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plot_time_seris_csv.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
plot multi axes ####
The data for statistics is seprated from timeseries plot.
"""
__author__ = "Saeed Moghimi"
__copyright__ = "Copyright 2017, UCAR/NOAA"
__license__ = "GPL"
__version__ = "1.0"
__email__ = "[email protected]"
import os,sys
#sys.path.append('/home/moghimis/linux_working/00-working/04-test-adc_plot/')
#sys.path.append('/home/moghimis/linux_working/00-working/04-test-adc_plot/csdlpy')
import matplotlib
if os.name == 'nt':
matplotlib.rc('font', family='Arial')
else: # might need tweaking, must support black triangle for N arrow
matplotlib.rc('font', family='DejaVu Sans')
import netCDF4
from collections import defaultdict
# import cPickle as pickle
import matplotlib.pyplot as plt
import numpy as np
import datetime
import string
import glob
import time
import pandas as pd
import seaborn as sns
import itertools
import cftime
sns.set_style(style='dark')
#sns.set_style(style='ticks')
from pynmd.plotting.vars_param import *
from pynmd.plotting import plot_routines as pr
import pynmd.models.adcirc.post as adcp
from pynmd.tools.tide_analysis import tappy_filters
from pynmd.plotting import colormaps as cmaps
import pynmd.plotting.plot_settings as ps
import matplotlib.tri as Tri
from geo_regions import get_region_extent
try:
os.system('rm base_info.pyc')
except:
pass
if 'base_info' in sys.modules:
del(sys.modules["base_info"])
import base_info
tim_lim = base_info.tim_lim
plot_timeseries = True
pandas_plots = True
plot_taylor = True
plot_rtofs = False
sub_tidal = False
pandas_plots_time_series = True
print ('\n\n ##################################################################')
print (' >>>>>>>>>>>>>>>>> Plot timeseries ',plot_timeseries)
print (' >>>>>>>>>>>>>>>>> PANDAS_plots ',pandas_plots)
print (' >>>>>>>>>>>>>>>>> Plot RTOFS ',plot_rtofs)
print (' >>>>>>>>>>>>>>>>> Plot Sub-tidal ',sub_tidal)
print (' >>>>>>>>>>>>>>>>> Pre-selected stations to plot ',base_info.station_selected_list)
print (' >>>>>>>>>>>>>>>>> pandas_plots_time_series ',pandas_plots_time_series)
print (' ##################################################################\n\n\n\n\n')
# calculate statistics
elev_lim = 2.2 # m
############################
curr_time = time.strftime("%Y%m%d_h%H_m%M_s%S")
fs = 12
fs2 = 10
# plt.rc('font', family='serif')
plt.rcParams.update(\
{'axes.labelsize': fs,
#'text.fontsize': fs,
'xtick.labelsize': fs,
'ytick.labelsize': fs,
'axes.titlesize': fs,
'legend.fontsize': fs / 1.5,
})
def read_csv(obs_dir, name, year, label):
"""
examples
print(' > write csv files')
write_csv(base_dir, name, year, table=wnd_ocn_table, data= wnd_ocn , label='ndbc_wind' )
write_csv(base_dir, name, year, table=wav_ocn_table, data= wav_ocn , label='ndbc_wave' )
write_csv(base_dir, name, year, table=ssh_table , data= ssh , label='coops_ssh' )
write_csv(base_dir, name, year, table=wnd_obs_table, data= wnd_obs , label='coops_wind')
"""
outt = os.path.join(obs_dir, name+year,label)
outd = os.path.join(outt,'data')
if not os.path.exists(outd):
sys.exit('ERROR',outd )
table = pd.read_csv(os.path.join(outt,'table.csv')).set_index('station_name')
table['station_code'] = table['station_code'].astype('str')
stations = table['station_code']
data = []
metadata = []
for ista in range(len(stations)):
sta = stations [ista]
fname8 = os.path.join(outd,sta)+'.csv'
df = pd.read_csv(fname8,parse_dates = ['date_time']).set_index('date_time')
fmeta = os.path.join(outd,sta) + '_metadata.csv'
