def write_mhw_analyse_csv(mhws,file_name,location_type="onshore"):
'''
This function will create a csv file that contains some characteristics of marine heatwaves.
Inputs:
mhws This is the output of detect funtion.
It contains all the characteristics of marine heatwaves in the box from 1993 to 2019.
file_name This is the name of the netCDF file.
Options:
location_type This should be "onshore" or "offshore". It represents the location of the box.
The default value if "onshore".
'''
category = mhws['category']
# change the intensity category into the corresponding intensity levels
#1=strong, 2=moderate, 3=severe, 4=extreme
intensity_level = []
for ca in category:
if ca == 'Strong':
intensity_level.append(1)
elif ca == 'Moderate':
intensity_level.append(2)
elif ca == 'Severe':
intensity_level.append(3)
elif ca == 'Extreme':
intensity_level.append(4)
else:
intensity_level.append('ERROR')
# variables(characteristics of Marine Heatwaves) in the csv file
start = [date.fromordinal(int(st)) for st in mhws['time_start']] # date format
end = [date.fromordinal(int(st)) for st in mhws['time_end']]
duration = mhws['duration']
peak_time = [date.fromordinal(int(st)) for st in mhws['time_peak']]
int_max = mhws['intensity_max']
int_cum = mhws['intensity_cumulative']
int_mean = mhws['intensity_mean']
f = file_name+"_"+location_type
with open(f+'_analys.csv','w',newline = '') as a_file:
writer = csv.writer(a_file)
writer.writerow(["index_number","time_start","time_end","duration","peak_time","max_intensity","mean_intensity","cumulative_intensity","intensity_level","intensity_category"])
i = 0
while i < len(start):
writer.writerow([i+1,start[i],end[i],duration[i],peak_time[i],int_max[i],int_mean[i],int_cum[i],intensity_level[i],category[i]])
i += 1
mhwBlock = mhw.blockAverage(t, mhws)
mhwBlock_3y = mhw.blockAverage(t, mhws,blockLength=3)
mhwBlock_9y = mhw.blockAverage(t, mhws,blockLength=9)
def write_mhwBlock_csv(t,mhws,block_length,file_name,location_type="onshore"):
'''
This function creates a csv file that contains the characteristics of all marine heatwave in a time block.
Inputs:
t The time vector.
mhws This is the output of detect funtion.
It contains all the characteristics of marine heatwaves in the box from 1993 to 2019.
block_length The length of time blocks in year.
file_name The name of the netCDF file.
Options:
location_type This should be "onshore" or "offshore". It represents the location of the box.
The default value is "onshore"
'''
mhwBlock = mhw.blockAverage(t, mhws,blockLength=block_length)
# mhwBlock
start_year = mhwBlock['years_start'] # Start year blocks (inclusive)
end_year = mhwBlock['years_end'] # End year of blocks (inclusive)
centre_year = mhwBlock['years_centre'] # Decimal year at centre of blocks
frequency = mhwBlock['count']# Total MHW count in each block
duration = mhwBlock['duration']# Average MHW duration in each block [days]
max_intensity = mhwBlock['intensity_max']# Average MHW "maximum (peak) intensity" in each block [deg. C]
max_max_intensity = mhwBlock['intensity_max_max']# Maximum MHW "maximum (peak) intensity" in each block [deg. C]
mean_intensity = mhwBlock['intensity_mean'] #Average MHW "mean intensity" in each block [deg. C]
