In [1]:
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
/home/eolson/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/home/eolson/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
In [2]:
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 [3]:
d=dict()
d['orig']=loadSOGHoff('/data/eolson/SOG/SOG-runs/temp/profiles/hoff-SOG.dat')
d['E4']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/E4/profiles/hoff-SOG.dat')
d['E5']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/E5/profiles/hoff-SOG.dat')
d['E6']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/E6/profiles/hoff-SOG.dat')
d['Q9']=loadSOGHoff('/data/eolson/SOG/SOG-runs/exp/Q9/profiles/hoff-SOG.dat')
In [4]:
d.keys()
Out[4]:
dict_keys(['orig', 'E5', 'E4', 'E6', 'Q9'])
In [5]:
d['Q9'].keys()
Out[5]:
dict_keys(['u velocity', 'nitrate', 'micro phytoplankton', 'POC detritus', 'total salinity eddy diffusivity', 'micro zooplankton', 'nano phytoplankton', 'sigma-t', 'total temperature eddy diffusivity', 'biogenic Si detritus', 'PON detritus', 'dissolved oxygen', 'depth', 'dissolved inorganic carbon', 'DON detritus', 'times', 'silicon', 'total momentum eddy diffusivity', 'temperature', 'v velocity', 'pico phytoplankton', 'alkalinity', 'salinity', 'refractory N detritus', 'photosynthetic available radiation', 'DOC detritus', 'ammonium'])
In [6]:
style={'micro phytoplankton':'-','nano phytoplankton':'--','pico phytoplankton':'.-',
'nitrate':'-o','silicon':'->','ammonium':'-x','micro zooplankton':'+'}
col={'orig':'k','Q9':'goldenrod','E4':'blueviolet','E5':'deeppink','E6':'dodgerblue'}
#'R1':'dodgerblue','A3':'lime','N3':'m','A5':'pink','C2':'gold','A6':'brown',
#'F1':'blue','C4':'turquoise','C5':'mediumseagreen','C6':'red','C7':'c','C8':'pink',
#'M1':'darkorange','R5':'purple','M3':'darkorange','M5':'limegreen','M6':'firebrick',
#'M7':'darkkhaki','N2':'aquamarine','M8':'peru','R2':'deeppink','R3':'darkkhaki','Si3':'orange'}
In [12]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','Q9','E4','E5','E6'):
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(2005,6,1))
axs.legend(loc=1)
Out[12]:
<matplotlib.legend.Legend at 0x7fb57a2aa2b0>
In [9]:
fig, axs = plt.subplots(2,1,figsize=(12,8))
for el in ('orig','Q9','E4','E5','E6'):
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 [15]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','Q9','E4','E5','E6'):
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(2005,8,1))
axs.legend(loc=1)
Out[15]:
<matplotlib.legend.Legend at 0x7f4d9dd84fd0>
In [10]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','Q9','E4','E5','E6'):
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(2005,10,1))
axs.legend(loc=1)
# axs.plot(d['C5']['times'][128],10,'rs')
Out[10]:
<matplotlib.legend.Legend at 0x7fb57a238b00>
In [11]:
fig, axs = plt.subplots(1,1,figsize=(20,7))
for el in ('orig','Q9','E4','E5','E6'):
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[11]:
<matplotlib.legend.Legend at 0x7fb57a15d0b8>
In [ ]:
In [ ]:
Q9