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alamo2csv.py
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#!/usr/bin/env python
"""
alamo2csv.py
[u'TEMP',
u'JULD',
u'FLOAT_SERIAL_NO',
u'PSAL',
u'REFERENCE_DATE_TIME',
u'longitude',
u'time', u'latitude', u'profileid', u'CYCLE_NUMBER', u'PRES']
History:
--------
2016-10-26
"""
#System Stack
import datetime
import os
import argparse
#Science Stack
from netCDF4 import Dataset, date2num, num2date
import numpy as np
# Plotting Stack
import matplotlib as mpl
mpl.use('Agg')
from mpl_toolkits.basemap import Basemap, shiftgrid
import matplotlib.pyplot as plt
import matplotlib as mpl
import cmocean
#%%
#User Stack
from io_utils.EcoFOCI_netCDF_read import EcoFOCI_netCDF
__author__ = 'Shaun Bell'
__email__ = '[email protected]'
__created__ = datetime.datetime(2014, 05, 22)
__modified__ = datetime.datetime(2016, 8, 10)
__version__ = "0.1.0"
__status__ = "Development"
__keywords__ = 'netCDF','meta','header', 'csv'
### Plot settings
mpl.rcParams['axes.grid'] = False
mpl.rcParams['axes.edgecolor'] = 'black'
mpl.rcParams['axes.linewidth'] = 0.5
mpl.rcParams['grid.linestyle'] = '--'
mpl.rcParams['grid.linestyle'] = '--'
mpl.rcParams['xtick.major.size'] = 4
mpl.rcParams['xtick.minor.size'] = 2
mpl.rcParams['xtick.major.width'] = 2
mpl.rcParams['xtick.minor.width'] = 1
mpl.rcParams['ytick.major.size'] = 6
mpl.rcParams['ytick.minor.size'] = 2
mpl.rcParams['ytick.major.width'] = 2
mpl.rcParams['ytick.minor.width'] = 1
mpl.rcParams['ytick.direction'] = 'out'
mpl.rcParams['xtick.direction'] = 'out'
mpl.rcParams['ytick.color'] = 'k'
mpl.rcParams['xtick.color'] = 'k'
mpl.rcParams['font.size'] = 18
mpl.rcParams['font.sans-serif'] = "Arial"
mpl.rcParams['font.family'] = "sans-serif"
mpl.rcParams['font.weight'] = 'medium'
mpl.rcParams['svg.fonttype'] = 'none'
def etopo1_subset(file='etopo1.nc', region=None):
""" read in ardemV2 topography/bathymetry. """
file='/Volumes/WDC_internal/Users/bell/in_and_outbox/Ongoing_Analysis/MapGrids/etopo_subsets/etopo1_chukchi.nc'
bathydata = Dataset(file)
topoin = bathydata.variables['Band1'][:]
lons = bathydata.variables['lon'][:]
lats = bathydata.variables['lat'][:]
bathydata.close()
return(topoin, lats, lons)
def etopo5_data():
""" read in etopo5 topography/bathymetry. """
file = '/Volumes/WDC_internal/Users/bell/in_and_outbox/MapGrids/etopo5.nc'
etopodata = Dataset(file)
topoin = etopodata.variables['bath'][:]
lons = etopodata.variables['X'][:]
lats = etopodata.variables['Y'][:]
etopodata.close()
topoin,lons = shiftgrid(0.,topoin,lons,start=False) # -360 -> 0
#lons, lats = np.meshgrid(lons, lats)
return(topoin, lats, lons)
def IBCAO_data():
""" read in IBCAO topography/bathymetry. """
file_in = '/Volumes/WDC_internal/Users/bell/in_and_outbox/MapGrids/ARDEMv2.0.nc'
IBCAOtopodata = Dataset(file_in)
topoin = IBCAOtopodata.variables['z'][:]
lons = IBCAOtopodata.variables['lon'][:] #degrees east
lats = IBCAOtopodata.variables['lat'][:]
IBCAOtopodata.close()