intensity_var = mhwBlock['intensity_var'] #Average MHW "intensity variability" in each block [deg. C]
cumulative_intensity = mhwBlock['intensity_cumulative'] #Average MHW "cumulative intensity" in each block [deg. C x days]
rate_onset = mhwBlock['rate_onset'] #Average MHW onset rate in each block [deg. C / days]
rate_decline = mhwBlock['rate_decline'] #Average MHW decline rate in each block [deg. C / days]
mhw_days = mhwBlock['total_days'] #Total number of MHW days in each block [days]
total_cumu_int = mhwBlock['total_icum'] # Total cumulative intensity over all MHWs in each block [deg. C x days]
f = "average_"+block_length+"_year_"+file_name+"_"+location_type
with open(f+'_analys.csv','w',newline = '') as a_file:
writer = csv.writer(a_file)
writer.writerow(["index_number","start_year","end_year","centre_year","frequency","duration","max_intensity","max_max_intensity","mean_intensity","intensity_variablity","cumulative_intensity","rate_onset","rate_decline","mhw_days","total_cumulative_intensity"])
i = 0
while i < len(start_year):
writer.writerow([i+1,start_year[i],end_year[i],centre_year[i],frequency[i],duration[i],max_intensity[i],max_max_intensity[i],mean_intensity[i],intensity_var[i],cumulative_intensity[i],rate_onset[i],rate_decline[i],mhw_days[i],total_cumu_int[i]])
i += 1
fileobj = Dataset(file0, mode='r')
lon = fileobj.variables['lon'][:].astype(float)
lat = fileobj.variables['lat'][:].astype(float)
fill_value = fileobj.variables['sst']._FillValue.astype(float)
scale = fileobj.variables['sst'].scale_factor.astype(float)
offset = fileobj.variables['sst'].add_offset.astype(float)
fileobj.close()
#
# Size of mhwBlock variable
#
matobj = io.loadmat(header + 'timeseries/avhrr-only-v2.ts.' +
str(300).zfill(4) + '.mat')
mhws, clim = mhw.detect(t, matobj['sst_ts'][300, tt_1982_2005])
mhwBlock = mhw.blockAverage(t, mhws)
years = mhwBlock['years_centre']
NB = len(years)
#
# initialize some variables
#
pctile = 90 # Percentile for calculation of MHWs
X = len(lon)
Y = len(lat)
i_which = range(0, X)
j_which = range(0, Y)
DIM = (len(j_which), len(i_which))
SST_mean = np.NaN * np.zeros(DIM)
MHW_total = np.NaN * np.zeros(DIM)
if ens > NENS[model]['hist'] - 1:
ens_clim = NENS[model]['hist'] - 1
else:
ens_clim = ens
else:
ens_clim = ens
mhws, clim = mhw.detect(t[model][exp][which_pre2100],
sst[exp][which_pre2100, j, ens],
climatologyPeriod=climPeriod,
alternateClimatology=[
t[model]['hist'],
sst['hist'][:, j, ens_clim]
])
mhwBlock = mhw.blockAverage(t[model][exp][which_pre2100],
mhws,
temp=sst[exp][which_pre2100, j,
ens],
clim=clim)
#else:
# mhws, clim = mhw.detect(t[model][exp], sst[exp][:,j,ens], climatologyPeriod=climPeriod)
# mhwBlock = mhw.blockAverage(t[model][exp], mhws, temp=sst[exp][:,j,ens], clim=clim)
# Calculate new measure of annual duration, intensity
mhwBlock['count_new'] = np.zeros(mhwBlock['count'].shape)
mhwBlock['duration_new'] = np.zeros(
mhwBlock['duration'].shape)
mhwBlock['intensity_max_max_new'] = np.zeros(
mhwBlock['intensity_max_max'].shape)
for ev in range(mhws['n_events']):
# Block index for year of each MHW (MHW year defined by start year)
iBlock = np.where((mhwBlock['years_start'] >=
