], STORMS4['labels'], STORMS4['trends']): validYears = [] validCounts = [] for year in years: count = year.summary[label] # correct for detection/recording trend count = count * (1 - (trend * (y2 - year.year))) validYears.append(year.year) validCounts.append(count) axes = plt.gca() plt.bar(validYears, validCounts, color=color, label=label, alpha=0.8) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validCounts) points = [slope * y + intercept for y in validYears] print(label, slope) plt.plot(validYears, points, color=color, alpha=0.9) plt.legend(ncol = 4, loc='upper left') plt.title("Adjusted storm counts " + str(yearStart) + "-" + str(yearEnd)) pngFile = "count" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotACE(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) fig = plt.figure(figsize=(9,3)) color = "black" validYears = [] validACE = [] for year in years: aceTotal = 0 for label, trend in zip(STORMS4['labels'], STORMS4['trends']): if label == '>=120': ace = year.summary['ace' + label] # correct for detection/recording trend aceTotal = aceTotal + ace * (1 - (trend * (y2 - year.year))) validYears.append(year.year) validACE.append(aceTotal) axes = plt.gca() plt.bar(validYears, validACE, color=color, label="ACE", alpha=0.5) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validACE) points = [slope * y + intercept for y in validYears] print(label, slope) plt.plot(validYears, points, color=color, alpha=0.9) plt.legend(loc='upper center') plt.title("Adjusted ACE using only '>=120' " + str(yearStart) + "-" + str(yearEnd)) #plt.title("Adjusted ACE " + str(yearStart) + "-" + str(yearEnd)) pngFile = "ace" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotWeakening(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) fig = plt.figure(figsize=(9,3)) color = "black" validYears = [] validAvgs = [] for year in years: avg = year.summary['avgWeakening'] if avg != 999: validYears.append(year.year) validAvgs.append(avg) axes = plt.gca() plt.scatter(validYears, validAvgs, color=color, alpha=0.5) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validAvgs) points = [slope * y + intercept for y in validYears] print('slope', slope, points[0], points[-1]) #plt.plot(validYears, points, color=color, alpha=0.9) plt.title("Average weakening in 24 hours after landfall " + str(yearStart) + "-" + str(yearEnd)) pngFile = "weakening" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() def plotLFStrengthening(y1, y2): yearStart = y1 yearEnd = y2 years = read_data(FILENAME, yearStart, yearEnd) table = LFTable() for year in years: table.addYear(year) html = table.fini() f = open("lfstorms.html", "w") f.write(html) f.close() fig = plt.figure(figsize=(9,3)) color = "black" label = "6h" validYears = [] validAvgs = [] for year in years: avg = year.summary['avgInc6LF'] if avg != 999: validYears.append(year.year) validAvgs.append(avg) axes = plt.gca() plt.scatter(validYears, validAvgs, color=color, label=label, alpha=0.5) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validAvgs) points = [slope * y + intercept for y in validYears] print('slope', slope, points[0], points[-1]) plt.plot(validYears, points, color=color, alpha=0.9) color = "red" label = "12h" validYears = [] validAvgs = [] for year in years: avg = year.summary['avgInc12LF'] if avg != 999: validYears.append(year.year) validAvgs.append(avg) axes = plt.gca() plt.scatter(validYears, validAvgs, color=color, label=label, alpha=0.5) slope, intercept, r_value, p_value, std_err = stats.linregress(validYears, validAvgs) points = [slope * y + intercept for y in validYears] print('slope', slope, points[0], points[-1]) plt.plot(validYears, points, color=color, alpha=0.9) plt.legend(ncol=2, loc='upper right') plt.title("Average strengthening before landfall " + str(yearStart) + "-" + str(yearEnd)) pngFile = "strenthening" + str(yearStart) + ".png" plt.savefig(pngFile) plt.show() if __name__ == '__main__': #plotSpeed(1950, 2020) #plotSpeed(1990, 2020) #plotRI(1950, 2020) #plotRI(1990, 2020) #plotRIRatio(1982, 2009) #plotRD(1950, 2020) #plotSW(1851, 2020) #plotNearLand(1851, 2020) #plotNearLand(1920, 2020) #plotCounts(1920, 2020) #plotACE(1920, 2020) #plotWeakening(1920, 2020) #plotLFStrengthening(1950, 2020)