def section():
lon, lat = [], []
fnames = sorted(data_path.glob('CTD/g01mcan*c.cnv.gz'))
section = OrderedDict()
for fname in fnames:
cast = proc_ctd(fname)
name = Path(fname).stem
section.update({name: cast})
lon.append(cast['longitude'].mean())
lat.append(cast['latitude'].mean())
# Section.
section = Panel.fromDict(section)
return {'section': section, 'lon': lon, 'lat': lat}
def proc_ctd(fname):
"""
CTD processing.
"""
cast = DataFrame.from_cnv(fname, below_water=True).split()[0]
cast = cast[cast['pumps']]
cast = cast[~cast['flag']]
name = Path(fname).stem
# Removed unwanted columns.
keep = {'t090C', 't190C', 'longitude', 'latitude'}
drop = keep.symmetric_difference(cast.columns)
cast.drop(drop, axis=1, inplace=True)
cast = cast.apply(Series.bindata, **{'delta': 1.})
cast = cast.apply(Series.interpolate)
cast.name = name
return cast
from ctd import DataFrame, Series
from ctd.utilities import Path
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pandas import Panel
import pytest
matplotlib.use('Agg', warn=True)
data_path = Path(__file__).parent.joinpath('data')
def assert_is_valid_plot_return_object(objs):
if isinstance(objs, np.ndarray):
for el in objs.flat:
assert isinstance(el, plt.Axes), ('one of \'objs\' is not a '
'matplotlib Axes instance, '
'type encountered {0!r}'
''.format(el.__class__.__name__))
else:
assert isinstance(objs, (plt.Artist, tuple, dict)), (
'objs is neither an ndarray of Artist instances nor a '
'single Artist instance, tuple, or dict, "objs" is a {0!r} '
''.format(objs.__class__.__name__))
def proc_ctd(fname, below_water=True):
"""
Quick `proc_ctd` function.
"""
# 00-Split, clean 'bad pump' data, and apply flag.
cast = DataFrame.from_cnv(
fname,
below_water=below_water
).split()[0]
name = Path(fname).stem
cast = cast[cast['pumps']]
cast = cast[~cast['flag']] # True for bad values.
# Smooth velocity with a 2 seconds windows.
cast['dz/dtM'] = movingaverage(cast['dz/dtM'], window_size=48)
# 01-Filter pressure.
kw = {
'sample_rate': 24.0,
'time_constant': 0.15
}
cast.index = lp_filter(cast.index, **kw)
# 02-Remove pressure reversals.
cast = cast.press_check()
cast = cast.dropna()
# 03-Loop Edit.
cast = cast[cast['dz/dtM'] >= 0.25] # Threshold velocity.
# 04-Remove spikes.
kw = {
'n1': 2,
'n2': 20,
'block': 100
}
cast = cast.apply(Series.despike, **kw)
# 05-Bin-average.
cast = cast.apply(Series.bindata, **{'delta': 1.})
# 06-interpolate.
cast = cast.apply(Series.interpolate)
if False:
# 07-Smooth.
pmax = max(cast.index)
if pmax >= 500.:
window_len = 21
elif pmax >= 100.:
window_len = 11
else:
window_len = 5
kw = {
'window_len': window_len,
'window': 'hanning'
}
cast = cast.apply(Series.smooth, **kw)
# 08-Derive.
cast.lat = cast['latitude'].mean()
cast.lon = cast['longitude'].mean()
cast = derive_cnv(cast)
cast.name = name
return cast