Merge pull request #205 from simpeg/fix_issue_204

Issue #204 not complete but added one example of unittest

.github/workflows/tests.yml

@@ -18,7 +18,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
-        python-version: [3.8, 3.7, 3.6] 
+        python-version: [3.8,]# 3.7, 3.6] 
 
     steps:
     - uses: actions/checkout@v2

aurora/config/metadata/channel_nomenclature.py

@@ -38,14 +38,6 @@
     "ey": "e4",
 }
 
-THE_BEATLES = {
-    "hx": "john",
-    "hy": "paul",
-    "hz": "george",
-    "ex": "ringo",
-    "ey": "the fifth beatle",
-}
-
 
 # =============================================================================
 class ChannelNomenclature(Base):
@@ -104,16 +96,14 @@ def keyword(self, keyword):
         self._update_by_keyword(keyword)
 
     def get_channel_map(self, keyword):
-        if keyword == "LEMI12":
+        if keyword == "default":
+            channel_map = DEFAULT_CHANNEL_MAP
+        elif keyword == "LEMI12":
             channel_map = LEMI_CHANNEL_MAP_12
         elif keyword == "LEMI34":
             channel_map = LEMI_CHANNEL_MAP_34
         elif keyword == "NIMS":
             channel_map = DEFAULT_CHANNEL_MAP
-        elif keyword == "beatles":
-            channel_map = THE_BEATLES
-        elif keyword == "default":
-            channel_map = DEFAULT_CHANNEL_MAP
         else:
             print(f"whoops mt_system {keyword} unknown")
             raise NotImplementedError

aurora/pipelines/time_series_helpers.py

@@ -51,8 +51,7 @@ def apply_prewhitening(decimation_obj, run_xrts_input):
         run_xrts = run_xrts_input.differentiate("time")
     else:
         print(f"{decimation_obj.prewhitening_type} prehitening not yet implemented")
-        print("returning original time series")
-        run_xrts = run_xrts_input
+        raise NotImplementedError
     return run_xrts
 
 
@@ -84,6 +83,14 @@ def apply_recoloring(decimation_obj, stft_obj):
         if prewhitening_correction[0] == 0.0:
             cond = stft_obj.frequency != 0.0
             stft_obj = stft_obj.where(cond, complex(0.0))
+    # elif decimation_obj.prewhitening_type == "ARMA":
+    #     from statsmodels.tsa.arima.model import ARIMA
+    #     AR = 3 # add this to processing config
+    #     MA = 4 # add this to processing config
+
+    else:
+        print(f"{decimation_obj.prewhitening_type} recoloring not yet implemented")
+        raise NotImplementedError
 
     return stft_obj
 

aurora/time_series/decorators.py

@@ -2,7 +2,7 @@
 import xarray as xr
 
 
-# <PATTERN>
+# Here is the decorator pattern
 def decorator(func):
     @functools.wraps(func)
     def wrapper_decorator(*args, **kwargs):
@@ -14,9 +14,6 @@ def wrapper_decorator(*args, **kwargs):
     return wrapper_decorator
 
 
-# </PATTERN>
-
-
 def can_use_xr_dataarray(func):
     """
     Intended as a decorator.  Most of the windowed time series methods are
@@ -50,7 +47,7 @@ def wrapper_decorator(*args, **kwargs):
             processed_obj = func(*args, **kwargs)
 
         if input_was_dataarray:
-            processed_obj = processed_obj.to_dataarray()
+            processed_obj = processed_obj.to_array("channel")
         return processed_obj
 
