Merge pull request #349 from simpeg/fix_issue_96

rename TRME to RME

aurora/pipelines/transfer_function_helpers.py

@@ -12,8 +12,8 @@
 from aurora.time_series.xarray_helpers import handle_nan
 from aurora.transfer_function.regression.base import RegressionEstimator
 from aurora.transfer_function.regression.iter_control import IterControl
-from aurora.transfer_function.regression.TRME import TRME
-from aurora.transfer_function.regression.TRME_RR import TRME_RR
+from aurora.transfer_function.regression.RME import RME
+from aurora.transfer_function.regression.RME_RR import RME_RR
 
 # from aurora.transfer_function.weights.coherence_weights import compute_multiple_coherence_weights
 from aurora.transfer_function.weights.edf_weights import (
@@ -24,7 +24,7 @@
 import numpy as np
 import xarray as xr
 
-ESTIMATOR_LIBRARY = {"OLS": RegressionEstimator, "RME": TRME, "RME_RR": TRME_RR}
+ESTIMATOR_LIBRARY = {"OLS": RegressionEstimator, "RME": RME, "RME_RR": RME_RR}
 
 
 def get_estimator_class(estimation_engine: str) -> RegressionEstimator:

aurora/transfer_function/regression/TRME.py → aurora/transfer_function/regression/RME.py

@@ -79,7 +79,7 @@
 from scipy.linalg import solve_triangular
 
 
-class TRME(MEstimator):
+class RME(MEstimator):
     def __init__(self, **kwargs):
         """
         Constructor.
@@ -102,7 +102,7 @@ def __init__(self, **kwargs):
             Its the number of points that didn't get weighted /total number of points
 
         """
-        super(TRME, self).__init__(**kwargs)
+        super(RME, self).__init__(**kwargs)
         self.qr_input = "X"
 
     def update_y_hat(self) -> None:

aurora/transfer_function/regression/TRME_RR.py → aurora/transfer_function/regression/RME_RR.py

@@ -14,15 +14,15 @@
 from loguru import logger
 
 
-class TRME_RR(MEstimator):
+class RME_RR(MEstimator):
     def __init__(self, **kwargs):
         """
         Constructor.
 
         Robust remote reference estimator.  Z is the reference station data,
         and is the same size as X (see regression.base.RegressionEstimator(
         """
-        super(TRME_RR, self).__init__(**kwargs)
+        super(RME_RR, self).__init__(**kwargs)
         self._Z = kwargs.get("Z", None)
         self.Z = self._Z.to_array().data.T
         self.qr_input = "Z"
@@ -38,7 +38,7 @@ def check_for_nan(self):
         cond3 = np.isnan(self.Z).any()
         nans_present = cond1 or cond2 or cond3
         if nans_present:
-            logger.error("Missing data not allowed for TRME_RR class")
+            logger.error("Missing data not allowed for RME_RR class")
             raise Exception
 
     def check_for_enough_data_for_rr_estimate(self) -> None:

aurora/transfer_function/regression/base.py

@@ -342,8 +342,8 @@ def qr_decomposition(
         Parameters
         ----------
         X: numpy array
-            In TRME this is the Input channels X
-            In TRME_RR this is the RR channels Z
+            In RME this is the Input channels X
+            In RME_RR this is the RR channels Z
         sanity_check: boolean
             check QR decomposition is working correctly.  Set to True for debugging.
             Can probably be deprecated.
@@ -505,7 +505,7 @@ def _get_channel_names(
     Returns list of channel names.
 
     If X is a numpy array, names will be created.
-    These are needed by TRME.estimate() to return xarrays.
+    These are needed by RME.estimate() to return xarrays.
 
     Parameters
     ----------

aurora/transfer_function/regression/helper_functions.py

@@ -10,8 +10,8 @@ def rme_beta(r0: float) -> float:
     Returns a normalization factor to correct for bias in residual variance.
 
     Details:
-    - This is an RME specific property.  It represents a bias in the calculation of
-    residual_variance which we correct for in TRME and TRME_RR. The implemented
+    - This is a Robust M-estimator specific property.  It represents a bias in the calculation of
+    residual_variance which we correct for in RME and RME_RR. The implemented
     formula is an approximation.  This is approximately equal to 1/beta where beta is
     defined by Equation A3 in Egbert & Booker 1986.
 

aurora/transfer_function/regression/m_estimator.py

@@ -1,7 +1,7 @@
 """
 This module contains the MEstimator class - an extension of RegressionEstimator.
 
- MEstimator has the class methods that are common to both TRME and TRME_RR.
+ MEstimator has the class methods that are common to both RME and RME_RR.
  See Notes in RME, RME_RR for more details.
 
 """
@@ -33,8 +33,7 @@ def __init__(self, **kwargs):
             then it increases linearly to 1 (r0)
             Psi' is something like 1 between -r0, and r0
             Psi' is zero outside
-            So the expectiaon value of psi' is the number of points outside
-            its the number of points that didnt get weighted /total number of points
+            So the expectation value of psi' is the number of points outside, (that didn't get weighted) divided by the total number of points
 
         """
         super(MEstimator, self).__init__(**kwargs)
@@ -117,8 +116,8 @@ def residual_variance_method1(self) -> np.ndarray:
         """
         returns the residual variance of the output channels.
 
-        This is the method that was originally in TRME_RR.m.  It seems more correct
-        than the one in TRME, but also has more computational overhead.
+        This is the method that was originally in RME_RR.m.  It seems more correct
+        than the one in RME, but also has more computational overhead.
         """
         res = self.Yc - self.Y_hat  # initial estimate of error variance
         residual_variance = np.sum(np.abs(res * np.conj(res)), axis=0) / self.n_data
@@ -196,7 +195,7 @@ def update_y_cleaned_via_huber_weights(self) -> None:
             w = np.minimum(r0s / residuals, 1.0)
             self.Yc[:, k] = w * self.Y[:, k] + (1 - w) * self.Y_hat[:, k]
             self.expectation_psi_prime[k] = 1.0 * np.sum(w == 1) / self.n_data
-        self.update_QHYc()  # note the QH is different in TRME_RR vs TRME
+        self.update_QHYc()  # note the QH is different in RME_RR vs RME
         return
 
     def initial_estimate(self) -> None:
@@ -210,7 +209,7 @@ def initial_estimate(self) -> None:
 
     def apply_huber_regression(self) -> None:
         """
-            This is the 'convergence loop' from TRME, TRME_RR
+            This is the 'convergence loop' from RME, RME_RR
 
         TODO: Consider not setting iter_control.number_of_iterations
          - Instead, Initialize a new iter_control object

tests/synthetic/test_processing.py

@@ -318,7 +318,7 @@ def main():
 #     Just like process_synthetic_1, but the window is ridiculously long so that we
 #     encounter the underdetermined problem. We actually pass that test but in testing
 #     I found that at the next band over, which has more data because there are multipe
-#     FCs the sigma in TRME comes out as negative. see issue #4 and issue #55.
+#     FCs the sigma in RME comes out as negative. see issue #4 and issue #55.
 #     Returns
 #     -------
 #