meta = pd.read_csv(fmeta, header=0, names = ['names','info']).set_index('names')
meta_dict = meta.to_dict()['info']
meta_dict['lon'] = float(meta_dict['lon'])
meta_dict['lat'] = float(meta_dict['lat'])
df._metadata = meta_dict
data.append(df)
return table,data
def get_station_ssh(fort61,lim):
"""
Read model ssh
"""
nc0 = netCDF4.Dataset(fort61)
ncv0 = nc0.variables
sta_lon = ncv0['x'][:]
sta_lat = ncv0['y'][:]
sta_nam = ncv0['station_name'][:].squeeze()
sta_zeta = ncv0['zeta'] [:].squeeze()
sta_date = netCDF4.num2date(ncv0['time'][:], ncv0['time'].units)
stationIDs = []
mod = []
ind = np.arange(len(sta_lat))
[ind_bbox] = np.where((sta_lon > lim['xmin']) &
(sta_lon < lim['xmax']) &
(sta_lat > lim['ymin']) &
(sta_lat < lim['ymax']))
if False:
indi = ind
else:
indi = ind_bbox
for ista in indi:
stationID = sta_nam[ista].tostring().decode().rstrip()
stationIDs.append(stationID)
mod_tmp = pd.DataFrame(data = np.c_[sta_date, sta_zeta[:,ista]], columns=['date_time', 'ssh']).set_index('date_time')
mod_tmp._metadata = stationID
mod.append(mod_tmp)
stationIDs = np.array(stationIDs)
data = np.c_[indi, stationIDs]
mod_table = pd.DataFrame(data = data, columns=['ind', 'station_code'])
return mod,mod_table
###################################################################
def return_index(pattern, plist):
for i in range(len(plist)):
if pattern in plist[i]:
return i
break
return None
#############################################################
def some_thing():
############# Sea Surface height analysis ########################
# For simplicity we will use only the stations that have both wind speed and sea surface height and reject those that have only one or the other.
common = set(ssh_table['station_code']).intersection(mod_table ['station_code'].values)
ssh_obs, ssh_mod = [], []
for station in common:
ssh_obs.extend([obs for obs in ssh if obs._metadata['station_code'] == station])
ssh_mod.extend([obm for obm in mod if obm._metadata == station])
index = pd.date_range(
start = start_dt.replace(tzinfo=None),
end = end_dt.replace (tzinfo=None),
freq=freq
)
#############################################################
#organize and re-index both observations
# Re-index and rename series.
ssh_observations = []
for series in ssh_obs:
_metadata = series._metadata
obs = series.reindex(index=index, limit=1, method='nearest')
obs._metadata = _metadata
obs.dropna(inplace=True)
obs.name = _metadata['station_name']
ssh_observations.append(obs)
##############################################################
#model
ssh_from_model = []
for series in ssh_mod:
_metadata = series._metadata
obs = series.reindex(index=index, limit=1, method='nearest')
obs._metadata = _metadata
obs.name = _metadata
obs['ssh'][np.abs(obs['ssh']) > 10] = np.nan
obs.dropna(inplace=True)
ssh_from_model.append(obs)
for ssh1, model1 in zip(ssh_observations, ssh_from_model):
fname = ssh1._metadata['station_code']
location = ssh1._metadata['lat'], ssh1._metadata['lon']
p = make_plot(ssh1, model1, label='SSH [m]',remove_mean_diff=remove_mean_diff, bbox_bias = bbox_bias)
#p = make_plot(ssh1, ssh1)
marker = make_marker(p, location=location, fname=fname)
marker.add_to(marker_cluster_coops)
#del ssh_observations, ssh_from_model
###################################################
def get_rtofs_elev(stationID):
"""
inp: filename, station_name
out: date and elev
"""
fort61 = '/scratch4/COASTAL/coastal/save/Saeed.Moghimi/models/NEMS/NEMS_inps/data/ARTOFS-ike/rtofs_fort61.nc'
fort61_sta_ids = get_station_ids(fort61)
nc = netCDF4.Dataset(fort61)
ncv = nc.variables
dates = netCDF4.num2date(ncv['time_rtofs'][:], ncv['time_rtofs'].units)
