chore: logging 3 - remove prints from tools directory

src/icesat2_tracks/ICEsat2_SI_tools/angle_optimizer.py

@@ -1,10 +1,14 @@
 """
 This library contains method, and classes used to search for the best angle given x,y data using single frequecy fits.
 """
+import logging
+
 from numba import jit
 import numpy as np
 
 
+_logger = logging.getLogger(__name__)
+
 numba_parallel = False
 
 def get_wavenumbers_polar( amp, angle_rad):
@@ -196,9 +200,9 @@ def set_parameters(self, par_dict, verbose= False):
         self.seeds = np.vstack([pxx.flatten(), pyy.flatten() ]).T
         self.params = params
         if verbose:
-            print('Nwalker: ', self.nwalkers)
-            print('Seeds: ', self.seeds.shape)
-            print(self.params)
+            _logger.debug('Nwalker: %s', self.nwalkers)
+            _logger.debug('Seeds: %s', self.seeds.shape)
+            _logger.debug("Params: %s", self.params)
 
     def test_objective_func(self):
         return self.objective_func(self.params, *self.fitting_args, **self.fitting_kargs)
@@ -212,8 +216,8 @@ def sample(self, fitting_args= None , method='emcee', steps=100, verbose= True,
                         args=fitting_args, kws=fitting_kargs ,
                         nwalkers=self.nwalkers, steps=steps, pos= self.seeds,nan_policy='omit' , **kargs)
         if verbose:
-            print(self.LM.report_fit(self.fitter))
-            print('results at self.fitter')
+            _logger.debug("%s", self.LM.report_fit(self.fitter))
+            _logger.debug('results at self.fitter')
 
     def plot_sample(self, **kargs ):
         import matplotlib.pyplot as plt
@@ -232,8 +236,8 @@ def optimize(self, fitting_args= None , method='dual_annealing', verbose= True):
         self.fitter_optimize = self.LM.minimize(self.objective_func, self.params,  method=method,
                         args=fitting_args, kws=fitting_kargs )
         if verbose:
-            print(self.LM.report_fit(self.fitter_optimize))
-            print('results at self.fitter_optimize')
+            _logger.debug("fit report: %s", self.LM.report_fit(self.fitter_optimize))
+            _logger.debug('results at self.fitter_optimize')
 
     def plot_optimze(self, **kargs):
         import matplotlib.pyplot as plt
@@ -249,8 +253,8 @@ def brute(self, fitting_args= None , method='brute', verbose= True, N_grid = 30)
                         args=fitting_args, kws=fitting_kargs, Ns=N_grid  )
 
         if verbose:
-            print(self.LM.report_fit(self.fitter_brute))
-            print('results at self.fitter_brute')
+            _logger.debug("%s", self.LM.report_fit(self.fitter_brute))
+            _logger.debug('results at self.fitter_brute')
 
 
     def plot_brute(self, clevel = np.linspace(-3.2, 3.2, 30), **kargs):

src/icesat2_tracks/ICEsat2_SI_tools/beam_stats.py

@@ -1,3 +1,5 @@
+import logging
+
 import numpy as np
 import pandas as pd
 import icesat2_tracks.ICEsat2_SI_tools.spectral_estimates as spec
@@ -6,6 +8,8 @@
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 
+_logger = logging.getLogger(__name__)
+
 
 def derive_beam_statistics(Gd, all_beams, Lmeter=10e3, dx=10):
     """
@@ -24,7 +28,7 @@ def derive_beam_statistics(Gd, all_beams, Lmeter=10e3, dx=10):
         elif isinstance(Gd, h5py.File):
             Gi = io_local.get_beam_hdf_store(Gd[k])
         else:
-            print("Gd is neither dict nor hdf5 file")
+            _logger.debug("Gd is neither dict nor hdf5 file")
             break
 
         dd = Gi["h_mean"]

src/icesat2_tracks/ICEsat2_SI_tools/filter_regrid.py

@@ -1,7 +1,9 @@
+import logging
 
 import numpy as np
 from numba import jit
 
+_logger = logging.getLogger(__name__)
 
 def correct_heights(T03, T03c, coord = 'delta_time'):
     """
@@ -37,7 +39,7 @@ def track_pole_ward_file(hdf5_file, product='ALT03'):
         T_lat = hdf5_file['gt1r/freeboard_beam_segment/latitude'][:]
         T_time = hdf5_file['gt1r/freeboard_beam_segment/delta_time'][:]
     #return ( T_lat[T_time.argmax()] - T_lat[T_time.argmin()] ) < 0
-    print('1st lat =' + str(abs(T_lat[T_time.argmin()])) , ';last lat =' + str(abs(T_lat[T_time.argmax()])) )
+    _logger.debug('1st lat = %s, ; last lat= %s', abs(T_lat[T_time.argmin()]), abs(T_lat[T_time.argmax()]))
     return abs(T_lat[T_time.argmax()]) > abs(T_lat[T_time.argmin()])
 
