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PlotMapAndFewMeasurements.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Sep 13 16:11:18 2017
@author: siirias
"""
import sys
sys.path.insert(0,'D:\\svnfmi_merimallit\\qa\\nemo')
import datetime as dt
import calendar
import matplotlib as mp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.io import netcdf
from mpl_toolkits.basemap import Basemap, shiftgrid, cm
import ModelQATools as qa
import math
import argohelper as ah
#runfile('D:/ArgoData/plot_full_data.py', wdir='D:/ArgoData')
draw_images=True
#km/day range
day_in_km=3
how_many_shown=10
"""
nc Variable Variable Units Description
metavar1 Cruise
metavar2 Station
metavar3 Type
longitude Longitude degrees_east
latitude Latitude degrees_north
metavar4 Bot. Depth m
metavar5 Secchi Depth m
date_time Decimal Gregorian Days of the station days since 2013-01-01 00:00:00 UTC Relative Gregorian Days with decimal part
var1 PRES db
var2 TEMP deg C
var3 PSAL psu
var4 DOXY ml/l
var5 PHOS umol/l
var6 TPHS umol/l
var7 SLCA umol/l
var8 NTRA umol/l
var9 NTRI umol/l
var10 AMON umol/l
var11 NTOT umol/l
var12 H2SX umol/l
var13 PHPH
var14 ALKY meq/l
var15 CPHL ug/l
var16 Year (station date)
"""
#Gotlands deep
#lon_min=17;lat_min=56;lon_max=22;lat_max=59;
lon_min=16.5;lat_min=55.5;lon_max=22.5;lat_max=59.5;
target_lat=57.3; target_lon=20; target_rad=1.8*6 #rad in km
filetype='csv' # 'nc' tai 'csv'
if filetype=='nc':
invalid_val=-10000000000.000
file_n='./Siiriaetal2017/2013-2016_GotlDeep_data_from_helcom.nc'
fmk=netcdf.netcdf_file(file_n,'r')
press=-1.0*fmk.variables['var1'][:]
longitude=fmk.variables['longitude'][:]
latitude=fmk.variables['latitude'][:]
start_epoch=dt.datetime(2013,1,1,0,0)
start_secs=(start_epoch-dt.datetime.utcfromtimestamp(0.0)).total_seconds() #this should be amount of seconds to add to actual timestamps
#a=dt.datetime.fromtimestamp(times[0]*24.0*60.0*60.0)
times_s=fmk.variables['date_time'][:]
times=[]
for i in range(len(times_s)):
times.append(dt.datetime.utcfromtimestamp(times_s[i]*24.0*60.0*60.0+start_secs))
d=fmk.variables['var2'][:]
d=np.ma.masked_where(d==invalid_val,d)
else:
if filetype=='csv':
invalid_val=-10000000000.000
#file_n='./Siiriaetal2017/uudet_CTDt_16102017.csv'
file_n=ah.ctd_data_file
fmk=pd.read_csv(file_n)
press=-1.0*fmk[u'PRES [db]'][:]
longitude=fmk[u'Longitude [degrees_east]'][:]
latitude=fmk[ u'Latitude [degrees_north]'][:]
temperature=fmk[u'TEMP [deg C]'][:]
salinity=fmk[u'PSAL [psu]'][:]
oxygen=fmk[u'DOXY [ml/l]'][:]
#purkka, koska happi eri muodossa:
for i in range(len(oxygen)):
if(type(oxygen[i])==str and oxygen[i][0]=='<'):
oxygen[i]=None
else:
oxygen[i]=float(oxygen[i])
times_s=fmk[u'yyyy-mm-ddThh:mm'][:]
times=[]
for i in range(len(times_s)):
times.append(dt.datetime.strptime(times_s[i],'%Y-%m-%dT%H:%M'))
ts=[]
for i in range(len(times)):
ts.append(calendar.timegm(times[i].timetuple()))
ctd_data=ah.split_csv_profiles(press,[longitude,latitude,temperature,salinity,ts,oxygen])
#tehdän se perus aikajana
timesx=ctd_data[:,0,5]
times=[]
for i in range(len(timesx)):
times.append(dt.datetime.utcfromtimestamp(timesx[i]))
# d=np.ma.masked_where(d==invalid_val,d)
#create mask, for values close to main point:
distance_mask=[False]*len(times_s)
for i in range(len(times_s)):
if(target_rad>ah.distance((target_lat,target_lon),(latitude[i],longitude[i]))):
distance_mask[i]=True
#Ja perus paikkajanatkin
pressure=ctd_data[:,:,0]
latitude=ctd_data[:,0,2]
longitude=ctd_data[:,0,1]
temperature=ctd_data[:,:,3]
salinity=ctd_data[:,:,4]
oxygen=ctd_data[:,:,6]
else:
print "wrong fieltype,",filetype," aborting!"