ind2 = return_index (stationID, fort61_sta_ids)
elev = ncv['zeta_rtofs'][:, ind2]
nc.close()
return dates, elev
def datetime64todatetime(dt):
tmp = []
for it in range(len(dt)):
d = dt[it]
if isinstance(d, cftime.DatetimeGregorian):
tmp.append(datetime.datetime(d.year, d.month, d.day, d.hour, d.minute, d.second))
else:
tmp.append(pd.Timestamp(dt[it]).to_pydatetime())
return np.array(tmp)
def do_tappy_tide_analysis(dates, elev):
# filtered_dates, result = tappy_filters('transform', dates, elev)
obs = pd.DataFrame(elev, columns=['elev'])
se = pd.Series(dates)
obs = obs.set_index (se)
obs.dropna()
obsh = obs.resample('H').mean()
def do_tappy_tide_analysis(dates, val):
"""
"""
obs = pd.DataFrame(val, columns=['val'])
se = pd.Series(dates)
obs = obs.set_index (se)
obs.dropna()
obsh = obs.resample('H').mean()
dates = datetime64todatetime(obsh.index)
val = obsh.val
# ##
from tappy.tappy import tappy
# ## Saeed tries to understand! from here
data_filename = 'test'
def_filename = None
config = None
quiet = False
debug = False
outputts = False
outputxml = ''
ephemeris = False
rayleigh = 0.9
print_vau_table = False
missing_data = 'ignore'
# missing_data = 'fill'
linear_trend = False
remove_extreme = False
zero_ts = None
filter = None
pad_filters = None
include_inferred = True
xmlname = 'test'
xmlcountry = 'US'
xmllatitude = 100
xmllongitude = 100
xmltimezone = '0000'
xmlcomments = 'No comment'
xmlunits = 'm or ms-1'
xmldecimalplaces = None
############## model
x = tappy(
outputts=outputts,
outputxml='model.xml',
quiet=quiet,
debug=debug,
ephemeris=ephemeris,
rayleigh=rayleigh,
print_vau_table=print_vau_table,
missing_data=missing_data,
linear_trend=linear_trend,
remove_extreme=remove_extreme,
zero_ts=zero_ts,
filter=filter,
pad_filters=pad_filters,
include_inferred=include_inferred,
)
x.dates = dates
x.elevation = val
package = x.astronomic(x.dates)
(x.zeta, x.nu, x.nup, x.nupp, x.kap_p, x.ii, x.R, x.Q, x.T, x.jd, x.s, x.h, x.N, x.p, x.p1) = package
ray = 1.0
(x.speed_dict, x.key_list) = x.which_constituents(len(x.dates), package, rayleigh_comp=ray)
# x.constituents()
# x.print_con()
# x.print_con_file(filedat = out_file, lon = lon, lat = lat)
x.dates_filled, x.elevation_filled = x.missing(x.missing_data, x.dates, x.elevation)
x.write_file(x.dates_filled,
x.elevation_filled,
fname='outts_filled.dat')
x_dates_filter, x_eleva_filter = x.filters('transform', x.dates, x.elevation)
# x.filter='usgs'
# x.filter='cd'
# x.filter='boxcar'
# x.filter='doodson'
# x.filter='transform'
# if x.filter:
# for item in x.filter.split(','):
# if item in ['mstha', 'wavelet', 'cd', 'boxcar', 'usgs', 'doodson', 'lecolazet1', 'kalman', 'transform']:# 'lecolazet', 'godin', 'sfa']:
# filtered_dates, result = x.filters(item, x.dates, x.elevation)
# x.write_file(filtered_dates, result, fname='outts_filtered_%s.dat' % (item,))
# x_dates_filter= filtered_dates
# x_eleva_filter= result
# (x.speed_dict, x.key_list) = x.which_constituents(len(x.dates),package,rayleigh_comp = ray)
units = "seconds since 1970-01-01 00:00:00 UTC"
yd_time = x.dates
yd_sec = netCDF4.date2num(yd_time , units)
ft_sec = netCDF4.date2num(x_dates_filter , units)
ft_lev_new = np.interp(yd_sec, ft_sec, x_eleva_filter)
elev_filter = ft_lev_new
return yd_time , ft_lev_new
#sys.path.append('/scratch2/COASTAL/coastal/save/Saeed.Moghimi/opt/pycodes/csdlpy')
#import adcirc
from atcf import readTrack
def plot_track(ax,track,date=None,color = 'r'):
if date is not None:
dates = np.array(track['dates'])
#ind = np.array(np.where((dates==date))).squeeze().item()