 
@@ -175,7 +177,7 @@ def derive_axis(TT, lat_lims = None):
         # decending track
         lon_min = TT['lons'].min()
 
-    #print(lon_min)
+    #_logger.debug(lon_min)
     TT['x']     = (TT['lons'] - lon_min) * np.cos( TT['lats']*np.pi/180.0 ) * dy
     #TT['x']     = (TT['lons'] ) * np.cos( TT['lats']*np.pi/180.0 ) * dy
     TT['dist']  =   np.sqrt(TT['x']**2 + TT['y']**2)
@@ -448,7 +450,7 @@ def bin_means(T2, dist_grid):
 
     for i in np.arange(1,ilim-1, 1):
         if i % 5000 ==0:
-            print(i)
+            _logger.debug("i= %s", i)
         i_mask=(T2['dist'] >= dist_grid[i-1])  & (T2['dist'] < dist_grid[i+1])
         #if ( (T2['dist'] >= dist_grid[i-1])  & (T2['dist'] < dist_grid[i+1]) ).sum() > 0:
         dF_mean[i] = T2[i_mask].mean()

src/icesat2_tracks/ICEsat2_SI_tools/generalized_FT.py

@@ -1,4 +1,5 @@
 import copy
+import logging
 import time
 
 from numpy import linalg
@@ -13,6 +14,8 @@
 from icesat2_tracks.ICEsat2_SI_tools import lanczos, spectral_estimates as spec
 import icesat2_tracks.local_modules.JONSWAP_gamma as spectal_models
 
+_logger = logging.getLogger(__name__)
+
 
 def rebin(data, dk):
     """
@@ -256,7 +259,7 @@ def calc_gFT_apply(stancil, prior):
             if (
                 x.size / Lpoints < 0.40
             ):  # if there are not enough photos set results to nan
-                print(" -- data density to low, skip stancil")
+                _logger.debug(" -- data density to low, skip stancil")
                 return {
                     "stancil_center": stancil[1],
                     "p_hat": np.concatenate([self.k * np.nan, self.k * np.nan]),
@@ -288,7 +291,7 @@ def calc_gFT_apply(stancil, prior):
             # define error
             err = ERR[x_mask] if ERR is not None else 1
 
-            print("compute time weights : ", time.perf_counter() - ta)
+            _logger.debug("compute time weights : %s", time.perf_counter() - ta)
 
             ta = time.perf_counter()
             FT.define_problem(weight, err)
@@ -297,17 +300,15 @@ def calc_gFT_apply(stancil, prior):
             p_hat = FT.solve()
 
             if np.isnan(np.mean(p_hat)):
-                print(" -- inversion nan!")
-                print(" -- data fraction", x.size / Lpoints)
-                print(
-                    " -- weights:",
+                _logger.debug(" -- inversion nan!")
+                _logger.debug(" -- data fraction %s", x.size / Lpoints)
+                _logger.debug(
+                    " -- weights: %s err: %s y: %s",
                     np.mean(weight),
-                    "err:",
                     np.mean(err),
-                    "y:",
                     np.mean(y),
                 )
-                print(" -- skip stancil")
+                _logger.debug(" -- skip stancil")
                 return {
                     "stancil_center": stancil[1],
                     "p_hat": np.concatenate([self.k * np.nan, self.k * np.nan]),
@@ -320,7 +321,7 @@ def calc_gFT_apply(stancil, prior):
                     "spec_adjust": np.nan,
                 }
 
-            print("compute time solve : ", time.perf_counter() - ta)
+            _logger.debug("compute time solve : %s", time.perf_counter() - ta)
             ta = time.perf_counter()
 
             x_pos = (np.round((x - stancil[0]) / self.dx, 0)).astype("int")
@@ -337,7 +338,7 @@ def calc_gFT_apply(stancil, prior):
             for k, I in prior_pars.items():
                 inverse_stats[k] = I.value if hasattr(I, "value") else np.nan
 
