Remove print statements

aurora/config/processing_config.py

@@ -73,7 +73,6 @@ def to_json(self, json_fn=None, indent=" " * 4):
         json_dict["local_station_id"] = self.local_station_id
         json_dict["reference_station_id"] = self.reference_station_id
         json_dict["channel_scale_factors"] = self.channel_scale_factors
-        print(self.decimation_level_ids)
         for dec_level_id in self.decimation_level_ids:
             json_dict[dec_level_id] = self_dict[dec_level_id].__dict__
 

aurora/pipelines/process_mth5.py

@@ -179,9 +179,6 @@ def process_mth5_decimation_level(config, local, remote, units="MT"):
     )
 
     transfer_function_obj.apparent_resistivity(units=units)
-    # print(transfer_function_obj.rho.shape)
-    # print(transfer_function_obj.rho[0])
-    # print(transfer_function_obj.rho[-1])
     return transfer_function_obj
 
 
@@ -327,7 +324,7 @@ def process_mth5_run(
                 processing_config, mth5_obj, run_id
             )
 
-            print("APPLY TIMING CORRECTIONS HERE")
+            # APPLY TIMING CORRECTIONS HERE
         else:
             local = prototype_decimate(processing_config, local)
             if processing_config.reference_station_id:

aurora/pipelines/time_series_helpers.py

@@ -177,9 +177,7 @@ def calibrate_stft_obj(stft_obj, run_obj, units="MT", channel_scale_factors=None
         channel_filter = mth5_channel.channel_response_filter
         if not channel_filter.filters_list:
             print("WARNING UNEXPECTED CHANNEL WITH NO FILTERS")
-            # ONE OFF HACK FOR SAO missing data
             if channel_id == "hy":
-                print("WARNING ONE-OFF PKD SAO RR")
                 channel_filter = run_obj.get_channel("hx").channel_response_filter
         calibration_response = channel_filter.complex_response(stft_obj.frequency.data)
         if channel_scale_factors:

aurora/pipelines/transfer_function_helpers.py

@@ -247,7 +247,6 @@ def process_transfer_functions(
                 )
                 regression_estimator.estimate()
                 transfer_function_obj.set_tf(regression_estimator, band.center_period)
-            print("Add method for compute residuals and noise covariance")
         else:
             X, Y, RR = handle_nan(X, Y, RR, drop_dim="observation")
             regression_estimator = regression_class(

aurora/time_series/frequency_band_helpers.py

@@ -127,7 +127,7 @@ def configure_frequency_bands(config):
         )
     elif config["band_setup_style"] == "band edges":
         frequency_bands.band_edges = config["band_edges"]
-        print("Not Yet Supported")
+        # "Not Yet Supported"
         raise NotImplementedError
     elif config["band_setup_style"] == "logarithmic range":
         lower_bound = config["frequency_bands_lower_bound"]

aurora/time_series/windowed_time_series.py

@@ -152,16 +152,13 @@ def detrend(data=None, detrend_axis=None, detrend_type=None, inplace=True):
         if detrend_axis is None:
             detrend_axis = get_time_coordinate_axis(data)
         if not inplace:
-            print("deep copy dataset and then overwrite")
             raise NotImplementedError
 
         for channel in data.keys():
-            print(f"channel {channel}")
 
             # windowed_array = data[key].data
             nanless_data = data[channel].dropna(dim="time")
             ensembles = nanless_data.data
-            print("Nan Checker goes here")
             if detrend_type:  # neither False nor None
                 ensembles = ssig.detrend(
                     ensembles, axis=detrend_axis, type=detrend_type
@@ -180,7 +177,6 @@ def detrend(data=None, detrend_axis=None, detrend_type=None, inplace=True):
                 else:
                     data[channel].data = ensembles
             else:
-                print("deep copy dataset and then overwrite")
                 raise NotImplementedError
         return data
 
@@ -225,7 +221,7 @@ def delay_correction(self, dataset, run_obj):
         -------
 
         """
-        print("NOT TESTED - PSEUDOCODE ONLY")
+        # "NOT TESTED - PSEUDOCODE ONLY"
         for channel_id in dataset.keys():
             mth5_channel = run_obj.get_channel(channel_id)
             channel_filter = mth5_channel.channel_response_filter

aurora/time_series/windowing_scheme.py

@@ -420,11 +420,10 @@ def fft_xr_ds(dataset, sample_rate, detrend_type=None, prewhitening=None):
     # TODO:
     from aurora.time_series.frequency_domain_helpers import get_fft_harmonics
 
