chore: logging 3 - remove prints from tools directory

src/icesat2waves/tools/beam_stats.py

@@ -1,3 +1,5 @@
+import logging
+
 import numpy as np
 import pandas as pd
 import icesat2waves.tools.spectral_estimates as spec
@@ -7,6 +9,8 @@
 import matplotlib.gridspec as gridspec
 import h5py
 
+_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/icesat2waves/tools/generalized_FT.py

@@ -1,4 +1,5 @@
 import copy
+import logging
 import time
 
 from numpy import linalg
@@ -13,6 +14,8 @@
 from icesat2waves.tools import lanczos, spectral_estimates as spec
 import icesat2waves.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
 

src/icesat2waves/tools/iotools.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import re
 import json
@@ -17,6 +18,9 @@
 import icesat2waves.tools.convert_GPS_time as cGPS
 
 
+_logger = logging.getLogger(__name__)
+
+
 def init_from_input(arguments):
     """
     Initializes the variables track_name, batch_key, and test_flag based on the input arguments.
@@ -32,7 +36,7 @@ def init_from_input(arguments):
         track_name = "20190605061807_10380310_004_01"
         batch_key = "SH_batch01"
         test_flag = True
-        print("use standard values")
+        _logger.debug("use standard values")
 
     else:
         track_name = arguments[1]
@@ -65,7 +69,7 @@ def init_data(ID_name, batch_key, ID_flag, ID_root, prefix="A01b_ID"):
 
     """
 
-    print(ID_name, batch_key, ID_flag)
+    _logger.debug("id name: %s, batch key: %s, id flag: %s", ID_name, batch_key, ID_flag)
     hemis, batch = batch_key.split("_")
 
     if ID_flag:
@@ -98,13 +102,13 @@ def get_atl06p(ATL03_track_name, params_yapc, maximum_height):
     Returns:
     geopandas.GeoDataFrame: The geodataframe containing the ATL06 data.
     """
-    print("Retrieving data from sliderule ...")
+    _logger.debug("Retrieving data from sliderule ...")
     gdf = icesat2.atl06p(params_yapc, resources=[ATL03_track_name])
 
     if gdf.empty:
         raise ValueError("Empty Geodataframe. No data could be retrieved.")
 
-    print("Initial data retrieved")
+    _logger.debug("Initial data retrieved")
     gdf = sct.correct_and_remove_height(gdf, maximum_height)
     return gdf
 
@@ -264,7 +268,7 @@ def nsidc_icesat2_get_associated_file(
     remote_names = []
 
     for input_file in file_list:
-        # print(input_file)
+        _logger.debug("input file: %s", input_file)
         # -- extract parameters from ICESat-2 ATLAS HDF5 file name
         SUB, PRD, HEM, YY, MM, DD, HH, MN, SS, TRK, CYC, GRN, RL, VRS = rx.findall(
             input_file
@@ -288,10 +292,10 @@ def nsidc_icesat2_get_associated_file(
         colnames, collastmod, colerror = icesat2_toolkit.utilities.nsidc_list(
             PATH, build=False, timeout=TIMEOUT, parser=parser, pattern=R1, sort=True
         )
-        print(colnames)
+        _logger.debug("colnames: %s", colnames)
         # -- print if file was not found
         if not colnames:
-            print(colerror)
+            _logger.debug("colerror: %s", colerror)
             continue
         # -- add to lists
         for colname, remote_mtime in zip(colnames, collastmod):
@@ -312,7 +316,7 @@ def json_load(name, path, verbose=False):
     with open(full_name, "r") as ifile:
         data = json.load(ifile)
     if verbose:
-        print("loaded from: ", full_name)
+        _logger.debug("loaded from: %s", full_name)
     return data
 
 
@@ -328,7 +332,7 @@ def ATL03_download(username, password, dpath, product_directory, sd, file_name):
     """
 
     HOST = ["https://n5eil01u.ecs.nsidc.org", "ATLAS", product_directory, sd, file_name]
-    print("download to:", dpath + "/" + HOST[-1])
+    _logger.debug("download to: %s", dpath + "/" + HOST[-1])
     buffer, error = icesat2_toolkit.utilities.from_nsidc(
         HOST,
         username=username,
@@ -389,7 +393,7 @@ def write_track_to_HDF5(data_dict, name, path, verbose=False, mode="w"):
                 store1[kk] = I
 
     if verbose:
-        print(f"saved at: {full_name}")
+        _logger.debug("saved at: %s", full_name)
 
 
 def get_time_for_track(delta_time, atlas_epoch):
@@ -465,7 +469,7 @@ def getATL03_beam(fileT, numpy=False, beam="gt1l", maxElev=1e6):
     mask_total = mask_seaice | mask_ocean
 
     if sum(~mask_total) == beam_data[:, 1].size:
-        print("zero photons, lower photon quality to 2 or higher")
+        _logger.debug("zero photons, lower photon quality to 2 or higher")
         # lower quality threshold and recompute
         quality_threshold = 1
         mask_ocean = beam_data[:, 1] > quality_threshold
@@ -506,8 +510,8 @@ def getATL03_beam(fileT, numpy=False, beam="gt1l", maxElev=1e6):
             }
         )
         # Filter out high elevation values
-        print("seg_dist shape ", segment_dist_x.shape)
-        print("df shape ", dF.shape)
+        _logger.debug("seg_dist shape %s", segment_dist_x.shape)
+        _logger.debug("df shape %s", dF.shape)
 
         dF = dF[mask_total]
         return dF, dF_seg

src/icesat2waves/tools/sliderule_converter_tools.py

@@ -1,3 +1,5 @@
+import logging
+
 from ipyleaflet import (
     Map,
     basemaps,
@@ -11,6 +13,8 @@
 
 import matplotlib.pyplot as plt
 
+_logger = logging.getLogger(__name__)
+
 
 # height correction tools
 def correct_and_remove_height(Gi, height_limit):
@@ -362,11 +366,11 @@ def check_RGT_in_domain(Gtrack_lowest, gdf):
     interect_list = list(result)
     interect_list.sort()
 
-    print("RGT in domain: ", len(Gtrack_lowest["RGT"].unique()))
-    print("RGT with data found: ", len(gdf_list))
-    print("RGT in both: ", len(interect_list))
+    _logger.debug("RGT in domain: %s", len(Gtrack_lowest["RGT"].unique()))
+    _logger.debug("RGT with data found: %s", len(gdf_list))
+    _logger.debug("RGT in both: %s", len(interect_list))
     if len(interect_list) != len(gdf_list):
-        print("RGT not in both: ", list(set(gdf_list) - result))
+        _logger.debug("RGT not in both: %s", list(set(gdf_list) - result))
     return interect_list