-            print("compute time stats : ", time.perf_counter() - ta)
+            _logger.debug("compute time stats : %s", time.perf_counter() - ta)
 
             # multiply with the standard deviation of the data to get dimensions right
             PSD = power_from_model(p_hat, dk, self.k.size, x.size, Lpoints)
@@ -366,7 +367,7 @@ def calc_gFT_apply(stancil, prior):
                 plt.legend()
                 plt.show()
 
-                print("---------------------------------")
+                _logger.debug("---------------------------------")
 
             # return dict with all relevant data
             return_dict = {
@@ -397,18 +398,18 @@ def calc_gFT_apply(stancil, prior):
         N_stencil = len(self.stancil_iter_list.T)
         Ni = 1
         for ss in copy.copy(self.stancil_iter):
-            print(Ni, "/", N_stencil, "Stancils")
+            _logger.debug("%s / %s Stancils", Ni, N_stencil)
             # prior step
             if prior[0] is False:  # make NL fit of piors do not exist
-                print("1st step: with NL-fit")
+                _logger.debug("1st step: with NL-fit")
                 I_return = calc_gFT_apply(ss, prior=prior)
                 prior = I_return["PSD"], I_return["weight"]
             # 2nd step
             if prior[0] is False:
-                print("1st GFD failed (priors[0]=false), skip 2nd step")
+                _logger.debug("1st GFD failed (priors[0]=false), skip 2nd step")
             else:
-                print("2nd step: use set priors:", type(prior[0]), type(prior[1]))
-                print(prior[0][0:3], prior[1][0:3])
+                _logger.debug("2nd step: use set priors of types: %s %s", type(prior[0]), type(prior[1]))
+                _logger.debug("first three elements of priors: %s %s", prior[0][0:3], prior[1][0:3])
                 I_return = calc_gFT_apply(ss, prior=prior)
                 prior = I_return["PSD"], I_return["weight"]
 
@@ -471,7 +472,7 @@ def calc_gFT_apply(stancil, prior):
             N_per_stancil.append(I["x_size"])
             Spec_adjust_per_stancil.append(spec_adjust)
 
-        print("# of x-coordinates" + str(len(Spec_returns)))
+        _logger.debug("# of x-coordinates %s", len(Spec_returns))
 
         self.N_per_stancil = N_per_stancil
         chunk_positions = np.array(list(D_specs.keys()))
@@ -578,10 +579,10 @@ def calc_gFT_apply(stancil, prior):
                 # check sizes and adjust if necessary.
                 if x_pos.size > I["model_error_x"].size:
                     x_pos = x_pos[0 : I["model_error_x"].size]
-                    print("adjust x")
+                    _logger.debug("adjust x")
                 elif x_pos.size < I["model_error_x"].size:
                     I["model_error_x"] = I["model_error_x"][0:-1]
-                    print("adjust y")
+                    _logger.debug("adjust y")
 
                 x_err[x_pos] = I["model_error_x"]
                 model_error_x[xi] = xr.DataArray(
@@ -657,10 +658,10 @@ def get_stancil_var_apply(stancil):
 
         stancil_weighted_variance = np.nansum(np.array(stancil_vars)) / Nphotons
 
-        print("Parcevals Theorem:")
-        print("variance of timeseries: ", DATA.var())
-        print("mean variance of stancils: ", stancil_weighted_variance)
-        print("variance of the optimzed windowed LS Spectrum: ", self.calc_var())
+        _logger.debug("Parcevals Theorem:")
+        _logger.debug("variance of timeseries: %s", DATA.var())
+        _logger.debug("mean variance of stancils: %s", stancil_weighted_variance)
+        _logger.debug("variance of the optimzed windowed LS Spectrum: %s", self.calc_var())
 
         if add_attrs:
             self.G.attrs["variance_unweighted_data"] = DATA.var()
@@ -874,7 +875,7 @@ def get_stats(self, dk, Nx_full, print_flag=False):
                 "var_spec_complete",
                 "spec_adjust",
             ]:
-                print(ki.ljust(20) + str(pars[ki]))
+                _logger.debug("%s", ki.ljust(20) + str(pars[ki]))
 
         return pars
 
@@ -972,7 +973,7 @@ def runningmean(self, var, m, tailcopy=False):
         m = int(m)
         s = var.shape
         if s[0] <= 2 * m:
-            print("0 Dimension is smaller then averaging length")
+            _logger.debug("0 Dimension is smaller then averaging length")
             return
         rr = np.asarray(var) * np.nan