mhws['date_start'][ev].year) *
def simulate(t, sst_obs, seas_obs, sst_trend_obs, N_ens, params=None):
'''
Fit AR1 model to sst time series and simulate MHW property trends
t is time vector, daily
sst_trend_obs is trend in units of decade^-1
N_ens is Number of ensembles, per trend value
params=(a, sig_eps) specified AR1 model parameters, None by defaule
which forces the AR1 model to be fit to sst data
'''
# Variables for AR1 process (simulated SST)
if params == None:
a, tmp, sig_eps = ar1fit(signal.detrend(sst_obs - seas_obs))
else:
a = params[0]
sig_eps = params[1]
tau = -1 / np.log(a)
var_eps = sig_eps**2
var_sst = var_eps * a / (1 - a**2) # AR1 process variance
sig_sst = np.sqrt(var_sst)
# Variables for large-ensemble experiment with multiple trend values
keys = ['count', 'intensity_mean', 'duration', 'intensity_max_max']
N_keys = len(keys)
trends = {}
means = {}
for key in keys:
trends[key] = np.zeros((N_ens))
means[key] = np.zeros((N_ens))
# Loop over trend values and ensemble members, save MHW property trends
T = len(t)
for i_ens in range(N_ens):
# Initialize sst and noise variables
#sst = np.zeros(T)
#eps = sig_eps*np.random.randn(T)
# Initial condition of sst is Normal random variable with mean 0, variance given by theoretical AR1 variance
#sst[0] = sig_sst*np.random.randn(1)
# Generate AR1 process
#for tt in range(1,T):
# sst[tt] = a*sst[tt-1] + eps[tt]
sst = tsa.arima_process.arma_generate_sample([1, -a], [1],
T,
sigma=sig_eps,
burnin=100)
# Add trend
sst = sst + sst_trend_obs * (t - t[0]) / 10. / 365.25
# Apply Marine Heat Wave definition
mhws, clim = mhw.detect(t, sst)
mhwBlock = mhw.blockAverage(t, mhws)
mean, trend, dtrend = meanTrend_TS(mhwBlock) #mhw.meanTrend(mhwBlock)
# Save trends
for key in keys:
trends[key][i_ens] = trend[key]
means[key][i_ens] = mean[key]
# Output results
return tau, sig_eps, trends, means
j = np.where(lat > locations['lat'][n])[0][0]
matobj = io.loadmat(header + 'timeseries/avhrr-only-v2.ts.' +
str(i + 1).zfill(4) + '.mat')
sst[site] = matobj['sst_ts'][j, :]
#site = 'E_Tas'
site = 'WA'
site = 'Med'
site = 'NW_Atl'
site = 'Blob'
t_obs, dates_obs, T_obs, year_obs, month_obs, day_obs, doy_obs = ecj.timevector(
[1982, 1, 1], [2016, 12, 31])
sst_obs = sst[site] #np.loadtxt('data/sst_' + site + '.csv', delimiter=',')
mhws_obs, clim_obs = mhw.detect(t_obs, sst_obs)
mhwBlock_obs = mhw.blockAverage(t_obs, mhws_obs)
mean_obs, trend_obs, dtrend_obs = meanTrend_TS(
mhwBlock_obs) #mhw.meanTrend(mhwBlock_obs)
# SST trend
years = mhwBlock_obs['years_centre']
SST_block = np.zeros(years.shape)
for yr in range(len(years)):
SST_block[yr] = np.mean(sst_obs[year_obs == years[yr]])
X = np.array([np.ones(years.shape), years - years.mean()]).T
beta = linalg.lstsq(X, SST_block)[0]
sst_trend_obs = beta[1] * 10
# Loop over SST trend values and simulate MHW property trends
else:
j1 = np.where(lat > lat_data[j] - res_data / 2.)[0][0]
if lat_data[j] + res_data / 2. > lat.max():
j2 = len(lat) - 1
else:
j2 = np.where(lat > lat_data[j] + res_data / 2.)[0][0]
sst = np.nanmean(np.nanmean(sst_ts[j1:j2 + 1, :, :], axis=2), axis=0)
if np.logical_not(np.isfinite(sst.sum())) + (