ind = find_nearest_time(dates,date)
ax.plot(track['lon'][ind],track['lat'][ind],'ro',alpha=1,ms=8)
ax.plot(track['lon'],track['lat'],lw=3,color=color,ls='dashed',alpha=1)
def read_track(fname=None):
if fname is None:
fname = '/scratch2/COASTAL/coastal/save/Saeed.Moghimi/models/NEMS/NEMS_inps/01_data/tracks/ike_bal092008.dat'
track = readTrack(fname)
keys = ['dates', 'lon', 'vmax', 'lat']
for key in keys:
tmp = pd.DataFrame(track[key],columns=[key])
#dfh = df
if 'trc' not in locals():
trc = tmp
else:
trc = pd.concat([trc,tmp],axis=1)
trc = trc.drop_duplicates(subset='dates',keep='first')
trc = trc.set_index (trc.dates)
trc = trc.resample('H').interpolate()
trc.drop('dates',axis=1,inplace=True)
dates = datetime64todatetime(trc.index)
return dict(dates=dates,lon=trc.lon.values, lat=trc.lat.values)
def plot_map(sta_tab=[],comm_tab=[],prefix='',iplot=0):
print ('Plot map for recording points ...')
#construct HSOFS Tri mask
fname = base_info.cases[base_info.key1]['dir'] + '/maxele.63.nc'
nc0 = netCDF4.Dataset(fname)
ncv0 = nc0.variables
depth = ncv0['depth'][:]
depth [depth.mask] = -10.0
lon0 = ncv0['x'][:]
lat0 = ncv0['y'][:]
elems = ncv0['element'][:,:]-1 # Move to 0-indexing by subtracting 1
tri = Tri.Triangulation(lon0,lat0, triangles=elems)
nc0.close()
fig, ax = adcp.make_map(res = None)
fig.set_size_inches(7,7)
lim = get_region_extent(region = base_info.regions[0])
extent = [lim['xmin'],lim['xmax'],lim['ymin'],lim['ymax']+0]
ax.set_extent(extent)
try:
track = read_track(fname = base_info.track_fname)
plot_track(ax,track,date=None)
except:
pass
cond1 = ax.tricontour(tri,depth+0.1 ,levels=[0.0] ,colors='k',lw=0.02, alpha=0.8)
#plot mesh
if False:
ax.triplot(tri, 'w-', lw=0.1, alpha=0.4)
#plot all stations
for il in range(len(comm_tab)):
ax.scatter(comm_tab.iloc[il]['lon'],comm_tab.iloc[il]['lat'],s=15,c='y',zorder=3,edgecolors='None')
#plot these stations
for il in range(len(sta_tab)):
ax.scatter(sta_tab.iloc[il]['lon'],sta_tab.iloc[il]['lat'],s=50,c='r',zorder=3,marker=ps.marker[il],label=sta_tab.iloc[il]['station_code'])
#ax.plot(ncv['x'][ind2],ncv['y'][ind2],color='r')
plt.legend(loc=4,ncol=4,frameon=True,numpoints=1,scatterpoints=1,prop={'size':8},framealpha=0.5)
#ax.set_title(header)
filename = out_dir + '/' +str(100+iplot)+'_map_' +prefix + '_' + '.png'
plt.savefig(filename,dpi=dpi//1.4)
plt.close('all')
#### END of Funcs
#read Obs data
print (' read from OBS CSV files')
#try:
print (' > ssh from CSV files')
ssh_table,ssh = read_csv (base_info.base_dir_obs, base_info.name, base_info.year, label='coops_ssh' )
"""
except:
print (' > No ssh CSV files')
try:
print (' > wind from CSV files')
wnd_obs_table,wnd_obs = read_csv (base_info.base_dir_obs, name, year, label='coops_wind')
except:
print (' > No wind CSV files')
try:
print (' > NDBC wind from CSV files')
wnd_ocn_table,wnd_ocn = read_csv (base_info.base_dir_obs, name, year, label='ndbc_wind' )
except:
print (' > No ndbc wind CSV files')
try:
print (' > NDBC wave from CSV files')
wav_ocn_table, wav_ocn = read_csv (base_info.base_dir_obs, name, year, label='ndbc_wave' )
except:
print (' > no NDBC wave CSV files')
"""
for x in base_info.cases[base_info.key0]['dir'].split('/'):
if 'rt_' in x:
prefix = x
else:
prefix = ''.join(base_info.cases[base_info.key0]['dir'].split('/')[-3:])
prefix = 'time_series_' + prefix
t1 = tim_lim['xmin'].isoformat()[2:13] + '_' + tim_lim['xmax'].isoformat()[2:13] + '_' + curr_time
out_dir = base_info.out_dir + prefix + t1 + '/'
# out dir and scr back up
scr_dir = out_dir + '/scr/'
os.system('mkdir -p ' + scr_dir)