return(topoin, lats, lons)
def find_nearest(a, a0):
"Element in nd array `a` closest to the scalar value `a0`"
idx = np.abs(a - a0).argmin()
return idx
"""---------------------------------- Main --------------------------------------------"""
parser = argparse.ArgumentParser(description='Convert .nc to .csv screen output')
parser.add_argument('infile', metavar='infile', type=str, help='input file path')
parser.add_argument("-csv","--csv", action="store_true",
help='output non-epic formatted netcdf as csv')
parser.add_argument("-is_whoi","--is_whoi", action="store_true",
help='flag if is directly from WHOI')
parser.add_argument("-plots","--plots", action="store_true",
help='generate plots')
args = parser.parse_args()
###nc readin/out
file1 = '/Volumes/WDC_internal/Users/bell/ecoraid/2016/Additional_FieldData/ArcticHeat/AlamoFloats/netcdf/arctic_heat_alamo_profiles_9058_9f75_d5e5_f5f9.nc'
df = EcoFOCI_netCDF(file1)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
dims = df.get_dims()
data0 = df.ncreadfile_dic()
df.close()
file2 = '/Volumes/WDC_internal/Users/bell/ecoraid/2016/Additional_FieldData/ArcticHeat/AlamoFloats/netcdf/arctic_heat_alamo_profiles_9115_bb97_cc7e_a9c0.nc'
df = EcoFOCI_netCDF(file2)
global_atts = df.get_global_atts()
vars_dic = df.get_vars()
dims = df.get_dims()
data1 = df.ncreadfile_dic()
if args.is_whoi:
timestr = 'days since 1950-01-01T00:00:00Z'
else:
timestr = 'seconds since 1970-01-01'
skipped_vars = ['STATION_PARAMETERS','FLOAT_SERIAL_NO',
'REFERENCE_DATE_TIME','time', 'profileid',
'PLATFORM_NUMBER','JULD','JULD_LOCATION']
if args.csv:
if args.is_whoi:
line = 'time, CycleNumber'
for k in vars_dic.keys():
if k in ['PRES','TEMP','PSAL']:
line = line + ', ' + str(k)
print line
for j in range(0,dims['N_LEVELS'].size):
line = num2date(data1['JULD'][0],timestr).strftime('%Y-%m-%d %H:%M:%S')
line = line + ', ' + str(data1['CYCLE_NUMBER'][0])
for k in vars_dic.keys():
if k in ['PRES','TEMP','PSAL']:
line = line + ', ' + str(data1[k][0][j])
print line
else:
line = 'time'
for k in vars_dic.keys():
if k not in ['FLOAT_SERIAL_NO','REFERENCE_DATE_TIME','time', 'profileid']:
line = line + ', ' + str(k)
print line
for i, val in enumerate(data1['time']):
line = num2date(val,timestr).strftime('%Y-%m-%d %H:%M:%S')
for k in vars_dic.keys():
if k not in ['FLOAT_SERIAL_NO','REFERENCE_DATE_TIME','time', 'profileid']:
line = line + ', ' + str(data1[k][i])
print line
df.close()
#--
if args.plots:
doy_plt = False
if doy_plt:
dtime = num2date(data0['time'],timestr)
doy0 = [x.timetuple().tm_yday for x in dtime]
dtime = num2date(data1['time'],timestr)
doy1 = [x.timetuple().tm_yday for x in dtime]
mono_col_plt = True
if doy_plt:
dtime = num2date(data0['time'],timestr)
doy0 = [x.timetuple().tm_yday for x in dtime]
dtime = num2date(data1['time'],timestr)
doy1 = [x.timetuple().tm_yday for x in dtime]
sfc_tmp_plt = False
if sfc_tmp_plt:
temp_data0,lat_data0, lon_data0 = [],[],[]
for ind in list(set(data0['CYCLE_NUMBER'])):