filetype='exitnow'
if(filetype!='exitnow'):
#
#
#
#
#
#create mask, for values close to main point:
"""
distance_mask=[False]*len(times_s)
for i in range(len(times_s)):
if(target_rad>distance((target_lat,target_lon),(latitude[i],longitude[i]))):
distance_mask[i]=True
"""
distance_mask=[False]*len(times)
for i in range(len(times)):
if(target_rad>ah.distance((target_lat,target_lon),(latitude[i],longitude[i]))):
distance_mask[i]=True
#
#
#
#
#Map...
if draw_images==True:
plt.figure(figsize=(15,15))
bmap = Basemap(projection='merc',\
resolution='i', llcrnrlat=lat_min, llcrnrlon=lon_min, urcrnrlat=lat_max, urcrnrlon=lon_max)
bmap.drawcoastlines()
bmap.fillcontinents()
bmap.drawparallels(np.arange(50.,69,0.5),labels=[1,0,0,0],linewidth=1,dashes=[1,1],color='#bbbbbb',zorder=0)
bmap.drawmeridians(np.arange(12.,30,1.0),labels=[0,0,0,1],linewidth=1,dashes=[1,1],color='#bbbbbb',zorder=0)
# for i in range(len(x)):
# if(distance_mask[i]):
# bmap.plot(x[i],y[i],'xg',markersize=15)
def format_coord(x, y):
return 'x=%.4f, y=%.4f'%(bmap(x, y, inverse = True))
plt.gca().format_coord = format_coord
#
#
#
#
#
#then plot the argo routes
plot_legends=False
plot_routes=True
print "PLOTTING ARGO ROUTES!"
# files_to_plot=["6902014_prof.nc","6902019_prof.nc", \
# "6902020_prof.nc"]
files_to_plot=ah.file_names_cleaned
# colors=["#ff0000","#00ff00","#0000ff"]
colors=["#5555ff"]*3
labels=[\
"6902020", "6902021", "6902022", \
]
# start=mp.dates.datetime.datetime(1000,5,5)
# end=mp.dates.datetime.datetime(3030,5,5)
start=mp.dates.datetime.datetime(2013,10,19)
end=mp.dates.datetime.datetime(2013,11,4)
argo_lats=np.array([])
argo_lons=np.array([])
argo_times=np.array([])
argo_depth=[]
argo_tem=[]
argo_sal=[]
argo_lats_all=np.array([])
argo_lons_all=np.array([])
argo_times_all=np.array([])
argo_depth_all=[]
argo_tem_all=[]
argo_sal_all=[]
if plot_routes:
for f,col,lab in zip(files_to_plot,colors,labels):
a=qa.PointData(f,1,start,end,"argonc")
print "File {} has {} profiles".format(f,len(a.obs['ape']['lat'][:]))
argo_lats=np.concatenate((argo_lats,a.obs['ape']['lat'][:]))
argo_lons=np.concatenate((argo_lons,a.obs['ape']['lon'][:]))
argo_times=np.concatenate((argo_times,mp.dates.num2date(a.obs['ape']['date'][:])))
tmp_val=a.obs['ape']['tem'][:]
for i in range(tmp_val.shape[0]):
argo_tem.append(tmp_val[i,:])
tmp_val=a.obs['ape']['sal'][:]