args = sys.argv
scr_name = args[0]
os.system('cp -fr ' + scr_name + ' ' + scr_dir)
os.system('cp -fr *.py ' + scr_dir)
print (out_dir)
#####################################################################
# graphic settings
ftypes = ['png', 'pdf']
#
left1 = 0.1 # the left side of the subplots of the figure
right1 = 0.9
bottom1 = 0.3 # the bottom of the subplots of the figure (ntr==16 bottom=0.05)
top1 = 0.9
wspace1 = 0.1
hspace1 = 0.15
#
dpi = 300
#for map plot
lon,lat,tri = adcp.ReadTri(base_info.cases[base_info.key1]['dir'])
####################################################3###############
#
defs['elev']['cmap'] = cmaps.jetWoGn()
#
defs['elevdif']['vmin'] = -0.05
defs['elevdif']['vmax'] = 0.05
#
defs['hs']['vmax'] = 2
defs['hs']['vmin'] = 0.2
#
params = ['elev']
defs['elev']['label'] = 'Elev. [m]'
#list of COOPS data files
#fpiks = np.array(glob.glob(base_info.base_dir_coops_piks + '/*.pik'))
#sta_coops_list = []
#for fpik in fpiks:
# stationID_new = fpik.split('/')[-1][0:7]
# sta_coops_list.append(stationID_new)
#get stations in bounding box
defs['lim'] = get_region_extent(region = base_info.regions[0] )
#ind_sta_all, ncv0, stationIDs_model = get_station_list(fort61, defs['lim'])
#sta_old_list = []
#for fpik in fpiks:
# stationID_new = fpik.split('/')[-1][0:7]
# sta_old_list.append(stationID_new)
if base_info.station_selected_list is not None:
stationIDs = np.array(base_info.station_selected_list)
else:
#stationIDs = np.array(ssh_table.station_code)
##############3
#This is just to generate the common list of stations
fort61 = base_info.cases[np.sort(list(base_info.cases.keys()))[0]]['dir'] + '/fort.61.nc'
mod , mod_table = get_station_ssh(fort61=fort61,lim=defs['lim'] )
tmp_ssh_table = ssh_table.sort_values('lat')
common = set(mod_table['station_code']).intersection(tmp_ssh_table ['station_code'].values)
stationIDs = np.sort(list(common))
#Get matched stations lists
#sta_list_all = return_matched_items(stationIDs_model,stationIDs)
sta_list_all = stationIDs
all_stations = {}
if plot_timeseries:
icol = 2
irow_max = 2
nplot = len(sta_list_all)// (irow_max * icol)
iloc = 0
for iplot in range(nplot):
if len(sta_list_all) < (irow_max * icol):
print (' ... All list')
sta_list = sta_list_all
# match the numebr to clumns
sta_list = sta_list[:n]
else:
print (' ... partial list')
sta_list = sta_list_all[iloc:iloc+icol*irow_max]
iloc = iloc + icol * irow_max
n = len(sta_list) // icol * icol
irow = len(params) * len(sta_list) // icol
fwidth = icol * 10
fheight = irow * 4
#
fig, axgrid = plt.subplots(nrows=irow, ncols=icol, sharex=True, sharey=False,
figsize=(fwidth, fheight), facecolor='w', edgecolor='k', dpi=dpi)
#
plt.subplots_adjust(left=left1, bottom=bottom1, right=right1, top=top1,
wspace=wspace1, hspace=hspace1)
#
axgrid = np.array(axgrid)
axgrid = axgrid.reshape(icol * irow)
#
nn = 0
#sta_list = []
for sta in sta_list:
for param in params:
ax = axgrid[nn]
ic = 0
defs['elev']['vmin'] = -0.5
defs['elev']['vmax'] = 0.5
cl = itertools.cycle(sns.color_palette(palette='dark', n_colors=2 * len(base_info.cases.keys())))
# cl = itertools.cycle(sns.color_palette(palette = 'mute', n_colors = 2 * len(base_info.cases.keys()) ) )
keys = np.sort(list(base_info.cases.keys()))
for key in keys:
# try:
print (key)
fort61 = base_info.cases[key]['dir'] + '/fort.61.nc'
mod , mod_table = get_station_ssh(fort61,defs['lim'])
############# Sea Surface height analysis ########################
# For simplicity we will use only the stations that have both wind speed and sea surface height and reject those that have only one or the other.
