def progressive_lowcut(wind, vapor, co2):
"""Apply progressive lowcut filter to wind, vapor, and CO2 series.
Uses wavelet decompostion to yield a sequence of (w, q, c) series
in which low frequency (large scale) components are progressively
removed from w, q, c.
Parameters
----------
wind,vapor,co2 : array-like
1D time series of vertical wind velocity (w), water vapor
concentration (q), and carbon dioxide concentration (c).
Yields
------
(lc_w, lc_q, lc_c) : tuple of arrays
Arrays lc_w, lc_q, and lc_c are low cut (high pass) filtered
versions of the passed w,q,c series.
Notes
-----
The time series data are truncated at the maxium dyadic length
(power of 2) before the filter is applied.
"""
max_dyadic_len = 2**int(np.log2(np.asarray(co2).shape[0]))
trunc_w = np.asarray(wind)[:max_dyadic_len]
trunc_q = np.asarray(vapor)[:max_dyadic_len]
trunc_c = np.asarray(co2)[:max_dyadic_len]
lowcut_w = util.progressive_lowcut_series(trunc_w)
lowcut_q = util.progressive_lowcut_series(trunc_q)
lowcut_c = util.progressive_lowcut_series(trunc_c)
for lowcut_series in zip(lowcut_w, lowcut_q, lowcut_c):
yield lowcut_series
def _progressive_lowcut(wind, vapor, co2):
"""Apply progressive lowcut filter to wind, vapor, and CO2 series.
Use wavelet decomposition to yield a sequence of (w, q, c) series
in which low frequency (large scale) components are progressively
removed from w, q, c.
Parameters
----------
wind,vapor,co2 : array-like
1D time series of vertical wind velocity (w), water vapor
concentration (q), and carbon dioxide concentration (c).
Yields
------
(lc_w, lc_q, lc_c) : tuple of arrays
Arrays lc_w, lc_q, and lc_c are low cut (high pass) filtered
versions of the passed w,q,c series.
Notes
-----
Before the filter is applied, the data are truncated so that the
length is a power of 2.
"""
max_pow2_len = 2 ** int(np.log2(np.asarray(co2).shape[0]))
trunc_w = np.asarray(wind)[:max_pow2_len]
trunc_q = np.asarray(vapor)[:max_pow2_len]
trunc_c = np.asarray(co2)[:max_pow2_len]
lowcut_w = util.progressive_lowcut_series(trunc_w)
lowcut_q = util.progressive_lowcut_series(trunc_q)
lowcut_c = util.progressive_lowcut_series(trunc_c)
for lowcut_series in zip(lowcut_w, lowcut_q, lowcut_c):
yield lowcut_series