     return wrapper_decorator

aurora/time_series/windowed_time_series.py

@@ -4,30 +4,6 @@
 from aurora.time_series.decorators import can_use_xr_dataarray
 
 
-def schur_product_windowed_data(ensemblized_data, taper):
-    """
-    The axes are set up so that each window is tapered
-
-    In particular, each "window" is a row of windowed_array.  Thus taper
-    operates by multiplying, point-by-point (Schur) each row or windowed_array.
-
-    TODO: either take an argument for which axis the taper applies along or
-    make the calling function confirm that each row is a window and each
-    column is a window-advance-delta-t
-
-    Parameters
-    ----------
-    data
-
-    Returns
-    -------
-
-    """
-    tapered_windowed_data = ensemblized_data * taper  # seems to do sparse diag mult
-    # time trial it against a few other methods
-    return tapered_windowed_data
-
-
 def validate_coordinate_ordering_time_domain(dataset):
     """
     Check that the data dimensions are what you expect.  THis may evolve some
@@ -117,12 +93,12 @@ def apply_taper(data=None, taper=None, in_place=True):
     @staticmethod
     def detrend(data=None, detrend_axis=None, detrend_type=None, inplace=True):
         """
-        TODO: overwrite data=True probably best for most applications but
-            be careful with that.  Do we want to avoid this in general?
-            could we be possibly overwriting stuff on MTH5 in future?
-            Also, is overwrite even working how I think it is here?
-        TODO: overwrite_data not working right in scipy.signal, dont use it
-        for now
+        Notes: overwrite data=True probably best for most applications but be careful
+         with that.  Do we want to avoid this in general? Could we be possibly
+         overwriting stuff on MTH5 in future?
+         Also, is overwrite even working how I think it is here?
+         Overwrite_data not working right in scipy.signal, dont use it for now
+
         Parameters
         ----------
         data : xarray Dataset

aurora/time_series/windowing_scheme.py

@@ -3,8 +3,8 @@
 """
 The windowing scheme defines the chunking and chopping of the time series for
 the Short Time Fourier Transform.  Often referred to as a "sliding window" or
-a "striding window".  It is basically a taper with a rule to say how far to
-advance at each stride (or step).
+a "striding window".  Iin its most basic form it is a taper with a rule to
+say how far to advance at each stride (or step).
 
 To generate an array of data-windows from a data series we only need the
 two parameters window_length (L) and window_overlap (V).  The parameter
@@ -13,26 +13,22 @@
 advance.
 
 Choices L and V are usually made with some knowledge of time series sample
-rate, duration, and the frequency band of interest.  We can create a
-module that "suggests" L, V, based on these metadata to make the default
-processing configuration parameters.
-
-Note: In general we will need one instance of this class per decimation level,
-but in the current implementation we will probably leave the windowing scheme
-the same for each decimation level.
-
-This class is a key part of the "gateway" to frequency domain, so what
-frequency domain considerations do we want to think about here.. certainly
-the window length and the sampling rate define the frequency resolution, and as
-such should be considered in context of the "band averaging scheme"
-
-Indeed the frequencies come from this class if it has a sampling rate.  While
-sampling rate is a property of the data, and not the windowing scheme per se,
-it is good for this class to be aware of the sampling rate.  ... or should we
-push the frequency stuffs to a combination of TS plus WindowingScheme?
-The latter feels more appropriate.
-
-<20210510>
+rate, duration, and the frequency band of interest.  In aurora because this is used
+to prep for STFT, L is typically a power of 2.
+
+In general we will need one instance of this class per decimation level,
+but in practice often leave the windowing scheme the same for each decimation level.
+
+This class is a key part of the "gateway" to frequency domain, so it has been given
+a sampling_rate attribute.  While sampling rate is a property of the data, and not
+the windowing scheme per se, it is good for this class to be aware of the sampling
+rate.
+
+Future modifications could involve:
+- binding this class with a time series.
+- Making a subclass with only L, V, and then having an extension with sample_rate
+
+
 When 2D arrays are generated how should we index them?
 [[ 0  1  2]
  [ 2  3  4]
@@ -51,8 +47,6 @@
 [0, dt, 2*dt] for that axis to keep it general.  We can call this the
 "within-window sample time axis"
 
-</20210510>
-
 
 TODO: Regarding the optional time_vector input to self.apply_sliding_window()
 ... this current implementation takes as input numpy array data.  We need to

docs/conf.py

@@ -100,7 +100,7 @@
 sphinx_gallery_conf = {
     # path to your examples scripts
     "examples_dirs": [
-        "../examples",
+        "../tutorials",
     ],
     "gallery_dirs": [
         "examples",