#tmp_ssh_table = ssh_table.sort_values('lat')
common = set(ssh_table['station_code']).intersection(mod_table ['station_code'].values)
#common = set(mod_table['station_code']).intersection(tmp_ssh_table ['station_code'].values)
common = np.sort(list(common))
#ssh_obs, ssh_mod , comm_tab = [], [], []
#for station in common:
# ssh_obs.extend([obs for obs in ssh if obs._metadata['station_code'] == station])
# ssh_mod.extend([obm for obm in mod if obm._metadata == station])
comm_tab = ssh_table[ssh_table['station_code'].isin(common)]
#station_names, stationIDs = [],[]
#for obs in ssh_obs:
# station_names.append(obs._metadata['station_name'])
# stationIDs.append(obs._metadata['station_code'])
stationID = sta#stationIDs[ista]
#sta_list.append(stationID)
print (stationID)
indo = return_index (stationID, ssh_table['station_code'].values)
indm = return_index (stationID, mod_table['station_code'].values)
header = 'Station: ' + stationID + ' at ' + ssh_table.index[indo]
prefix = header.replace(':','_').replace(' ','_').replace(',','_').replace(',','_')
print (header)
defs['elev']['vmax'] = 1.2 * max(ssh[indo].max().values,mod[indm].max().values ).astype(float)
defs['elev']['vmin'] = 1.1 * min(ssh[indo].min().values,mod[indm].min().values ).astype(float)
# model
dates1 = datetime64todatetime(mod[indm].index)
val1 = mod[indm].values
#model data bias
bias = np.mean(ssh[indo].values)- np.mean(val1)
if base_info.local_bias_cor:
val1 = val1 + bias
data = dict(xx = dates1 ,
val = val1 ,
#
var = defs[param],
lim = tim_lim,
)
#
args = dict(
title=header,
label=base_info.cases[key]['label'],
color=ps.cl[ic],
# color = next(cl),
plot_leg=True,
#panel_num = nn,
)
#
if sub_tidal:
print ('sub_tidal')
dates1, val1 = do_tappy_tide_analysis(dates, val)
data['xx'] = dates1
data['val'] = val1
pr.TimeSeriesPlot(ax, data, args)
else:
pr.TimeSeriesPlot(ax, data, args)
# ax.plot(dates,val)
ic += 1
# except:
# print (' > unsucessful key >' , key)
# #sys.exit()
# pass
# plot OBS station data
dates = datetime64todatetime(ssh[indo].index)
val = ssh[indo].values
data = dict(
xx = dates[::5] ,
val = val [::5] ,
#
# xx = stations['tides']['dates'] ,
# val = stations['tides']['values'] ,
#
# xx = stations['wl_obs']['dates'] ,
# val = stations['wl_obs']['values'] ,
var=defs[param],
lim=tim_lim,
)
#
args = dict(
title=header,
label='Data',
color='k',
plot_leg=True,
obs=True,
panel_num = nn,
ms=4,
alpha = 0.5
)
#
no_data = False
if not no_data:
# pr.TimeSeriesPlot(ax,data,args)
if sub_tidal:
dates1, val1 = do_tappy_tide_analysis(dates, val)
data['xx'] = dates1
data['val'] = val1
pr.TimeSeriesPlot(ax, data, args)
else:
pr.TimeSeriesPlot(ax, data, args)
if plot_rtofs:
dates, val = get_rtofs_elev(stationID)
dates = dates - datetime.timedelta(1) # dates changed without any reason
key = 'artofs'
stations[key] = {}
stations[key]['dates'] = dates
stations[key]['values'] = val
data = dict(xx=dates ,
val=val ,
#
var=defs[param],
lim=tim_lim,
)
#
args = dict(
label='ARTOFS',
color=ps.cl[ic + 1],
plot_leg=True,
)
# pr.TimeSeriesPlot(ax,data,args)
if sub_tidal:
dates1, val1 = do_tappy_tide_analysis(dates, val)
data['xx'] = dates1
data['val'] = val1