temp_data0 = temp_data0 + [max(data0['TEMP'][data0['CYCLE_NUMBER'] == ind])]
lat_data0 = lat_data0 + [max(data0['latitude'][data0['CYCLE_NUMBER'] == ind])]
lon_data0 = lon_data0 + [max(data0['longitude'][data0['CYCLE_NUMBER'] == ind])]
temp_data1,lat_data1, lon_data1 = [],[],[]
for ind in list(set(data1['CYCLE_NUMBER'])):
temp_data1 = temp_data1 + [max(data1['TEMP'][data1['CYCLE_NUMBER'] == ind])]
lat_data1 = lat_data1 + [max(data1['latitude'][data1['CYCLE_NUMBER'] == ind])]
lon_data1 = lon_data1 + [max(data1['longitude'][data1['CYCLE_NUMBER'] == ind])]
btm_tmp_plt = False
if btm_tmp_plt:
temp_data0,lat_data0, lon_data0 = [],[],[]
for ind in list(set(data0['CYCLE_NUMBER'])):
temp_data0 = temp_data0 + [min(data0['TEMP'][data0['CYCLE_NUMBER'] == ind])]
lat_data0 = lat_data0 + [min(data0['latitude'][data0['CYCLE_NUMBER'] == ind])]
lon_data0 = lon_data0 + [min(data0['longitude'][data0['CYCLE_NUMBER'] == ind])]
temp_data1,lat_data1, lon_data1 = [],[],[]
for ind in list(set(data1['CYCLE_NUMBER'])):
temp_data1 = temp_data1 + [min(data1['TEMP'][data1['CYCLE_NUMBER'] == ind])]
lat_data1 = lat_data1 + [min(data1['latitude'][data1['CYCLE_NUMBER'] == ind])]
lon_data1 = lon_data1 + [min(data1['longitude'][data1['CYCLE_NUMBER'] == ind])]
ave_tmp_plt = False
if ave_tmp_plt:
temp_data0,lat_data0, lon_data0 = [],[],[]
for ind in list(set(data0['CYCLE_NUMBER'])):
temp_data0 = temp_data0 + [np.mean(data0['TEMP'][np.where((data0['PRES'][data0['CYCLE_NUMBER'] == ind] > 0.5) & (data0['PRES'][data0['CYCLE_NUMBER'] == ind] <= 35))])]
lat_data0 = lat_data0 + [min(data0['latitude'][data0['CYCLE_NUMBER'] == ind])]
lon_data0 = lon_data0 + [min(data0['longitude'][data0['CYCLE_NUMBER'] == ind])]
temp_data1,lat_data1, lon_data1 = [],[],[]
for ind in list(set(data1['CYCLE_NUMBER'])):
temp_data1 = temp_data1 + [np.mean(data1['TEMP'][np.where((data1['PRES'][data1['CYCLE_NUMBER'] == ind] > 0.5) & (data1['PRES'][data1['CYCLE_NUMBER'] == ind] <= 35))])]
lat_data1 = lat_data1 + [min(data1['latitude'][data1['CYCLE_NUMBER'] == ind])]
lon_data1 = lon_data1 + [min(data1['longitude'][data1['CYCLE_NUMBER'] == ind])]
plot_moorings = False
if plot_moorings:
#2015
mlat = [71.23013333,72.46685,71.04641667,71.23075,70.8385,71.04785,71.23048333,70.83565,71.24101667]
mlon = -1.*np.array([164.2206167,156.5496167,160.5148667,164.2158833,163.10535,160.51155,164.21015,163.12385,164.30135])
#### plot
etopo_levels=[-1000, -100, -50, -25, ] #chuckchi
(topoin, elats, elons) = etopo1_subset()
#(topoin, elats, elons) = etopo5_data()
#determine regional bounding
y1 = np.floor(data0['latitude'].min()-2.5)
y2 = np.ceil(data0['latitude'].max()+2.5)
x1 = np.ceil((data0['longitude'].min()-5))
x2 = np.floor((data0['longitude'].max()+5))
print y1,y2,x1,x2
y1=64.0
y2=76.0
x1=-173.0
x2=-137.0
fig = plt.figure()
ax = plt.subplot(111)
m = Basemap(resolution='i',projection='merc', llcrnrlat=66, \
urcrnrlat=74,llcrnrlon=-170,urcrnrlon=-150,\
lat_ts=45)
elons, elats = np.meshgrid(elons, elats)
ex, ey = m(elons, elats)
if doy_plt:
