In [11]:
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.dates as dts
import netCDF4 as nc
import os
import re
import pytz
import csv
import time
%matplotlib inline
best guess is 4k; if not look at 4hij
In [12]:
def loadSOGHoff(fname):
data2=dict()
with open(fname, 'rt') as file_obj:
for index, line in enumerate(file_obj):
line = line.strip()
if line.startswith('*FieldNames:'):
field_names = line.split(': ', 1)[1].split(', ')
elif line.startswith('*FieldUnits:'):
field_units = line.split(': ', 1)[1].split(', ')
elif line.startswith('*HoffmuellerStartYr:'):
year_start = line.split(': ', 1)[1]
elif line.startswith('*HoffmuellerStartDay:'):
day_start = line.split(': ', 1)[1]
elif line.startswith('*HoffmuellerStartSec:'):
sec_start = line.split(': ', 1)[1]
elif line.startswith('*HoffmuellerInterval:'):
interval = line.split(': ', 1)[1]
elif line.startswith('*EndOfHeader'):
break
# Timestamp in matplotlib time
dt_start = dts.date2num(dt.datetime.strptime(year_start + ' ' + day_start, '%Y %j')) + float(sec_start)/86400
data = pd.read_csv(fname, delim_whitespace=True, header=0, names=field_names, skiprows=index, chunksize=82)
# Extract dataframe chunks into dictionary
times=list()
for index, chunk in enumerate(data):
times.append(dts.num2date(dt_start + index*float(interval), tz=pytz.timezone('UTC')))
if index==0:
da=chunk
else:
da=np.dstack((da,chunk))
data2['times']=np.array(times)
for iel in range(0,len(field_names)):
data2[field_names[iel]]=da[:,iel,:].transpose()
return data2
In [13]:
d=dict()
d['orig']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/HC1812/profiles/hoff-SOG.dat')
d['test']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/TestNoSiTGrowth4b/profiles/hoff-SOG.dat')
d['TestNoSiTGrowth']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/TestNoSiTGrowth4k/profiles/hoff-SOG.dat')
In [14]:
d.keys()
Out[14]:
dict_keys(['orig', 'TestNoSiTGrowth', 'test'])
In [15]:
style={'micro phytoplankton':'-','nano phytoplankton':'--','pico phytoplankton':'.-',
'nitrate':'-o','silicon':'->','ammonium':'-x','micro zooplankton':'+'}
col={'orig':'k','test':'goldenrod','TestNoSiTGrowth':'blueviolet','c':'deeppink','d':'dodgerblue','e':'aqua'}
In [16]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','test','TestNoSiTGrowth'):#,'b','f',):#,'HCi'):
for var in ('micro phytoplankton','nano phytoplankton','pico phytoplankton'):
axs.plot(d[el]['times'],np.mean(d[el][var][:,0:6],1),style[var],color=col[el],label=el+' '+var)
axs.set_xlim(dt.datetime(2005,2,1),dt.datetime(2005,11,1))
axs.legend(loc=1)
Out[16]:
<matplotlib.legend.Legend at 0x7f850258ccf8>
In [17]:
fig, axs = plt.subplots(2,1,figsize=(12,8))
for el in ('orig','test','TestNoSiTGrowth'):
axs[0].plot(np.mean(d[el]['nitrate'][:,0:6],1),np.mean(d[el]['silicon'][:,0:6],1),'s',alpha=.3,color=col[el],label=el+' Si:N')
#ts=d[el]['times']
#xs=np.mean(d[el]['silicon'][:,0:6]+.002,1)/np.mean(d[el]['nitrate'][:,0:6]+.001,1)
#ys=np.mean(d[el]['nitrate'][:,0:6]+.001,1)
#axs.plot(ts[ys>1],xs[ys>1],'s',color=col[el],label=el+' Si:N')
axs[1].plot(d[el]['times'],np.mean(d[el]['silicon'][:,0:6],1)-2*np.mean(d[el]['nitrate'][:,0:6],1),color=col[el])
#axs.set_xlim(dt.datetime(2005,2,1),dt.datetime(2005,10,1))
axs[0].legend(loc=4)
axs[0].set_aspect(.5)
In [18]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','test','TestNoSiTGrowth'):
for var in ('nitrate','silicon','ammonium'):
axs.plot(d[el]['times'],np.mean(d[el][var][:,0:6],1),style[var],color=col[el],label=el+' '+var)
axs.set_xlim(dt.datetime(2005,2,1),dt.datetime(2005,12,1))
axs.legend(loc=1)
Out[18]:
<matplotlib.legend.Legend at 0x7f850257b828>
In [19]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','test','TestNoSiTGrowth'):
for var in ('micro phytoplankton','nano phytoplankton','pico phytoplankton','micro zooplankton'):
axs.plot(d[el]['times'],np.mean(d[el][var][:,0:6],1),style[var],color=col[el],label=el+' '+var)
#axs.plot(d['M5']['times'],np.mean(d['M5']['micro phytoplankton'][:,0:6],1),'g*')
axs.set_xlim(dt.datetime(2005,2,1),dt.datetime(2006,2,1))
axs.legend(loc=1)
# axs.plot(d['C5']['times'][128],10,'rs')
Out[19]:
<matplotlib.legend.Legend at 0x7f85026ce710>
In [20]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','test','TestNoSiTGrowth'):
for var in ('pico phytoplankton',):
axs.plot(d[el]['times'],np.mean(d[el][var][:,0:6],1),style[var],color=col[el],label=el+' '+var)
#axs.plot(d['M5']['times'],np.mean(d['M5']['micro phytoplankton'][:,0:6],1),'g*')
axs.set_xlim(dt.datetime(2005,2,1),dt.datetime(2005,10,1))
axs.legend(loc=1)
# axs.plot(d['C5']['times'][128],10,'rs')
Out[20]:
<matplotlib.legend.Legend at 0x7f8502495b38>
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