-    print(
-        "Modify this so that demeaning and detrending is happening before "
-        "the application of the tapering window.  Add a second demean right "
-        "before the FFT"
-    )
+    # TODO: Modify this so that demeaning and detrending
+    # is happening before the application of the tapering
+    # window.  Add a second demean right "
+    # before the FFT
 
     samples_per_window = len(dataset.coords["within-window time"])
     n_fft_harmonics = int(samples_per_window / 2)  # no bin at Nyquist,
@@ -442,7 +441,6 @@ def fft_xr_ds(dataset, sample_rate, detrend_type=None, prewhitening=None):
             data=dataset, detrend_axis=time_coordinate_index, detrend_type="linear"
         )
     for channel_id in dataset.keys():
-        print(f"channel_id {channel_id}")
         data = dataset[channel_id].data
         # Here is where you would add segment-by-segment prewhitening
         fspec_array = np.fft.fft(data, axis=time_coordinate_index)

aurora/transfer_function/base.py

@@ -76,7 +76,6 @@ def __init__(self, tf_header, frequency_bands, **kwargs):
         tf_header
         frequency_bands
         """
-        print("TODO: change self.T to self.period")
         self.tf_header = tf_header
         self.frequency_bands = frequency_bands
         self.num_segments = None

aurora/transfer_function/iter_control.py

@@ -98,17 +98,19 @@ def converged(self, b, b0):
         iteration_cond = self.number_of_iterations >= self.max_number_of_iterations
         if tolerance_cond or iteration_cond:
             converged = True
-            if tolerance_cond:
-                print(
-                    f"Converged Due to MaxChange < Tolerance after "
-                    f" {self.number_of_iterations} of "
-                    f" {self.max_number_of_iterations} iterations"
-                )
-            elif iteration_cond:
-                print(
-                    f"Converged Due to maximum number_of_iterations "
-                    f" {self.max_number_of_iterations}"
-                )
+            # These print statments are not very clear and
+            # Should be reworded.
+            # if tolerance_cond:
+            #    print(
+            #        f"Converged Due to MaxChange < Tolerance after "
+            #        f" {self.number_of_iterations} of "
+            #        f" {self.max_number_of_iterations} iterations"
+            #    )
+            # elif iteration_cond:
+            #    print(
+            #        f"Converged Due to maximum number_of_iterations "
+            #        f" {self.max_number_of_iterations}"
+            #    )
         else:
             converged = False
 

aurora/transfer_function/regression/TRME.py

@@ -168,7 +168,6 @@ def sigma(self, QHY_or_QHYc, Y_or_Yc, correction_factor=1.0):
             assert (sigma > 0).all()
         except AssertionError:
             print("WARNING - Negative error variances observed")
-            print(sigma)
             print("Setting sigma to zero - Negative sigma_squared observed")
             sigma *= 0
             # raise Exception
@@ -296,7 +295,6 @@ def estimate(self):
             b0 = self.b
 
         if self.iter_control.max_number_of_redescending_iterations:
-            print(b0)
             # self.iter_control.number_of_redescending_iterations = 0;
             while self.iter_control.continue_redescending:
                 self.iter_control.number_of_redescending_iterations += 1

aurora/transfer_function/regression/base.py

@@ -118,7 +118,6 @@ def noise_covariance(self):
         return self.cov_nn
 
     def b_to_xarray(self):
-        print("TEST IMPLEMENTATION")
         xra = xr.DataArray(
             np.transpose(self.b),
             dims=["output_channel", "input_channel"],
@@ -302,7 +301,5 @@ def estimate_ols(self, mode="solve"):
         return b
 
     def estimate(self):
-        print("this method is not defined for the abstract base class")
-        print("But we put OLS in here for dev")
         Z = self.estimate_ols(mode="qr")
         return Z

aurora/transfer_function/transfer_function_collection.py

@@ -159,7 +159,6 @@ def check_all_channels_present(self):
             tmp = tmp.sel(output_channel=output_channels)
             self.merged_tf = tmp
 
-            print("NOW ADD Hz to cov_nn")
             n_output_ch = len(self.merged_tf.output_channel)  # 3
             n_periods = len(self.merged_tf.period)
             cov_nn_dims = (n_output_ch, n_output_ch, n_periods)
@@ -379,7 +378,6 @@ def write_emtf_z_file(self, z_file_path, run_obj=None, orientation_strs=None):
                 line = ""
                 for out_ch in tf.tf_header.output_channels:
                     for inp_ch in tf.tf_header.input_channels:
-                        print(out_ch, inp_ch)
                         chchtf = tf_xr.loc[out_ch, inp_ch, :]
                         real_part = np.real(chchtf.data[period_index])
                         imag_part = np.imag(chchtf.data[period_index])