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chore: clean up generalized_FT #69

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Feb 2, 2024
Merged

chore: clean up generalized_FT #69

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d586a66
chore: clean up generalized_FT
cpaniaguam Jan 18, 2024
e6132b1
uncommenting step5 to test in CI
kmilo9999 Feb 2, 2024
343d7bc
updating the branch with latest changes in main
kmilo9999 Feb 2, 2024
8dc15b1
Merge branch 'main' into fix-step2-update-generalized_FT
kmilo9999 Feb 2, 2024
1ad8502
Merge branch 'fix-step2-update-generalized_FT' of https://github.com/…
kmilo9999 Feb 2, 2024
550193e
imports moved to top of the file
kmilo9999 Feb 2, 2024
d88898a
Update generalized_FT.py
cpaniaguam Feb 2, 2024
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1 change: 1 addition & 0 deletions .github/workflows/test-B01_SL_load_single_file.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -37,3 +37,4 @@ jobs:
run: python src/icesat2_tracks/analysis_db/B05_define_angle.py SH_20190502_05180312 SH_testSLsinglefile2 True
- name: Seventh step B06_correct_separate_var
run: python src/icesat2_tracks/analysis_db/B06_correct_separate_var.py SH_20190502_05180312 SH_testSLsinglefile2 True

58 changes: 16 additions & 42 deletions src/icesat2_tracks/ICEsat2_SI_tools/generalized_FT.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,16 @@
import numpy as np
import copy
import time

import icesat2_tracks.ICEsat2_SI_tools.spectral_estimates as spec
import icesat2_tracks.ICEsat2_SI_tools.lanczos as lanczos
from numpy import linalg
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
from scipy.signal import detrend
import lmfit as LM

from icesat2_tracks.ICEsat2_SI_tools import lanczos, spectral_estimates as spec
import icesat2_tracks.local_modules.JONSWAP_gamma as spectal_models


def rebin(data, dk):
Expand Down Expand Up @@ -69,15 +77,15 @@ def get_weights_from_data(
pars = Spec_fft.set_parameters(flim=np.sqrt(9.81 * k[-1]) / 2 / np.pi)
k_max = (pars["f_max"].value * 2 * np.pi) ** 2 / 9.81

if method is "gaussian":
if method == "gaussian":
# simple gaussian weight
def gaus(x, x_0, amp, sigma_g):
return amp * np.exp(-0.5 * ((x - x_0) / sigma_g) ** 2)

weight = gaus(k, k_max, 1, 0.02) ** (1 / 2)
params = None

elif method is "parametric":
elif method == "parametric":
# JONSWAP weight
f = np.sqrt(9.81 * k) / (2 * np.pi)
weight = Spec_fft.create_weight(freq=f, plot_flag=False, max_nfev=max_nfev)
Expand Down Expand Up @@ -121,8 +129,6 @@ def define_weight_shutter(weight, k, Ncut=3):


def make_xarray_from_dict(D, name, dims, coords):
import xarray as xr

D_return = dict()
for xi, I in D.items():
coords["x"] = xi
Expand All @@ -136,7 +142,7 @@ def define_weights(stancil, prior, x, y, dx, k, max_nfev, plot_flag=False):
return weights normalized to 1, prior_pars used for the next iteration
"""

if (type(prior[0]) is bool) and not prior[0]:
if isinstance(prior[0], bool) and not prior[0]:
# fit function to data
weight, prior_pars = get_weights_from_data(
x, y, dx, stancil, k, max_nfev, plot_flag=plot_flag, method="parametric"
Expand All @@ -157,8 +163,6 @@ def define_weights(stancil, prior, x, y, dx, k, max_nfev, plot_flag=False):
weight = weight * define_weight_shutter(weight, k, Ncut=3)

if plot_flag:
import matplotlib.pyplot as plt

plt.plot(k, weight, zorder=12, c="darkgreen", linewidth=0.8, label=weight_name)

# peak normlize weights by std of data
Expand Down Expand Up @@ -228,9 +232,6 @@ def cal_spectrogram(
self.GG, params_dataframe
params_dataframe is a pd.DataFrame that containes all the parameters of the fitting process (and may contain uncertainties too once they are calculated)
"""
import xarray as xr
import copy
import pandas as pd