for i in range(tmp_val.shape[0]):
argo_sal.append(tmp_val[i,:])
tmp_val=a.obs['ape']['depth'][:]
for i in range(tmp_val.shape[0]):
argo_depth.append(tmp_val[i,:])
argo_lats_all=np.concatenate((argo_lats_all,argo_lats))
argo_lons_all=np.concatenate((argo_lons_all,argo_lons))
argo_times_all=np.concatenate((argo_times_all,argo_times))
argo_tem_all+=argo_tem
argo_sal_all+=argo_sal
argo_depth_all+=argo_depth
if(draw_images):
x,y=bmap(a.obs['ape']['lon'][:],a.obs['ape']['lat'][:])
bmap.plot(x,y,'x-',markersize=4,color=col,linewidth=2, alpha=0.8)
bmap.plot(x[0],y[0],'o',markersize=5,color=col,linewidth=2, alpha=0.8)
#bmap.plot(x[-1],y[-1],'o',color=col,markersize=10,alpha=1.0,label=lab)
if(draw_images):
x,y=bmap(longitude,latitude)
bmap.plot(x,y,'*',markersize=4,color='#773300')
#add BY15 place:
#BY15 (57.32\,N 20.05\,E)
x,y=bmap([20.05],[57.32])
bmap.plot(x,y,'o',markersize=10, markerfacecolor='None',color='#ffaaaa')
plt.savefig('CTD_Argo_places.eps',dpi=300)
#plot 30 km circle
angle=np.arange(0,2.0*np.pi,0.01)
x,y=bmap(20.05+np.sin(angle)*0.5,57.32+np.cos(angle)*0.27)
bmap.plot(x,y,'r-',color='#aaaaff')
if(draw_images and False):
ref_point=(57.315,20.04)
mbi_dat=0
tolerance=5.0
plt.figure(figsize=(12,8))
for i in range(len(latitude)):
if(ah.distance(ref_point,(latitude[i],longitude[i]))<50.0):
plt.plot(oxygen[i],pressure[i])
fill_data={'lat':latitude[i],'lon':longitude[i],'time':pd.to_datetime(str(times[i])),
'sal120':ah.get_closest(pressure[i],salinity[i],-120.0,tolerance)[1],
'oxy120':ah.get_closest(pressure[i],oxygen[i],-120.0,tolerance)[1],
'sal150':ah.get_closest(pressure[i],salinity[i],-150.0,tolerance)[1],
'oxy150':ah.get_closest(pressure[i],oxygen[i],-150.0,tolerance)[1],
'sal200':ah.get_closest(pressure[i],salinity[i],-200.0,tolerance)[1],
'oxy200':ah.get_closest(pressure[i],oxygen[i],-200.0,tolerance)[1]}
if(type(mbi_dat)==int):
mbi_dat=pd.DataFrame(fill_data,index=[0])
else:
idx=len(mbi_dat)
mbi_dat=mbi_dat.append(fill_data,ignore_index=idx)
# mbi_dat=mbi_dat.dropna()
mbi_dat=mbi_dat.sort_values(by='time')
plt.figure(figsize=(12,8))
plt.plot(mbi_dat['time'],mbi_dat['oxy120'],'b*')
plt.plot(mbi_dat['time'],mbi_dat['oxy150'],'r*')
plt.plot(mbi_dat['time'],mbi_dat['oxy200'],'g*')
print mbi_dat.shape
# plt.plot([[i]],[longitude[i]],'x',markersize=4,color=col,linewidth=2, alpha=0.8)
# if(type(mbi_dat)==int):
# pd.DataFrame()