pr.TimeSeriesPlot(ax, data, args)
else:
pr.TimeSeriesPlot(ax, data, args)
nn += 1
#if not no_data:
# all_stations[stationID] = stations
plt.savefig(out_dir + '/' +str(100+iplot)+ '_tim_' +prefix + '_' + '.png', dpi=dpi)
#plt.savefig(out_dir + '/' +str(100+iplot)+ '_tim_' +prefix + '_' + '.pdf')
plt.close('all')
##################################################################################
sta_tab = ssh_table[ssh_table['station_code'].isin(sta_list)]
plot_map(sta_tab=sta_tab,comm_tab=comm_tab,prefix=prefix,iplot=iplot)
# if plot_rtofs:
# base_info.cases['artofs'] = {}
if False:
###########################################################################################
###########################################################################################
def model_on_data(data_dates, model_dates, model_val):
print (' >>>>>>>>>>>>>>> ')
units = 'seconds since 2012-04-01 00:00:00'
data_sec = netCDF4.date2num(data_dates , units)
model_sec = netCDF4.date2num(model_dates, units)
return np.interp(data_sec, model_sec, model_val)
# read and construct timeseries
all_stations = {}
nn = 0
for ista in range(len(sta_list_all)):
stationID = sta_list_all[ista]
print (stationID)
ind1 = return_index (stationID, fpiks)
if ind1 is not None:
sta_pik = fpiks [ind1]
else:
print (' Remove this station from the list: no data is avaiable for the time of storm > ' , stationID)
sys.exit(' ERROR !')
f2 = file(sta_pik , 'r')
stations = pickle.load(f2)
f2.close()
header = 'Station: ' + stationID + ' at ' + stations['info']['name'] + ', ' + stations['info']['state']
print (stations['info']['name'], stations['info']['lon'], stations['info']['lat'], ' ! ', stationID)
for param in params:
keys = np.sort(base_info.cases.keys())
for key in keys:
print (key)
fort61 = base_info.cases[key]['dir'] + '/fort.61.nc'
fort61_sta_ids = get_station_ids(fort61)
nc = netCDF4.Dataset(fort61)
ncv = nc.variables
dates = netCDF4.num2date(ncv['time'][:], ncv['time'].units)
ind2 = return_index (stationID, fort61_sta_ids)
val = ncv[defs['elev']['var']][:, ind2]
nc.close()
#
stations['cwlev']['dates' ] = np.array(stations['cwlev']['dates' ]).squeeze()
stations['cwlev']['values'] = np.array(stations['cwlev']['values']).squeeze()
stations[key] = {}
stations[key]['dates'] = dates
stations[key]['values'] = val
#
if len(stations['cwlev']['values']) > 1:
all_stations[stationID] = stations
print (' >>> Map for Taylor diagram')
plot_map(sta_list_all=all_stations.keys(),sta_list=[],prefix='for_taylor',iplot=0)
from pynmd.tools.compute_statistics import statatistics
taylor_data = {}
for sta in all_stations.keys():
# find stations with exterme levels
max_elev = 0
keys = np.sort(base_info.cases.keys())
for key in keys:
max_elev = max (max_elev, max(all_stations[sta]['cwlev']['values']), all_stations[sta][key]['values'].max())
base_info.cases[key]['model_on_data'] = []
base_info.cases[key]['data_on_data'] = []
base_info.cases[key]['data_on_data_stations'] = []
# interpolate model on data points
if max_elev > elev_lim:
keys = np.sort(base_info.cases.keys())
for key in keys:
ind = np.where ((np.array(all_stations[sta]['cwlev']['dates']) >= tim_lim['xmin']) &