X = self.x if x is None else x # all x positions
DATA = self.data if data is None else data # all data points
Expand All @@ -246,9 +247,6 @@ def calc_gFT_apply(stancil, prior):
windows the data accoding to stencil and applies LS spectrogram
returns: stancil center, spectrum for this stencil, number of datapoints in stancil
"""
from scipy.signal import detrend
import matplotlib.pyplot as plt
import time

ta = time.perf_counter()
x_mask = (stancil[0] <= X) & (X <= stancil[-1])
Expand Down Expand Up @@ -277,8 +275,6 @@ def calc_gFT_apply(stancil, prior):
FT = generalized_Fourier(x, y, self.k)

if plot_flag:
import matplotlib.pyplot as plt

plt.figure(figsize=(3.34, 1.8), dpi=300)

# define weights. Weights are normalized to 1
Expand Down Expand Up @@ -338,10 +334,7 @@ def calc_gFT_apply(stancil, prior):
inverse_stats = FT.get_stats(self.dk, Lpoints_full, print_flag=plot_flag)
# add fitting parameters of Prior to stats dict
for k, I in prior_pars.items():
try:
inverse_stats[k] = I.value
except:
inverse_stats[k] = np.nan
inverse_stats[k] = I.value if hasattr(I, "value") else np.nan

print("compute time stats : ", time.perf_counter() - ta)

Expand Down Expand Up @@ -634,15 +627,11 @@ def calc_var(self):

def parceval(self, add_attrs=True, weight_data=False):
"test Parceval theorem"
import copy

DATA = self.data
L = self.Lmeters
X = self.x

def get_stancil_var_apply(stancil):
from scipy.signal import detrend

"returns the variance of yy for stancil"
x_mask = (stancil[0] < X) & (X <= stancil[-1])
idata = DATA[x_mask]
Expand Down Expand Up @@ -756,8 +745,6 @@ def __init__(self, x, ydata, k):
"""
non_dimensionalize (bool, default=True) if True, then the data and R_data_uncertainty is non-dimensionalized by the std of the data
"""
import numpy as np
from numpy import linalg

self.x, self.ydata, self.k = x, ydata, k
self.M = self.k.size # number of wavenumbers
Expand All @@ -769,7 +756,7 @@ def __init__(self, x, ydata, k):
# test if the data is real, not nan and not inf
assert np.isrealobj(self.ydata), "data is not real"
assert np.isfinite(self.ydata).all(), "data is not finite"
assert np.isnan(self.ydata).all() == False, "data is not nan"
assert not np.isnan(self.ydata).all(), "data is not nan"

# data matrix
def get_H(self, xx=None):
Expand All @@ -792,8 +779,6 @@ def define_problem(self, P_weight, R_data_uncertainty):
self.R_1d = R_data_uncertainty

def solve(self):
from numpy import linalg

inv = linalg.inv
"""
solves the linear inverse problem, return hessian and p_hat
Expand Down Expand Up @@ -865,7 +850,6 @@ def parceval(self, dk, Nx_full):
def get_stats(self, dk, Nx_full, print_flag=False):
residual = self.ydata - self.model()

Lmeters = self.x[-1] - self.x[0]
pars = {
"data_var": self.ydata.var(),
"model_var": self.model().var(),
Expand Down Expand Up @@ -896,8 +880,6 @@ def get_stats(self, dk, Nx_full, print_flag=False):

class get_prior_spec:
def __init__(self, freq, data):
import lmfit as LM

self.LM = LM
self.data = data
self.freq = freq
Expand All @@ -919,7 +901,6 @@ def set_parameters(self, flim=None):
self.params LMfit.parameters class needed for optimization

"""
import numpy as np

params = self.LM.Parameters()

Expand Down Expand Up @@ -949,9 +930,6 @@ def model_func(self, f, params):
)

def non_dim_spec_model(self, f, f_max, amp, gamma=1, angle_rad=0):
import icesat2_tracks.local_modules.JONSWAP_gamma as spectal_models

U = 20 # results are incensitive to U
f_true = f * np.cos(angle_rad)
model = spectal_models.JONSWAP_default_alt(f_true, f_max, 20, gamma=gamma)
model = amp * model / np.nanmean(model)
Expand Down Expand Up @@ -984,13 +962,9 @@ def optimize(self, fitting_args=None, method="dual_annealing", max_nfev=None):
return self.fitter

def plot_data(self):
import matplotlib.pyplot as plt

plt.plot(self.freq, self.data, "k")

def plot_model(self, pars):
import matplotlib.pyplot as plt

plt.plot(self.freq, self.model_func(self.freq, pars), "b--")

def runningmean(self, var, m, tailcopy=False):
Expand Down
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