Merge pull request #264 from simpeg/fix_issue_139

Fix issue 139

.github/workflows/tests.yml

@@ -35,7 +35,7 @@ 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
+        pip install git+https://github.com/kujaku11/mth5.git@fc
 
     - name: Install Our Package
       run: |

aurora/config/metadata/processing.py

@@ -9,6 +9,7 @@
 
 from aurora.time_series.windowing_scheme import window_scheme_from_decimation
 from mt_metadata.transfer_functions.processing.aurora.processing import Processing
+from mt_metadata.utils.list_dict import ListDict
 from mth5.utils.helpers import initialize_mth5
 
 
@@ -81,7 +82,7 @@ def save_as_json(self, filename=None, nested=True, required=False):
         with open(filename, "w") as f:
             f.write(json_str)
 
-    def make_tf_header(self, dec_level_id):
+    def emtf_tf_header(self, dec_level_id):
         """
 
         Parameters
@@ -93,19 +94,14 @@ def make_tf_header(self, dec_level_id):
         -------
         tfh: mt_metadata.transfer_functions.processing.aurora.transfer_function_header.TransferFunctionHeader
         """
-        from aurora.transfer_function.transfer_function_header import (
-            TransferFunctionHeader,
-        )
-
-        tfh = TransferFunctionHeader(
-            processing_scheme=self.decimations[dec_level_id].estimator.engine,
-            local_station=self.stations.local,
-            reference_station=self.stations.remote,
-            input_channels=self.decimations[dec_level_id].input_channels,
-            output_channels=self.decimations[dec_level_id].output_channels,
-            reference_channels=self.decimations[dec_level_id].reference_channels,
-            decimation_level_id=dec_level_id,
-        )
+        tfh = ListDict()
+        tfh.processing_scheme = self.decimations[dec_level_id].estimator.engine
+        tfh.local_station = self.stations.local
+        tfh.remote_station = self.stations.remote
+        tfh.input_channels = self.decimations[dec_level_id].input_channels
+        tfh.output_channels = self.decimations[dec_level_id].output_channels
+        tfh.reference_channels = self.decimations[dec_level_id].reference_channels
+        tfh.decimation_level_id = dec_level_id
 
         return tfh
 
@@ -125,7 +121,53 @@ def make_tf_level(self, dec_level_id):
         # from aurora.transfer_function.base import TransferFunction
         from aurora.transfer_function.TTFZ import TTFZ
 
-        tf_header = self.make_tf_header(dec_level_id)
-        tf_obj = TTFZ(tf_header, self.decimations[dec_level_id].frequency_bands_obj())
+        tf_obj = TTFZ(
+            dec_level_id,
+            self.decimations[dec_level_id].frequency_bands_obj(),
+            processing_config=self,
+        )
 
         return tf_obj
+
+
+class EMTFTFHeader(ListDict):
+    """
+    Convenince class for storing metadata for a TF estimate.
+    Based on Gary Egbert's TFHeader.m originally in
+    iris_mt_scratch/egbert_codes-20210121T193218Z-001/egbert_codes/matlabPrototype_10-13-20/TF/classes
+
+    It completely depends on the Processing class
+    """
+
+    def __init__(self, **kwargs):
+        """
+        Parameters
+        _local_station : mt_metadata.transfer_functions.tf.station.Station()
+            Station metadata object for the station to be estimated (
+            location, channel_azimuths, etc.)
+        _remote_station: same object type as local station
+            if no remote reference then this can be None
+        output_channels: list
+            Probably a list of channel keys -- usually ["ex","ey","hz"]
+        input_channels : list
+            Probably a list of channel keys -- usually ["hx","hy"]
+            These are the channels being provided as input to the regression.
+        reference_channels : list
+            These are the channels being used from the RR station. This is a
+            channel list -- usually ["hx", "hy"]
+        processing_scheme: str
+            Denotes the regression engine used to estimate the transfer
+            function.  One of "OLS" or "RME", "RME_RR.  Future
+            versions could include , "multivariate array", "multiple remote",
+            etc.
+
+        """
+        super().__init__()
+        self.processing_scheme = kwargs.get("processing_scheme", None)
+        self.local_station = kwargs.get("local_station", None)
+        self.remote_station = kwargs.get("remote_station", None)
+        self.input_channels = kwargs.get("input_channels", ["hx", "hy"])
+        self.output_channels = kwargs.get("output_channels", ["ex", "ey"])
+        self.reference_channels = kwargs.get("reference_channels", [])
+        self.decimation_level_id = kwargs.get("decimation_level_id", None)
+        self.user_meta_data = None  # placeholder for anything

aurora/pipelines/process_mth5.py

@@ -128,8 +128,7 @@ def process_tf_decimation_level(
         The transfer function values packed into an object
     """
     frequency_bands = config.decimations[i_dec_level].frequency_bands_obj()
-    tf_header = config.make_tf_header(i_dec_level)
-    transfer_function_obj = TTFZ(tf_header, frequency_bands, processing_config=config)
+    transfer_function_obj = TTFZ(i_dec_level, frequency_bands, processing_config=config)
 
     transfer_function_obj = process_transfer_functions(
         config, i_dec_level, local_stft_obj, remote_stft_obj, transfer_function_obj

aurora/pipelines/transfer_function_helpers.py

@@ -100,7 +100,7 @@ def get_band_for_tf_estimate(
 
     Parameters
     ----------
-    band : aurora.time_series.frequency_band.FrequencyBand
+    band : mt_metadata.transfer_functions.processing.aurora.FrequencyBands
         object with lower_bound and upper_bound to tell stft object which
         subarray to return
     config : mt_metadata.transfer_functions.processing.aurora.decimation_level.DecimationLevel

aurora/sandbox/plot_helpers.py

@@ -266,7 +266,7 @@ def plot_tf_obj(tf_obj, out_filename=None, show=True):
 
     plotter = RhoPlot(tf_obj)
     fig, axs = plt.subplots(nrows=2)
-    ttl_str = tf_obj.tf_header.local_station_id
+    ttl_str = tf_obj.tf_header.local_station.id
     plotter.rho_sub_plot(axs[0], ttl_str=ttl_str)
     plotter.phase_sub_plot(axs[1], ttl_str=ttl_str)
     if out_filename:

aurora/test_utils/synthetic/processing_helpers.py

@@ -46,3 +46,28 @@ class that has a df that describes the runs to be processed.
         return_collection=return_collection,
     )
     return tf_collection
+
+
+def tf_obj_from_synthetic_data(mth5_path):
+    """Helper function for test_issue_139"""
+    from aurora.config.config_creator import ConfigCreator
+    from aurora.pipelines.run_summary import RunSummary
+    from aurora.transfer_function.kernel_dataset import KernelDataset
+
+    run_summary = RunSummary()
+    run_summary.from_mth5s(list((mth5_path,)))
+
+    kernel_dataset = KernelDataset()
+    kernel_dataset.from_run_summary(run_summary, "test1", "test2")
+
+    # Define the processing Configuration
+    cc = ConfigCreator()
+    config = cc.create_from_kernel_dataset(kernel_dataset)
+
+    tf_cls = process_mth5(
+        config,
+        kernel_dataset,
+        units="MT",
+        z_file_path="zzz.zz",
+    )
+    return tf_cls