(np.array(all_stations[sta]['cwlev']['dates']) <= tim_lim['xmax']))
#
ind = np.array(ind).squeeze()
all_stations[sta][key]['val_on_data'] = model_on_data(
data_dates = all_stations[sta]['cwlev']['dates'][ind],
model_dates = all_stations[sta][key]['dates'],
model_val = all_stations[sta][key]['values'])
base_info.cases[key]['model_on_data'].append(all_stations[sta][key]['val_on_data'])
base_info.cases[key]['data_on_data' ].append(all_stations[sta] ['cwlev']['values' ][ind])
base_info.cases[key]['data_on_data_stations'].append(sta)
# constract model and data for statistics calculation
keys = np.sort(base_info.cases.keys())
for key in keys:
model = np.array(base_info.cases[key]['model_on_data']).squeeze()
data = np.array(base_info.cases[key]['data_on_data' ]).squeeze()
base_info.cases[key]['stats'] = statatistics (data, model)
taylor_data.update({base_info.cases[key]['label']:[model.std(ddof=1), np.corrcoef(data, model)[0, 1]]})
if plot_taylor:
#####
print ('Plot Taylor Dig.')
from pynmd.plotting import taylor
markersize = 8
fig = plt.figure(6, figsize=(9, 9))
fig.clf()
refstd = data.std(ddof=1)
# Taylor diagram
dia = taylor.TaylorDiagram(refstd, fig=fig, rect=111, label="Reference")
colors = plt.matplotlib.cm.jet(np.linspace(0, 1, len(taylor_data.keys())))
# Add samples to Taylor diagram
for imodel in range(len(taylor_data.keys())):
key = taylor_data.keys()[imodel]
stddev = taylor_data[key][0]
corrcoef = taylor_data[key][1]
marker = ps.marker[imodel]
dia.add_sample(stddev, corrcoef, ref=False, marker=marker, ls='', c=ps.colors[imodel],
markersize=markersize, label=key)
# add refrence point for data
dia.add_sample(refstd, 1.0 , ref=True, marker='*', ls='', c='k',
markersize=markersize * 1.5, label='Ref.')
# Add RMS contours, and label them
contours = dia.add_contours(levels=8, data_std=refstd, colors='0.5')
plt.clabel(contours, inline=1, fmt='%.3g', fontsize=10)
dia.smin = 0.15
dia.smax = 0.5
# Add a figure legend
if True:
leg2 = fig.legend(dia.samplePoints,
[ p.get_label() for p in dia.samplePoints ],
numpoints=1, prop=dict(size='small'), loc='upper right', ncol=1)
frame = leg2.get_frame()
frame.set_edgecolor('None')
frame.set_facecolor('None')
plt.title('COOPS data', position=(0.1, 1.04))
plt.subplots_adjust(left=left1, bottom=bottom1, right=right1, top=top1,
wspace=wspace1, hspace=hspace1)
plt.savefig(out_dir + '/taylor_coops' + '.png', dpi=dpi)
plt.close('all')
print ('plot stats on fig ..')
params = ['cor', 'r2', 'rmse', 'rbias', 'bias', 'mae', 'peak', 'ia', 'skill']
params = ['cor', 'rmse', 'rbias', 'bias', 'peak', 'ia']
nall = len(params)
icol = 2
irow = nall // icol
params = params[:icol * irow]
# figure
fwidth, fheight = ps.get_figsize(300)
fwidth = fwidth * icol
fheight = fheight * irow
fig, axgrid = plt.subplots(nrows=irow, ncols=icol, sharex=True, sharey=False,
figsize=(fwidth, fheight),
facecolor='w', edgecolor='k'
# ,gridspec_kw = {'width_ratios':[1, 1]}
)
axgrid = np.array(axgrid)
axgrid = axgrid.reshape(icol * irow)
nn = -1
colors = plt.matplotlib.cm.jet(np.linspace(0, 1, len(base_info.cases.keys())))
for param in params:
nn += 1
ax = axgrid[nn]
imodel = 0
samplePoints = []
labs = []
keys = np.sort(base_info.cases.keys())