Fix issue 194

.flake8

@@ -3,6 +3,6 @@ ignore = E501, W605, W503, E203, F401, E722
 #,E402, E203,E722 
 #F841, E402, E722
 #ignore = E203, E266, E501, W503, F403, F401
-max-line-length = 120
+max-line-length = 88
 max-complexity = 18
 select = B,C,E,F,W,T4,B9

.github/workflows/tests.yml

@@ -35,12 +35,39 @@ jobs:
         conda install -c conda-forge pytest pytest-cov certifi">=2017.4.17" pandoc
         pip install -r requirements-dev.txt
         pip install git+https://github.com/kujaku11/mt_metadata.git@fcs
-        pip install git+https://github.com/kujaku11/mth5.git@fc
+        pip install git+https://github.com/kujaku11/mth5.git
 
     - name: Install Our Package
       run: |
         pip install -e .
         conda list
+
+    - name: Install Jupyter and dependencies
+      run: |
+        pip install jupyter
+        pip install ipykernel
+        python -m ipykernel install --user --name aurora-test
+        # Install any other dependencies you need
+
+    - name: Execute Jupyter Notebooks
+      run: |
+        jupyter nbconvert --to notebook --execute docs/examples/dataset_definition.ipynb
+        jupyter nbconvert --to notebook --execute docs/examples/make_cas04_single_station_h5.ipynb
+        jupyter nbconvert --to notebook --execute docs/examples/operate_aurora.ipynb
+        jupyter nbconvert --to notebook --execute tests/test_run_on_commit.ipynb
+        jupyter nbconvert --to notebook --execute tutorials/pole_zero_fitting/lemi_pole_zero_fitting_example.ipynb
+        jupyter nbconvert --to notebook --execute tutorials/processing_configuration.ipynb
+        jupyter nbconvert --to notebook --execute tutorials/synthetic_data_processing.ipynb
+        # Replace "notebook.ipynb" with your notebook's filename
+
+#    - name: Commit changes (if any)
+#      run: |
+#        git config --local user.email "action@github.com"
+#        git config --local user.name "GitHub Action"
+#        git commit -a -m "Execute Jupyter notebook"
+#        git push
+#      if: ${{ success() }}
+
 
     - name: Run Tests
       run: |

aurora/sandbox/mth5_helpers.py

@@ -121,10 +121,14 @@ def get_time_period_bounds(ch):
         else:
             if start is None:
                 ch_start = '1970-01-01 00:00:00'
+            else:
+                ch_start = start
             if end is None:
                 ch_end = datetime.datetime.now()
                 ch_end = ch_end.replace(hour=0, minute=0, second=0, microsecond=0)
                 ch_end = str(ch_end)
+            else:
+                ch_end = end
         return ch_start, ch_end
 
     fdsn_object = FDSN(mth5_version=mth5_version)

aurora/time_series/apodization_window.py

@@ -201,24 +201,21 @@ def enbw(self, fs):
         return fs * self.S2 / (self.S1**2)
 
     def test_linear_spectral_density_factor(self):
-        """
-        This is just a test to verify some algebra
+        """This is just a test to verify some algebra
         Claim:
         The lsd_calibration factors
-        A      (1./coherent_gain)*np.sqrt((2*dt)/(nenbw*N))
+        A      (1./coherent\_gain)\*np.sqrt((2\*dt)/(nenbw\*N))
         and
-        B      np.sqrt(2/(sample_rate*self.S2))
+        B      np.sqrt(2/(sample\_rate\*self.S2))
         are identical.
 
-        Note sqrt(2*dt)==sqrt(2*sample_rate) so we can cancel these terms and
+        Note sqrt(2\*dt)==sqrt(2\*sample_rate) so we can cancel these terms and
         A=B IFF
-        (1./coherent_gain) * np.sqrt(1/(nenbw*N)) == 1/np.sqrt(S2)
+        (1./coherent\_gain) * np.sqrt(1/(nenbw\*N)) == 1/np.sqrt(S2)
         which I show in githib aurora issue #3 via .
-        (CG**2) * NENBW *N   =  S2
-
-        Returns
-        -------
+        (CG\*\*2) \* NENBW \*N   =  S2
 
+
         """
         lsd_factor1 = (1.0 / self.coherent_gain) * np.sqrt(
             1.0 / (self.nenbw * self.num_samples_window)

aurora/time_series/time_axis_helpers.py

@@ -45,14 +45,13 @@ def make_time_axis(t0, n_samples, sample_rate):
 
 
 def test_generate_time_axis(t0, n_samples, sample_rate):
-    """
-    Two obvious ways to generate an axis of timestanps here. One method is slow and
+    """Two obvious ways to generate an axis of timestamps here. One method is slow and
     more precise, the other is fast but drops some nanoseconds due to integer
     roundoff error.
 
     To see this, consider the example of say 3Hz, we are 333333333ns between samples,
-     which drops 1ns per second if we scale a nanoseconds=np.arange(N)
-    The issue here is that the nanoseconds granularity forces a roundoff error,
+    which drops 1ns per second if we scale a nanoseconds=np.arange(N)
+    The issue here is that the nanoseconds granularity forces a roundoff error
 
 
     Probably will use logic like:
@@ -74,20 +73,20 @@ def test_generate_time_axis(t0, n_samples, sample_rate):
     """
     t0 = np.datetime64(t0)
 
-    # <SLOW>
+    # SLOW
     tt = time.time()
     time_index_1 = slow_comprehension(t0, n_samples, sample_rate)
     processing_time_1 = tt - time.time()
     print(f"processing_time_1 = {processing_time_1}")
-    # </SLOW>
+
 
-    # <FAST>
+    # FAST
     tt = time.time()
     time_index_2 = fast_arange(t0, n_samples, sample_rate)
     processing_time_2 = tt - time.time()
     print(f"processing_time_2 {processing_time_2}")
     print(f"ratio of processing times {processing_time_1/processing_time_2}")
-    # </FAST>
+
     if (np.abs(time_index_2 - time_index_1)).sum() == 0:
         pass
     else:

aurora/transfer_function/plot/comparison_plots.py

@@ -8,8 +8,8 @@
 def compare_two_z_files(
     z_path1,
     z_path2,
-    angle1=0.0,
-    angle2=0.0,
+    angle1 = 0.0,
+    angle2 = 0.0,
     label1="",
     label2="",
     scale_factor1=1.0,

aurora/transfer_function/regression/m_estimator.py

@@ -318,7 +318,7 @@ def compute_noise_covariance(self):
         """
         res_clean = self.Yc - self.Y_hat
         SSR_clean = np.conj(res_clean.conj().T @ res_clean)
-        inv_psi_prime2 = np.diag(1.0 / (self.expectation_psi_prime**2))
+        inv_psi_prime2 = np.diag(1.0 / (self.expectation_psi_prime ** 2))
         cov_nn = inv_psi_prime2 @ SSR_clean / self.degrees_of_freedom
 
         self.cov_nn = xr.DataArray(

aurora/transfer_function/weights/edf_weights.py

@@ -44,15 +44,15 @@ def p1(self):
         Threshold applied to edf.  All edf below  this value
         are set to weight=0
         """
-        return self.c1 * (self.n_data**self.alpha)
+        return self.c1 * (self.n_data ** self.alpha)
 
     @property
     def p2(self):
         """
         Threshold applied to edf.  All edf above th  this value
         are set to weight=0
         """
-        return self.c2 * (self.n_data**self.alpha)
+        return self.c2 * (self.n_data ** self.alpha)
 
     def compute_weights(self, X, use):
         """