(sst < -1).sum() > 0): # check for land, ice
continue
# Count number of MHWs of each length
mhws, clim = mhw.detect(
t[avhrr_match],
sst,
climatologyPeriod=[1983, np.min([dataEnd, 2012])])
mhwBlock = mhw.blockAverage(t[avhrr_match], mhws)
years = mhwBlock['years_centre']
# Skip proxy_fit calculation if not enough data
if ((proxy['threshCount'][avhrr, j, i] > 0).sum() <= 5) + (
(~np.isnan(proxy['maxAnom'][avhrr, j, i])).sum() <= 5) + (
(~np.isnan(proxy['threshAnom'][avhrr, j, i])).sum() <= 5):
continue
# Loop over all combinations of MHW properties and proxy keys, to build and test all models
for key in MHW_keys:
if (key == 'total_days') + (key == 'total_icum'):
continue
else:
for pkey in proxy.keys():
rho[key][pkey][j, i], p[key][pkey][
j, i], MHW_ts[key][pkey][j, i, :], MHW_CI[key][pkey][
j, i, :, :] = proxy_fit(key,
if sw_write:
outfile_train.write(
str(year.min().astype(int)) + '-' + str(year.max().astype(int)) +
', ')
outfile_valid.write(
str(year.min().astype(int)) + '-' + str(year.max().astype(int)) +
', ')
# Apply Marine Heat Wave definition
print '\n' + station + '\n'
print 'Proportion of valid values: ' + str(100. - 100. *
np.isnan(sst).sum() / len(sst))
mhws, clim = mhw.detect(t,
sst,
climatologyPeriod=climPeriod,
maxPadLength=maxPadLength)
mhwBlock = mhw.blockAverage(t, mhws, clim, removeMissing=True, temp=sst)
mean, trend, dtrend = mhw.meanTrend(mhwBlock)
# Number of years with no MHW events pre and post 1950
#count_pre50 = mhwBlock['count'][(mhwBlock['years_centre']<=1950)*(~np.isnan(mhwBlock['count']))]
#count_post50 = mhwBlock['count'][(mhwBlock['years_centre']>1950)*(~np.isnan(mhwBlock['count']))]
#print 'Pre-1950 0-count: ' + str(1.*np.sum(count_pre50==0)/len(count_pre50)) + ' post-1950 0-count: ' + str(1.*np.sum(count_post50==0)/len(count_post50)) + ' Difference: ' + str(1.*np.sum(count_post50==0)/len(count_post50) - 1.*np.sum(count_pre50==0)/len(count_pre50))
years = mhwBlock['years_centre']
#Ny = len(mhwBlock['years_centre'])/2
ttPre = (years >= 1925) * (years <= 1954)
ttPost = (years >= 1984) * (years <= 2013)
count_preMid = ecj.nonans(mhwBlock['count'][ttPre])
count_postMid = ecj.nonans(mhwBlock['count'][ttPost])
print 'Pre-midpoint 0-count: ' + str(
1. * np.sum(count_preMid == 0) /
len(count_preMid)) + ' post-midpoint 0-count: ' + str(
1. * np.sum(count_postMid == 0) /
fileobj = Dataset(file0, 'r')
lon = fileobj.variables['lon'][:].astype(float)
lat = fileobj.variables['lat'][:].astype(float)
fill_value = fileobj.variables['sst']._FillValue.astype(float)
scale = fileobj.variables['sst'].scale_factor.astype(float)
offset = fileobj.variables['sst'].add_offset.astype(float)
fileobj.close()
#
# Size of mhwBlock variable
#
matobj = io.loadmat(header + 'timeseries/avhrr-only-v2.ts.' +
str(300).zfill(4) + '.mat')
mhws, clim = mhw.detect(t, matobj['sst_ts'][300, :])
mhwBlock = mhw.blockAverage(t, mhws)
years = mhwBlock['years_centre']
NB = len(years)
#
# initialize some variables
#
X = len(lon)
Y = len(lat)
i_which = range(0, X) #,10)
j_which = range(0, Y) #,10)
#i_which = range(0,X,4)
#j_which = range(0,Y,4)
DIM = (len(j_which), len(i_which))
N_ts = np.zeros((len(j_which), len(i_which), NB))