xd0,yd0 = m(data0['longitude'],data0['latitude'])
xd1,yd1 = m(data1['longitude'],data1['latitude'])
if sfc_tmp_plt:
xd0,yd0 = m(lon_data0,lat_data0)
xd1,yd1 = m(lon_data1,lat_data1)
if btm_tmp_plt:
xd0,yd0 = m(lon_data0,lat_data0)
xd1,yd1 = m(lon_data1,lat_data1)
if ave_tmp_plt:
xd0,yd0 = m(lon_data0,lat_data0)
xd1,yd1 = m(lon_data1,lat_data1)
if plot_moorings:
mx, my = m(mlon, mlat)
if mono_col_plt:
xd0,yd0 = m(data0['longitude'],data0['latitude'])
#manually add another point
#xd0,yd0 = m(np.hstack([data0['longitude'],[-156.6]]),np.hstack([data0['latitude'],[71.6]]))
xd1,yd1 = m(data1['longitude'],data1['latitude'])
#CS = m.imshow(topoin, cmap='Greys_r') #
CS_l = m.contour(ex,ey,topoin, levels=etopo_levels, linestyle='--', linewidths=0.2, colors='black', alpha=.75)
CS = m.contourf(ex,ey,topoin, levels=etopo_levels, colors=('#737373','#969696','#bdbdbd','#d9d9d9','#f0f0f0'), extend='both', alpha=.75)
plt.clabel(CS_l, inline=1, fontsize=8, fmt='%1.0f')
if doy_plt:
m.scatter(xd0,yd0,100,marker='.', edgecolors='none', c=doy0, vmin=245, vmax=360, cmap='viridis')
m.scatter(xd1,yd1,100,marker='.', edgecolors='none', c=doy1, vmin=245, vmax=360, cmap='viridis')
c = plt.colorbar()
c.set_label("DOY")
if sfc_tmp_plt:
m.scatter(xd0,yd0,100,marker='.', edgecolors='none', c=temp_data0, vmin=-4, vmax=10, cmap=cmocean.cm.thermal)
m.scatter(xd1,yd1,100,marker='.', edgecolors='none', c=temp_data1, vmin=-4, vmax=10, cmap=cmocean.cm.thermal)
c = plt.colorbar()
c.set_label("~SFC Temperature")
if btm_tmp_plt:
m.scatter(xd0,yd0,100,marker='.', edgecolors='none', c=temp_data0, vmin=-4, vmax=10, cmap=cmocean.cm.thermal)
m.scatter(xd1,yd1,100,marker='.', edgecolors='none', c=temp_data1, vmin=-4, vmax=10, cmap=cmocean.cm.thermal)
c = plt.colorbar()
c.set_label("~BTM Temperature")
if ave_tmp_plt:
m.scatter(xd0,yd0,100,marker='.', edgecolors='none', c=temp_data0, vmin=-4, vmax=10, cmap=cmocean.cm.thermal)
m.scatter(xd1,yd1,100,marker='.', edgecolors='none', c=temp_data1, vmin=-4, vmax=10, cmap=cmocean.cm.thermal)
c = plt.colorbar()
c.set_label("~BTM Temperature")
if mono_col_plt:
m.scatter(xd0,yd0,60,marker='.', edgecolors='none', c='#004499')
m.scatter(xd1,yd1,60,marker='.', edgecolors='none', c='#299387')
m.plot(xd0[0],yd0[0], '+', markersize=10, color='k')
m.plot(xd1[0],yd1[0], '+', markersize=10, color='k')
if plot_moorings:
m.plot(mx,my,'o', markersize=8, markerfacecolor='None', color='k',markeredgewidth=2)
#m.drawcountries(linewidth=0.5)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(y1-10,y2+10,2.),labels=[1,0,0,0],color='black',dashes=[1,1],labelstyle='+/-',linewidth=0.2) # draw parallels
m.drawmeridians(np.arange(x1-10,x2+10,4.),labels=[0,0,0,1],color='black',dashes=[1,1],labelstyle='+/-',linewidth=0.2) # draw meridians
m.fillcontinents(color='white')
#
DefaultSize = fig.get_size_inches()
fig.set_size_inches( (DefaultSize[0]*1.5, DefaultSize[1]*1.5) )
plt.savefig('images/ArcticHeat_Alamo_etopo1_temp_doy.png', bbox_inches='tight', dpi=300)
plt.close()